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5.3. Kubernetes架构

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《Kubernetes权威指南》
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参考《Kubernetes权威指南》

Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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《Kubernetes权威指南》
Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/
Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20 @@ -3197,7 +3197,7 @@

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  • 《Kubernetes权威指南》
    • Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    /pkg/yurttunnel/agent/anpagent.go
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    4、Store:

    用于处理etcd支持的各类功能的事务，包括数据索引、节点状态变更、监控与反馈、事件处理与执行等等，是etcd对用户提供的大多数API功能的具体实现。

    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    Use KubeVirt | KubeVirt.io
    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    Use "virtual-kubelet [command] --help" for more information about a command.
    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    配置CNI插件
    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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+++ b/network/kubernetes-network.html @@ -3106,7 +3106,7 @@

    具体参考Flannel介绍

    参考《Kubernetes权威指南》

    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    k8s serviceaccount创建后没有生成对应的secret
    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    https://kubernetes.io/zh/docs/reference/kubectl/cheatsheet/
    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    文章参考《Kubernetes权威指南》

    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    http://github.com/google/cadvisor

    参考《Kubernetes权威指南》

    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    优先从备选节点列表中选择各项资源使用率最均衡的节点。

    参考《Kubernetes权威指南》

    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    https://fuckcloudnative.io/posts/what-happens-when-k8s/
    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/
    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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-{"./":{"url":"./","title":"序言","keywords":"","body":"Kubernetes 学习笔记微信公众号云原生体系 安装与配置 基本概念 核心原理 容器网络 容器存储 资源隔离 运维指南 开发指南 问题排查 源码分析 Runtime Etcd 多集群管理 边缘容器 虚拟化 监控体系 赞赏Kubernetes 学习笔记 本系列是 Kubernetes 学习笔记 更多的学习笔记请参考： Kubernetes 学习笔记 Kubernetes 源码分析笔记 Docker 学习笔记 Golang 学习笔记 Linux 学习笔记 数据结构学习笔记 个人博客： www.huweihuang.com blog.huweihuang.com k8s.huweihuang.com 微信公众号 微信公众号：容器云架构 云原生体系 12-Factor K8S知识体系 安装与配置 部署k8s集群 使用kubeadm安装生产环境kubernetes 使用kubespray安装kubernetes 使用minikube安装kubernetes 使用kind安装kubernetes 安装k8s dashboard kubeadm升级k8s集群 kubeadm管理证书 k8s证书及秘钥 k8s版本说明 k8s版本记录 基本概念 kubernetes架构 Kubernetes总架构图 基于Docker及Kubernetes技术构建容器云（PaaS）平台概述 kubernetes对象 理解kubernetes对象 kubernetes常用对象说明 Pod Pod介绍 Pod定义文件 Pod生命周期 Pod健康检查 Pod存储卷 Pod控制器 Pod伸缩与升级 配置 ConfigMap Workload 核心原理 核心组件 Api Server Controller Manager Scheduler Kubelet 流程图 Pod创建流程 PVC创建流程 容器网络 Docker网络 K8S网络 Pod的DNS策略 网络插件 Flannel介绍 CNI CNI接口介绍 Macvlan介绍 容器存储 存储卷概念 Volume Persistent Volume Persistent Volume Claim Storage Class Dynamic Volume Provisioning CSI csi-cephfs-plugin 部署csi-cephfs 部署cephfs-provisioner FlexVolume介绍 资源隔离 资源配额 Pod限额 资源服务质量 Lxcfs资源视图隔离 运维指南 kubernetes集群问题排查 kubectl工具 kubectl安装与配置 kubectl命令说明 kubectl命令别名 kubectl进入node shell helm工具 helm的使用 节点迁移 安全迁移节点 指定Node调度与隔离 镜像仓库 配置私有的镜像仓库 拉取私有镜像 访问控制 使用RBAC鉴权 版本发布 金丝雀发布 开发指南 client-go的使用及源码分析 CSI插件开发 nfs-client-provisioner源码分析 csi-provisioner源码分析 operator开发 kubebuilder的使用 如何开发一个Operator k8s社区开发指南 问题排查 节点相关问题 keycreate permission denied Cgroup不支持pid资源 Cgroup子系统无法挂载 Pod驱逐 镜像拉取失败问题 PVC Terminating 源码分析 Kubernetes源码分析笔记 kubelet NewKubeletCommand NewMainKubelet startKubelet syncLoopIteration syncPod kube-controller-manager NewControllerManagerCommand DeploymentController Informer机制 kube-scheduler NewSchedulerCommand registerAlgorithmProvider scheduleOne findNodesThatFit PrioritizeNodes preempt kube-apiserver NewAPIServerCommand Runtime Runtime Runc和Containerd概述 Containerd 安装Containerd Docker Docker学习笔记 Kata Container kata容器简介 kata配置 GPU nvidia-device-plugin介绍 Etcd Etcd介绍 Raft算法 Etcd启动配置参数 Etcd访问控制 etcdctl命令工具 etcdctl命令工具-V3 etcdctl命令工具-V2 Etcd中的k8s数据 Etcd-Operator的使用 多集群管理 k8s多集群管理的思考 Virtual Kubelet Virtual Kubelet介绍 Virtual Kubelet 命令 Karmada Karmada介绍 边缘容器 KubeEdge介绍 KubeEdge源码分析 cloudcore edgecore OpenYurt OpenYurt部署 OpenYurt部署之调整k8s配置 OpenYurt源码分析 YurtHub源码分析（1) TunnelServer源码分析（1） Tunnel-Agent源码分析 虚拟化 虚拟化相关概念 KubeVirt KubeVirt的介绍 KubeVirt的使用 监控体系 监控体系介绍 kube-prometheus-stack的使用 cAdvisor介绍 Heapster介绍 Influxdb介绍 赞赏 如果觉得文章有帮助的话，可以打赏一下，谢谢！ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2023-10-29 11:09:37 "},"paas/12-factor.html":{"url":"paas/12-factor.html","title":"12-Factor","keywords":"","body":"简介1. 基准代码1.1. 一份基准代码1.2. 多份部署2. 依赖3. 配置4. 后端服务5. 构建，发布，运行6. 进程7. 端口绑定8. 并发9. 易处理10. 开发环境与线上环境等价11. 日志12. 管理进程 以下主要介绍PaaS平台设计架构中使用到的方法论，统称为12-Factor(要素) 简介 软件通常会作为一种服务来交付，即软件即服务(SaaS)。12-Factor原则为构建SaaS应用提供了以下的方法论： 使用标准化流程自动配置，减少开发者的学习成本。 和操作系统解耦，使其可以在各个系统间提供最大的移植性。 适合部署在现代的云计算平台上，从而在服务器和系统管理方面节省资源。 将开发环境与生产环境的差异降至最低，并使用持续交付实施敏捷开发。 可以在工具、架构和开发流程不发生明显变化的前提下实现拓展 该理论适应于任何语言和后端服务(数据库、消息队列、缓存等)开发的应用程序。 1. 基准代码 一份基准代码，多份部署 应用代码使用版本控制系统来管理，常用的有Git、SVN等。一份用来跟踪代码所有修订版本的数据库称为代码库。 1.1. 一份基准代码 基准代码和应用之间总是保持一一对应的关系： 一旦有多个基准代码，则不能称之为一个应用，而是一个分布式系统。分布式系统中的每个组件都是一个应用，每个应用都可以使用12-Factor原则进行开发。 多个应用共享一份基准代码有悖于12-Factor原则。解决方法是将共享的代码拆成独立的类库，通过依赖管理去使用它们。 1.2. 多份部署 每个应用只对应一份基准代码，但可以同时存在多份的部署，每份部署相当于运行了一个应用的实例。 多份部署的区别在于： 可以存在不同的配置文件对应不同的环境。例如开发环境、预发布环境、生产环境等。 可以使用不同的版本。例如开发环境的版本可能高于预发布环境版本，还没同步到预发布环境版本，同理，预发布环境版本可能高于生产环境版本。 2. 依赖 显式声明依赖关系 大多数的编程语言都会提供一个包管理系统或工具，其中包含所有的依赖库，例如Golang的vendor目录存放了该应用的所有依赖包。 12-Factor原则下的应用会通过依赖清单来显式确切地声明所有的依赖项。在运行工程中通过依赖隔离工具来保证应用不会去调用系统中存在但依赖清单中未声明的依赖项。 显式声明依赖项的优点在于可以简化环境配置流程，开发者关注应用的基准代码，而依赖库则由依赖库管理工具来管理和配置。例如，Golang中的包管理工具dep等。 3. 配置 在环境中存储配置 通常，应用的配置在不同的发布环境中(例如：开发、预发布、生产环境)会有很大的差异，其中包括： 数据库、Redis等后端服务的配置 每份部署特有的配置，例如域名 第三方服务的证书等 12-Factor原则要求代码和配置严格分离，而不应该通过代码常量的形式写在代理里面。配置在不同的部署环境中存在大幅差异，但是代码却是完全一致的。 判断一个应用是否正确地将配置排除在代码外，可以看应用的基准代码是否可以立即开源而不担心暴露敏感信息。 12-Factor原则建议将应用的配置存储在环境变量中，环境变量可以方便在不同的部署环境中修改，而不侵入原有的代码。(例如，k8s的大部分代码配置是通过环境变量的方式来传入的)。 12-Factor应用中，环境变量的粒度要足够小且相对独立。当应用需要拓展时，可以平滑过渡。 4. 后端服务 把后端服务当作附加资源 后端服务指程序运行时所需要通过网络调用的各种服务，例如：数据库（MySQL，CouchDB），消息/队列系统（RabbitMQ，Beanstalkd），SMTP 邮件发送服务（Postfix），以及缓存系统（Memcached）。 其中可以根据管理对象分为本地服务(例如本地数据库)和第三方服务(例如Amason S3)。对于12-Factor应用来说都是附加资源，没有区别对待，当其中一份后端服务失效后，可以通过切换到原先备份的后端服务中，而不需要修改代码(但可能需要修改配置)。12-Factor应用与后端服务保持松耦合的关系。 5. 构建，发布，运行 严格分离构建和运行 基准代码转化成一份部署需要经过三个阶段： 构建阶段：指代码转化为可执行包的过程。构建过程会使用指定版本的代码，获取依赖项，编译生成二进制文件和资源文件。 发布阶段：将构建的结果与当前部署所需的配置结合，并可以在运行环境中使用。 运行阶段（运行时）：指针对指定的发布版本在执行环境中启动一系列应用程序的进程。 12-Factor应用严格区分构建、发布、运行三个步骤，每一个发布版本对应一个唯一的发布ID，可以使用时间戳或递增的版本序列号。 如果需要修改则需要产生一个新的发布版本，如果需要回退，则回退到之前指定的发布版本。 新代码部署之前，由开发人员触发构建操作，构建阶段可以相对复杂一些，方便错误信息可以展示出来得到妥善处理。运行阶段可以人为触发或自动运行，运行阶段应该保持尽可能少的模块。 6. 进程 以一个或多个无状态进程运行应用 12-Factor应有的进程必须是无状态且无共享的，任何需要持久化的数据存储在后端服务中，例如数据库。 内存区域和磁盘空间可以作为进程的缓存，12-Factor应用不需要关注这些缓存的持久化，而是允许其丢失，例如重启的时候。 进程的二进制文件应该在构建阶段执行编译而不是运行阶段。 当应用使用到粘性Session，即将用户的session数据缓存到进程的内存中，将同一用户的后续请求路由到同一个进程。12-Factor应用反对这种处理方式，而是建议将session的数据保存在redis/memcached带有过期时间的缓存中。 7. 端口绑定 通过端口绑定提供服务 应用通过端口绑定来提供服务，并监听发送至该端口的请求。端口绑定的方式意味着一个应用也可以成为另一个应用的后端服务，例如提供某些API请求。 8. 并发 通过进程模型进行扩展 12-Factor应用中，开发人员可以将不同的工作分配给不同类型进程，例如HTTP请求由web进程来处理，常驻的后台工作由worker进程来处理（k8s的设计中就经常用不同类型的manager来处理不同的任务）。 12-Factor应用的进程具备无共享、水平分区的特性，使得水平扩展较为容易。 12-Factor应用的进程不需要守护进程或是写入PID文件，而是通过进程管理器（例如 systemd）来管理输出流，响应崩溃的进程，以及处理用户触发的重启或关闭超级进程的操作。 9. 易处理 快速启动和优雅终止可最大化健壮性 12-Factor应用的进程是易处理的，即它们可以快速的开启或停止，这样有利于快速部署迭代和弹性伸缩实例。 进程应该追求最小的启动时间，这样可以敏捷发布，增加健壮性，当出现问题可以快速在别的机器部署一个实例。 进程一旦接收到终止信号(SIGTERM)就会优雅终止。优雅终止指停止监听服务的端口，拒绝所有新的请求，并继续执行当前已接收的请求，然后退出。 进程还需在面对突然挂掉的情况下保持健壮性，例如通过任务队列的方式来解决进程突然挂掉而没有完成处理的事情，所以应该设计为任务执行是幂等的，可以被重复执行，重复执行的结果是一致的。 10. 开发环境与线上环境等价 尽可能的保持开发，预发布，线上环境相同 不同的发布环境可能存在以下差异： 时间差异：开发到部署的周期较长。 人员差异：开发人员只负责开发，运维人员只负责部署。分工过于隔离。 工具差异：不同环境的配置和运行环境，使用的后端类型可能存在不同。 应尽量缩小本地与线上的差异，缩短上线周期，开发运维一体化，保证开发环境与线上运行的环境一致（例如，可以通过Docker容器的方式）。 11. 日志 把日志当作事件流 日志应该是事件流的汇总。12-Factor应用本身不考虑存储自己的日志输出流，不去写或管理日志文件，而是通过标准输出（stdout）的方式。 日志的标准输出流可以通过其他组件截获，整合其他的日志输出流，一并发给统一的日志中心处理，用于查看或存档。例如：日志收集开源工具Fluentd。 截获的日志流可以输出至文件，或者在终端实时查看。最重要的是可以发送到Splunk这样的日志索引及分析系统，提供后续的分析统计及监控告警等功能。例如： 找出过去一段时间的特殊事件。 图形化一个大规模的趋势，如每分钟的请求量。 根据用户定义的条件触发告警，如每分钟报错数超过某个警戒线。 12. 管理进程 后台管理任务当作一次性进程运行 开发人员经常需要执行一些管理或维护应用的一次性任务，一次性管理进程应该和常驻进程使用相同的运行环境，开发人员可以通过ssh方式来执行一次性脚本或任务。 参考： https://12factor.net/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"paas/k8s.html":{"url":"paas/k8s.html","title":"K8S知识体系","keywords":"","body":"1. k8s知识体系2. k8s重点开源项目1. k8s知识体系 以下整理了k8s涉及的相关知识体系。 图片 - k8s体系 思维导图：https://www.processon.com/view/link/5d7f7d08e4b03461a3a937e2 2. k8s重点开源项目 TODO Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"setup/installer/_index.html":{"url":"setup/installer/_index.html","title":"部署k8s集群","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"setup/installer/install-k8s-by-kubeadm.html":{"url":"setup/installer/install-k8s-by-kubeadm.html","title":"使用kubeadm安装生产环境kubernetes","keywords":"","body":"1. 环境准备1.0. master硬件配置1.1. 设置防火墙端口策略1.1.1. master节点端口配置1.1.2. worker节点端口配置1.2. 关闭​​swap​​分区1.3. 开启br_netfilter和bridge-nf-call-iptables2. 安装容器运行时2.1. Containerd2.2. Docker2.3. Container Socket3. 安装kubeadm,kubelet,kubectl4. 配置kubeadm config4.1. 配置项说明4.1.1. 配置类型4.1.2. Init配置4.2. Init配置示例5. 安装Master控制面5.1. 安装master5.2. 添加其他master6. 添加Node节点7. 安装网络插件7.1. 问题8. 部署dashboard9. 重置部署10. 问题排查10.1. kubeadm token过期10.2. 修改kubeadm join的master IP或端口10.3. conntrack not found10.4. Kubelet: unable to determine runtime API version 本文为基于kubeadm搭建生产环境级别高可用的k8s集群。 1. 环境准备 1.0. master硬件配置 参考： Master节点规格 高可靠推荐配置 - 容器服务 ACK - 阿里云 Kubernetes集群Master节点上运行着etcd、kube-apiserver、kube-controller等核心组件，对于Kubernetes集群的稳定性有着至关重要的影响，对于生产环境的集群，必须慎重选择Master规格。Master规格跟集群规模有关，集群规模越大，所需要的Master规格也越高。 说明 ：可从多个角度衡量集群规模，例如节点数量、Pod数量、部署频率、访问量。这里简单的认为集群规模就是集群里的节点数量。 对于常见的集群规模，可以参见如下的方式选择Master节点的规格（对于测试环境，规格可以小一些。下面的选择能尽量保证Master负载维持在一个较低的水平上）。 节点规模 Master规格 磁盘 1~5个节点 4核8 GB（不建议2核4 GB） 6~20个节点 4核16 GB 21~100个节点 8核32 GB 100~200个节点 16核64 GB 1000个节点 32核128GB 1T SSD 注意事项： 由于Etcd的性能瓶颈，Etcd的数据存储盘尽量选择SSD磁盘。 为了实现多机房容灾，可将三台master分布在一个可用区下三个不同机房。（机房之间的网络延迟在10毫秒及以下级别） 申请LB来做master节点的负载均衡实现高可用，LB作为apiserver的访问地址。 1.1. 设置防火墙端口策略 生产环境设置k8s节点的iptables端口访问规则。 1.1.1. master节点端口配置 协议 方向 端口范围 目的 使用者 TCP 入站 6443 Kubernetes API server 所有 TCP 入站 2379-2380 etcd server client API kube-apiserver, etcd TCP 入站 10250 Kubelet API 自身, 控制面 TCP 入站 10259 kube-scheduler 自身 TCP 入站 10257 kube-controller-manager 自身 1.1.2. worker节点端口配置 协议 方向 端口范围 目的 使用者 TCP 入站 10250 Kubelet API 自身, 控制面 TCP 入站 30000-32767 NodePort Services 所有 添加防火墙iptables规则 master节点开放6443、2379、2380端口。 iptables -A INPUT -p tcp -m multiport --dports 6443,2379,2380,10250 -j ACCEPT 1.2. 关闭​​swap​​分区 [root@master ~]#swapoff -a [root@master ~]# [root@master ~]# free -m total used free shared buff/cache available Mem: 976 366 135 6 474 393 Swap: 0 0 0 # swap 一栏为0，表示已经关闭了swap 1.3. 开启br_netfilter和bridge-nf-call-iptables 参考：https://imroc.cc/post/202105/why-enable-bridge-nf-call-iptables/ # 设置加载br_netfilter模块 cat 2. 安装容器运行时 在所有主机上安装容器运行时，推荐使用containerd为runtime。以下分别是containerd与docker的安装命令。 2.1. Containerd 1、参考：安装containerd # for ubuntu apt install -y containerd.io 2、生成默认配置 containerd config default > /etc/containerd/config.toml 3、修改CgroupDriver为systemd k8s官方推荐使用systemd类型的CgroupDriver。 [plugins.\"io.containerd.grpc.v1.cri\".containerd.runtimes.runc] ... [plugins.\"io.containerd.grpc.v1.cri\".containerd.runtimes.runc.options] SystemdCgroup = true 4、重启containerd systemctl restart containerd 2.2. Docker # for ubuntu apt install -y docker.io 官方建议配置cgroupdriver为systemd。 # 修改docker进程管理器 vi /etc/docker/daemon.json { \"exec-opts\": [\"native.cgroupdriver=systemd\"] } systemctl daemon-reload && systemctl restart docker docker info | grep -i cgroup 2.3. Container Socket 运行时 Unix 域套接字 Containerd unix:///var/run/containerd/containerd.sock CRI-O unix:///var/run/crio/crio.sock Docker Engine (使用 cri-dockerd) unix:///var/run/cri-dockerd.sock 3. 安装kubeadm,kubelet,kubectl 安装脚本可参考仓库：https://github.com/huweihuang/kubeadm-scripts.git 在所有主机上安装kubeadm，kubelet，kubectl。最好版本与需要安装的k8s的版本一致。 # 以Ubuntu系统为例 # 安装仓库依赖 sudo apt-get update sudo apt-get install -y apt-transport-https ca-certificates curl # use google registry sudo curl -fsSLo /usr/share/keyrings/kubernetes-archive-keyring.gpg https://packages.cloud.google.com/apt/doc/apt-key.gpg echo \"deb [signed-by=/usr/share/keyrings/kubernetes-archive-keyring.gpg] https://apt.kubernetes.io/ kubernetes-xenial main\" | sudo tee /etc/apt/sources.list.d/kubernetes.list # or use aliyun registry curl -s https://mirrors.aliyun.com/kubernetes/apt/doc/apt-key.gpg | sudo apt-key add - tee /etc/apt/sources.list.d/kubernetes.list 离线下载安装 #!/bin/bash Version=${Version:-1.24.2} wget https://dl.k8s.io/release/v${Version}/bin/linux/amd64/kubeadm wget https://dl.k8s.io/release/v${Version}/bin/linux/amd64/kubelet wget https://dl.k8s.io/release/v${Version}/bin/linux/amd64/kubectl chmod +x kubeadm kubelet kubectl cp kubeadm kubelet kubectl /usr/bin/ # add kubelet serivce cat > /lib/systemd/system/kubelet.service /etc/systemd/system/kubelet.service.d/10-kubeadm.conf 4. 配置kubeadm config 参考： kubeadm Configuration (v1beta3) | Kubernetes kubeadm Configuration (v1beta2) | Kubernetes 4.1. 配置项说明 4.1.1. 配置类型 kubeadm config支持以下几类配置。 apiVersion: kubeadm.k8s.io/v1beta3 kind: InitConfiguration apiVersion: kubeadm.k8s.io/v1beta3 kind: ClusterConfiguration apiVersion: kubelet.config.k8s.io/v1beta1 kind: KubeletConfiguration apiVersion: kubeproxy.config.k8s.io/v1alpha1 kind: KubeProxyConfiguration apiVersion: kubeadm.k8s.io/v1beta3 kind: JoinConfiguration 可以使用以下命令打印init和join的默认配置。 kubeadm config print init-defaults kubeadm config print join-defaults 4.1.2. Init配置 kubeadm init配置中只有InitConfiguration 和 ClusterConfiguration 是必须的。 InitConfiguration: apiVersion: kubeadm.k8s.io/v1beta3 kind: InitConfiguration bootstrapTokens: ... nodeRegistration: ... bootstrapTokens nodeRegistration criSocket：runtime的socket name：节点名称 localAPIEndpoint advertiseAddress：apiserver的广播IP bindPort：k8s控制面安全端口 ClusterConfiguration: apiVersion: kubeadm.k8s.io/v1beta3 kind: ClusterConfiguration networking: ... etcd: ... apiServer: extraArgs: ... extraVolumes: ... ... networking: podSubnet：Pod CIDR范围 serviceSubnet： service CIDR范围 dnsDomain etcd: dataDir：Etcd的数据存储目录 apiserver certSANs：设置额外的apiserver的域名签名证书 imageRepository：镜像仓库 controlPlaneEndpoint：控制面LB的域名 kubernetesVersion：k8s版本 4.2. Init配置示例 在master节点生成默认配置，并修改配置参数。 kubeadm config print init-defaults > kubeadm-config.yaml 修改配置内容 apiVersion: kubeadm.k8s.io/v1beta3 bootstrapTokens: - groups: - system:bootstrappers:kubeadm:default-node-token token: abcdef.0123456789abcdef ttl: 24h0m0s usages: - signing - authentication kind: InitConfiguration localAPIEndpoint: advertiseAddress: 1.2.3.4 # 修改为apiserver的IP 或者去掉localAPIEndpoint则会读取默认IP。 bindPort: 6443 nodeRegistration: criSocket: unix:///var/run/containerd/containerd.sock imagePullPolicy: IfNotPresent name: node taints: null --- apiServer: certSANs: - lb.k8s.domain # 添加额外的apiserver的域名 - timeoutForControlPlane: 4m0s apiVersion: kubeadm.k8s.io/v1beta3 certificatesDir: /etc/kubernetes/pki clusterName: kubernetes controllerManager: {} dns: {} # 默认为coredns etcd: local: dataDir: /data/etcd # 修改etcd的存储盘目录 imageRepository: k8s.gcr.io # 修改镜像仓库地址 controlPlaneEndpoint: lb.k8s.domain # 修改控制面域名 kind: ClusterConfiguration kubernetesVersion: 1.24.0 # k8s 版本 networking: dnsDomain: cluster.local serviceSubnet: 10.96.0.0/12 podSubnet: 10.244.0.0/16 # 设置pod的IP范围 scheduler: {} --- kind: KubeletConfiguration apiVersion: kubelet.config.k8s.io/v1beta1 cgroupDriver: systemd # 设置为systemd 安装完成后可以查看kubeadm config kubectl get cm -n kube-system kubeadm-config -oyaml 5. 安装Master控制面 提前拉取镜像： kubeadm config images pull 5.1. 安装master sudo kubeadm init --config kubeadm-config.yaml --upload-certs --node-name 部署参数说明： --control-plane-endpoint：指定控制面(kube-apiserver)的IP或DNS域名地址。 --apiserver-advertise-address：kube-apiserver的IP地址。 --pod-network-cidr：pod network范围，控制面会自动给每个节点分配CIDR。 --service-cidr：service的IP范围，default \"10.96.0.0/12\"。 --kubernetes-version：指定k8s的版本。 --image-repository：指定k8s镜像仓库地址。 --upload-certs ：标志用来将在所有控制平面实例之间的共享证书上传到集群。 --node-name：hostname-override，作为节点名称。 执行完毕会输出添加master和添加worker的命令如下： ... You can now join any number of control-plane node by running the following command on each as a root: kubeadm join 192.168.0.200:6443 --token 9vr73a.a8uxyaju799qwdjv --discovery-token-ca-cert-hash sha256:7c2e69131a36ae2a042a339b33381c6d0d43887e2de83720eff5359e26aec866 --control-plane --certificate-key f8902e114ef118304e561c3ecd4d0b543adc226b7a07f675f56564185ffe0c07 Please note that the certificate-key gives access to cluster sensitive data, keep it secret! As a safeguard, uploaded-certs will be deleted in two hours; If necessary, you can use kubeadm init phase upload-certs to reload certs afterward. Then you can join any number of worker nodes by running the following on each as root: kubeadm join 192.168.0.200:6443 --token 9vr73a.a8uxyaju799qwdjv --discovery-token-ca-cert-hash sha256:7c2e69131a36ae2a042a339b33381c6d0d43887e2de83720eff5359e26aec866 5.2. 添加其他master 添加master和添加worker的差别在于添加master多了--control-plane 参数来表示添加类型为master。 kubeadm join :6443 --token \\ --discovery-token-ca-cert-hash sha256: \\ --control-plane --certificate-key \\ --node-name 6. 添加Node节点 kubeadm join :6443 --token \\ --discovery-token-ca-cert-hash sha256: \\ --cri-socket /run/containerd/containerd.sock \\ --node-name 7. 安装网络插件 ## 如果安装之后node的状态都改为ready，即为成功 wget https://raw.githubusercontent.com/coreos/flannel/master/Documentation/kube-flannel.yml kubectl apply -f ./kube-flannel.yml kubectl get nodes 如果Pod CIDR的网段不是10.244.0.0/16，则需要加flannel配置中的网段更改为与Pod CIDR的网段一致。 7.1. 问题 Warning FailedCreatePodSandBox 4m6s kubelet Failed to create pod sandbox: rpc error: code = Unknown desc = failed to setup network for sandbox \"300d9b570cc1e23b6335c407b8e7d0ef2c74dc2fe5d7a110678c2dc919c62edf\": plugin type=\"flannel\" failed (add): failed to delegate add: failed to set bridge addr: \"cni0\" already has an IP address different from 10.244.3.1/24 原因： 宿主机节点有cni0网卡，且网卡的IP段与flannel的CIDR网段不同，因此需要删除该网卡，让其重建。 查看cni0网卡 # ifconfig cni0 |grep -w inet inet 10.244.5.1 netmask 255.255.255.0 broadcast 10.244.116.255 查看flannel配置 # cat /run/flannel/subnet.env FLANNEL_NETWORK=10.244.0.0/16 FLANNEL_SUBNET=10.244.116.1/24 FLANNEL_MTU=1450 FLANNEL_IPMASQ=true 发现cni0 IP与FLANNEL_SUBNET网段不一致，因此删除cni0重建。 解决： ifconfig cni0 down ip link delete cni0 8. 部署dashboard kubectl apply -f https://raw.githubusercontent.com/kubernetes/dashboard/v2.5.0/aio/deploy/recommended.yaml 镜像： kubernetesui/dashboard:v2.5.0 默认端口：8443 登录页面需要填入token或kubeconfig 参考：dashboard/creating-sample-user apiVersion: v1 kind: ServiceAccount metadata: name: admin-user namespace: kubernetes-dashboard --- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: admin-user roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: cluster-admin subjects: - kind: ServiceAccount name: admin-user namespace: kubernetes-dashboard 创建用户 kubectl -n kubernetes-dashboard create token admin-user 9. 重置部署 # kubeadm重置 kubeadm reset # 清空数据目录 rm -fr /data/etcd rm -fr /etc/kubernetes rm -fr ~/.kube/ 删除flannel ifconfig cni0 down ip link delete cni0 ifconfig flannel.1 down ip link delete flannel.1 rm -rf /var/lib/cni/ rm -f /etc/cni/net.d/* 10. 问题排查 10.1. kubeadm token过期 问题描述: 添加节点时报以下错误： [discovery] The cluster-info ConfigMap does not yet contain a JWS signature for token ID \"abcdef\", will try again 原因：token过期，初始化token后会在24小时候会被master删除。 解决办法： # 重新生成token kubeadm token create --print-join-command kubeadm token list # kubeadm token create oumnnc.aqlxuvdbntlvzoiv # 重新生成hash openssl x509 -pubkey -in /etc/kubernetes/pki/ca.crt | openssl rsa -pubin -outform der 2>/dev/null | openssl dgst -sha256 -hex | sed 's/^.* //' 基于新生成的token重新添加节点。 10.2. 修改kubeadm join的master IP或端口 kubeadm join命令会去kube-public命名空间获取名为cluster-info的ConfigMap。如果需要修改kubeadm join使用的master的IP或端口，则需要修改cluster-info的configmap。 # 查看cluster-info kubectl -n kube-public get configmaps cluster-info -o yaml # 修改cluster-info kubectl -n kube-public edit configmaps cluster-info 修改配置文件中的server字段 clusters: - cluster: certificate-authority-data: xxx server: https://lb.k8s.domain:36443 name: \"\" 执行kubeadm join的命令时指定新修改的master地址。 10.3. conntrack not found [preflight] Some fatal errors occurred: [ERROR FileExisting-conntrack]: conntrack not found in system path 解决方法： apt -y install conntrack 10.4. Kubelet: unable to determine runtime API version Error: failed to run Kubelet: unable to determine runtime API version: rpc error:code = Unavailable desc = connection error: desc = \"transport: Error while dialing dial unix: missing address\" 解决方法： 检查kubelet的启动参数，可以用二进制直接添加参数debug # 查看启动参数是否遗漏，比如10-kubeadm.conf 文件参数缺失 systemctl cat --no-pager kubelet cat /lib/systemd/system/kubelet.service cat /etc/systemd/system/kubelet.service.d/10-kubeadm.conf 参考： 利用 kubeadm 创建高可用集群 | Kubernetes 使用 kubeadm 创建集群 | Kubernetes 高可用拓扑选项 | Kubernetes kubeadm init | Kubernetes v1.24.2|kubeadm|v1beta3 Installing kubeadm | Kubernetes Ports and Protocols | Kubernetes 容器运行时 | Kubernetes https://github.com/Mirantis/cri-dockerd 配置 cgroup 驱动|Kubernetes GitHub: flannel is a network fabric for containers 部署和访问 Kubernetes 仪表板（Dashboard） | Kubernetes Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"setup/installer/install-k8s-by-kubespray.html":{"url":"setup/installer/install-k8s-by-kubespray.html","title":"使用kubespray安装kubernetes","keywords":"","body":"1. 环境准备1.1. 部署机器1.2. 配置管理机1.3. 配置部署机器1.4. 涉及镜像2. 部署集群2.1. 下载kubespary的源码2.2. 编辑配置文件2.2.1. hosts.ini2.2.2. k8s-cluster.yml2.3. 执行部署操作3. 确认部署结果3.1. ansible的部署结果3.2. k8s集群运行结果4. k8s集群扩容节点4.1. 修改hosts.ini文件4.2. 执行扩容命令4.3. 检查扩容结果5. 部署高可用集群6. 升级k8s集群7. troubles shooting7.1. python-netaddr未安装7.2. swap未关闭7.3. 部署机器内存过小7.4. kube-scheduler组件运行失败7.5. docker安装包冲突1. 环境准备 1.1. 部署机器 以下机器为虚拟机 机器IP 主机名 角色 系统版本 备注 172.16.94.140 kube-master-0 k8s master Centos 4.17.14 内存：3G 172.16.94.141 kube-node-41 k8s node Centos 4.17.14 内存：3G 172.16.94.142 kube-node-42 k8s node Centos 4.17.14 内存：3G 172.16.94.135 部署管理机 - 1.2. 配置管理机 管理机主要用来部署k8s集群，需要安装以下版本的软件，具体可参考： https://github.com/kubernetes-incubator/kubespray#requirements https://github.com/kubernetes-incubator/kubespray/blob/master/requirements.txt ansible>=2.4.0 jinja2>=2.9.6 netaddr pbr>=1.6 ansible-modules-hashivault>=3.9.4 hvac 1、安装及配置ansible 参考ansible的使用。 给部署机器配置SSH的免密登录权限，具体参考ssh免密登录。 2、安装python-netaddr # 安装pip yum -y install epel-release yum -y install python-pip # 安装python-netaddr pip install netaddr 3、升级Jinja # Jinja 2.9 (or newer) pip install --upgrade jinja2 1.3. 配置部署机器 部署机器即用来运行k8s集群的机器，包括Master和Node。 1、确认系统版本 本文采用centos7的系统，建议将系统内核升级到4.x.x以上。 2、关闭防火墙 systemctl stop firewalld systemctl disable firewalld iptables -F 3、关闭swap Kubespary v2.5.0的版本需要关闭swap，具体参考 https://github.com/kubernetes-incubator/kubespray/blob/02cd5418c22d51e40261775908d55bc562206023/roles/kubernetes/preinstall/tasks/verify-settings.yml#L75 - name: Stop if swap enabled assert: that: ansible_swaptotal_mb == 0 when: kubelet_fail_swap_on|default(true) ignore_errors: \"{{ ignore_assert_errors }}\" V2.6.0 版本去除了swap的检查，具体参考： https://github.com/kubernetes-incubator/kubespray/commit/b902602d161f8c147f3d155d2ac5360244577127#diff-b92ae64dd18d34a96fbeb7f7e48a6a9b 执行关闭swap命令swapoff -a。 [root@master ~]#swapoff -a [root@master ~]# [root@master ~]# free -m total used free shared buff/cache available Mem: 976 366 135 6 474 393 Swap: 0 0 0 # swap 一栏为0，表示已经关闭了swap 4、确认部署机器内存 由于本文采用虚拟机部署，内存可能存在不足的问题，因此将虚拟机内存调整为3G或以上；如果是物理机一般不会有内存不足的问题。具体参考： https://github.com/kubernetes-incubator/kubespray/blob/95f1e4634a1c50fa77312d058a2b713353f4307e/roles/kubernetes/preinstall/tasks/verify-settings.yml#L52 - name: Stop if memory is too small for masters assert: that: ansible_memtotal_mb >= 1500 ignore_errors: \"{{ ignore_assert_errors }}\" when: inventory_hostname in groups['kube-master'] - name: Stop if memory is too small for nodes assert: that: ansible_memtotal_mb >= 1024 ignore_errors: \"{{ ignore_assert_errors }}\" when: inventory_hostname in groups['kube-node'] 1.4. 涉及镜像 Docker版本为17.03.2-ce。 1、Master节点 镜像 版本 大小 镜像ID 备注 gcr.io/google-containers/hyperkube v1.9.5 620 MB a7e7fdbc5fee k8s quay.io/coreos/etcd v3.2.4 35.7 MB 498ffffcfd05 gcr.io/google_containers/pause-amd64 3.0 747 kB 99e59f495ffa quay.io/calico/node v2.6.8 282 MB e96a297310fd calico quay.io/calico/cni v1.11.4 70.8 MB 4c4cb67d7a88 calico quay.io/calico/ctl v1.6.3 44.4 MB 46d3aace8bc6 calico 2、Node节点 镜像 版本 大小 镜像ID 备注 gcr.io/google-containers/hyperkube v1.9.5 620 MB a7e7fdbc5fee k8s gcr.io/google_containers/pause-amd64 3.0 747 kB 99e59f495ffa quay.io/calico/node v2.6.8 282 MB e96a297310fd calico quay.io/calico/cni v1.11.4 70.8 MB 4c4cb67d7a88 calico quay.io/calico/ctl v1.6.3 44.4 MB 46d3aace8bc6 calico gcr.io/google_containers/k8s-dns-dnsmasq-nanny-amd64 1.14.8 40.9 MB c2ce1ffb51ed dns gcr.io/google_containers/k8s-dns-sidecar-amd64 1.14.8 42.2 MB 6f7f2dc7fab5 dns gcr.io/google_containers/k8s-dns-kube-dns-amd64 1.14.8 50.5 MB 80cc5ea4b547 dns gcr.io/google_containers/cluster-proportional-autoscaler-amd64 1.1.2 50.5 MB 78cf3f492e6b gcr.io/google_containers/kubernetes-dashboard-amd64 v1.8.3 102 MB 0c60bcf89900 dashboard nginx 1.13 109 MB ae513a47849c - 3、说明 镜像被墙并且全部镜像下载需要较多时间，建议提前下载到部署机器上。 hyperkube镜像主要用来运行k8s核心组件（例如kube-apiserver等）。 此处使用的网络组件为calico。 2. 部署集群 2.1. 下载kubespary的源码 git clone https://github.com/kubernetes-incubator/kubespray.git 2.2. 编辑配置文件 2.2.1. hosts.ini hosts.ini主要为部署节点机器信息的文件，路径为：kubespray/inventory/sample/hosts.ini。 cd kubespray # 复制一份配置进行修改 cp -rfp inventory/sample inventory/k8s vi inventory/k8s/hosts.ini 例如： hosts.ini文件可以填写部署机器的登录密码，也可以不填密码而设置ssh的免密登录。 # Configure 'ip' variable to bind kubernetes services on a # different ip than the default iface # 主机名 ssh登陆IP ssh用户名 ssh登陆密码 机器IP 子网掩码 kube-master-0 ansible_ssh_host=172.16.94.140 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.140 mask=/24 kube-node-41 ansible_ssh_host=172.16.94.141 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.141 mask=/24 kube-node-42 ansible_ssh_host=172.16.94.142 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.142 mask=/24 # configure a bastion host if your nodes are not directly reachable # bastion ansible_ssh_host=x.x.x.x [kube-master] kube-master-0 [etcd] kube-master-0 [kube-node] kube-node-41 kube-node-42 [k8s-cluster:children] kube-node kube-master [calico-rr] 2.2.2. k8s-cluster.yml k8s-cluster.yml主要为k8s集群的配置文件，路径为：kubespray/inventory/k8s/group_vars/k8s-cluster.yml。该文件可以修改安装的k8s集群的版本，参数为：kube_version: v1.9.5。具体可参考： https://github.com/kubernetes-incubator/kubespray/blob/master/inventory/sample/group_vars/k8s-cluster.yml#L22 2.3. 执行部署操作 涉及文件为cluster.yml。 # 进入主目录 cd kubespray # 执行部署命令 ansible-playbook -i inventory/k8s/hosts.ini cluster.yml -b -vvv -vvv 参数表示输出运行日志 如果需要重置可以执行以下命令： 涉及文件为reset.yml。 ansible-playbook -i inventory/k8s/hosts.ini reset.yml -b -vvv 3. 确认部署结果 3.1. ansible的部署结果 ansible命令执行完，出现以下日志，则说明部署成功，否则根据报错内容进行修改。 PLAY RECAP ***************************************************************************** kube-master-0 : ok=309 changed=30 unreachable=0 failed=0 kube-node-41 : ok=203 changed=8 unreachable=0 failed=0 kube-node-42 : ok=203 changed=8 unreachable=0 failed=0 localhost : ok=2 changed=0 unreachable=0 failed=0 以下为部分部署执行日志： kubernetes/preinstall : Update package management cache (YUM) --------------------23.96s /root/gopath/src/kubespray/roles/kubernetes/preinstall/tasks/main.yml:121 kubernetes/master : Master | wait for the apiserver to be running ----------------23.44s /root/gopath/src/kubespray/roles/kubernetes/master/handlers/main.yml:79 kubernetes/preinstall : Install packages requirements ----------------------------20.20s /root/gopath/src/kubespray/roles/kubernetes/preinstall/tasks/main.yml:203 kubernetes/secrets : Check certs | check if a cert already exists on node --------13.94s /root/gopath/src/kubespray/roles/kubernetes/secrets/tasks/check-certs.yml:17 gather facts from all instances --------------------------------------------------9.98s /root/gopath/src/kubespray/cluster.yml:25 kubernetes/node : install | Compare host kubelet with hyperkube container --------9.66s /root/gopath/src/kubespray/roles/kubernetes/node/tasks/install_host.yml:2 kubernetes-apps/ansible : Kubernetes Apps | Start Resources -----------------------9.27s /root/gopath/src/kubespray/roles/kubernetes-apps/ansible/tasks/main.yml:37 kubernetes-apps/ansible : Kubernetes Apps | Lay Down KubeDNS Template ------------8.47s /root/gopath/src/kubespray/roles/kubernetes-apps/ansible/tasks/kubedns.yml:3 download : Sync container ---------------------------------------------------------8.23s /root/gopath/src/kubespray/roles/download/tasks/main.yml:15 kubernetes-apps/network_plugin/calico : Start Calico resources --------------------7.82s /root/gopath/src/kubespray/roles/kubernetes-apps/network_plugin/calico/tasks/main.yml:2 download : Download items ---------------------------------------------------------7.67s /root/gopath/src/kubespray/roles/download/tasks/main.yml:6 download : Download items ---------------------------------------------------------7.48s /root/gopath/src/kubespray/roles/download/tasks/main.yml:6 download : Sync container ---------------------------------------------------------7.35s /root/gopath/src/kubespray/roles/download/tasks/main.yml:15 download : Download items ---------------------------------------------------------7.16s /root/gopath/src/kubespray/roles/download/tasks/main.yml:6 network_plugin/calico : Calico | Copy cni plugins from calico/cni container -------7.10s /root/gopath/src/kubespray/roles/network_plugin/calico/tasks/main.yml:62 download : Download items ---------------------------------------------------------7.04s /root/gopath/src/kubespray/roles/download/tasks/main.yml:6 download : Download items ---------------------------------------------------------7.01s /root/gopath/src/kubespray/roles/download/tasks/main.yml:6 download : Sync container ---------------------------------------------------------7.00s /root/gopath/src/kubespray/roles/download/tasks/main.yml:15 download : Download items ---------------------------------------------------------6.98s /root/gopath/src/kubespray/roles/download/tasks/main.yml:6 download : Download items ---------------------------------------------------------6.79s /root/gopath/src/kubespray/roles/download/tasks/main.yml:6 3.2. k8s集群运行结果 1、k8s组件信息 # kubectl get all --namespace=kube-system NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE ds/calico-node 3 3 3 3 3 2h NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE deploy/kube-dns 2 2 2 2 2h deploy/kubedns-autoscaler 1 1 1 1 2h deploy/kubernetes-dashboard 1 1 1 1 2h NAME DESIRED CURRENT READY AGE rs/kube-dns-79d99cdcd5 2 2 2 2h rs/kubedns-autoscaler-5564b5585f 1 1 1 2h rs/kubernetes-dashboard-69cb58d748 1 1 1 2h NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE ds/calico-node 3 3 3 3 3 2h NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE deploy/kube-dns 2 2 2 2 2h deploy/kubedns-autoscaler 1 1 1 1 2h deploy/kubernetes-dashboard 1 1 1 1 2h NAME DESIRED CURRENT READY AGE rs/kube-dns-79d99cdcd5 2 2 2 2h rs/kubedns-autoscaler-5564b5585f 1 1 1 2h rs/kubernetes-dashboard-69cb58d748 1 1 1 2h NAME READY STATUS RESTARTS AGE po/calico-node-22vsg 1/1 Running 0 2h po/calico-node-t7zgw 1/1 Running 0 2h po/calico-node-zqnx8 1/1 Running 0 2h po/kube-apiserver-kube-master-0 1/1 Running 0 22h po/kube-controller-manager-kube-master-0 1/1 Running 0 2h po/kube-dns-79d99cdcd5-f2t6t 3/3 Running 0 2h po/kube-dns-79d99cdcd5-gw944 3/3 Running 0 2h po/kube-proxy-kube-master-0 1/1 Running 2 22h po/kube-proxy-kube-node-41 1/1 Running 3 22h po/kube-proxy-kube-node-42 1/1 Running 3 22h po/kube-scheduler-kube-master-0 1/1 Running 0 2h po/kubedns-autoscaler-5564b5585f-lt9bb 1/1 Running 0 2h po/kubernetes-dashboard-69cb58d748-wmb9x 1/1 Running 0 2h po/nginx-proxy-kube-node-41 1/1 Running 3 22h po/nginx-proxy-kube-node-42 1/1 Running 3 22h NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE svc/kube-dns ClusterIP 10.233.0.3 53/UDP,53/TCP 2h svc/kubernetes-dashboard ClusterIP 10.233.27.24 443/TCP 2h 2、k8s节点信息 # kubectl get nodes NAME STATUS ROLES AGE VERSION kube-master-0 Ready master 22h v1.9.5 kube-node-41 Ready node 22h v1.9.5 kube-node-42 Ready node 22h v1.9.5 3、组件健康信息 # kubectl get cs NAME STATUS MESSAGE ERROR scheduler Healthy ok controller-manager Healthy ok etcd-0 Healthy {\"health\": \"true\"} 4. k8s集群扩容节点 4.1. 修改hosts.ini文件 如果需要扩容Node节点，则修改hosts.ini文件，增加新增的机器信息。例如，要增加节点机器kube-node-43（IP为172.16.94.143），修改后的文件内容如下： # Configure 'ip' variable to bind kubernetes services on a # different ip than the default iface # 主机名 ssh登陆IP ssh用户名 ssh登陆密码 机器IP 子网掩码 kube-master-0 ansible_ssh_host=172.16.94.140 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.140 mask=/24 kube-node-41 ansible_ssh_host=172.16.94.141 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.141 mask=/24 kube-node-42 ansible_ssh_host=172.16.94.142 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.142 mask=/24 kube-node-43 ansible_ssh_host=172.16.94.143 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.143 mask=/24 # configure a bastion host if your nodes are not directly reachable # bastion ansible_ssh_host=x.x.x.x [kube-master] kube-master-0 [etcd] kube-master-0 [kube-node] kube-node-41 kube-node-42 kube-node-43 [k8s-cluster:children] kube-node kube-master [calico-rr] 4.2. 执行扩容命令 涉及文件为scale.yml。 # 进入主目录 cd kubespray # 执行部署命令 ansible-playbook -i inventory/k8s/hosts.ini scale.yml -b -vvv 4.3. 检查扩容结果 1、ansible的执行结果 PLAY RECAP *************************************** kube-node-41 : ok=228 changed=11 unreachable=0 failed=0 kube-node-42 : ok=197 changed=6 unreachable=0 failed=0 kube-node-43 : ok=227 changed=69 unreachable=0 failed=0 # 新增Node节点 localhost : ok=2 changed=0 unreachable=0 failed=0 2、k8s的节点信息 # kubectl get nodes NAME STATUS ROLES AGE VERSION kube-master-0 Ready master 1d v1.9.5 kube-node-41 Ready node 1d v1.9.5 kube-node-42 Ready node 1d v1.9.5 kube-node-43 Ready node 1m v1.9.5 #该节点为新增Node节点 可以看到新增的kube-node-43节点已经扩容完成。 3、k8s组件信息 # kubectl get po --namespace=kube-system -o wide NAME READY STATUS RESTARTS AGE IP NODE calico-node-22vsg 1/1 Running 0 10h 172.16.94.140 kube-master-0 calico-node-8fz9x 1/1 Running 2 27m 172.16.94.143 kube-node-43 calico-node-t7zgw 1/1 Running 0 10h 172.16.94.142 kube-node-42 calico-node-zqnx8 1/1 Running 0 10h 172.16.94.141 kube-node-41 kube-apiserver-kube-master-0 1/1 Running 0 1d 172.16.94.140 kube-master-0 kube-controller-manager-kube-master-0 1/1 Running 0 10h 172.16.94.140 kube-master-0 kube-dns-79d99cdcd5-f2t6t 3/3 Running 0 10h 10.233.100.194 kube-node-41 kube-dns-79d99cdcd5-gw944 3/3 Running 0 10h 10.233.107.1 kube-node-42 kube-proxy-kube-master-0 1/1 Running 2 1d 172.16.94.140 kube-master-0 kube-proxy-kube-node-41 1/1 Running 3 1d 172.16.94.141 kube-node-41 kube-proxy-kube-node-42 1/1 Running 3 1d 172.16.94.142 kube-node-42 kube-proxy-kube-node-43 1/1 Running 0 26m 172.16.94.143 kube-node-43 kube-scheduler-kube-master-0 1/1 Running 0 10h 172.16.94.140 kube-master-0 kubedns-autoscaler-5564b5585f-lt9bb 1/1 Running 0 10h 10.233.100.193 kube-node-41 kubernetes-dashboard-69cb58d748-wmb9x 1/1 Running 0 10h 10.233.107.2 kube-node-42 nginx-proxy-kube-node-41 1/1 Running 3 1d 172.16.94.141 kube-node-41 nginx-proxy-kube-node-42 1/1 Running 3 1d 172.16.94.142 kube-node-42 nginx-proxy-kube-node-43 1/1 Running 0 26m 172.16.94.143 kube-node-43 5. 部署高可用集群 将hosts.ini文件中的master和etcd的机器增加到多台，执行部署命令。 ansible-playbook -i inventory/k8s/hosts.ini cluster.yml -b -vvv 例如： # Configure 'ip' variable to bind kubernetes services on a # different ip than the default iface # 主机名 ssh登陆IP ssh用户名 ssh登陆密码 机器IP 子网掩码 kube-master-0 ansible_ssh_host=172.16.94.140 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.140 mask=/24 kube-master-1 ansible_ssh_host=172.16.94.144 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.144 mask=/24 kube-master-2 ansible_ssh_host=172.16.94.145 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.145 mask=/24 kube-node-41 ansible_ssh_host=172.16.94.141 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.141 mask=/24 kube-node-42 ansible_ssh_host=172.16.94.142 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.142 mask=/24 kube-node-43 ansible_ssh_host=172.16.94.143 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.143 mask=/24 # configure a bastion host if your nodes are not directly reachable # bastion ansible_ssh_host=x.x.x.x [kube-master] kube-master-0 kube-master-1 kube-master-2 [etcd] kube-master-0 kube-master-1 kube-master-2 [kube-node] kube-node-41 kube-node-42 kube-node-43 [k8s-cluster:children] kube-node kube-master [calico-rr] 6. 升级k8s集群 选择对应的k8s版本信息，执行升级命令。涉及文件为upgrade-cluster.yml。 ansible-playbook upgrade-cluster.yml -b -i inventory/k8s/hosts.ini -e kube_version=v1.10.4 -vvv 7. troubles shooting 在使用kubespary部署k8s集群时，主要遇到以下报错。 7.1. python-netaddr未安装 报错内容： fatal: [node1]: FAILED! => {\"failed\": true, \"msg\": \"The ipaddr filter requires python-netaddr be installed on the ansible controller\"} 解决方法： 需要安装 python-netaddr，具体参考上述[环境准备]内容。 7.2. swap未关闭 报错内容： fatal: [kube-master-0]: FAILED! => { \"assertion\": \"ansible_swaptotal_mb == 0\", \"changed\": false, \"evaluated_to\": false } fatal: [kube-node-41]: FAILED! => { \"assertion\": \"ansible_swaptotal_mb == 0\", \"changed\": false, \"evaluated_to\": false } fatal: [kube-node-42]: FAILED! => { \"assertion\": \"ansible_swaptotal_mb == 0\", \"changed\": false, \"evaluated_to\": false } 解决方法： 所有部署机器执行swapoff -a关闭swap，具体参考上述[环境准备]内容。 7.3. 部署机器内存过小 报错内容： TASK [kubernetes/preinstall : Stop if memory is too small for masters] ********************************************************************************************************************************************************************************************************* task path: /root/gopath/src/kubespray/roles/kubernetes/preinstall/tasks/verify-settings.yml:52 Friday 10 August 2018 21:50:26 +0800 (0:00:00.940) 0:01:14.088 ********* fatal: [kube-master-0]: FAILED! => { \"assertion\": \"ansible_memtotal_mb >= 1500\", \"changed\": false, \"evaluated_to\": false } TASK [kubernetes/preinstall : Stop if memory is too small for nodes] *********************************************************************************************************************************************************************************************************** task path: /root/gopath/src/kubespray/roles/kubernetes/preinstall/tasks/verify-settings.yml:58 Friday 10 August 2018 21:50:27 +0800 (0:00:00.570) 0:01:14.659 ********* fatal: [kube-node-41]: FAILED! => { \"assertion\": \"ansible_memtotal_mb >= 1024\", \"changed\": false, \"evaluated_to\": false } fatal: [kube-node-42]: FAILED! => { \"assertion\": \"ansible_memtotal_mb >= 1024\", \"changed\": false, \"evaluated_to\": false } to retry, use: --limit @/root/gopath/src/kubespray/cluster.retry 解决方法： 调大所有部署机器的内存，本示例中调整为3G或以上。 7.4. kube-scheduler组件运行失败 kube-scheduler组件运行失败，导致http://localhost:10251/healthz调用失败。 报错内容： FAILED - RETRYING: Master | wait for kube-scheduler (1 retries left). FAILED - RETRYING: Master | wait for kube-scheduler (1 retries left). fatal: [node1]: FAILED! => {\"attempts\": 60, \"changed\": false, \"content\": \"\", \"failed\": true, \"msg\": \"Status code was not [200]: Request failed: \", \"redirected\": false, \"status\": -1, \"url\": \"http://localhost:10251/healthz\"} 解决方法： 可能是内存不足导致，本示例中调大了部署机器的内存。 7.5. docker安装包冲突 报错内容： failed: [k8s-node-1] (item={u'name': u'docker-engine-1.13.1-1.el7.centos'}) => { \"attempts\": 4, \"changed\": false, ... \"item\": { \"name\": \"docker-engine-1.13.1-1.el7.centos\" }, \"msg\": \"Error: docker-ce-selinux conflicts with 2:container-selinux-2.66-1.el7.noarch\\n\", \"rc\": 1, \"results\": [ \"Loaded plugins: fastestmirror\\nLoading mirror speeds from cached hostfile\\n * elrepo: mirrors.tuna.tsinghua.edu.cn\\n * epel: mirrors.tongji.edu.cn\\nPackage docker-engine is obsoleted by docker-ce, trying to install docker-ce-17.03.2.ce-1.el7.centos.x86_64 instead\\nResolving Dependencies\\n--> Running transaction check\\n---> Package docker-ce.x86_64 0:17.03.2.ce-1.el7.centos will be installed\\n--> Processing Dependency: docker-ce-selinux >= 17.03.2.ce-1.el7.centos for package: docker-ce-17.03.2.ce-1.el7.centos.x86_64\\n--> Processing Dependency: libltdl.so.7()(64bit) for package: docker-ce-17.03.2.ce-1.el7.centos.x86_64\\n--> Running transaction check\\n---> Package docker-ce-selinux.noarch 0:17.03.2.ce-1.el7.centos will be installed\\n---> Package libtool-ltdl.x86_64 0:2.4.2-22.el7_3 will be installed\\n--> Processing Conflict: docker-ce-selinux-17.03.2.ce-1.el7.centos.noarch conflicts docker-selinux\\n--> Restarting Dependency Resolution with new changes.\\n--> Running transaction check\\n---> Package container-selinux.noarch 2:2.55-1.el7 will be updated\\n---> Package container-selinux.noarch 2:2.66-1.el7 will be an update\\n--> Processing Conflict: docker-ce-selinux-17.03.2.ce-1.el7.centos.noarch conflicts docker-selinux\\n--> Finished Dependency Resolution\\n You could try using --skip-broken to work around the problem\\n You could try running: rpm -Va --nofiles --nodigest\\n\" ] } 解决方法： 卸载旧的docker版本，由kubespary自动安装。 sudo yum remove -y docker \\ docker-client \\ docker-client-latest \\ docker-common \\ docker-latest \\ docker-latest-logrotate \\ docker-logrotate \\ docker-selinux \\ docker-engine-selinux \\ docker-engine 参考文章： https://github.com/kubernetes-incubator/kubespray https://github.com/kubernetes-incubator/kubespray/blob/master/docs/upgrades.md Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"setup/installer/install-k8s-by-minikube.html":{"url":"setup/installer/install-k8s-by-minikube.html","title":"使用minikube安装kubernetes","keywords":"","body":"1. 安装kubectl2. 安装minikube2.1 安装minikube2.2 minikube命令帮助3. 使用minikube安装k8s集群3.1. minikube start3.2. minikube status3.3. minikube stop3.4. minikube delete4. 查看部署结果4.1. 部署组件4.2. dashboard5. troubleshooting5.1. 没有安装VirtualBox5.2. 没有安装Docker 以下内容基于Linux系统，特别为Ubuntu系统 1. 安装kubectl curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/linux/amd64/kubectl && chmod +x kubectl && sudo mv kubectl /usr/local/bin/ 下载指定版本，例如下载v1.9.0版本 curl -LO https://storage.googleapis.com/kubernetes-release/release/v1.9.0/bin/linux/amd64/kubectl && chmod +x kubectl && sudo mv kubectl /usr/local/bin/ 2. 安装minikube minikube的源码地址：https://github.com/kubernetes/minikube 2.1 安装minikube 以下命令为安装latest版本的minikube。 curl -Lo minikube https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64 && chmod +x minikube && sudo mv minikube /usr/local/bin/ 安装指定版本可到https://github.com/kubernetes/minikube/releases下载对应版本。 例如：以下为安装v0.28.2版本 curl -Lo minikube https://storage.googleapis.com/minikube/releases/v0.28.2/minikube-linux-amd64 && chmod +x minikube && sudo mv minikube /usr/local/bin/ 2.2 minikube命令帮助 Minikube is a CLI tool that provisions and manages single-node Kubernetes clusters optimized for development workflows. Usage: minikube [command] Available Commands: addons Modify minikube's kubernetes addons cache Add or delete an image from the local cache. completion Outputs minikube shell completion for the given shell (bash or zsh) config Modify minikube config dashboard Opens/displays the kubernetes dashboard URL for your local cluster delete Deletes a local kubernetes cluster docker-env Sets up docker env variables; similar to '$(docker-machine env)' get-k8s-versions Gets the list of Kubernetes versions available for minikube when using the localkube bootstrapper ip Retrieves the IP address of the running cluster logs Gets the logs of the running localkube instance, used for debugging minikube, not user code mount Mounts the specified directory into minikube profile Profile sets the current minikube profile service Gets the kubernetes URL(s) for the specified service in your local cluster ssh Log into or run a command on a machine with SSH; similar to 'docker-machine ssh' ssh-key Retrieve the ssh identity key path of the specified cluster start Starts a local kubernetes cluster status Gets the status of a local kubernetes cluster stop Stops a running local kubernetes cluster update-check Print current and latest version number update-context Verify the IP address of the running cluster in kubeconfig. version Print the version of minikube Flags: --alsologtostderr log to standard error as well as files -b, --bootstrapper string The name of the cluster bootstrapper that will set up the kubernetes cluster. (default \"localkube\") --log_backtrace_at traceLocation when logging hits line file:N, emit a stack trace (default :0) --log_dir string If non-empty, write log files in this directory --loglevel int Log level (0 = DEBUG, 5 = FATAL) (default 1) --logtostderr log to standard error instead of files -p, --profile string The name of the minikube VM being used. This can be modified to allow for multiple minikube instances to be run independently (default \"minikube\") --stderrthreshold severity logs at or above this threshold go to stderr (default 2) -v, --v Level log level for V logs --vmodule moduleSpec comma-separated list of pattern=N settings for file-filtered logging Use \"minikube [command] --help\" for more information about a command. 3. 使用minikube安装k8s集群 3.1. minikube start 可以以Docker的方式运行k8s的组件，但需要先安装Docker(可参考Docker安装)，启动参数使用--vm-driver=none。 minikube start --vm-driver=none 例如： root@ubuntu:~# minikube start --vm-driver=none Starting local Kubernetes v1.10.0 cluster... Starting VM... Getting VM IP address... Moving files into cluster... Downloading kubeadm v1.10.0 Downloading kubelet v1.10.0 ^[[DFinished Downloading kubelet v1.10.0 Finished Downloading kubeadm v1.10.0 Setting up certs... Connecting to cluster... Setting up kubeconfig... Starting cluster components... Kubectl is now configured to use the cluster. =================== WARNING: IT IS RECOMMENDED NOT TO RUN THE NONE DRIVER ON PERSONAL WORKSTATIONS The 'none' driver will run an insecure kubernetes apiserver as root that may leave the host vulnerable to CSRF attacks When using the none driver, the kubectl config and credentials generated will be root owned and will appear in the root home directory. You will need to move the files to the appropriate location and then set the correct permissions. An example of this is below: sudo mv /root/.kube $HOME/.kube # this will write over any previous configuration sudo chown -R $USER $HOME/.kube sudo chgrp -R $USER $HOME/.kube sudo mv /root/.minikube $HOME/.minikube # this will write over any previous configuration sudo chown -R $USER $HOME/.minikube sudo chgrp -R $USER $HOME/.minikube This can also be done automatically by setting the env var CHANGE_MINIKUBE_NONE_USER=true Loading cached images from config file. 安装指定版本的kubernetes集群 # 查阅版本 minikube get-k8s-versions # 选择版本启动 minikube start --kubernetes-version v1.7.3 --vm-driver=none 3.2. minikube status $ minikube status minikube: Running cluster: Running kubectl: Correctly Configured: pointing to minikube-vm at 172.16.94.139 3.3. minikube stop minikube stop 命令可以用来停止集群。 该命令会关闭 minikube 虚拟机，但将保留所有集群状态和数据。 再次启动集群将恢复到之前的状态。 3.4. minikube delete minikube delete 命令可以用来删除集群。 该命令将关闭并删除 minikube 虚拟机。没有数据或状态会被保存下来。 4. 查看部署结果 4.1. 部署组件 root@ubuntu:~# kubectl get all --namespace=kube-system NAME READY STATUS RESTARTS AGE pod/etcd-minikube 1/1 Running 0 38m pod/kube-addon-manager-minikube 1/1 Running 0 38m pod/kube-apiserver-minikube 1/1 Running 1 39m pod/kube-controller-manager-minikube 1/1 Running 0 38m pod/kube-dns-86f4d74b45-bdfnx 3/3 Running 0 38m pod/kube-proxy-dqdvg 1/1 Running 0 38m pod/kube-scheduler-minikube 1/1 Running 0 38m pod/kubernetes-dashboard-5498ccf677-c2gnh 1/1 Running 0 38m pod/storage-provisioner 1/1 Running 0 38m NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/kube-dns ClusterIP 10.96.0.10 53/UDP,53/TCP 38m service/kubernetes-dashboard NodePort 10.104.48.227 80:30000/TCP 38m NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE daemonset.apps/kube-proxy 1 1 1 1 1 38m NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE deployment.apps/kube-dns 1 1 1 1 38m deployment.apps/kubernetes-dashboard 1 1 1 1 38m NAME DESIRED CURRENT READY AGE replicaset.apps/kube-dns-86f4d74b45 1 1 1 38m replicaset.apps/kubernetes-dashboard-5498ccf677 1 1 1 38m 4.2. dashboard 通过访问ip:port，例如：http://172.16.94.139:30000/，可以访问k8s的`dashboard`控制台。 5. troubleshooting 5.1. 没有安装VirtualBox [root@minikube ~]# minikube start Starting local Kubernetes v1.10.0 cluster... Starting VM... Downloading Minikube ISO 160.27 MB / 160.27 MB [============================================] 100.00% 0s E0727 15:47:08.655647 9407 start.go:174] Error starting host: Error creating host: Error executing step: Running precreate checks. : VBoxManage not found. Make sure VirtualBox is installed and VBoxManage is in the path. Retrying. E0727 15:47:08.656994 9407 start.go:180] Error starting host: Error creating host: Error executing step: Running precreate checks. : VBoxManage not found. Make sure VirtualBox is installed and VBoxManage is in the path ================================================================================ An error has occurred. Would you like to opt in to sending anonymized crash information to minikube to help prevent future errors? To opt out of these messages, run the command: minikube config set WantReportErrorPrompt false ================================================================================ Please enter your response [Y/n]: 解决方法，先安装VirtualBox。 5.2. 没有安装Docker [root@minikube ~]# minikube start --vm-driver=none Starting local Kubernetes v1.10.0 cluster... Starting VM... E0727 15:56:54.936706 9441 start.go:174] Error starting host: Error creating host: Error executing step: Running precreate checks. : docker cannot be found on the path for this machine. A docker installation is a requirement for using the none driver: exec: \"docker\": executable file not found in $PATH. Retrying. E0727 15:56:54.938930 9441 start.go:180] Error starting host: Error creating host: Error executing step: Running precreate checks. : docker cannot be found on the path for this machine. A docker installation is a requirement for using the none driver: exec: \"docker\": executable file not found in $PATH 解决方法，先安装Docker。 文章参考： https://github.com/kubernetes/minikube https://kubernetes.io/docs/setup/minikube/ https://kubernetes.io/docs/tasks/tools/install-minikube/ https://kubernetes.io/docs/tasks/tools/install-kubectl/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"setup/installer/install-k8s-by-kind.html":{"url":"setup/installer/install-k8s-by-kind.html","title":"使用kind安装kubernetes","keywords":"","body":"1. 安装kind2. 创建k8s集群3. kindest/node容器内进程1. 安装kind On mac or linux curl -Lo ./kind \"https://github.com/kubernetes-sigs/kind/releases/download/v0.7.0/kind-$(uname)-amd64\" chmod +x ./kind mv ./kind /some-dir-in-your-PATH/kind 2. 创建k8s集群 $ kind create cluster Creating cluster \"kind\" ... ✓ Ensuring node image (kindest/node:v1.17.0) 🖼 ✓ Preparing nodes 📦 ✓ Writing configuration 📜 ✓ Starting control-plane 🕹️ ✓ Installing CNI 🔌 ✓ Installing StorageClass 💾 Set kubectl context to \"kind-kind\" You can now use your cluster with: kubectl cluster-info --context kind-kind Not sure what to do next? 😅 Check out https://kind.sigs.k8s.io/docs/user/quick-start/ 查看集群信息 $ kubectl cluster-info --context kind-kind Kubernetes master is running at https://127.0.0.1:32768 KubeDNS is running at https://127.0.0.1:32768/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy 查看node $ kubectl get node -o wide NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME kind-control-plane Ready master 35h v1.17.0 172.17.0.2 Ubuntu 19.10 3.10.107-1-tlinux2_kvm_guest-0049 containerd://1.3.2 查看pod $ kubectl get po --all-namespaces -o wide NAMESPACE NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES kube-system coredns-6955765f44-lqk9v 1/1 Running 0 35h 10.244.0.4 kind-control-plane kube-system coredns-6955765f44-zpsmc 1/1 Running 0 35h 10.244.0.3 kind-control-plane kube-system etcd-kind-control-plane 1/1 Running 0 35h 172.17.0.2 kind-control-plane kube-system kindnet-8mt7d 1/1 Running 0 35h 172.17.0.2 kind-control-plane kube-system kube-apiserver-kind-control-plane 1/1 Running 0 35h 172.17.0.2 kind-control-plane kube-system kube-controller-manager-kind-control-plane 1/1 Running 0 35h 172.17.0.2 kind-control-plane kube-system kube-proxy-5w25s 1/1 Running 0 35h 172.17.0.2 kind-control-plane kube-system kube-scheduler-kind-control-plane 1/1 Running 0 35h 172.17.0.2 kind-control-plane local-path-storage local-path-provisioner-7745554f7f-dckzr 1/1 Running 0 35h 10.244.0.2 kind-control-plane docker ps $ docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 93b291f99dd4 kindest/node:v1.17.0 \"/usr/local/bin/entr…\" 2 minutes ago Up 2 minutes 127.0.0.1:32768->6443/tcp kind-control-plane 3. kindest/node容器内进程 $ docker exec -it 93b291f99dd4 bash root@kind-control-plane:/# ps auxw USER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND root 1 0.1 0.0 19512 7480 ? Ss 03:18 0:00 /sbin/init root 105 0.0 0.0 26396 7344 ? S root 1080 0.0 0.0 0 0 ? Z 03:18 0:00 [iptables-nft-sa] root 1241 0.0 0.0 112540 5156 ? Sl 03:19 0:00 /usr/local/bin/containerd-shim-runc-v2 -namespace k8s.io -id 5cbd7bbe186cf5847786c7a03aa4c6f82e6c805d0a189f0f3e8fb1750594260d -address /run/containerd/containerd.sock root 1262 0.0 0.0 1012 4 ? Ss 03:19 0:00 /pause root 1303 0.1 0.0 134372 14088 ? Ssl 03:19 0:00 local-path-provisioner --debug start --helper-image k8s.gcr.io/debian-base:v2.0.0 --config /etc/config/config.json root 1411 0.0 0.0 112540 4876 ? Sl 03:19 0:00 /usr/local/bin/containerd-shim-runc-v2 -namespace k8s.io -id 196b440345cb5ef47a6c31222323d35bbfef85d1d79c149ec0e3a6e22022a5f0 -address /run/containerd/containerd.sock root 1437 0.0 0.0 1012 4 ? Ss 03:19 0:00 /pause root 1450 0.0 0.0 112540 4380 ? Sl 03:19 0:00 /usr/local/bin/containerd-shim-runc-v2 -namespace k8s.io -id de7bdf052083978c78708383f842567d4fb38adff22a56792437a4de82425afe -address /run/containerd/containerd.sock root 1480 0.0 0.0 1012 4 ? Ss 03:19 0:00 /pause root 1530 0.1 0.1 144324 19056 ? Ssl 03:19 0:00 /coredns -conf /etc/coredns/Corefile root 1531 0.1 0.1 144580 19204 ? Ssl 03:19 0:00 /coredns -conf /etc/coredns/Corefile 参考： https://github.com/kubernetes-sigs/kind https://kind.sigs.k8s.io/docs/user/quick-start/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"setup/installer/install-dashboard.html":{"url":"setup/installer/install-dashboard.html","title":"安装k8s dashboard","keywords":"","body":"1. 部署dashboard2. 登录dashboard2.1. 创建超级管理员2.2. 创建Namespace管理员2.3. 创建只读账户3. 集成SSO登录1. 部署dashboard kubectl apply -f https://raw.githubusercontent.com/kubernetes/dashboard/v2.7.0/aio/deploy/recommended.yaml 镜像： kubernetesui/dashboard:v2.5.0 默认端口：8443 登录页面需要填入token或kubeconfig 2. 登录dashboard 2.1. 创建超级管理员 参考：dashboard/creating-sample-user 创建dashboard-adminuser.yaml文件如下： k8s 1.24+版本需要自行创建secret绑定serviceaccount apiVersion: v1 kind: ServiceAccount metadata: name: admin-user namespace: kubernetes-dashboard --- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: admin-user roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: cluster-admin subjects: - kind: ServiceAccount name: admin-user namespace: kubernetes-dashboard --- apiVersion: v1 kind: Secret metadata: name: admin-user-secret namespace: kubernetes-dashboard annotations: kubernetes.io/service-account.name: \"admin-user\" type: kubernetes.io/service-account-token 创建serviceaccount和ClusterRoleBinding，绑定cluster-admin的超级管理员的权限。 kubectl apply -f dashboard-adminuser.yaml 创建用户token kubectl -n kubernetes-dashboard create token admin-user --duration 8760h 或者通过secret查询token kubectl get secret admin-user-secret -n kubernetes-dashboard -o jsonpath={\".data.token\"} | base64 -d 移除账号 kubectl -n kubernetes-dashboard delete serviceaccount admin-user kubectl -n kubernetes-dashboard delete clusterrolebinding admin-user 2.2. 创建Namespace管理员 1、创建角色权限（role） kind: Role apiVersion: rbac.authorization.k8s.io/v1 metadata: namespace: name: -admin-role rules: - apiGroups: - '*' resources: - '*' verbs: - '*' 2、创建用户账号（ServiceAccount） apiVersion: v1 kind: ServiceAccount metadata: name: -admin-user namespace: 创建secret 可自动生成token apiVersion: v1 kind: Secret metadata: name: ${SecretName} namespace: ${ServiceAccountNS} annotations: kubernetes.io/service-account.name: \"${ServiceAccountName}\" type: kubernetes.io/service-account-token 3、创建角色绑定关系 apiVersion: rbac.authorization.k8s.io/v1 kind: RoleBinding metadata: name: -admin-user namespace: roleRef: apiGroup: rbac.authorization.k8s.io kind: Role name: -admin-role subjects: - kind: ServiceAccount name: -admin-user namespace: 4、生成token kubectl -n create token --duration 8760h 或者通过上述secret中的token获得 kubectl get secret ${SecretName} -n ${ServiceAccountNS} -o jsonpath={\".data.token\"} | base64 -d 2.3. 创建只读账户 集群默认提供了几种命名空间级别的权限，分别设置ClusterRole: [admin, edit, view], 将授权设置为ClusterRole为view即可。 apiVersion: rbac.authorization.k8s.io/v1 kind: RoleBinding metadata: name: -admin-user namespace: roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: view subjects: - kind: ServiceAccount name: -admin-user namespace: 3. 集成SSO登录 社区提供了添加Authorization header的方式来集成自定义的SSO登录。即在HTTP请求中增加Header: Authorization: Bearer 。该操作可以通过apisix或Nginx等插件注入Header。 参考： 部署和访问 Kubernetes 仪表板（Dashboard） | Kubernetes dashboard/creating-sample-user.md at master · kubernetes/dashboard · GitHub dashboard/docs/user/access-control at master · kubernetes/dashboard · GitHub Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"setup/kubeadm-upgrade.html":{"url":"setup/kubeadm-upgrade.html","title":"kubeadm升级k8s集群","keywords":"","body":"1. 版本注意事项1.1. 版本跨度过大1.2. 可升级的版本计划2. 版本升级步骤2.1. 准备工作2.2. 升级master节点2.2.1. 升级第一个master节点2.2.2. 手动升级你的 CNI 驱动插件。2.2.3. 升级其他master节点2.2.4. 升级master的kubelet组件2.3. 升级worker节点3. 升级版本回滚4. 问题排查本文主要说明如何使用kubeadm来升级k8s集群。 1. 版本注意事项 假设k8s的版本格式为x.y.z，那么使用kubeadm最多只能升级到y+1版本，或者是当前y版本的最新版本。例如你k8s集群的版本为1.24.x，那么你最大版本只能下载1.25.x的kubeadm来升级版本。 因此升级前需要执行以下命令来验证可升级的版本。 kubeadm upgrade plan 1.1. 版本跨度过大 如果出现以下报错，说明升级的版本跨度过大。 # kubeadm的版本为v1.26.7 ./kubeadm version kubeadm version: &version.Info{Major:\"1\", Minor:\"26\", GitVersion:\"v1.26.7\", GitCommit:\"84e1fc493a47446df2e155e70fca768d2653a398\", GitTreeState:\"clean\", BuildDate:\"2023-07-19T12:22:13Z\", GoVersion:\"go1.20.6\", Compiler:\"gc\", Platform:\"linux/amd64\"} # 当前k8s集群版本1.24.2 版本跨度过大。 ./kubeadm upgrade plan [upgrade/config] Making sure the configuration is correct: [upgrade/config] Reading configuration from the cluster... [upgrade/config] FYI: You can look at this config file with 'kubectl -n kube-system get cm kubeadm-config -o yaml' [upgrade/config] FATAL: this version of kubeadm only supports deploying clusters with the control plane version >= 1.25.0. Current version: v1.24.2 To see the stack trace of this error execute with --v=5 or higher 1.2. 可升级的版本计划 可升级的版本计划如下 当前的k8s版本为1.24.2，可升级的版本是1.24.16或1.25.12，其中etcd升级的版本为3.5.6-0。 ./kubeadm upgrade plan [upgrade/config] Making sure the configuration is correct: [upgrade/config] Reading configuration from the cluster... [upgrade/config] FYI: You can look at this config file with 'kubectl -n kube-system get cm kubeadm-config -o yaml' [upload-config] Storing the configuration used in ConfigMap \"kubeadm-config\" in the \"kube-system\" Namespace [preflight] Running pre-flight checks. [upgrade] Running cluster health checks [upgrade] Fetching available versions to upgrade to [upgrade/versions] Cluster version: v1.24.2 [upgrade/versions] kubeadm version: v1.25.12 I0815 21:41:34.096199 2934255 version.go:256] remote version is much newer: v1.27.4; falling back to: stable-1.25 [upgrade/versions] Target version: v1.25.12 [upgrade/versions] Latest version in the v1.24 series: v1.24.16 Components that must be upgraded manually after you have upgraded the control plane with 'kubeadm upgrade apply': COMPONENT CURRENT TARGET kubelet 4 x v1.24.2 v1.24.16 Upgrade to the latest version in the v1.24 series: COMPONENT CURRENT TARGET kube-apiserver v1.24.2 v1.24.16 kube-controller-manager v1.24.2 v1.24.16 kube-scheduler v1.24.2 v1.24.16 kube-proxy v1.24.2 v1.24.16 CoreDNS v1.8.6 v1.9.3 etcd 3.5.3-0 3.5.6-0 You can now apply the upgrade by executing the following command: kubeadm upgrade apply v1.24.16 _____________________________________________________________________ Components that must be upgraded manually after you have upgraded the control plane with 'kubeadm upgrade apply': COMPONENT CURRENT TARGET kubelet 4 x v1.24.2 v1.25.12 Upgrade to the latest stable version: COMPONENT CURRENT TARGET kube-apiserver v1.24.2 v1.25.12 kube-controller-manager v1.24.2 v1.25.12 kube-scheduler v1.24.2 v1.25.12 kube-proxy v1.24.2 v1.25.12 CoreDNS v1.8.6 v1.9.3 etcd 3.5.3-0 3.5.6-0 You can now apply the upgrade by executing the following command: kubeadm upgrade apply v1.25.12 _____________________________________________________________________ The table below shows the current state of component configs as understood by this version of kubeadm. Configs that have a \"yes\" mark in the \"MANUAL UPGRADE REQUIRED\" column require manual config upgrade or resetting to kubeadm defaults before a successful upgrade can be performed. The version to manually upgrade to is denoted in the \"PREFERRED VERSION\" column. API GROUP CURRENT VERSION PREFERRED VERSION MANUAL UPGRADE REQUIRED kubeproxy.config.k8s.io v1alpha1 v1alpha1 no kubelet.config.k8s.io v1beta1 v1beta1 no _____________________________________________________________________ 2. 版本升级步骤 2.1. 准备工作 下载指定版本的kubeadm二进制 查看升级计划：kubeadm upgrade plan 2.2. 升级master节点 2.2.1. 升级第一个master节点 kubeadm upgrade apply v1.25.x -f 升级结束会查看都以下输出： [upgrade/successful] SUCCESS! Your cluster was upgraded to \"v1.25.x\". Enjoy! [upgrade/kubelet] Now that your control plane is upgraded, please proceed with upgrading your kubelets if you haven't already done so. 2.2.2. 手动升级你的 CNI 驱动插件。 你的容器网络接口（CNI）驱动应该提供了程序自身的升级说明。 参阅插件页面查找你的 CNI 驱动， 并查看是否需要其他升级步骤。 如果 CNI 驱动作为 DaemonSet 运行，则在其他控制平面节点上不需要此步骤。 2.2.3. 升级其他master节点 下载指定版本的kubeadm组件。使用以下命令升级，注意区别于第一个master的升级命令。 kubeadm upgrade node 2.2.4. 升级master的kubelet组件 将节点标记为不可调度并驱逐所有负载，准备节点的维护： # 将 替换为你要腾空的控制面节点名称 kubectl drain --ignore-daemonsets 下载kubelet和kubectl # 用最新的补丁版本替换 1.27.x-00 中的 x apt-mark unhold kubelet kubectl && \\ apt-get update && apt-get install -y kubelet=1.27.x-00 kubectl=1.27.x-00 && \\ apt-mark hold kubelet kubectl 重启kubelet sudo systemctl daemon-reload sudo systemctl restart kubelet 解除节点保护 # 将 替换为你的节点名称 kubectl uncordon 2.3. 升级worker节点 同 2.2.4. 升级master的kubelet组件的步骤，worker节点只需要升级kubelet。 3. 升级版本回滚 kubeadm升级过程中会把相关目录备份到/etc/kubernetes/tmp目录，备份内容如下： tmp/ ├── kubeadm-backup-etcd-2023-08-16-14-50-50 │ └── etcd └── kubeadm-backup-manifests-2023-08-16-14-50-50 ├── etcd.yaml ├── kube-apiserver.yaml ├── kube-controller-manager.yaml └── kube-scheduler.yaml 如果 kubeadm upgrade 失败并且没有回滚，例如由于执行期间节点意外关闭， 你可以再次运行 kubeadm upgrade。 此命令是幂等的，并最终确保实际状态是你声明的期望状态。 要从故障状态恢复，你还可以运行 kubeadm upgrade apply --force 而无需更改集群正在运行的版本。 在升级期间，kubeadm 向 /etc/kubernetes/tmp 目录下的如下备份文件夹写入数据： kubeadm-backup-etcd-- kubeadm-backup-manifests-- kubeadm-backup-etcd 包含当前控制面节点本地 etcd 成员数据的备份。 如果 etcd 升级失败并且自动回滚也无法修复，则可以将此文件夹中的内容复制到 /var/lib/etcd 进行手工修复。如果使用的是外部的 etcd，则此备份文件夹为空。 kubeadm-backup-manifests 包含当前控制面节点的静态 Pod 清单文件的备份版本。 如果升级失败并且无法自动回滚，则此文件夹中的内容可以复制到 /etc/kubernetes/manifests 目录实现手工恢复。 如果由于某些原因，在升级前后某个组件的清单未发生变化，则 kubeadm 也不会为之生成备份版本。 4. 问题排查 在升级k8s 从1.25.12到1.26.7的过程中，遇到master节点的服务起不来，报错如下： \"CreatePodSandbox for pod failed\" err=\"open /run/systemd/resolve/resolv.conf: no such file or directory\" pod=\"kube-system/kube-apiserver\" 现象主要是静态pod起不来，包括etcd等。 具体解决方法参考：open /run/systemd/resolve/resolv.conf # 查看以下的resolv.conf是否存在 cat /var/lib/kubelet/config.yaml | grep resolvConf /run/systemd/resolve/resolv.conf # 如果不存在，检查systemd-resolved是否正常运行， systemctl status systemd-resolved # 如果没有运行，则运行该服务 systemctl start systemd-resolved # 或者新建文件/run/systemd/resolve/resolv.conf，并将其他master的文件拷贝过来。 参考： 升级 kubeadm 集群 | Kubernetes 升级 Linux 节点 | Kubernetes 安装扩展（Addon） | Kubernetes Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"setup/kubeadm-certs.html":{"url":"setup/kubeadm-certs.html","title":"kubeadm管理证书","keywords":"","body":"1. 检查证书是否过期2. 手动更新过期的证书2.1. 备份/etc/kubernetes目录2.2. 执行更新证书命令2.3. 重启k8s组件2.4. 更新默认的kubeconfig文件2.5. 配置kubelet证书轮转3. 修改kubeadm源码证书时间3.1. ca文件的有效期（默认为10年）3.2. 证书文件的有效期（默认为1年）通过kubeadm搭建的集群默认的证书时间是1年（由于官方期望每年更新一次k8s的版本，在更新的时候会默认更新证书），当你执行命令出现以下报错，说明你的证书已经到期了，则需要手动更新证书。 # kubectl get node Unable to connect to the server: x509: certificate has expired or is not yet valid: current time 2023-08-03T18:06:23+08:00 is after 2023-07-04T06:30:54Z # 或者出现以下报错 You must be logged in to the server(unauthorized) 以下说明手动更新证书的流程。 具体可以参考：使用 kubeadm 进行证书管理 | Kubernetes 1. 检查证书是否过期 kubeadm certs check-expiration 会输出以下的内容： CERTIFICATE EXPIRES RESIDUAL TIME CERTIFICATE AUTHORITY EXTERNALLY MANAGED admin.conf Dec 30, 2020 23:36 UTC 364d no apiserver Dec 30, 2020 23:36 UTC 364d ca no apiserver-etcd-client Dec 30, 2020 23:36 UTC 364d etcd-ca no apiserver-kubelet-client Dec 30, 2020 23:36 UTC 364d ca no controller-manager.conf Dec 30, 2020 23:36 UTC 364d no etcd-healthcheck-client Dec 30, 2020 23:36 UTC 364d etcd-ca no etcd-peer Dec 30, 2020 23:36 UTC 364d etcd-ca no etcd-server Dec 30, 2020 23:36 UTC 364d etcd-ca no front-proxy-client Dec 30, 2020 23:36 UTC 364d front-proxy-ca no scheduler.conf Dec 30, 2020 23:36 UTC 364d no CERTIFICATE AUTHORITY EXPIRES RESIDUAL TIME EXTERNALLY MANAGED ca Dec 28, 2029 23:36 UTC 9y no etcd-ca Dec 28, 2029 23:36 UTC 9y no front-proxy-ca Dec 28, 2029 23:36 UTC 9y no 2. 手动更新过期的证书 分别在master节点执行以下命令。 2.1. 备份/etc/kubernetes目录 cp -fr /etc/kubernetes /etc/kubernetes.bak cp -fr ~/.kube ~/.kube.bak 2.2. 执行更新证书命令 kubeadm certs renew all 输出如下： # kubeadm certs renew all [renew] Reading configuration from the cluster... [renew] FYI: You can look at this config file with 'kubectl -n kube-system get cm kubeadm-config -o yaml' [renew] Error reading configuration from the Cluster. Falling back to default configuration certificate embedded in the kubeconfig file for the admin to use and for kubeadm itself renewed certificate for serving the Kubernetes API renewed certificate the apiserver uses to access etcd renewed certificate for the API server to connect to kubelet renewed certificate embedded in the kubeconfig file for the controller manager to use renewed certificate for liveness probes to healthcheck etcd renewed certificate for etcd nodes to communicate with each other renewed certificate for serving etcd renewed certificate for the front proxy client renewed certificate embedded in the kubeconfig file for the scheduler manager to use renewed Done renewing certificates. You must restart the kube-apiserver, kube-controller-manager, kube-scheduler and etcd, so that they can use the new certificates. 2.3. 重启k8s组件 先重启etcd 注意事项：需要将三个master节点的证书都重新更新后，然后三个master的etcd服务一起重启，使得etcd集群使用新的证书可以正常运行，否则会导致kube-apiserver也启动失败。 crictl ps |grep \"etcd\"|awk '{print $1}'|xargs crictl stop 再重启kube-apiserver、kube-controller、kube-scheduler容器。 crictl ps |egrep \"kube-apiserver|kube-scheduler|kube-controller\"|awk '{print $1}'|xargs crictl stop 2.4. 更新默认的kubeconfig文件 cp -fr /etc/kubernetes/admin.conf $HOME/.kube/config 2.5. 配置kubelet证书轮转 由于kubelet默认支持证书轮转，当证书过期时，可以自动生成新的密钥，并从 Kubernetes API 申请新的证书。可以查看kubelet的配置检查是否已经开启。 # cat /var/lib/kubelet/config.yaml |grep rotate rotateCertificates: true 3. 修改kubeadm源码证书时间 由于社区不允许用户配置超过1年的证书，因此自定义证书时间的参数不被允许开发。 相关issue如下： https://github.com/kubernetes/kubernetes/issues/119350 如果要实现自定义参数设置证书时间，可参考一下pr： https://github.com/kubernetes/kubernetes/pull/100907/files 如果需要修改kubeadm源码证书可以参考如下代码修改。 kubeadm中跟证书相关的代码有： 3.1. ca文件的有效期（默认为10年） 代码文件：./staging/src/k8s.io/client-go/util/cert/cert.go 中 NewSelfSignedCACert 函数的NotAfter字段 代码如下： // NewSelfSignedCACert creates a CA certificate func NewSelfSignedCACert(cfg Config, key crypto.Signer) (*x509.Certificate, error) { now := time.Now() // returns a uniform random value in [0, max-1), then add 1 to serial to make it a uniform random value in [1, max). serial, err := cryptorand.Int(cryptorand.Reader, new(big.Int).SetInt64(math.MaxInt64-1)) if err != nil { return nil, err } serial = new(big.Int).Add(serial, big.NewInt(1)) notBefore := now.UTC() if !cfg.NotBefore.IsZero() { notBefore = cfg.NotBefore.UTC() } tmpl := x509.Certificate{ SerialNumber: serial, Subject: pkix.Name{ CommonName: cfg.CommonName, Organization: cfg.Organization, }, DNSNames: []string{cfg.CommonName}, NotBefore: notBefore, NotAfter: now.Add(duration365d * 10).UTC(), # 默认为10年 KeyUsage: x509.KeyUsageKeyEncipherment | x509.KeyUsageDigitalSignature | x509.KeyUsageCertSign, BasicConstraintsValid: true, IsCA: true, } certDERBytes, err := x509.CreateCertificate(cryptorand.Reader, &tmpl, &tmpl, key.Public(), key) if err != nil { return nil, err } return x509.ParseCertificate(certDERBytes) } 3.2. 证书文件的有效期（默认为1年） 代码文件：cmd/kubeadm/app/util/pkiutil/pki_helpers.go中 NewSignedCert 函数的 notAfter 字段 常量参数kubeadmconstants.CertificateValidity ： /cmd/kubeadm/app/constants/constants.go 代码如下： // NewSignedCert creates a signed certificate using the given CA certificate and key func NewSignedCert(cfg *CertConfig, key crypto.Signer, caCert *x509.Certificate, caKey crypto.Signer, isCA bool) (*x509.Certificate, error) { // returns a uniform random value in [0, max-1), then add 1 to serial to make it a uniform random value in [1, max). serial, err := cryptorand.Int(cryptorand.Reader, new(big.Int).SetInt64(math.MaxInt64-1)) if err != nil { return nil, err } serial = new(big.Int).Add(serial, big.NewInt(1)) if len(cfg.CommonName) == 0 { return nil, errors.New(\"must specify a CommonName\") } keyUsage := x509.KeyUsageKeyEncipherment | x509.KeyUsageDigitalSignature if isCA { keyUsage |= x509.KeyUsageCertSign } RemoveDuplicateAltNames(&cfg.AltNames) # 此处引用了一个常量 notAfter := time.Now().Add(kubeadmconstants.CertificateValidity).UTC() if cfg.NotAfter != nil { notAfter = *cfg.NotAfter } certTmpl := x509.Certificate{ Subject: pkix.Name{ CommonName: cfg.CommonName, Organization: cfg.Organization, }, DNSNames: cfg.AltNames.DNSNames, IPAddresses: cfg.AltNames.IPs, SerialNumber: serial, NotBefore: caCert.NotBefore, NotAfter: notAfter, KeyUsage: keyUsage, ExtKeyUsage: cfg.Usages, BasicConstraintsValid: true, IsCA: isCA, } certDERBytes, err := x509.CreateCertificate(cryptorand.Reader, &certTmpl, caCert, key.Public(), caKey) if err != nil { return nil, err } return x509.ParseCertificate(certDERBytes) } 其中常量文件为： /cmd/kubeadm/app/constants/constants.go 代码如下： # 常量默认证书为1年。 // CertificateValidity defines the validity for all the signed certificates generated by kubeadm CertificateValidity = time.Hour * 24 * 365 可以修改此处常量的值10年，例如： # 常量默认证书为1年。 // CertificateValidity defines the validity for all the signed certificates generated by kubeadm CertificateValidity = time.Hour * 24 * 365 * 10 修改源码后，就可以重新编译kubeadm二进制。生成10年的证书文件。 参考： 使用 kubeadm 进行证书管理 | Kubernetes Kubernetes v1.25 编译 kubeadm 修改证书有效期到 100 年 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"setup/k8s-cert.html":{"url":"setup/k8s-cert.html","title":"k8s证书及秘钥","keywords":"","body":"1. 证书分类1.1. k8s集群的证书分类2. CA证书及秘钥3. cfssl工具4. 创建 CA (Certificate Authority)4.1. 配置源文件4.2. 执行命令5. 创建 kubernetes 证书5.1. 配置源文件5.2. 执行命令6. 创建 admin 证书6.1. 配置源文件6.2. 执行命令7. 创建 kube-proxy 证书7.1. 配置源文件7.2. 执行命令8. 校验证书9. 分发证书1. 证书分类 服务器证书：server cert，用于客户端验证服务端的身份。 客户端证书：client cert，用于服务端验证客户端的身份。 对等证书：peer cert（既是server cert又是client cert），用户成员之间的身份验证，例如 etcd。 1.1. k8s集群的证书分类 etcd节点：需要标识自己服务的server cert，也需要client cert与etcd集群其他节点交互，因此需要一个对等证书。 master节点：需要标识 apiserver服务的server cert，也需要client cert连接etcd集群，也需要一个对等证书。 kubelet：需要标识自己服务的server cert，也需要client cert请求apiserver，也使用一个对等证书。 kubectl、kube-proxy、calico：需要client证书。 2. CA证书及秘钥 目录：/etc/kubernetes/ssl 分类 证书/秘钥 说明 组件 ca ca-key.pem ca.pem ca.csr Kubernetes kubernetes-key.pem kubernetes.pem kubernetes.csr Admin admin-key.pem admin.pem admin.csr Kubelet kubelet.crt kubelet.key 配置文件 分类 证书/秘钥 说明 ca ca-config.json ca-csr.json Kubernetes kubernetes-csr.json Admin admin-csr.json Kube-proxy kube-proxy-csr.json 3. cfssl工具 安装cfssl： # 下载cfssl $ curl https://pkg.cfssl.org/R1.2/cfssl_linux-amd64 -o /usr/local/bin/cfssl $ curl https://pkg.cfssl.org/R1.2/cfssljson_linux-amd64 -o /usr/local/bin/cfssljson $ curl https://pkg.cfssl.org/R1.2/cfssl-certinfo_linux-amd64 -o /usr/local/bin/cfssl-certinfo # 添加可执行权限 $ chmod +x /usr/local/bin/cfssl /usr/local/bin/cfssljson /usr/local/bin/cfssl-certinfo 4. 创建 CA (Certificate Authority) 4.1. 配置源文件 创建 CA 配置文件 ca-config.json cat ca-config.json { \"signing\": { \"default\": { \"expiry\": \"87600h\" }, \"profiles\": { \"kubernetes\": { \"usages\": [ \"signing\", \"key encipherment\", \"server auth\", \"client auth\" ], \"expiry\": \"876000h\" } } } } EOF 参数说明 ca-config.json：可以定义多个 profiles，分别指定不同的过期时间、使用场景等参数；后续在签名证书时使用某个 profile； signing：表示该证书可用于签名其它证书；生成的 ca.pem 证书中 CA=TRUE； server auth：表示client可以用该 CA 对server提供的证书进行验证； client auth：表示server可以用该CA对client提供的证书进行验证； 创建 CA 证书签名请求 ca-csr.json cat ca-csr.json { \"CN\": \"kubernetes\", \"key\": { \"algo\": \"rsa\", \"size\": 2048 }, \"names\": [ { \"C\": \"CN\", \"ST\": \"ShenZhen\", \"L\": \"ShenZhen\", \"O\": \"k8s\", \"OU\": \"System\" } ] } EOF 参数说明 ca-csr.json的参数 CN：Common Name，kube-apiserver 从证书中提取该字段作为请求的用户名 (User Name)；浏览器使用该字段验证网站是否合法； names中的字段： C : country，国家 ST: state，州或省份 L：location，城市 O：organization，组织，kube-apiserver 从证书中提取该字段作为请求用户所属的组 (Group) OU：organization unit 4.2. 执行命令 cfssl gencert -initca ca-csr.json | cfssljson -bare ca 输出如下： # cfssl gencert -initca ca-csr.json | cfssljson -bare ca 2019/12/13 14:35:52 [INFO] generating a new CA key and certificate from CSR 2019/12/13 14:35:52 [INFO] generate received request 2019/12/13 14:35:52 [INFO] received CSR 2019/12/13 14:35:52 [INFO] generating key: rsa-2048 2019/12/13 14:35:52 [INFO] encoded CSR 2019/12/13 14:35:52 [INFO] signed certificate with serial number 248379771349454958117219047414671162179070747780 生成以下文件： # 生成文件 -rw-r--r-- 1 root root 1005 12月 13 11:32 ca.csr -rw------- 1 root root 1675 12月 13 11:32 ca-key.pem -rw-r--r-- 1 root root 1363 12月 13 11:32 ca.pem # 配置源文件 -rw-r--r-- 1 root root 293 12月 13 11:31 ca-config.json -rw-r--r-- 1 root root 210 12月 13 11:31 ca-csr.json 5. 创建 kubernetes 证书 5.1. 配置源文件 创建 kubernetes 证书签名请求文件kubernetes-csr.json。 cat kubernetes-csr.json { \"CN\": \"kubernetes\", \"hosts\": [ \"127.0.0.1\", \"\", \"\", \"kubernetes\", \"kubernetes.default\", \"kubernetes.default.svc\", \"kubernetes.default.svc.cluster\", \"kubernetes.default.svc.cluster.local\" ], \"key\": { \"algo\": \"rsa\", \"size\": 2048 }, \"names\": [{ \"C\": \"\", \"ST\": \"\", \"L\": \"\", \"O\": \"\", \"OU\": \"\" }] } EOF 参数说明： MASTER_IP：master节点的IP或域名 MASTER_CLUSTER_IP：kube-apiserver 指定的 service-cluster-ip-range 网段的第一个IP，例如（10.254.0.1）。 5.2. 执行命令 $ cfssl gencert -ca=ca.pem -ca-key=ca-key.pem -config=ca-config.json -profile=kubernetes kubernetes-csr.json | cfssljson -bare kubernetes 输出如下： # cfssl gencert -ca=ca.pem -ca-key=ca-key.pem -config=ca-config.json -profile=kubernetes kubernetes-csr.json | cfssljson -bare kubernetes 2019/12/13 14:40:28 [INFO] generate received request 2019/12/13 14:40:28 [INFO] received CSR 2019/12/13 14:40:28 [INFO] generating key: rsa-2048 2019/12/13 14:40:28 [INFO] encoded CSR 2019/12/13 14:40:28 [INFO] signed certificate with serial number 392795299385191732458211386861696542628305189374 2019/12/13 14:40:28 [WARNING] This certificate lacks a \"hosts\" field. This makes it unsuitable for websites. For more information see the Baseline Requirements for the Issuance and Management of Publicly-Trusted Certificates, v.1.1.6, from the CA/Browser Forum (https://cabforum.org); specifically, section 10.2.3 (\"Information Requirements\"). 生成以下文件： # 生成文件 -rw-r--r-- 1 root root 1269 12月 13 14:40 kubernetes.csr -rw------- 1 root root 1679 12月 13 14:40 kubernetes-key.pem -rw-r--r-- 1 root root 1643 12月 13 14:40 kubernetes.pem # 配置源文件 -rw-r--r-- 1 root root 580 12月 13 14:40 kubernetes-csr.json 6. 创建 admin 证书 6.1. 配置源文件 创建 admin 证书签名请求文件 admin-csr.json： cat admin-csr.json { \"CN\": \"admin\", \"hosts\": [], \"key\": { \"algo\": \"rsa\", \"size\": 2048 }, \"names\": [ { \"C\": \"CN\", \"ST\": \"ShenZhen\", \"L\": \"ShenZhen\", \"O\": \"system:masters\", \"OU\": \"System\" } ] } EOF 6.2. 执行命令 $ cfssl gencert -ca=ca.pem -ca-key=ca-key.pem -config=ca-config.json -profile=kubernetes admin-csr.json | cfssljson -bare admin 输出如下： # cfssl gencert -ca=ca.pem -ca-key=ca-key.pem -config=ca-config.json -profile=kubernetes admin-csr.json | cfssljson -bare admin 2019/12/13 14:52:37 [INFO] generate received request 2019/12/13 14:52:37 [INFO] received CSR 2019/12/13 14:52:37 [INFO] generating key: rsa-2048 2019/12/13 14:52:37 [INFO] encoded CSR 2019/12/13 14:52:37 [INFO] signed certificate with serial number 465422983473444224050765004141217688748259757371 2019/12/13 14:52:37 [WARNING] This certificate lacks a \"hosts\" field. This makes it unsuitable for websites. For more information see the Baseline Requirements for the Issuance and Management of Publicly-Trusted Certificates, v.1.1.6, from the CA/Browser Forum (https://cabforum.org); specifically, section 10.2.3 (\"Information Requirements\"). 生成文件 # 生成文件 -rw-r--r-- 1 root root 1013 12月 13 14:52 admin.csr -rw------- 1 root root 1675 12月 13 14:52 admin-key.pem -rw-r--r-- 1 root root 1407 12月 13 14:52 admin.pem # 配置源文件 -rw-r--r-- 1 root root 231 12月 13 14:49 admin-csr.json 7. 创建 kube-proxy 证书 7.1. 配置源文件 创建 kube-proxy 证书签名请求文件 kube-proxy-csr.json： cat kube-proxy-csr.json { \"CN\": \"system:kube-proxy\", \"hosts\": [], \"key\": { \"algo\": \"rsa\", \"size\": 2048 }, \"names\": [ { \"C\": \"CN\", \"ST\": \"BeiJing\", \"L\": \"BeiJing\", \"O\": \"k8s\", \"OU\": \"System\" } ] } EOF 7.2. 执行命令 $ cfssl gencert -ca=ca.pem -ca-key=ca-key.pem -config=ca-config.json -profile=kubernetes kube-proxy-csr.json | cfssljson -bare kube-proxy 输出如下： # cfssl gencert -ca=ca.pem -ca-key=ca-key.pem -config=ca-config.json -profile=kubernetes kube-proxy-csr.json | cfssljson -bare kube-proxy 2019/12/13 19:37:48 [INFO] generate received request 2019/12/13 19:37:48 [INFO] received CSR 2019/12/13 19:37:48 [INFO] generating key: rsa-2048 2019/12/13 19:37:48 [INFO] encoded CSR 2019/12/13 19:37:48 [INFO] signed certificate with serial number 526712749765692443642491255093816136154324531741 2019/12/13 19:37:48 [WARNING] This certificate lacks a \"hosts\" field. This makes it unsuitable for websites. For more information see the Baseline Requirements for the Issuance and Management of Publicly-Trusted Certificates, v.1.1.6, from the CA/Browser Forum (https://cabforum.org); specifically, section 10.2.3 (\"Information Requirements\"). 生成文件： # 生成文件 -rw-r--r-- 1 root root 1009 12月 13 19:37 kube-proxy.csr -rw------- 1 root root 1675 12月 13 19:37 kube-proxy-key.pem -rw-r--r-- 1 root root 1407 12月 13 19:37 kube-proxy.pem # 配置源文件 -rw-r--r-- 1 root root 230 12月 13 19:37 kube-proxy-csr.json 8. 校验证书 openssl x509 -noout -text -in kubernetes.pem 输出如下： # openssl x509 -noout -text -in kubernetes.pem Certificate: Data: Version: 3 (0x2) Serial Number: 44:cd:8c:e6:a4:60:ff:3f:09:af:02:e7:68:5e:f2:0f:e6:a0:39:fe Signature Algorithm: sha256WithRSAEncryption Issuer: C=CN, ST=ShenZhen, L=ShenZhen, O=k8s, OU=System, CN=kubernetes Validity Not Before: Dec 13 06:35:00 2019 GMT Not After : Nov 19 06:35:00 2119 GMT Subject: C=CN, ST=ShenZhen, L=ShenZhen, O=k8s, OU=System, CN=kubernetes Subject Public Key Info: Public Key Algorithm: rsaEncryption Public-Key: (2048 bit) Modulus: 00:d7:91:4f:90:56:fb:ab:a9:de:c4:98:9e:d7:e6: 45:db:5a:14:9a:76:78:6a:4c:db:3c:47:3c:e7:1c: 3c:37:4e:8a:cf:9c:a1:8a:4c:51:4c:cd:45:b0:03: 87:06:b9:20:2c:3a:51:f9:21:55:1c:90:7c:f8:93: bc:6a:48:05:3d:8b:74:fd:f2:f1:e6:5e:ad:b4:a8: f6:6d:f9:63:9e:e4:b4:cc:68:9e:90:d7:ef:de:ce: c1:1d:1b:68:59:68:5e:5f:7d:5c:f3:49:4f:18:72: be:b5:c8:af:e2:8d:34:9c:d2:68:b7:8c:26:69:cc: a5:f4:ca:69:2d:d7:21:f5:19:2e:b2:b5:97:16:87: 9f:9c:fd:01:97:c2:0e:20:b4:88:27:9a:37:9a:af: 0a:cf:82:4f:26:24:cb:07:ac:8c:b1:34:20:42:22: 00:b2:b0:98:c5:53:01:fb:32:aa:15:1b:7e:39:44: ae:af:6e:c3:65:96:f6:38:7a:87:37:d0:31:63:d8: a4:15:13:f2:56:da:e6:09:45:2b:46:2c:cb:63:db: f7:ba:7f:44:0a:36:39:7c:cc:5b:42:e5:56:c7:7f: dd:64:5c:f2:4a:af:d3:a9:d1:6e:06:27:57:09:4d: db:08:62:87:66:c8:2c:36:00:41:f1:90:f6:5f:68: 20:3d Exponent: 65537 (0x10001) X509v3 extensions: X509v3 Key Usage: critical Digital Signature, Key Encipherment X509v3 Extended Key Usage: TLS Web Server Authentication, TLS Web Client Authentication X509v3 Basic Constraints: critical CA:FALSE X509v3 Subject Key Identifier: 3D:3F:FA:B8:36:D7:FE:B1:59:BE:B1:F5:C1:5D:88:3D:BC:78:9F:87 X509v3 Authority Key Identifier: keyid:40:A2:D4:30:22:12:2E:C2:FB:A2:55:2C:CB:F0:F6:3E:4D:B8:02:03 X509v3 Subject Alternative Name: DNS:kubernetes, DNS:kubernetes.default, DNS:kubernetes.default.svc, DNS:kubernetes.default.svc.cluster, DNS:kubernetes.default.svc.cluster.local, IP Address:127.0.0.1, IP Address:172.20.0.112, IP Address:172.20.0.113, IP Address:172.20.0.114, IP Address:172.20.0.115, IP Address:10.254.0.1 Signature Algorithm: sha256WithRSAEncryption 63:50:f6:2a:03:c7:35:dd:e9:10:8d:2f:b3:27:9a:64:f3:e1: 11:8a:18:1e:fa:6d:85:30:11:b4:59:a3:6c:86:cd:2b:5c:67: 17:4f:aa:0d:bb:4c:ee:c8:af:e7:3d:61:6d:03:9d:14:6f:00: 48:56:59:b5:76:13:a9:30:23:e0:b2:d2:12:64:0c:60:0d:76: ec:c6:4f:b1:bc:24:01:7a:48:c6:fd:9e:5d:68:da:b9:a1:ad: 30:7a:ba:90:e2:e3:4e:b4:92:1b:c5:f2:8c:c1:b0:3d:fc:14: d2:46:e8:f8:22:8f:d9:4d:85:4f:58:6b:0f:84:78:06:b4:b9: 92:b9:0d:bd:1d:95:e9:0d:42:d3:fd:dd:2a:59:60:3f:63:35: eb:07:25:d2:ea:0d:19:a6:f3:dc:92:8e:ee:73:04:15:5e:97: e8:da:51:c3:69:49:96:36:c7:cc:5b:e5:e5:cb:e5:ce:9f:21: 6f:6b:56:16:bf:85:ad:1c:8c:91:c1:91:0a:90:18:e2:4a:b0: 32:58:33:ef:55:8e:8f:4a:e3:0f:b8:f7:41:04:65:89:e1:1b: d8:41:28:6e:84:c3:1c:8e:a9:a0:8a:42:e4:fe:d7:fe:0e:24: dc:74:37:fa:5e:be:20:69:c5:9a:5a:e6:83:1c:0b:9e:e1:43: ef:4f:7a:37 字段说明： 确认 Issuer 字段的内容和 ca-csr.json 一致； 确认 Subject 字段的内容和 kubernetes-csr.json 一致； 确认 X509v3 Subject Alternative Name 字段的内容和 kubernetes-csr.json 一致； 确认 X509v3 Key Usage、Extended Key Usage 字段的内容和 ca-config.json 中 kubernetes profile 一致； 9. 分发证书 将生成的证书和秘钥文件（后缀名为.pem）拷贝到所有机器的 /etc/kubernetes/ssl 目录下。 mkdir -p /etc/kubernetes/ssl cp *.pem /etc/kubernetes/ssl 参考文章： https://kubernetes.io/docs/concepts/cluster-administration/certificates/ https://coreos.com/os/docs/latest/generate-self-signed-certificates.html https://jimmysong.io/kubernetes-handbook/practice/create-tls-and-secret-key.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"setup/k8s-version-release.html":{"url":"setup/k8s-version-release.html","title":"k8s版本说明","keywords":"","body":"1. k8s版本号说明2. 版本偏差策略2.1. 支持的版本偏差1）kube-apiserver2）kubelet3）kube-controller-manager、kube-scheduler 和 cloud-controller-manager4）kubectl5）kube-proxy2.2. 组件升级顺序3. k8s版本发布周期3.1. 发布周期参考：1. k8s版本号说明 k8s维护最新三个版本的发布分支（[2022.7.2]当前最新三个版本为1.24、1.23、1.22），Kubernetes 1.19 和更新的版本获得大约 1 年的补丁支持。 Kubernetes 版本表示为 x.y.z， 其中 x 是主要版本，y 是次要版本，z 是补丁版本。遵循语义化版本规范。 2. 版本偏差策略 2.1. 支持的版本偏差 总结： kubelet 版本不能比 kube-apiserver 版本新，最多只可落后两个次要版本。 kube-controller-manager、kube-scheduler 和 cloud-controller-manager 不能比 kube-apiserver 版本新。最多落后一个次要版本（允许实时升级）。 kubectl 在 kube-apiserver 的一个次要版本（较旧或较新）中支持。 kube-proxy 和节点上的 kubelet 必须是相同的次要版本。 1）kube-apiserver 在高可用性（HA）集群中， 最新版和最老版的 kube-apiserver 实例版本偏差最多为一个次要版本。 例如： 最新的 kube-apiserver 实例处于 1.24 版本 其他 kube-apiserver 实例支持 1.24 和 1.23 版本 2）kubelet kubelet 版本不能比 kube-apiserver 版本新，并且最多只可落后两个次要版本。 例如： kube-apiserver 处于 1.24 版本 kubelet 支持 1.24、1.23 和 1.22 版本 说明： 如果 HA 集群中的 kube-apiserver 实例之间存在版本偏差，这会缩小允许的 kubelet 版本范围。 例如： kube-apiserver 实例处于 1.24 和 1.23 版本 kubelet 支持 1.23 和 1.22 版本， （不支持 1.24 版本，因为这将比 kube-apiserver 1.23 版本的实例新） 3）kube-controller-manager、kube-scheduler 和 cloud-controller-manager kube-controller-manager、kube-scheduler 和 cloud-controller-manager 不能比与它们通信的 kube-apiserver 实例新。 它们应该与 kube-apiserver 次要版本相匹配，但可能最多旧一个次要版本（允许实时升级）。 例如： kube-apiserver 处于 1.24 版本 kube-controller-manager、kube-scheduler 和 cloud-controller-manager 支持 1.24 和 1.23 版本 说明： 如果 HA 集群中的 kube-apiserver 实例之间存在版本偏差， 并且这些组件可以与集群中的任何 kube-apiserver 实例通信（例如，通过负载均衡器），这会缩小这些组件所允许的版本范围。 例如： kube-apiserver 实例处于 1.24 和 1.23 版本 kube-controller-manager、kube-scheduler 和 cloud-controller-manager 与可以路由到任何 kube-apiserver 实例的负载均衡器通信 kube-controller-manager、kube-scheduler 和 cloud-controller-manager 支持 1.23 版本（不支持 1.24 版本，因为它比 1.23 版本的 kube-apiserver 实例新） 4）kubectl kubectl 在 kube-apiserver 的一个次要版本（较旧或较新）中支持。 例如： kube-apiserver 处于 1.24 版本 kubectl 支持 1.25、1.24 和 1.23 版本 说明： 如果 HA 集群中的 kube-apiserver 实例之间存在版本偏差，这会缩小支持的 kubectl 版本范围。 例如： kube-apiserver 实例处于 1.24 和 1.23 版本 kubectl 支持 1.24 和 1.23 版本（其他版本将与 kube-apiserver 组件之一相差不止一个的次要版本） 5）kube-proxy kube-proxy 和节点上的 kubelet 必须是相同的次要版本。 kube-proxy 版本不能比 kube-apiserver 版本新。 kube-proxy 最多只能比 kube-apiserver 落后两个次要版本。 例如： 如果 kube-proxy 版本处于 1.22 版本： kubelet 必须处于相同的次要版本 1.22。 kube-apiserver 版本必须介于 1.22 和 1.24 之间，包括两者。 2.2. 组件升级顺序 优先升级kube-apiserver，其他的组件按照上述的版本要求进行升级，最好保持一致的版本。 3. k8s版本发布周期 k8s每年大概发布三次，即3-4个月发布一次大版本（发布版本为 vX.Y 里程碑创建的 Git 分支 release-X.Y）。 发布过程可被认为具有三个主要阶段： 特性增强定义 实现 稳定 3.1. 发布周期 1）正常开发（第 1-11 周） /sig {name} /sig {name} /kind {type} /lgtm /approved 2）代码冻结（第 12-14 周） /milestone {v1.y} /sig {name} /kind {bug, failing-test} /lgtm /approved 3）发布后（第 14 周以上） 回到“正常开发”阶段要求： /sig {name} /kind {type} /lgtm /approved 参考： 发行版本 | Kubernetes kubernetes/CHANGELOG at master · GitHub sig-release/releases at master · kubernetes/sig-release · GitHub design-proposals-archive/versioning.md at main · kubernetes/design-proposals-archive · GitHub Kubernetes 发布周期 | Kubernetes Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"setup/k8s-changelog.html":{"url":"setup/k8s-changelog.html","title":"k8s版本记录","keywords":"","body":"1.27（一）重要更新k8s.gcr.io 重定向到 registry.k8s.io 相关说明原地调整 Pod 资源 (alpha)StatefulSet PVC 自动删除功能特性 Beta优化大型集群中 kube-proxy 的 iptables 模式性能Kubelet 事件驱动 PLEG 升级为 BetaPod 调度就绪态功能增强Deployment 滚动更新过程中的调度优化关于加快 Pod 启动的进展（二）弃用变更1.26（一）重要更新Kubelet Evented PLEG for Better Performance优化 kube-proxy 性能，它只发送在调用 iptables-restore 中更改的规则，而不是整个规则集PR#112200 client-go 的 SharedInformerFactory 增加 Shutdown 方法，来等待 Factory 内所有运行的 informer 都结束。（二）弃用变更1.25（一）重要更新cgroup v2 升级到 GACSI 内联存储卷正式发布GAEphemeral Containers进入稳定版本（二）弃用变更1.24（一）重要更新1）kubelet完全移除Dockershim【最重大更新】2）Beta API 默认关闭3）存储容量和卷扩展到GA4）避免 IP 分配给service的冲突（二）弃用变更1）kubeadm2）kube-apiserver3）kube-controller-manager4）kubelet1.23（一） 重要更新1）FlexVolume 已弃用2）IPv4/IPv6 双栈网络到 GA3）Horizo​​ntalPodAutoscaler v2 到 GA4）Scheduler简化多点插件配置（二）已知问题1.22（—）重要更新1）kubeadm2）etcd3）kubelet1.27 参考： Kubernetes 在 v1.27 中移除的特性和主要变更 https://github.com/kubernetes/kubernetes/blob/master/CHANGELOG/CHANGELOG-1.27.md#changelog-since-v1260 202304 | K8s 1.27 正式发布 - DaoCloud Enterprise （一）重要更新 k8s.gcr.io 重定向到 registry.k8s.io 相关说明 Kubernetes 项目为了托管其容器镜像，使用社区拥有的一个名为 registry.k8s.io. 的镜像仓库。从 3 月 20 日起，所有来自过期 k8s.gcr.io 仓库的流量将被重定向到 registry.k8s.io。 已弃用的 k8s.gcr.io 仓库最终将被淘汰。Kubernetes v1.27 版本不会发布到旧的仓库。 原地调整 Pod 资源 (alpha) 参考：Kubernetes 1.27: 原地调整 Pod 资源 (alpha) | Kubernetes 在 Kubernetes v1.27 中，添加了一个新的 alpha 特性，允许用户调整分配给 Pod 的 CPU 和内存资源大小，而无需重新启动容器。 首先，API 层面现在允许修改 Pod 容器中的 resources 字段下的 cpu 和 memory 资源。资源修改只需 patch 正在运行的 pod 规约即可。 StatefulSet PVC 自动删除功能特性 Beta 在 v1.23 中引入的 StatefulSetAutoDeletePVC 功能将在 1.27 版本中升级为 Beta，并默认开启。 然而，默认开启并不意味着所有 StatefulSet 的 PVC 都将自动删除。 优化大型集群中 kube-proxy 的 iptables 模式性能 功能 MinimizeIPTablesRestore 在 1.26 版本中引入，并在 1.27 版本中升级为 Beta 并默认启用。 该功能旨在改善大型集群中 kube-proxy 的 iptables 模式性能。 如果您遇到 Service 信息未正确同步到 iptables 的问题，您可以通过将 kube-proxy 启动参数设置为 --feature-gates=MinimizeIPTablesRestore=false 来禁用该功能（并向社区提交问题）。 您还可以查看 kube-proxy 的 metrics 信息中的 sync_proxy_rules_iptables_partial_restore_failures_total 指标来监控规则同步失败的次数。 Kubelet 事件驱动 PLEG 升级为 Beta 在节点 Pod 较多的情况下，通过容器运行时的 Event 驱动 Pod 状态更新，能够有效地提升效率。 在 1.27 中，该功能已经达到了 Beta 条件，基础的 E2E 测试任务已经添加。 之所以默认关闭该功能，是因为社区认为该功能还需要补充以下验证：压力测试、恢复测试和带退避逻辑的重试。 Pod 调度就绪态功能增强 调度就绪态功能 PodSchedulingReadiness，在 v1.26 作为 Alpha 功能引入，从 v1.27 开始该功能升级为 Beta，默认开启。 Deployment 滚动更新过程中的调度优化 在 v1.27 中，PodTopologySpread 调度策略可以区分调度 Pod 标签的值 （这里通常指 Pod 的 pod-template-hash 标签，不同 replica set 对应的 Pod 该标签的值不同）， 这样滚动更新后，新的 Pod 实例会被调度得更加均匀 。 关于加快 Pod 启动的进展 要启用并行镜像拉取，请在 kubelet 配置中将 serializeImagePulls 字段设置为 false。 当 serializeImagePulls 被禁用时，将立即向镜像服务发送镜像拉取请求，并可以并行拉取多个镜像。 为了在节点上具有多个 Pod 的场景中加快 Pod 启动，特别是在突然扩缩的情况下， kubelet 需要同步 Pod 状态并准备 ConfigMap、Secret 或卷。这就需要大带宽访问 kube-apiserver。在 v1.27 中，kubelet 为了提高 Pod 启动性能，将这些默认值分别提高到了 50 和 100。 （二）弃用变更 kubelet 移除了命令行参数 --container-runtime。 弃用的命令行参数 --pod-eviction-timeout 将被从 kube-controller-manager 中移除。 1.26 参考： Kubernetes 1.26 中的移除、弃用和主要变更 | Kubernetes https://github.com/kubernetes/kubernetes/blob/master/CHANGELOG/CHANGELOG-1.26.md#changelog-since-v1250 https://docs.daocloud.io/blogs/221209-k8s-1.26/ https://www.alibabacloud.com/help/zh/ack/product-overview/kubernetes-1-26-release-notes?spm=a2c63.p38356.0.0.64624df9xXfEkZ （一）重要更新 Kubelet Evented PLEG for Better Performance 该功能让 kubelet 在跟踪节点中 Pod 状态时，通过尽可能依赖容器运行时接口(CRI) 的通知来减少定期轮训，这会减少 kubelet 对 CPU 的使用 新增 Alpha Feature Gate —— EventedPLEG 来控制是否开启该功能。 优化 kube-proxy 性能，它只发送在调用 iptables-restore 中更改的规则，而不是整个规则集 PR#112200 client-go 的 SharedInformerFactory 增加 Shutdown 方法，来等待 Factory 内所有运行的 informer 都结束。 （二）弃用变更 Kubelet 不再支持 v1alpha2 版本的 CRI，接入的容器运行时必须实现 v1 版本的容器运行时接口。 Kubernetes v1.26 将不支持 containerd 1.5.x 及更早的版本；需要升级到 containerd 1.6.x 或更高版本后，才能将该节点的 kubelet 升级到 1.26。 1.25 参考： https://github.com/kubernetes/kubernetes/blob/master/CHANGELOG/CHANGELOG-1.25.md#whats-new-major-themes Kubernetes 1.25 的移除说明和主要变更 | Kubernetes （一）重要更新 cgroup v2 升级到 GA Kubernetes 1.25 将 cgroup v2 正式发布（GA）， 让kubelet使用最新的容器资源管理能力。一些 Kubernetes 特性专门使用 cgroup v2 来增强资源管理和隔离。 例如，MemoryQoS 特性提高了内存利用率并依赖 cgroup v2 功能来启用它。kubelet 中的新资源管理特性也将利用新的 cgroup v2 特性向前发展。 CSI 内联存储卷正式发布GA CSI 内联存储卷与其他类型的临时卷相似，如 configMap、downwardAPI 和 secret。 重要的区别是，存储是由 CSI 驱动提供的，它允许使用第三方供应商提供的临时存储。 卷被定义为 Pod 规约的一部分，并遵循 Pod 的生命周期，这意味着卷随着 Pod 的调度而创建，并随着 Pod 的销毁而销毁。 Ephemeral Containers进入稳定版本 当pod crash的时候，无法通过kubectl exec 进入容器，这个时候可以通过临时容器Ephemeral Containers) （二）弃用变更 Kubernetes v1.25 将移除 PodSecurityPolicy，取而代之的是 Pod Security Admission（即 PodSecurity 安全准入控制器）。 清理 IPTables 链的所有权 从 v1.25 开始，Kubelet 将逐渐迁移为不在 nat 表中创建以下 iptables 链： KUBE-MARK-DROP KUBE-MARK-MASQ KUBE-POSTROUTING 1.24 最新发行版本：1.24.2 (发布日期: 2022-06-15） 不再支持：2023-09-29 补丁版本： 1.24.1、 1.24.2 Complete 1.24 Schedule and Changelog Kubernetes 1.24 使用 go1.18构建，默认情况下将不再验证使用 SHA-1 哈希算法签名的证书。 （一）重要更新 1.24.0主要参考kubernetes/CHANGELOG-1.24.major-themes 1）kubelet完全移除Dockershim【最重大更新】 在 v1.20 中弃用后，dockershim 组件已从 kubelet 中删除。从 v1.24 开始，您将需要使用其他受支持的运行时之一（例如 containerd 或 CRI-O），或者如果您依赖 Docker 引擎作为容器运行时，则使用 cri-dockerd。有关确保您的集群已准备好进行此移除的更多信息，请参阅本指南)。 2）Beta API 默认关闭 默认情况下，不会在集群中启用新的 beta API。默认情况下，现有的 beta API 和现有 beta API 的新版本将继续启用。 3）存储容量和卷扩展到GA 存储容量跟踪支持通过 CSIStorageCapacity 对象公开当前可用的存储容量，并增强使用具有后期绑定的 CSI 卷的 pod 的调度。 卷扩展增加了对调整现有持久卷大小的支持。 4）避免 IP 分配给service的冲突 Kubernetes 1.24 引入了一项新的选择加入功能，允许您为服务的静态 IP 地址分配软预留范围。通过手动启用此功能，集群将更喜欢从服务 IP 地址池中自动分配，从而降低冲突风险。 可以分配 Service ClusterIP： 动态，这意味着集群将自动在配置的服务 IP 范围内选择一个空闲 IP。 静态，这意味着用户将在配置的服务 IP 范围内设置一个 IP。 Service ClusterIP 是唯一的，因此，尝试使用已分配的 ClusterIP 创建 Service 将返回错误。 （二）弃用变更 1）kubeadm kubeadm.k8s.io/v1beta2 已被弃用，并将在未来的版本中删除，可能在 3 个版本（一年）中。您应该开始将 kubeadm.k8s.io/v1beta3 用于新集群。要迁移磁盘上的旧配置文件，您可以使用 kubeadm config migrate 命令。 默认 k​​ubeadm 配置为 containerd 套接字（Unix：unix:///var/run/containerd/containerd.sock，Windows：npipe:////./pipe/containerd-containerd）而不是 Docker 的配置.如果在集群创建期间 Init|JoinConfiguration.nodeRegistration.criSocket 字段为空，并且在主机上发现多个套接字，则总是会抛出错误并要求用户通过设置字段中的值来指定要使用的套接字。使用 crictl 与 CRI 套接字进行所有通信，以执行诸如拉取图像和获取正在运行的容器列表等操作，而不是在 Docker 的情况下使用 docker CLI。 kubeadm 迁移到标签和污点中不再使用 master 一词。对于新的集群，标签 node-role.kubernetes.io/master 将不再添加到控制平面节点，只会添加标签 node-role.kubernetes.io/control-plane。 2）kube-apiserver 不安全的地址标志 --address、--insecure-bind-address、--port 和 --insecure-port（自 1.20 起惰性）被删除 弃用了--master-countflag 和--endpoint-reconciler-type=master-countreconciler，转而使用 lease reconciler。 已弃用Service.Spec.LoadBalancerIP。 3）kube-controller-manager kube-controller-manager 中的不安全地址标志 --address 和 --port 自 v1.20 起无效，并在 v1.24 中被删除。 4）kubelet --pod-infra-container-image kubelet 标志已弃用，将在未来版本中删除。 以下与 dockershim 相关的标志也与 dockershim 一起被删除 --experimental-dockershim-root-directory、--docker-endpoint、--image-pull-progress-deadline、--network-plugin、--cni-conf -dir，--cni-bin-dir，--cni-cache-dir，--network-plugin-mtu。(#106907, @cyclinder) 1.23 最新发行版本：1.23.8 (发布日期: 2022-06-15） 不再支持：2023-02-28 补丁版本： 1.23.1、 1.23.2、 1.23.3、 1.23.4、 1.23.5、 1.23.6、 1.23.7、 1.23.8 Complete 1.23 Schedule and Changelog Kubernetes 是使用 golang 1.17 构建的。此版本的 go 删除了使用 GODEBUG=x509ignoreCN=0 环境设置来重新启用将 X.509 服务证书的 CommonName 视为主机名的已弃用旧行为的能力。 （一） 重要更新 1）FlexVolume 已弃用 FlexVolume 已弃用。 Out-of-tree CSI 驱动程序是在 Kubernetes 中编写卷驱动程序的推荐方式。FlexVolume 驱动程序的维护者应实施 CSI 驱动程序并将 FlexVolume 的用户转移到 CSI。 FlexVolume 的用户应将其工作负载转移到 CSI 驱动程序。 2）IPv4/IPv6 双栈网络到 GA IPv4/IPv6 双栈网络从 GA 毕业。从 1.21 开始，Kubernetes 集群默认启用支持双栈网络。在 1.23 中，移除了 IPv6DualStack 功能门。双栈网络的使用不是强制性的。尽管启用了集群以支持双栈网络，但 Pod 和服务继续默认为单栈。要使用双栈网络：Kubernetes 节点具有可路由的 IPv4/IPv6 网络接口，使用支持双栈的 CNI 网络插件，Pod 配置为双栈，服务的 .spec.ipFamilyPolicy 字段设置为 PreferDualStack 或需要双栈。 3）Horizo​​ntalPodAutoscaler v2 到 GA Horizo​​ntalPodAutoscaler API 的第 2 版在 1.23 版本中逐渐稳定。 Horizo​​ntalPodAutoscaler autoscaling/v2beta2 API 已弃用，取而代之的是新的 autoscaling/v2 API，Kubernetes 项目建议将其用于所有用例。 4）Scheduler简化多点插件配置 kube-scheduler 正在为插件添加一个新的、简化的配置字段，以允许在一个位置启用多个扩展点。新的 multiPoint 插件字段旨在为管理员简化大多数调度程序设置。通过 multiPoint 启用的插件将自动为它们实现的每个单独的扩展点注册。例如，实现 Score 和 Filter 扩展的插件可以同时为两者启用。这意味着可以启用和禁用整个插件，而无需手动编辑单个扩展点设置。这些扩展点现在可以被抽象出来，因为它们与大多数用户无关。 （二）已知问题 在 1.22 Kubernetes 版本附带的 etcd v3.5.0 版本中发现了数据损坏问题。请阅读 etcd 的最新生产建议)。 运行etcd v3.5.2 v3.5.1和v3.5.0高负荷会导致数据损坏问题。如果etcd进程被杀,偶尔有些已提交的事务并不反映在所有的成员。建议升级到v3.5.3。 最低推荐etcd版本运行在生产3.3.18 + 3.4.2 + v3.5.3 +。 1.22 最新发行版本：1.22.11 (发布日期: 2022-06-15） 不再支持：2022-10-28 补丁版本： 1.22.1、 1.22.2、 1.22.3、 1.22.4、 1.22.5、 1.22.6、 1.22.7、 1.22.8、 1.22.9、 1.22.10、 1.22.11 Complete 1.22 Schedule and Changelog （—）重要更新 1）kubeadm 允许非root用户允许kubeadm。 现在V1beta3首选API版本;v1beta2 API也仍然是可用的,并没有弃用。 移除对docker cgroup driver的检查，kubeadm默认使用systemd cgroup driver，需要手动将runtime配置为systemd。 v1beta3中删除ClusterConfiguration.DNS字段，因为CoreDNS是唯一支持DNS类型。 2）etcd etcd使用v3.5.0版本。（但是在1.23版本中发现v3.5.0有数据损坏的问题） 3）kubelet 节点支持swap内存。 作为α特性,Kubernetes v1.22并且可以使用cgroup v2 API来控制内存分配和隔离。这个功能的目的是改善工作负载和节点可用性时对内存资源的争用。 参考： 发行版本 | Kubernetes kubernetes/CHANGELOG at master · GitHub sig-release/releases at master · kubernetes/sig-release · GitHub Kubernetes 1.24 正式发布，这里是更新功能总览 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/architecture/_index.html":{"url":"concepts/architecture/_index.html","title":"kubernetes架构","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/architecture/kubernetes-architecture.html":{"url":"concepts/architecture/kubernetes-architecture.html","title":"Kubernetes总架构图","keywords":"","body":"1. Kubernetes的总架构图2. Kubernetes各个组件介绍2.1 kube-master[控制节点]2.1.1 API Server[资源操作入口]2.1.2 Controller Manager[内部管理控制中心]2.1.3 Scheduler[集群分发调度器]2.2 kube-node[服务节点]2.2.1 Kubelet[节点上的Pod管家]2.2.2 Proxy[负载均衡、路由转发]2.2.3 kubectl[集群管理命令行工具集]1. Kubernetes的总架构图 2. Kubernetes各个组件介绍 2.1 kube-master[控制节点] master的工作流程图 Kubecfg将特定的请求，比如创建Pod，发送给Kubernetes Client。 Kubernetes Client将请求发送给API server。 API Server根据请求的类型，比如创建Pod时storage类型是pods，然后依此选择何种REST Storage API对请求作出处理。 REST Storage API对的请求作相应的处理。 将处理的结果存入高可用键值存储系统Etcd中。 在API Server响应Kubecfg的请求后，Scheduler会根据Kubernetes Client获取集群中运行Pod及Minion/Node信息。 依据从Kubernetes Client获取的信息，Scheduler将未分发的Pod分发到可用的Minion/Node节点上。 2.1.1 API Server[资源操作入口] 提供了资源对象的唯一操作入口，其他所有组件都必须通过它提供的API来操作资源数据，只有API Server与存储通信，其他模块通过API Server访问集群状态。 第一，是为了保证集群状态访问的安全。 第二，是为了隔离集群状态访问的方式和后端存储实现的方式：API Server是状态访问的方式，不会因为后端存储技术etcd的改变而改变。 作为kubernetes系统的入口，封装了核心对象的增删改查操作，以RESTFul接口方式提供给外部客户和内部组件调用。对相关的资源数据“全量查询”+“变化监听”，实时完成相关的业务功能。 更多API Server信息请参考：Kubernetes核心原理（一）之API Server 2.1.2 Controller Manager[内部管理控制中心] 实现集群故障检测和恢复的自动化工作，负责执行各种控制器，主要有： endpoint-controller：定期关联service和pod(关联信息由endpoint对象维护)，保证service到pod的映射总是最新的。 replication-controller：定期关联replicationController和pod，保证replicationController定义的复制数量与实际运行pod的数量总是一致的。 更多Controller Manager信息请参考：Kubernetes核心原理（二）之Controller Manager 2.1.3 Scheduler[集群分发调度器] Scheduler收集和分析当前Kubernetes集群中所有Minion/Node节点的资源(内存、CPU)负载情况，然后依此分发新建的Pod到Kubernetes集群中可用的节点。 实时监测Kubernetes集群中未分发和已分发的所有运行的Pod。 Scheduler也监测Minion/Node节点信息，由于会频繁查找Minion/Node节点，Scheduler会缓存一份最新的信息在本地。 最后，Scheduler在分发Pod到指定的Minion/Node节点后，会把Pod相关的信息Binding写回API Server。 更多Scheduler信息请参考：Kubernetes核心原理（三）之Scheduler 2.2 kube-node[服务节点] kubelet结构图 2.2.1 Kubelet[节点上的Pod管家] 负责Node节点上pod的创建、修改、监控、删除等全生命周期的管理 定时上报本Node的状态信息给API Server。 kubelet是Master API Server和Minion/Node之间的桥梁，接收Master API Server分配给它的commands和work，通过kube-apiserver间接与Etcd集群交互，读取配置信息。 具体的工作如下： 1) 设置容器的环境变量、给容器绑定Volume、给容器绑定Port、根据指定的Pod运行一个单一容器、给指定的Pod创建network 容器。 2) 同步Pod的状态、同步Pod的状态、从cAdvisor获取container info、 pod info、 root info、 machine info。 3) 在容器中运行命令、杀死容器、删除Pod的所有容器。 更多Kubelet信息请参考：Kubernetes核心原理（四）之Kubelet 2.2.2 Proxy[负载均衡、路由转发] Proxy是为了解决外部网络能够访问跨机器集群中容器提供的应用服务而设计的，运行在每个Minion/Node上。Proxy提供TCP/UDP sockets的proxy，每创建一种Service，Proxy主要从etcd获取Services和Endpoints的配置信息（也可以从file获取），然后根据配置信息在Minion/Node上启动一个Proxy的进程并监听相应的服务端口，当外部请求发生时，Proxy会根据Load Balancer将请求分发到后端正确的容器处理。 Proxy不但解决了同一主宿机相同服务端口冲突的问题，还提供了Service转发服务端口对外提供服务的能力，Proxy后端使用了随机、轮循负载均衡算法。 2.2.3 kubectl[集群管理命令行工具集] 通过客户端的kubectl命令集操作，API Server响应对应的命令结果，从而达到对kubernetes集群的管理。 参考文章： https://yq.aliyun.com/articles/47308?spm=5176.100240.searchblog.19.jF7FFa Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/architecture/paas-based-on-docker-and-kubernetes.html":{"url":"concepts/architecture/paas-based-on-docker-and-kubernetes.html","title":"基于Docker及Kubernetes技术构建容器云（PaaS）平台概述","keywords":"","body":"1. PaaS概述1.1. PaaS概念1.2. IaaS/PaaS/SaaS说明1.3. PaaS的特点（三种层次）2. App Engine概述2.1. App Engine概念2.2. 架构设计2.3. 组成模块说明3. 容器云平台技术栈4. Docker概述4.1. Docker介绍4.2. Docker的基本概念4.2.1. 镜像4.2.2. 容器4.2.3. 仓库4.3. Docker的优势5. Kubernetes概述5.1. Kubernetes介绍5.2. Kubernetes的基本概念5.2.1. Pod5.2.2. Replication Controller5.2.3. Service5.2.4. Label5.2.5. Node5.3. Kubernetes架构[编者的话] 目前很多的容器云平台通过Docker及Kubernetes等技术提供应用运行平台，从而实现运维自动化，快速部署应用、弹性伸缩和动态调整应用环境资源，提高研发运营效率。 从宏观到微观（从抽象到具体）的思路来理解：云计算→PaaS→ App Engine→XAE[XXX App Engine] （XAE泛指一类应用运行平台，例如GAE、SAE、BAE等）。 本文简要介绍了与容器云相关的几个重要概念：PaaS、App Engine、Dokcer、Kubernetes。 1. PaaS概述 1.1. PaaS概念 PaaS(Platform as a service)，平台即服务，指将软件研发的平台（或业务基础平台）作为一种服务，以SaaS的模式提交给用户。 PaaS是云计算服务的其中一种模式，云计算是一种按使用量付费的模式的服务，类似一种租赁服务，服务可以是基础设施计算资源（IaaS），平台（PaaS），软件（SaaS）。租用IT资源的方式来实现业务需要，如同水力、电力资源一样，计算、存储、网络将成为企业IT运行的一种被使用的资源，无需自己建设，可按需获得。 PaaS的实质是将互联网的资源服务化为可编程接口，为第三方开发者提供有商业价值的资源和服务平台。简而言之，IaaS就是卖硬件及计算资源，PaaS就是卖开发、运行环境，SaaS就是卖软件。 1.2. IaaS/PaaS/SaaS说明 类型 说明 比喻 例子 IaaS:Infrastructure-as-a-Service(基础设施即服务) 提供的服务是计算基础设施 地皮，需要自己盖房子 Amazon EC2（亚马逊弹性云计算） PaaS: Platform-as-a-Service(平台即服务) 提供的服务是软件研发的平台或业务基础平台 商品房，需要自己装修 GAE（谷歌开发者平台） SaaS: Software-as-a-Service(软件即服务) 提供的服务是运行在云计算基础设施上的应用程序 酒店套房，可以直接入住 谷歌的Gmail邮箱 1.3. PaaS的特点（三种层次） 特点 说明 平台即服务 PaaS提供的服务就是个基础平台，一个环境，而不是具体的应用 平台及服务 不仅提供平台，还提供对该平台的技术支持、优化等服务 平台级服务 “平台级服务”即强大稳定的平台和专业的技术支持团队，保障应用的稳定使用 2. App Engine概述 2.1. App Engine概念 App Engine是PaaS模式的一种实现方式，App Engine将应用运行所需的 IT 资源和基础设施以服务的方式提供给用户，包括了中间件服务、资源管理服务、弹性调度服务、消息服务等多种服务形式。App Engine的目标是对应用提供完整生命周期（包括设计、开发、测试和部署等阶段）的支持，从而减少了用户在购置和管理应用生命周期内所必须的软硬件以及部署应用和IT 基础设施的成本，同时简化了以上工作的复杂度。常见的App Engine有：GAE(Google App Engine)，SAE(Sina App Engine)，BAE(Baidu App Engine)。 App Engine利用虚拟化与自动化技术实现快速搭建部署应用运行环境和动态调整应用运行时环境资源这两个目标。一方面实现即时部署以及快速回收，降低了环境搭建时间，避免了手工配置错误，快速重复搭建环境，及时回收资源， 减少了低利用率硬件资源的空置。另一方面，根据应用运行时的需求对应用环境进行动态调整，实现了应用平台的弹性扩展和自优化，减少了非高峰时硬件资源的空置。 简而言之，App Engine主要目标是：Easy to maintain(维护), Easy to scale(扩容), Easy to build(构建)。 2.2. 架构设计 2.3. 组成模块说明 组成模块 模块说明 App Router[流量接入层] 接收用户请求，并转发到不同的App Runtime。 App Runtime[应用运行层] 应用运行环境，为各个应用提供基本的运行引擎，从而让app能够运行起来。 Services[基础服务层] 各个通用基础服务，主要是对主流的服务提供通用的接入，例如数据库等。 Platform Control[平台控制层] 整个平台的控制中心，实现业务调度，弹性扩容、资源审计、集群管理等相关工作。 Manage System[管理界面层] 提供友好可用的管理操作界面方便平台管理员来控制管理整个平台。 Platform Support[平台支持层] 为应用提供相关的支持，比如应用监控、问题定位、分布式日志重建、统计分析等。 Log Center[日志中心] 实时收集相关应用及系统的日志（日志收集），提供实时计算和分析平台（日志处理）。 Code Center[代码中心] 完成代码存储、部署上线相关的工作。 3. 容器云平台技术栈 功能组成部分 使用工具 应用载体 Docker 编排工具 Kubernetes 配置数据 Etcd 网络管理 Flannel 存储管理 Ceph 底层实现 Linux内核的Namespace[资源隔离]和CGroups[资源控制] Namespace[资源隔离] Namespaces机制提供一种资源隔离方案。PID,IPC,Network等系统资源不再是全局性的，而是属于某个特定的Namespace。每个namespace下的资源对于其他namespace下的资源都是透明，不可见的。 CGroups[资源控制] CGroup（control group）是将任意进程进行分组化管理的Linux内核功能。CGroup本身是提供将进程进行分组化管理的功能和接口的基础结构，I/O或内存的分配控制等具体的资源管理功能是通过这个功能来实现的。CGroups可以限制、记录、隔离进程组所使用的物理资源（包括：CPU、memory、IO等），为容器实现虚拟化提供了基本保证。CGroups本质是内核附加在程序上的一系列钩子（hooks），通过程序运行时对资源的调度触发相应的钩子以达到资源追踪和限制的目的。 4. Docker概述 更多详情请参考：Docker整体架构图 4.1. Docker介绍 Docker - Build, Ship, and Run Any App, Anywhere Docker是一种Linux容器工具集，它是为“构建（Build）、交付（Ship）和运行（Run）”分布式应用而设计的。 Docker相当于把应用以及应用所依赖的环境完完整整地打成了一个包，这个包拿到哪里都能原生运行。因此可以在开发、测试、运维中保证环境的一致性。 Docker的本质：Docker=LXC(Namespace+CGroups)+Docker Images，即在Linux内核的Namespace[资源隔离]和CGroups[资源控制]技术的基础上通过镜像管理机制来实现轻量化设计。 4.2. Docker的基本概念 4.2.1. 镜像 Docker 镜像就是一个只读的模板，可以把镜像理解成一个模子（模具），由模子（镜像）制作的成品（容器）都是一样的（除非在生成时加额外参数），修改成品（容器）本身并不会对模子（镜像）产生影响（除非将成品提交成一个模子），容器重启时，即由模子（镜像）重新制作成一个成品（容器），与其他由该模子制作成的成品并无区别。 例如：一个镜像可以包含一个完整的 ubuntu 操作系统环境，里面仅安装了 Apache 或用户需要的其它应用程序。镜像可以用来创建 Docker 容器。Docker 提供了一个很简单的机制来创建镜像或者更新现有的镜像，用户可以直接从其他人那里下载一个已经做好的镜像来直接使用。 4.2.2. 容器 Docker 利用容器来运行应用。容器是从镜像创建的运行实例。它可以被启动、开始、停止、删除。每个容器都是相互隔离的、保证安全的平台。可以把容器看做是一个简易版的 Linux 环境（包括root用户权限、进程空间、用户空间和网络空间等）和运行在其中的应用程序。 4.2.3. 仓库 仓库是集中存放镜像文件的场所。有时候会把仓库和仓库注册服务器（Registry）混为一谈，并不严格区分。实际上，仓库注册服务器上往往存放着多个仓库，每个仓库中又包含了多个镜像，每个镜像有不同的标签（tag）。 4.3. Docker的优势 容器的快速轻量 容器的启动，停止和销毁都是以秒或毫秒为单位的，并且相比传统的虚拟化技术，使用容器在CPU、内存，网络IO等资源上的性能损耗都有同样水平甚至更优的表现。 一次构建，到处运行 当将容器固化成镜像后，就可以非常快速地加载到任何环境中部署运行。而构建出来的镜像打包了应用运行所需的程序、依赖和运行环境， 这是一个完整可用的应用集装箱，在任何环境下都能保证环境一致性。 完整的生态链 容器技术并不是Docker首创，但是以往的容器实现只关注于如何运行，而Docker站在巨人的肩膀上进行整合和创新，特别是Docker镜像的设计，完美地解决了容器从构建、交付到运行，提供了完整的生态链支持。 5. Kubernetes概述 更多详情请参考：Kubernetes总架构图 5.1. Kubernetes介绍 Kubernetes是Google开源的容器集群管理系统。它构建Docker技术之上，为容器化的应用提供资源调度、部署运行、服务发现、扩容缩容等整一套功能，本质上可看作是基于容器技术的Micro-PaaS平台，即第三代PaaS的代表性项目。 5.2. Kubernetes的基本概念 5.2.1. Pod Pod是若干个相关容器的组合，是一个逻辑概念，Pod包含的容器运行在同一个宿主机上，这些容器使用相同的网络命名空间、IP地址和端口，相互之间能通过localhost来发现和通信，共享一块存储卷空间。在Kubernetes中创建、调度和管理的最小单位是Pod。一个Pod一般只放一个业务容器和一个用于统一网络管理的网络容器。 5.2.2. Replication Controller Replication Controller是用来控制管理Pod副本(Replica，或者称实例)，Replication Controller确保任何时候Kubernetes集群中有指定数量的Pod副本在运行，如果少于指定数量的Pod副本，Replication Controller会启动新的Pod副本，反之会杀死多余的以保证数量不变。另外Replication Controller是弹性伸缩、滚动升级的实现核心。 5.2.3. Service Service是真实应用服务的抽象，定义了Pod的逻辑集合和访问这个Pod集合的策略，Service将代理Pod对外表现为一个单一访问接口，外部不需要了解后端Pod如何运行，这给扩展或维护带来很大的好处，提供了一套简化的服务代理和发现机制。 5.2.4. Label Label是用于区分Pod、Service、Replication Controller的Key/Value键值对，实际上Kubernetes中的任意API对象都可以通过Label进行标识。每个API对象可以有多个Label，但是每个Label的Key只能对应一个Value。Label是Service和Replication Controller运行的基础，它们都通过Label来关联Pod，相比于强绑定模型，这是一种非常好的松耦合关系。 5.2.5. Node Kubernets属于主从的分布式集群架构，Kubernets Node（简称为Node，早期版本叫做Minion）运行并管理容器。Node作为Kubernetes的操作单元，将用来分配给Pod（或者说容器）进行绑定，Pod最终运行在Node上，Node可以认为是Pod的宿主机。 5.3. Kubernetes架构 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/object/_index.html":{"url":"concepts/object/_index.html","title":"kubernetes对象","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/object/understanding-kubernetes-objects.html":{"url":"concepts/object/understanding-kubernetes-objects.html","title":"理解kubernetes对象","keywords":"","body":"1. kubernetes对象概述2. Spec and Status3. 对象描述文件4. 必须字段1. kubernetes对象概述 kubernetes中的对象是一些持久化的实体，可以理解为是对集群状态的描述或期望。 包括： 集群中哪些node上运行了哪些容器化应用 应用的资源是否满足使用 应用的执行策略，例如重启策略、更新策略、容错策略等。 kubernetes的对象是一种意图（期望）的记录，kubernetes会始终保持预期创建的对象存在和集群运行在预期的状态下。 操作kubernetes对象（增删改查）需要通过kubernetes API，一般有以下几种方式： kubectl命令工具 Client library的方式，例如 client-go 2. Spec and Status 每个kubernetes对象的结构描述都包含spec和status两个部分。 spec：该内容由用户提供，描述用户期望的对象特征及集群状态。 status：该内容由kubernetes集群提供和更新，描述kubernetes对象的实时状态。 任何时候，kubernetes都会控制集群的实时状态status与用户的预期状态spec一致。 例如：当你定义Deployment的描述文件，指定集群中运行3个实例，那么kubernetes会始终保持集群中运行3个实例，如果任何实例挂掉，kubernetes会自动重建新的实例来保持集群中始终运行用户预期的3个实例。 3. 对象描述文件 当你要创建一个kubernetes对象的时候，需要提供该对象的描述信息spec，来描述你的对象在kubernetes中的预期状态。 一般使用kubernetes API来创建kubernetes对象，其中spec信息可以以JSON的形式存放在request body中，也可以以.yaml文件的形式通过kubectl工具创建。 例如，以下为Deployment对象对应的yaml文件： apiVersion: apps/v1beta2 # for versions before 1.8.0 use apps/v1beta1 kind: Deployment metadata: name: nginx-deployment spec: replicas: 3 selector: matchLabels: app: nginx template: metadata: labels: app: nginx spec: containers: - name: nginx image: nginx:1.7.9 ports: - containerPort: 80 执行kubectl create的命令 #create command kubectl create -f https://k8s.io/docs/user-guide/nginx-deployment.yaml --record #output deployment \"nginx-deployment\" created 4. 必须字段 在对象描述文件.yaml中，必须包含以下字段。 apiVersion：kubernetes API的版本 kind：kubernetes对象的类型 metadata：唯一标识该对象的元数据，包括name，UID，可选的namespace spec：标识对象的详细信息，不同对象的spec的格式不同，可以嵌套其他对象的字段。 文章参考： https://kubernetes.io/docs/concepts/overview/working-with-objects/kubernetes-objects/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/object/kubernetes-basic-concepts.html":{"url":"concepts/object/kubernetes-basic-concepts.html","title":"kubernetes常用对象说明","keywords":"","body":"1. Master1.1. Master的组件2. Node2.1. Node的组件2.2. Node相关命令2.3. describe命令的Node信息3. Pod4. Label5. Replication Controller(RC)6. Deployment7. Horizontal Pod Autoscaler(HPA)8. Service(服务)8.1. Service概述8.2. kubernetes的服务发现机制8.3. 外部系统访问Service的问题8.3.1. Node IP8.3.2. Pod IP8.3.3. Cluster IP8.3.4. 外部访问Kubernetes集群9. Volume(存储卷)9.1. Volume的功能9.2. Volume的特点9.3. Volume的使用方式9.4. Volume类型9.4.1. emptyDir9.4.2. hostPath9.4.3. gcePersistentDisk10. Persistent Volume11. Namespace12. Annotation(注解)1. Master 集群的控制节点，负责整个集群的管理和控制，kubernetes的所有的命令基本都是发给Master，由它来负责具体的执行过程。 1.1. Master的组件 kube-apiserver：资源增删改查的入口 kube-controller-manager：资源对象的大总管 kube-scheduler：负责资源调度（Pod调度） etcd Server:kubernetes的所有的资源对象的数据保存在etcd中。 2. Node Node是集群的工作负载节点，默认情况kubelet会向Master注册自己，一旦Node被纳入集群管理范围，kubelet会定时向Master汇报自身的情报，包括操作系统，Docker版本，机器资源情况等。 如果Node超过指定时间不上报信息，会被Master判断为“失联”，标记为Not Ready，随后Master会触发Pod转移。 2.1. Node的组件 kubelet:Pod的管家，与Master通信 kube-proxy：实现kubernetes Service的通信与负载均衡机制的重要组件 Docker：容器的创建和管理 2.2. Node相关命令 kubectl get nodes kuebctl describe node {node_name} 2.3. describe命令的Node信息 Node基本信息：名称、标签、创建时间等 Node当前的状态，Node启动后会进行自检工作，磁盘是否满，内存是否不足，若都正常则切换为Ready状态。 Node的主机地址与主机名 Node上的资源总量：CPU,内存，最大可调度Pod数量等 Node可分配资源量：当前Node可用于分配的资源量 主机系统信息：主机唯一标识符UUID，Linux kernel版本号，操作系统，kubernetes版本，kubelet与kube-proxy版本 当前正在运行的Pod列表及概要信息 已分配的资源使用概要，例如资源申请的最低、最大允许使用量占系统总量的百分比 Node相关的Event信息。 3. Pod Pod是Kubernetes中操作的基本单元。每个Pod中有个根容器(Pause容器)，Pause容器的状态代表整个容器组的状态，其他业务容器共享Pause的IP，即Pod IP，共享Pause挂载的Volume，这样简化了同个Pod中不同容器之间的网络问题和文件共享问题。 图片 - pod Kubernetes集群中，同宿主机的或不同宿主机的Pod之间要求能够TCP/IP直接通信，因此采用虚拟二层网络技术来实现，例如Flannel，Openvswitch(OVS)等，这样在同个集群中，不同的宿主机的Pod IP为不同IP段的IP，集群中的所有Pod IP都是唯一的，不同Pod之间可以直接通信。 Pod有两种类型：普通Pod和静态Pod。静态Pod即不通过K8S调度和创建，直接在某个具体的Node机器上通过具体的文件来启动。普通Pod则是由K8S创建、调度，同时数据存放在ETCD中。 Pod IP和具体的容器端口（ContainnerPort）组成一个具体的通信地址，即Endpoint。一个Pod中可以存在多个容器，可以有多个端口，Pod IP一样，即有多个Endpoint。 Pod Volume是定义在Pod之上，被各个容器挂载到自己的文件系统中，可以用分布式文件系统实现后端存储功能。 Pod中的Event事件可以用来排查问题，可以通过kubectl describe pod xxx 来查看对应的事件。 每个Pod可以对其能使用的服务器上的计算资源设置限额，一般为CPU和Memory。K8S中一般将千分之一个的CPU配置作为最小单位，用m表示，是一个绝对值，即100m对于一个Core的机器还是48个Core的机器都是一样的大小。Memory配额也是个绝对值，单位为内存字节数。 资源配额的两个参数 Requests:该资源的最小申请量，系统必须满足要求。 Limits:该资源最大允许使用量，当超过该量，K8S会kill并重启Pod。 图片 - pod2 4. Label Label是一个键值对，可以附加在任何对象上，比如Node,Pod,Service,RC等。Label和资源对象是多对多的关系，即一个Label可以被添加到多个对象上，一个对象也可以定义多个Label。 Label的作用主要用来实现精细的、多维度的资源分组管理，以便进行资源分配，调度，配置，部署等工作。 Label通俗理解就是“标签”，通过标签来过滤筛选指定的对象，进行具体的操作。k8s通过Label Selector(标签选择器)来筛选指定Label的资源对象，类似SQL语句中的条件查询（WHERE语句）。 Label Selector有基于等式和基于集合的两种表达方式，可以多个条件进行组合使用。 基于等式：name=redis-slave（匹配name=redis-slave的资源对象）;env!=product(匹配所有不具有标签env=product的资源对象) 基于集合：name in (redis-slave,redis-master);name not in (php-frontend)（匹配所有不具有标签name=php-frontend的资源对象） 使用场景 kube-controller进程通过资源对象RC上定义的Label Selector来筛选要监控的Pod副本数，从而实现副本数始终保持预期数目。 kube-proxy进程通过Service的Label Selector来选择对应Pod，自动建立每个Service到对应Pod的请求转发路由表，从而实现Service的智能负载均衡机制。 kube-scheduler实现Pod定向调度：对Node定义特定的Label，并且在Pod定义文件中使用NodeSelector标签调度策略。 5. Replication Controller(RC) RC是k8s系统中的核心概念，定义了一个期望的场景。 主要包括： Pod期望的副本数（replicas） 用于筛选目标Pod的Label Selector 用于创建Pod的模板（template） RC特性说明： Pod的缩放可以通过以下命令实现：kubectl scale rc redis-slave --replicas=3 删除RC并不会删除该RC创建的Pod，可以将副本数设置为0，即可删除对应Pod。或者通过kubectl stop /delete命令来一次性删除RC和其创建的Pod。 改变RC中Pod模板的镜像版本可以实现滚动升级（Rolling Update）。具体操作见https://kubernetes.io/docs/tasks/run-application/rolling-update-replication-controller/ Kubernetes1.2以上版本将RC升级为Replica Set，它与当前RC的唯一区别在于Replica Set支持基于集合的Label Selector(Set-based selector)，而旧版本RC只支持基于等式的Label Selector(equality-based selector)。 Kubernetes1.2以上版本通过Deployment来维护Replica Set而不是单独使用Replica Set。即控制流为：Delpoyment→Replica Set→Pod。即新版本的Deployment+Replica Set替代了RC的作用。 6. Deployment Deployment是kubernetes 1.2引入的概念，用来解决Pod的编排问题。Deployment可以理解为RC的升级版（RC+Reolicat Set）。特点在于可以随时知道Pod的部署进度，即对Pod的创建、调度、绑定节点、启动容器完整过程的进度展示。 使用场景 创建一个Deployment对象来生成对应的Replica Set并完成Pod副本的创建过程。 检查Deployment的状态来确认部署动作是否完成（Pod副本的数量是否达到预期值）。 更新Deployment以创建新的Pod(例如镜像升级的场景)。 如果当前Deployment不稳定，回退到上一个Deployment版本。 挂起或恢复一个Deployment。 可以通过kubectl describe deployment来查看Deployment控制的Pod的水平拓展过程。 7. Horizontal Pod Autoscaler(HPA) Horizontal Pod Autoscaler(HPA)即Pod横向自动扩容，与RC一样也属于k8s的资源对象。 HPA原理：通过追踪分析RC控制的所有目标Pod的负载变化情况，来确定是否针对性调整Pod的副本数。 Pod负载度量指标： CPUUtilizationPercentage：Pod所有副本自身的CPU利用率的平均值。即当前Pod的CPU使用量除以Pod Request的值。 应用自定义的度量指标，比如服务每秒内响应的请求数（TPS/QPS）。 8. Service(服务) 8.1. Service概述 图片 - service Service定义了一个服务的访问入口地址，前端应用通过这个入口地址访问其背后的一组由Pod副本组成的集群实例，Service与其后端的Pod副本集群之间是通过Label Selector来实现“无缝对接”。RC保证Service的Pod副本实例数目保持预期水平。 8.2. kubernetes的服务发现机制 主要通过kube-dns这个组件来进行DNS方式的服务发现。 8.3. 外部系统访问Service的问题 IP类型 说明 Node IP Node节点的IP地址 Pod IP Pod的IP地址 Cluster IP Service的IP地址 8.3.1. Node IP NodeIP是集群中每个节点的物理网卡IP地址，是真实存在的物理网络，kubernetes集群之外的节点访问kubernetes内的某个节点或TCP/IP服务的时候，需要通过NodeIP进行通信。 8.3.2. Pod IP Pod IP是每个Pod的IP地址，是Docker Engine根据docker0网桥的IP段地址进行分配的，是一个虚拟二层网络，集群中一个Pod的容器访问另一个Pod中的容器，是通过Pod IP进行通信的，而真实的TCP/IP流量是通过Node IP所在的网卡流出的。 8.3.3. Cluster IP Service的Cluster IP是一个虚拟IP，只作用于Service这个对象，由kubernetes管理和分配IP地址（来源于Cluster IP地址池）。 Cluster IP无法被ping通，因为没有一个实体网络对象来响应。 Cluster IP结合Service Port组成的具体通信端口才具备TCP/IP通信基础，属于kubernetes集群内，集群外访问该IP和端口需要额外处理。 k8s集群内Node IP 、Pod IP、Cluster IP之间的通信采取k8s自己的特殊的路由规则，与传统IP路由不同。 8.3.4. 外部访问Kubernetes集群 通过宿主机与容器端口映射的方式进行访问，例如：Service定位文件如下： 可以通过任意Node的IP 加端口访问该服务。也可以通过Nginx或HAProxy来设置负载均衡。 9. Volume(存储卷) 9.1. Volume的功能 Volume是Pod中能够被多个容器访问的共享目录，可以让容器的数据写到宿主机上或者写文件到网络存储中 可以实现容器配置文件集中化定义与管理，通过ConfigMap资源对象来实现。 9.2. Volume的特点 k8s中的Volume与Docker的Volume相似，但不完全相同。 k8s上Volume定义在Pod上，然后被一个Pod中的多个容器挂载到具体的文件目录下。 k8s的Volume与Pod生命周期相关而不是容器是生命周期，即容器挂掉，数据不会丢失但是Pod挂掉，数据则会丢失。 k8s中的Volume支持多种类型的Volume：Ceph、GlusterFS等分布式系统。 9.3. Volume的使用方式 先在Pod上声明一个Volume，然后容器引用该Volume并Mount到容器的某个目录。 9.4. Volume类型 9.4.1. emptyDir emptyDir Volume是在Pod分配到Node时创建的，初始内容为空，无须指定宿主机上对应的目录文件，由K8S自动分配一个目录，当Pod被删除时，对应的emptyDir数据也会永久删除。 作用： 临时空间，例如程序的临时文件，无须永久保留 长时间任务的中间过程CheckPoint的临时保存目录 一个容器需要从另一个容器中获取数据的目录（即多容器共享目录） 说明： 目前用户无法设置emptyVolume的使用介质，如果kubelet的配置使用硬盘则emptyDir将创建在该硬盘上。 9.4.2. hostPath hostPath是在Pod上挂载宿主机上的文件或目录。 作用： 容器应用日志需要持久化时，可以使用宿主机的高速文件系统进行存储 需要访问宿主机上Docker引擎内部数据结构的容器应用时，可以通过定义hostPath为宿主机/var/lib/docker目录，使容器内部应用可以直接访问Docker的文件系统。 注意点： 在不同的Node上具有相同配置的Pod可能会因为宿主机上的目录或文件不同导致对Volume上目录或文件的访问结果不一致。 如果使用了资源配额管理，则kubernetes无法将hostPath在宿主机上使用的资源纳入管理。 9.4.3. gcePersistentDisk 表示使用谷歌公有云提供的永久磁盘（Persistent Disk ,PD）存放Volume的数据，它与EmptyDir不同，PD上的内容会被永久保存。当Pod被删除时，PD只是被卸载时，但不会被删除。需要先创建一个永久磁盘，才能使用gcePersistentDisk。 使用gcePersistentDisk的限制条件： Node(运行kubelet的节点)需要是GCE虚拟机。 虚拟机需要与PD存在于相同的GCE项目中和Zone中。 10. Persistent Volume Volume定义在Pod上，属于“计算资源”的一部分，而Persistent Volume和Persistent Volume Claim是网络存储，简称PV和PVC，可以理解为k8s集群中某个网络存储中对应的一块存储。 PV是网络存储，不属于任何Node，但可以在每个Node上访问。 PV不是定义在Pod上，而是独立于Pod之外定义。 PV常见类型：GCE Persistent Disks、NFS、RBD等。 PV是有状态的对象，状态类型如下： Available:空闲状态 Bound:已经绑定到某个PVC上 Released:对应的PVC已经删除，但资源还没有回收 Failed:PV自动回收失败 11. Namespace Namespace即命名空间，主要用于多租户的资源隔离，通过将资源对象分配到不同的Namespace上，便于不同的分组在共享资源的同时可以被分别管理。 k8s集群启动后会默认创建一个“default”的Namespace。可以通过kubectl get namespaecs查看。 可以通过kubectl config use-context namespace配置当前k8s客户端的环境，通过kubectl get pods获取当前namespace的Pod。或者通过kubectl get pods --namespace=NAMESPACE来获取指定namespace的Pod。 Namespace yaml文件的定义 12. Annotation(注解) Annotation与Label类似，也使用key/value的形式进行定义，Label定义元数据（Metadata）,Annotation定义“附加”信息。 通常Annotation记录信息如下： build信息，release信息，Docker镜像信息等。 日志库、监控库等。 参考《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/pod/_index.html":{"url":"concepts/pod/_index.html","title":"Pod","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/pod/pod.html":{"url":"concepts/pod/pod.html","title":"Pod介绍","keywords":"","body":"1. Pod是什么（what）1.1. Pod概念1.1.1. Pod与Docker1.1.2. Pod中容器的运行方式1.2. Pod管理多个容器1.2.1. 网络1.2.2. 存储2. 为什么需要Pod(why)2.1. 管理需求2.2. 资源共享和通信2.3. Pod设计的优势3. Pod的使用(how)3.1. Controller3.2. Pod模板4. Pod的终止4.1. Pod终止的流程4.2. 强制删除Pod4.3. Pod特权模式1. Pod是什么（what） 1.1. Pod概念 Pod是kubernetes集群中最小的部署和管理的基本单元，协同寻址，协同调度。 Pod是一个或多个容器的集合，是一个或一组服务（进程）的抽象集合。 Pod中可以共享网络和存储（可以简单理解为一个逻辑上的虚拟机，但并不是虚拟机）。 Pod被创建后用一个UID来唯一标识，当Pod生命周期结束，被一个等价Pod替代，UID将重新生成。 1.1.1. Pod与Docker Docker是目前Pod最常用的容器环境，但仍支持其他容器环境。 Pod是一组被模块化的拥有共享命名空间和共享存储卷的容器，但并没有共享PID 命名空间（即同个Pod的不同容器中进程的PID是独立的，互相看不到非自己容器的进程）。 1.1.2. Pod中容器的运行方式 只运行一个单独的容器 即one-container-per-Pod模式，是最常用的模式，可以把这样的Pod看成单独的一个容器去管理。 运行多个强关联的容器 即sidecar模式，Pod 封装了一组紧耦合、共享资源、协同寻址的容器，将这组容器作为一个管理单元。 1.2. Pod管理多个容器 Pod是一组紧耦合的容器的集合，Pod内的容器作为一个整体以Pod形式进行协同寻址，协同调度、协同管理。相同Pod内的容器共享网络和存储。 1.2.1. 网络 每个Pod被分配了唯一的IP地址，该Pod内的所有容器共享一个网络空间，包括IP和端口。 同个Pod不同容器之间通过localhost通信，Pod内端口不能冲突。 不同Pod之间的通信则通过IP+端口的形式来访问到Pod内的具体服务（容器）。 1.2.2. 存储 可以在Pod中创建共享存储卷的方式来实现不同容器之间数据共享。 2. 为什么需要Pod(why) 2.1. 管理需求 Pod 是一种模式的抽象：互相协作的多个进程（容器）共同形成一个完整的服务。以一个或多个容器的方式组合成一个整体，作为管理的基本单元，通过Pod可以方便部署、水平扩展，协同调度等。 2.2. 资源共享和通信 Pod作为多个紧耦合的容器的集合，通过共享网络和存储的方式来简化紧耦合容器之间的通信，从这个角度，可以将Pod简单理解为一个逻辑上的“虚拟机”。而不同的Pod之间的通信则通过Pod的IP和端口的方式。 2.3. Pod设计的优势 调度器和控制器的可拔插性。 将Pod 的生存期从 controller 中剥离出来，从而减少相互影响。 高可用--在终止和删除 Pod 前，需要提前生成替代 Pod。 集群级别的功能和 Kubelet（Pod Controller） 级别的功能组合更加清晰。 3. Pod的使用(how) Pod一般是通过各种不同类型的Controller对Pod进行管理和控制，包括自我恢复（例如Pod因异常退出，则会再起一个相同的Pod替代该Pod，而该Pod则会被清除）。也可以不通过Controller单独创建一个Pod，但一般很少这么操作，因为这个Pod是一个孤立的实体，并不会被Controller管理。 3.1. Controller Controller是kubernetes中用于对Pod进行管理的控制器，通过该控制器让Pod始终维持在一个用户原本设定或期望的状态。如果节点宕机或者Pod因其他原因死亡，则会在其他节点起一个相同的Pod来替代该Pod。 常用的Controller有： Deployment StatefulSet DaemonSet Controller是通过用户提供的Pod模板来创建和控制Pod。 3.2. Pod模板 Pod模板用来定义Pod的各种属性，Controller通过Pod模板来生成对应的Pod。 Pod模板类似一个饼干模具，通过模具已经生成的饼干与原模具已经没有关系，即对原模具的修改不会影响已经生成的饼干，只会对通过修改后的模具生成的饼干有影响。这种方式可以更加方便地控制和管理Pod。 4. Pod的终止 用户发起一个删除Pod的请求，系统会先发送TERM信号给每个容器的主进程，如果在宽限期（默认30秒）主进程没有自主终止运行，则系统会发送KILL信号给该进程，接着Pod将被删除。 4.1. Pod终止的流程 用户发送一个删除 Pod 的命令， 并使用默认的宽限期（30s)。 把 API server 上的 pod 的时间更新成 Pod 与宽限期一起被认为 “dead” 之外的时间点。 使用客户端的命令，显示出的Pod的状态为 terminating。 （与第3步同时发生）Kubelet 发现某一个 Pod 由于时间超过第2步的设置而被标志成 terminating 状态时， Kubelet 将启动一个停止进程。 如果 pod 已经被定义成一个 preStop hook，这会在 pod 内部进行调用。如果宽限期已经过期但 preStop 锚依然还在运行，将调用第2步并在原来的宽限期上加一个小的时间窗口（2 秒钟）。 把 Pod 里的进程发送到 TERM信号。 （与第3步同时发生），Pod 被从终端的服务列表里移除，同时也不再被 replication controllers 看做时一组运行中的 pods。 在负载均衡（比如说 service proxy）会将它们从轮询中移除前， Pods 这种慢关闭的方式可以继续为流量提供服务。 当宽期限过期时， 任何还在 Pod 里运行的进程都会被 SIGKILL杀掉。 Kubelet 通过在 API server 把宽期限设置成0(立刻删除)的方式完成删除 Pod的过程。 这时 Pod 在 API 里消失，也不再能被用户看到。 4.2. 强制删除Pod 强制删除Pod是指从k8s集群状态和Etcd中立刻删除对应的Pod数据，API Server不会等待kubelet的确认信息。被强制删除后，即可重新创建一个相同名字的Pod。 删除默认的宽限期是30秒，通过将宽限期设置为0的方式可以强制删除Pod。 通过kubectl delete 命令后加--force和--grace-period=0的参数强制删除Pod。 kubectl delete pod --namespace= --force --grace-period=0 4.3. Pod特权模式 特权模式是指让Pod中的进程具有访问宿主机系统设备或使用网络栈操作等的能力，例如编写网络插件和卷插件。 通过将container spec中的SecurityContext设置为privileged即将该容器赋予了特权模式。特权模式的使用要求k8s版本高于v1.1。 参考文章： https://kubernetes.io/docs/concepts/workloads/pods/pod-overview/ https://kubernetes.io/docs/concepts/workloads/pods/pod/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/pod/pod-definition.html":{"url":"concepts/pod/pod-definition.html","title":"Pod定义文件","keywords":"","body":"1. Pod的基本用法1.1. 说明1.2. Pod相关命令2. Pod的定义文件3. 静态pod3.1. 通过配置文件方式1. Pod的基本用法 1.1. 说明 Pod实际上是容器的集合，在k8s中对运行容器的要求为：容器的主程序需要一直在前台运行，而不是后台运行。应用可以改造成前台运行的方式，例如Go语言的程序，直接运行二进制文件；java语言则运行主类；tomcat程序可以写个运行脚本。或者通过supervisor的进程管理工具，即supervisor在前台运行，应用程序由supervisor管理在后台运行。具体可参考supervisord。 当多个应用之间是紧耦合的关系时，可以将多个应用一起放在一个Pod中，同个Pod中的多个容器之间互相访问可以通过localhost来通信（可以把Pod理解成一个虚拟机，共享网络和存储卷）。 1.2. Pod相关命令 操作 命令 说明 创建 kubectl create -f frontend-localredis-pod.yaml 查询Pod运行状态 kubectl get pods --namespace= 查询Pod详情 kebectl describe pod --namespace= 该命令常用来排查问题，查看Event事件 删除 kubectl delete pod ;kubectl delete pod --all 更新 kubectl replace pod.yaml - 2. Pod的定义文件 apiVersion: v1 kind: Pod metadata: name: string namaspace: string labels: - name: string annotations: - name: string spec: containers: - name: string images: string imagePullPolice: [Always | Never | IfNotPresent] command: [string] args: [string] workingDir: string volumeMounts: - name: string mountPath: string readOnly: boolean ports: - name: string containerPort: int hostPort: int protocol: string env: - name: string value: string resources: limits: cpu: string memory: string requests: cpu: string memory: string livenessProbe: exec: command: [string] httpGet: path: string port: int host: string scheme: string httpHeaders: - name: string value: string tcpSocket: port: int initialDelaySeconds: number timeoutSeconds: number periodSeconds: number successThreshold: 0 failureThreshold: 0 securityContext: privileged: false restartPolicy: [Always | Never | OnFailure] nodeSelector: object imagePullSecrets: - name: string hostNetwork: false volumes: - name: string emptyDir: {} hostPath: path: string secret: secretName: string items: - key: string path: string configMap: name: string items: - key: string path: string 3. 静态pod 静态Pod是由kubelet进行管理，仅存在于特定Node上的Pod。它们不能通过API Server进行管理，无法与ReplicationController、Deployment或DaemonSet进行关联，并且kubelet也无法对其健康检查。 静态Pod总是由kubelet创建，并且总在kubelet所在的Node上运行。 创建静态Pod的方式： 3.1. 通过配置文件方式 需要设置kubelet的启动参数“–config”，指定kubelet需要监控的配置文件所在目录，kubelet会定期扫描该目录，并根据该目录的.yaml或.json文件进行创建操作。静态Pod无法通过API Server删除（若删除会变成pending状态），如需删除该Pod则将yaml或json文件从这个目录中删除。 例如： 配置目录为/etc/kubelet.d/，配置启动参数：--config=/etc/kubelet.d/，该目录下放入static-web.yaml。 apiVersion: v1 kind: Pod metadata: name: static-web labels: name: static-web spec: containers: - name: static-web image: nginx ports: - name: web containerPort: 80 参考文章 《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/pod/pod-lifecycle.html":{"url":"concepts/pod/pod-lifecycle.html","title":"Pod生命周期","keywords":"","body":"1. Pod phase2. Pod 状态3. 重启策略4. Pod的生命5. Pod状态转换5.1. 容器运行时内存超出限制5.2. 磁盘故障5.3. 运行节点挂掉1. Pod phase Pod的phase是Pod生命周期中的简单宏观描述，定义在Pod的PodStatus对象的phase 字段中。 phase有以下几种值： 状态值 说明 挂起（Pending） Pod 已被 Kubernetes 系统接受，但有一个或者多个容器镜像尚未创建。等待时间包括调度 Pod 的时间和通过网络下载镜像的时间。 运行中（Running） 该 Pod 已经绑定到了一个节点上，Pod 中所有的容器都已被创建。至少有一个容器正在运行，或者正处于启动或重启状态。 成功（Succeeded） Pod 中的所有容器都被成功终止，并且不会再重启。 失败（Failed） Pod 中的所有容器都已终止了，并且至少有一个容器是因为失败终止。也就是说，容器以非0状态退出或者被系统终止。 未知（Unknown） 因为某些原因无法取得 Pod 的状态，通常是因为与 Pod 所在主机通信失败。 2. Pod 状态 Pod 有一个 PodStatus 对象，其中包含一个 PodCondition 数组。 PodCondition包含以下以下字段： lastProbeTime：Pod condition最后一次被探测到的时间戳。 lastTransitionTime：Pod最后一次状态转变的时间戳。 message：状态转化的信息，一般为报错信息，例如：containers with unready status: [c-1]。 reason：最后一次状态形成的原因，一般为报错原因，例如：ContainersNotReady。 status：包含的值有 True、False 和 Unknown。 type：Pod状态的几种类型。 其中type字段包含以下几个值： PodScheduled：Pod已经被调度到运行节点。 Ready：Pod已经可以接收请求提供服务。 Initialized：所有的init container已经成功启动。 Unschedulable：无法调度该Pod，例如节点资源不够。 ContainersReady：Pod中的所有容器已准备就绪。 3. 重启策略 Pod通过restartPolicy字段指定重启策略，重启策略类型为：Always、OnFailure 和 Never，默认为 Always。 restartPolicy 仅指通过同一节点上的 kubelet 重新启动容器。 重启策略 说明 Always 当容器失效时，由kubelet自动重启该容器 OnFailure 当容器终止运行且退出码不为0时，由kubelet自动重启该容器 Never 不论容器运行状态如何，kubelet都不会重启该容器 说明： 可以管理Pod的控制器有Replication Controller，Job，DaemonSet，及kubelet（静态Pod）。 RC和DaemonSet：必须设置为Always，需要保证该容器持续运行。 Job：OnFailure或Never，确保容器执行完后不再重启。 kubelet：在Pod失效的时候重启它，不论RestartPolicy设置为什么值，并且不会对Pod进行健康检查。 4. Pod的生命 Pod的生命周期一般通过Controler 的方式管理，每种Controller都会包含PodTemplate来指明Pod的相关属性，Controller可以自动对pod的异常状态进行重新调度和恢复，除非通过Controller的方式删除其管理的Pod，不然kubernetes始终运行用户预期状态的Pod。 控制器的分类 使用 Job运行预期会终止的 Pod，例如批量计算。Job 仅适用于重启策略为 OnFailure 或 Never 的 Pod。 对预期不会终止的 Pod 使用 ReplicationController、ReplicaSet和 Deployment，例如 Web 服务器。 ReplicationController 仅适用于具有 restartPolicy 为 Always 的 Pod。 提供特定于机器的系统服务，使用 DaemonSet为每台机器运行一个 Pod 。 如果节点死亡或与集群的其余部分断开连接，则 Kubernetes 将应用一个策略将丢失节点上的所有 Pod 的 phase 设置为 Failed。 5. Pod状态转换 常见的状态转换 Pod的容器数 Pod当前状态 发生的事件 Pod结果状态 RestartPolicy=Always RestartPolicy=OnFailure RestartPolicy=Never 包含一个容器 Running 容器成功退出 Running Succeeded Succeeded 包含一个容器 Running 容器失败退出 Running Running Failure 包含两个容器 Running 1个容器失败退出 Running Running Running 包含两个容器 Running 容器被OOM杀掉 Running Running Failure 5.1. 容器运行时内存超出限制 容器以失败状态终止。 记录 OOM 事件。 如果restartPolicy为： Always：重启容器；Pod phase 仍为 Running。 OnFailure：重启容器；Pod phase 仍为 Running。 Never: 记录失败事件；Pod phase 仍为 Failed。 5.2. 磁盘故障 杀掉所有容器。 记录适当事件。 Pod phase 变成 Failed。 如果使用控制器来运行，Pod 将在别处重建。 5.3. 运行节点挂掉 节点控制器等待直到超时。 节点控制器将 Pod phase 设置为 Failed。 如果是用控制器来运行，Pod 将在别处重建。 参考文章： https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/pod/pod-probe.html":{"url":"concepts/pod/pod-probe.html","title":"Pod健康检查","keywords":"","body":"Pod健康检查1. 探针类型2. 探针使用场景3. 探针的配置3.1. 探针通用参数3.2. ReadinessProbe三种实现方式4. 探针相关源码4.1. Probe源码4.2. ProbeHandler源码4.3. ProbeActionPod健康检查 Pod的健康状态由两类探针来检查：LivenessProbe和ReadinessProbe。 1. 探针类型 1. livenessProbe(存活探针) 表明容器是否正在运行。 如果存活探测失败，则 kubelet 会杀死容器，并且容器将受到其 重启策略的影响。 如果容器不提供存活探针，则默认状态为 Success。 2. readinessProbe(就绪探针) 表明容器是否可以正常接受请求。 如果就绪探测失败，端点控制器将从与 Pod 匹配的所有 Service 的端点中删除该 Pod 的 IP 地址。 初始延迟之前的就绪状态默认为 Failure。 如果容器不提供就绪探针，则默认状态为 Success。 2. 探针使用场景 如果容器异常可以自动崩溃，则不一定要使用探针，可以由Pod的restartPolicy执行重启操作。 存活探针适用于希望容器探测失败后被杀死并重新启动，需要指定restartPolicy 为 Always 或 OnFailure。 就绪探针适用于希望Pod在不能正常接收流量的时候被剔除，并且在就绪探针探测成功后才接收流量。 探针是kubelet对容器执行定期的诊断，主要通过调用容器配置的四类Handler实现： Handler的类型： ExecAction：在容器内执行指定命令。如果命令退出时返回码为 0 则认为诊断成功。 TCPSocketAction：对指定端口上的容器的 IP 地址进行 TCP 检查。如果端口打开，则诊断被认为是成功的。 HTTPGetAction：对指定的端口和路径上的容器的 IP 地址执行 HTTP Get 请求。如果响应的状态码大于等于200 且小于 400，则诊断被认为是成功的。 GRPCAction：调用GRPC接口来判断服务是否健康。 如果响应的状态是 \"SERVING\"，则认为诊断成功。 探测结果为以下三种之一： 成功：容器通过了诊断。 失败：容器未通过诊断。 未知：诊断失败，因此不会采取任何行动。 3. 探针的配置 探针配置在pod的container结构体下，livenessProbe和readinessProbe参数基本一致。 3.1. 探针通用参数 常用的参数为timeoutSeconds、periodSeconds、periodSeconds，即接口超时时间，重试频率，重试次数三个值。 initialDelaySeconds：启动容器后首次进行健康检查的等待时间，单位为秒，默认值为0。 timeoutSeconds:健康检查接口超时响应的时间，默认为1秒，最小值为1秒。 periodSeconds：重试的频率，默认值为10秒，即10秒重试一次，最小值是1秒，建议可以设置为3-5秒。 failureThreshold：失败重试的次数，默认值为3，最小值为1。 successThreshold：最小探测成功次数，默认值为1，一般不设置。 除了以上的通用参数外，livenessProbe和readinessProbe参数基本一致。以下以readinessProbe为例说明探针的使用方式。 3.2. ReadinessProbe三种实现方式 3.2.1. HTTPGetAction 通过容器的IP地址、端口号及路径调用HTTP Get方法，如果响应的状态码大于等于200且小于等于400，则认为容器健康。 apiVersion: v1 kind: Pod metadata: name: pod-with-healthcheck spec: containers: - name: nginx image: nginx ports: - containnerPort: 80 readinessProbe: httpGet: path: /_status/healthz port: 80 scheme: HTTP initialDelaySeconds: 1 periodSeconds: 5 timeoutSeconds: 5 3.2.2. TCPSocketAction 通过容器IP地址和端口号执行TCP检查，如果能够建立TCP连接，则表明容器健康。 apiVersion: v1 kind: Pod metadata: name: pod-with-healthcheck spec: containers: - name: nginx image: nginx ports: - containnerPort: 80 readinessProbe: tcpSocket: port: 80 initialDelaySeconds: 1 timeoutSeconds: 5 3.2.3. ExecAction 在一个容器内部执行一个命令，如果该命令状态返回值为0，则表明容器健康。 apiVersion: v1 kind: Pod metadata: name: readiness-exec spec: containers: - name: readiness image: tomcagcr.io/google_containers/busybox args: - /bin/sh - -c - echo ok > /tmp/health;sleep 10;rm -fr /tmp/health;sleep 600 readinessreadinessProbe: exec: command: - cat - /tmp/health initialDelaySeconds: 1 timeoutSeconds: 5 4. 探针相关源码 探针配置在pod的container结构体下： // 存活探针 LivenessProbe *Probe `json:\"livenessProbe,omitempty\" protobuf:\"bytes,10,opt,name=livenessProbe\"` // 就绪探针 ReadinessProbe *Probe `json:\"readinessProbe,omitempty\" protobuf:\"bytes,11,opt,name=readinessProbe\"` 4.1. Probe源码 type Probe struct { // The action taken to determine the health of a container ProbeHandler `json:\",inline\" protobuf:\"bytes,1,opt,name=handler\"` // Number of seconds after the container has started before liveness probes are initiated. // More info: https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle#container-probes // +optional InitialDelaySeconds int32 `json:\"initialDelaySeconds,omitempty\" protobuf:\"varint,2,opt,name=initialDelaySeconds\"` // Number of seconds after which the probe times out. // Defaults to 1 second. Minimum value is 1. // More info: https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle#container-probes // +optional TimeoutSeconds int32 `json:\"timeoutSeconds,omitempty\" protobuf:\"varint,3,opt,name=timeoutSeconds\"` // How often (in seconds) to perform the probe. // Default to 10 seconds. Minimum value is 1. // +optional PeriodSeconds int32 `json:\"periodSeconds,omitempty\" protobuf:\"varint,4,opt,name=periodSeconds\"` // Minimum consecutive successes for the probe to be considered successful after having failed. // Defaults to 1. Must be 1 for liveness and startup. Minimum value is 1. // +optional SuccessThreshold int32 `json:\"successThreshold,omitempty\" protobuf:\"varint,5,opt,name=successThreshold\"` // Minimum consecutive failures for the probe to be considered failed after having succeeded. // Defaults to 3. Minimum value is 1. // +optional FailureThreshold int32 `json:\"failureThreshold,omitempty\" protobuf:\"varint,6,opt,name=failureThreshold\"` // Optional duration in seconds the pod needs to terminate gracefully upon probe failure. // The grace period is the duration in seconds after the processes running in the pod are sent // a termination signal and the time when the processes are forcibly halted with a kill signal. // Set this value longer than the expected cleanup time for your process. // If this value is nil, the pod's terminationGracePeriodSeconds will be used. Otherwise, this // value overrides the value provided by the pod spec. // Value must be non-negative integer. The value zero indicates stop immediately via // the kill signal (no opportunity to shut down). // This is a beta field and requires enabling ProbeTerminationGracePeriod feature gate. // Minimum value is 1. spec.terminationGracePeriodSeconds is used if unset. // +optional TerminationGracePeriodSeconds *int64 `json:\"terminationGracePeriodSeconds,omitempty\" protobuf:\"varint,7,opt,name=terminationGracePeriodSeconds\"` } 4.2. ProbeHandler源码 // ProbeHandler defines a specific action that should be taken in a probe. // One and only one of the fields must be specified. type ProbeHandler struct { // Exec specifies the action to take. // +optional Exec *ExecAction `json:\"exec,omitempty\" protobuf:\"bytes,1,opt,name=exec\"` // HTTPGet specifies the http request to perform. // +optional HTTPGet *HTTPGetAction `json:\"httpGet,omitempty\" protobuf:\"bytes,2,opt,name=httpGet\"` // TCPSocket specifies an action involving a TCP port. // +optional TCPSocket *TCPSocketAction `json:\"tcpSocket,omitempty\" protobuf:\"bytes,3,opt,name=tcpSocket\"` // GRPC specifies an action involving a GRPC port. // This is a beta field and requires enabling GRPCContainerProbe feature gate. // +featureGate=GRPCContainerProbe // +optional GRPC *GRPCAction `json:\"grpc,omitempty\" protobuf:\"bytes,4,opt,name=grpc\"` } 4.3. ProbeAction 4.3.1. HTTPGetAction // HTTPHeader describes a custom header to be used in HTTP probes type HTTPHeader struct { // The header field name Name string `json:\"name\" protobuf:\"bytes,1,opt,name=name\"` // The header field value Value string `json:\"value\" protobuf:\"bytes,2,opt,name=value\"` } // HTTPGetAction describes an action based on HTTP Get requests. type HTTPGetAction struct { // Path to access on the HTTP server. // +optional Path string `json:\"path,omitempty\" protobuf:\"bytes,1,opt,name=path\"` // Name or number of the port to access on the container. // Number must be in the range 1 to 65534. // Name must be an IANA_SVC_NAME. Port intstr.IntOrString `json:\"port\" protobuf:\"bytes,2,opt,name=port\"` // Host name to connect to, defaults to the pod IP. You probably want to set // \"Host\" in httpHeaders instead. // +optional Host string `json:\"host,omitempty\" protobuf:\"bytes,3,opt,name=host\"` // Scheme to use for connecting to the host. // Defaults to HTTP. // +optional Scheme URIScheme `json:\"scheme,omitempty\" protobuf:\"bytes,4,opt,name=scheme,casttype=URIScheme\"` // Custom headers to set in the request. HTTP allows repeated headers. // +optional HTTPHeaders []HTTPHeader `json:\"httpHeaders,omitempty\" protobuf:\"bytes,5,rep,name=httpHeaders\"` } // URIScheme identifies the scheme used for connection to a host for Get actions // +enum type URIScheme string const ( // URISchemeHTTP means that the scheme used will be http:// URISchemeHTTP URIScheme = \"HTTP\" // URISchemeHTTPS means that the scheme used will be https:// URISchemeHTTPS URIScheme = \"HTTPS\" ) 4.3.2. TCPSocketAction // TCPSocketAction describes an action based on opening a socket type TCPSocketAction struct { // Number or name of the port to access on the container. // Number must be in the range 1 to 65534. // Name must be an IANA_SVC_NAME. Port intstr.IntOrString `json:\"port\" protobuf:\"bytes,1,opt,name=port\"` // Optional: Host name to connect to, defaults to the pod IP. // +optional Host string `json:\"host,omitempty\" protobuf:\"bytes,2,opt,name=host\"` } 4.3.3. ExecAction // ExecAction describes a \"run in container\" action. type ExecAction struct { // Command is the command line to execute inside the container, the working directory for the // command is root ('/') in the container's filesystem. The command is simply exec'd, it is // not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use // a shell, you need to explicitly call out to that shell. // Exit status of 0 is treated as live/healthy and non-zero is unhealthy. // +optional Command []string `json:\"command,omitempty\" protobuf:\"bytes,1,rep,name=command\"` } 4.3.4. GRPCAction type GRPCAction struct { // Port number of the gRPC service. Number must be in the range 1 to 65534. Port int32 `json:\"port\" protobuf:\"bytes,1,opt,name=port\"` // Service is the name of the service to place in the gRPC HealthCheckRequest // (see https://github.com/grpc/grpc/blob/master/doc/health-checking.md). // // If this is not specified, the default behavior is defined by gRPC. // +optional // +default=\"\" Service *string `json:\"service\" protobuf:\"bytes,2,opt,name=service\"` } 参考文章： https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/ 配置存活、就绪和启动探针 | Kubernetes https://mp.weixin.qq.com/s/ApD8D0_UAPftUjw-0Txcyw 《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/pod/pod-volume.html":{"url":"concepts/pod/pod-volume.html","title":"Pod存储卷","keywords":"","body":"Pod VolumePod Volume 同一个Pod中的多个容器可以共享Pod级别的存储卷Volume,Volume可以定义为各种类型，多个容器各自进行挂载，将Pod的Volume挂载为容器内部需要的目录。 例如：Pod级别的Volume:\"app-logs\",用于tomcat向其中写日志文件，busybox读日志文件。 图片 - 这里写图片描述 pod-volumes-applogs.yaml apiVersion: v1 kind: Pod metadata: name: volume-pod spec: containers: - name: tomcat image: tomcat ports: - containerPort: 8080 volumeMounts: - name: app-logs mountPath: /usr/local/tomcat/logs - name: busybox image: busybox command: [\"sh\",\"-c\",\"tailf /logs/catalina*.log\"] volumeMounts: - name: app-logs mountPath: /logs volumes: - name: app-logs emptuDir: {} 查看日志 kubectl logs -c kubectl exec -it -c – tail /usr/local/tomcat/logs/catalina.xx.log 参考文章 《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/pod/pod-scheduler.html":{"url":"concepts/pod/pod-scheduler.html","title":"Pod控制器","keywords":"","body":"Pod调度1. RC、Deployment:全自动调度2. NodeSelector[定向调度]3. NodeAffinity[亲和性调度]4. DaemonSet：特定场景调度5. Job：批处理调度5.1. 批处理的三种模式5.2. Job的三种类型Pod调度 在kubernetes集群中，Pod（container）是应用的载体，一般通过RC、Deployment、DaemonSet、Job等对象来完成Pod的调度与自愈功能。 1. RC、Deployment:全自动调度 RC的功能即保持集群中始终运行着指定个数的Pod。 在调度策略上主要有： 系统内置调度算法[最优Node] NodeSelector[定向调度] NodeAffinity[亲和性调度] 2. NodeSelector[定向调度] k8s中kube-scheduler负责实现Pod的调度，内部系统通过一系列算法最终计算出最佳的目标节点。如果需要将Pod调度到指定Node上，则可以通过Node的标签（Label）和Pod的nodeSelector属性相匹配来达到目的。 1、kubectl label nodes {node-name} {label-key}={label-value} 2、nodeSelector: {label-key}:{label-value} 如果给多个Node打了相同的标签，则scheduler会根据调度算法从这组Node中选择一个可用的Node来调度。 如果Pod的nodeSelector的标签在Node中没有对应的标签，则该Pod无法被调度成功。 Node标签的使用场景： 对集群中不同类型的Node打上不同的标签，可控制应用运行Node的范围。例如role=frontend;role=backend;role=database。 3. NodeAffinity[亲和性调度] NodeAffinity意为Node亲和性调度策略，NodeSelector为精确匹配，NodeAffinity为条件范围匹配，通过In（属于）、NotIn（不属于）、Exists（存在一个条件）、DoesNotExist（不存在）、Gt（大于）、Lt（小于）等操作符来选择Node，使调度更加灵活。 RequiredDuringSchedulingRequiredDuringExecution：类似于NodeSelector，但在Node不满足条件时，系统将从该Node上移除之前调度上的Pod。 RequiredDuringSchedulingIgnoredDuringExecution：与上一个类似，区别是在Node不满足条件时，系统不一定从该Node上移除之前调度上的Pod。 PreferredDuringSchedulingIgnoredDuringExecution：指定在满足调度条件的Node中，哪些Node应更优先地进行调度。同时在Node不满足条件时，系统不一定从该Node上移除之前调度上的Pod。 如果同时设置了NodeSelector和NodeAffinity，则系统将需要同时满足两者的设置才能进行调度。 4. DaemonSet：特定场景调度 DaemonSet是kubernetes1.2版本新增的一种资源对象，用于管理在集群中每个Node上仅运行一份Pod的副本实例。 图片 - 这里写图片描述 该用法适用的应用场景： 在每个Node上运行一个GlusterFS存储或者Ceph存储的daemon进程。 在每个Node上运行一个日志采集程序：fluentd或logstach。 在每个Node上运行一个健康程序，采集该Node的运行性能数据，例如：Prometheus Node Exportor、collectd、New Relic agent或Ganglia gmond等。 DaemonSet的Pod调度策略与RC类似，除了使用系统内置算法在每台Node上进行调度，也可以通过NodeSelector或NodeAffinity来指定满足条件的Node范围进行调度。 5. Job：批处理调度 kubernetes从1.2版本开始支持批处理类型的应用，可以通过kubernetes Job资源对象来定义并启动一个批处理任务。批处理任务通常并行（或串行）启动多个计算进程去处理一批工作项（work item），处理完后，整个批处理任务结束。 5.1. 批处理的三种模式 图片 - 这里写图片描述 批处理按任务实现方式不同分为以下几种模式： Job Template Expansion模式 一个Job对象对应一个待处理的Work item，有几个Work item就产生几个独立的Job，通过适用于Work item数量少，每个Work item要处理的数据量比较大的场景。例如有10个文件（Work item）,每个文件（Work item）为100G。 Queue with Pod Per Work Item 采用一个任务队列存放Work item，一个Job对象作为消费者去完成这些Work item，其中Job会启动N个Pod，每个Pod对应一个Work item。 Queue with Variable Pod Count 采用一个任务队列存放Work item，一个Job对象作为消费者去完成这些Work item，其中Job会启动N个Pod，每个Pod对应一个Work item。但Pod的数量是可变的。 5.2. Job的三种类型 1）Non-parallel Jobs 通常一个Job只启动一个Pod,除非Pod异常才会重启该Pod,一旦此Pod正常结束，Job将结束。 2）Parallel Jobs with a fixed completion count 并行Job会启动多个Pod，此时需要设定Job的.spec.completions参数为一个正数，当正常结束的Pod数量达到该值则Job结束。 3）Parallel Jobs with a work queue 任务队列方式的并行Job需要一个独立的Queue，Work item都在一个Queue中存放，不能设置Job的.spec.completions参数。 此时Job的特性： 每个Pod能独立判断和决定是否还有任务项需要处理 如果某个Pod正常结束，则Job不会再启动新的Pod 如果一个Pod成功结束，则此时应该不存在其他Pod还在干活的情况，它们应该都处于即将结束、退出的状态 如果所有的Pod都结束了，且至少一个Pod成功结束，则整个Job算是成功结束 参考文章 《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/pod/pod-operation.html":{"url":"concepts/pod/pod-operation.html","title":"Pod伸缩与升级","keywords":"","body":"1. Pod伸缩1.1. 手动伸缩（scale）1.2. 自动伸缩（HPA）1.2.1. 通过kubectl autoscale创建1.2.2. 通过yaml配置文件创建2. Pod滚动升级2.1. 通过配置文件实现2.2. 通过kubectl rolling-update命令实现2.3. 升级回滚1. Pod伸缩 k8s中RC的用来保持集群中始终运行指定数目的实例，通过RC的scale机制可以完成Pod的扩容和缩容（伸缩）。 1.1. 手动伸缩（scale） kubectl scale rc redis-slave --replicas=3 1.2. 自动伸缩（HPA） Horizontal Pod Autoscaler（HPA）控制器用于实现基于CPU使用率进行自动Pod伸缩的功能。HPA控制器基于Master的kube-controller-manager服务启动参数--horizontal-pod-autoscaler-sync-period定义是时长（默认30秒），周期性监控目标Pod的CPU使用率，并在满足条件时对ReplicationController或Deployment中的Pod副本数进行调整，以符合用户定义的平均Pod CPU使用率。Pod CPU使用率来源于heapster组件，因此需安装该组件。 可以通过kubectl autoscale命令进行快速创建或者使用yaml配置文件进行创建。创建之前需已存在一个RC或Deployment对象，并且该RC或Deployment中的Pod必须定义resources.requests.cpu的资源请求值，以便heapster采集到该Pod的CPU。 1.2.1. 通过kubectl autoscale创建 例如： php-apache-rc.yaml apiVersion: v1 kind: ReplicationController metadata: name: php-apache spec: replicas: 1 template: metadata: name: php-apache labels: app: php-apache spec: containers: - name: php-apache image: gcr.io/google_containers/hpa-example resources: requests: cpu: 200m ports: - containerPort: 80 创建php-apache的RC kubectl create -f php-apache-rc.yaml php-apache-svc.yaml apiVersion: v1 kind: Service metadata: name: php-apache spec: ports: - port: 80 selector: app: php-apache 创建php-apache的Service kubectl create -f php-apache-svc.yaml 创建HPA控制器 kubectl autoscale rc php-apache --min=1 --max=10 --cpu-percent=50 1.2.2. 通过yaml配置文件创建 hpa-php-apache.yaml apiVersion: v1 kind: HorizontalPodAutoscaler metadata: name: php-apache spec: scaleTargetRef: apiVersion: v1 kind: ReplicationController name: php-apache minReplicas: 1 maxReplicas: 10 targetCPUUtilizationPercentage: 50 创建hpa kubectl create -f hpa-php-apache.yaml 查看hpa kubectl get hpa 2. Pod滚动升级 k8s中的滚动升级通过执行kubectl rolling-update命令完成，该命令创建一个新的RC（与旧的RC在同一个命名空间中），然后自动控制旧的RC中的Pod副本数逐渐减少为0，同时新的RC中的Pod副本数从0逐渐增加到附加值，但滚动升级中Pod副本数（包括新Pod和旧Pod）保持原预期值。 2.1. 通过配置文件实现 redis-master-controller-v2.yaml apiVersion: v1 kind: ReplicationController metadata: name: redis-master-v2 labels: name: redis-master version: v2 spec: replicas: 1 selector: name: redis-master version: v2 template: metadata: labels: name: redis-master version: v2 spec: containers: - name: master image: kubeguide/redis-master:2.0 ports: - containerPort: 6371 注意事项： RC的名字（name）不能与旧RC的名字相同 在selector中应至少有一个Label与旧的RC的Label不同，以标识其为新的RC。例如本例中新增了version的Label。 运行kubectl rolling-update kubectl rolling-update redis-master -f redis-master-controller-v2.yaml 2.2. 通过kubectl rolling-update命令实现 kubectl rolling-update redis-master --image=redis-master:2.0 与使用配置文件实现不同在于，该执行结果旧的RC被删除，新的RC仍使用旧的RC的名字。 2.3. 升级回滚 kubectl rolling-update加参数--rollback实现回滚操作 kubectl rolling-update redis-master --image=kubeguide/redis-master:2.0 --rollback 参考文章 《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/configmap/_index.html":{"url":"concepts/configmap/_index.html","title":"配置","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/configmap/pod-configmap.html":{"url":"concepts/configmap/pod-configmap.html","title":"ConfigMap","keywords":"","body":"Pod的配置管理1. ConfigMap：容器应用的配置管理2. 创建ConfigMap2.1. 通过yaml文件方式2.2. 通过kubectl命令行方式2.3. 通过环境变量的方式3. 使用ConfigMap的限制条件Pod的配置管理 Kubernetes v1.2的版本提供统一的集群配置管理方案–ConfigMap。 1. ConfigMap：容器应用的配置管理 使用场景： 生成为容器内的环境变量。 设置容器启动命令的启动参数（需设置为环境变量）。 以Volume的形式挂载为容器内部的文件或目录。 ConfigMap以一个或多个key:value的形式保存在kubernetes系统中供应用使用，既可以表示一个变量的值（例如：apploglevel=info），也可以表示完整配置文件的内容（例如：server.xml=...）。 可以通过yaml配置文件或者使用kubectl create configmap命令的方式创建ConfigMap。 2. 创建ConfigMap 2.1. 通过yaml文件方式 cm-appvars.yaml apiVersion: v1 kind: ConfigMap metadata: name: cm-appvars data: apploglevel: info appdatadir: /var/data 常用命令 kubectl create -f cm-appvars.yaml kubectl get configmap kubectl describe configmap cm-appvars kubectl get configmap cm-appvars -o yaml 2.2. 通过kubectl命令行方式 通过kubectl create configmap创建，使用参数--from-file或--from-literal指定内容，可以在一行中指定多个参数。 1）通过--from-file参数从文件中进行创建，可以指定key的名称，也可以在一个命令行中创建包含多个key的ConfigMap。 kubectl create configmap NAME --from-file=[key=]source --from-file=[key=]source 2）通过--from-file参数从目录中进行创建，该目录下的每个配置文件名被设置为key，文件内容被设置为value。 kubectl create configmap NAME --from-file=config-files-dir 3）通过--from-literal从文本中进行创建，直接将指定的key=value创建为ConfigMap的内容。 kubectl create configmap NAME --from-literal=key1=value1 --from-literal=key2=value2 容器应用对ConfigMap的使用有两种方法： 通过环境变量获取ConfigMap中的内容。 通过Volume挂载的方式将ConfigMap中的内容挂载为容器内部的文件或目录。 2.3. 通过环境变量的方式 ConfigMap的yaml文件:cm-appvars.yaml apiVersion: v1 kind: ConfigMap metadata: name: cm-appvars data: apploglevel: info appdatadir: /var/data Pod的yaml文件：cm-test-pod.yaml apiVersion: v1 kind: Pod metadata: name: cm-test-pod spec: containers: - name: cm-test image: busybox command: [\"/bin/sh\",\"-c\",\"env|grep APP\"] env: - name: APPLOGLEVEL valueFrom: configMapKeyRef: name: cm-appvars key: apploglevel - name: APPDATADIR valueFrom: configMapKeyRef: name: cm-appvars key: appdatadir 创建命令： kubectl create -f cm-test-pod.yaml kubectl get pods --show-all kubectl logs cm-test-pod 3. 使用ConfigMap的限制条件 ConfigMap必须在Pod之前创建 ConfigMap也可以定义为属于某个Namespace。只有处于相同Namespace中的Pod可以引用它。 kubelet只支持可以被API Server管理的Pod使用ConfigMap。静态Pod无法引用。 在Pod对ConfigMap进行挂载操作时，容器内只能挂载为“目录”，无法挂载为文件。 参考文章 《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"concepts/_index.html":{"url":"concepts/_index.html","title":"Workload","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"principle/component/_index.html":{"url":"principle/component/_index.html","title":"核心组件","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"principle/component/kubernetes-core-principle-api-server.html":{"url":"principle/component/kubernetes-core-principle-api-server.html","title":"Api Server","keywords":"","body":"1. API Server简介2. 如何访问kubernetes API2.1. 本地端口2.2. 安全端口2.3. 访问方式2.3.1. curl2.3.2. Kubectl Proxy2.3.3. kubectl客户端2.3.4. 编程方式调用3. 通过API Server访问Node、Pod和Service3.1. Node相关接口3.2. Pod相关接口3.3. Service相关接口4. 集群功能模块之间的通信4.1. kubelet与API Server交互4.2. kube-controller-manager与API Server交互4.3. kube-scheduler与API Server交互4.4. 特别说明1. API Server简介 k8s API Server提供了k8s各类资源对象（pod,RC,Service等）的增删改查及watch等HTTP Rest接口，是整个系统的数据总线和数据中心。 kubernetes API Server的功能： 提供了集群管理的REST API接口(包括认证授权、数据校验以及集群状态变更)； 提供其他模块之间的数据交互和通信的枢纽（其他模块通过API Server查询或修改数据，只有API Server才直接操作etcd）; 是资源配额控制的入口； 拥有完备的集群安全机制. kube-apiserver工作原理图 图片 - kube-apiserver 2. 如何访问kubernetes API k8s通过kube-apiserver这个进程提供服务，该进程运行在单个k8s-master节点上。默认有两个端口。 2.1. 本地端口 该端口用于接收HTTP请求； 该端口默认值为8080，可以通过API Server的启动参数“--insecure-port”的值来修改默认值； 默认的IP地址为“localhost”，可以通过启动参数“--insecure-bind-address”的值来修改该IP地址； 非认证或授权的HTTP请求通过该端口访问API Server。 2.2. 安全端口 该端口默认值为6443，可通过启动参数“--secure-port”的值来修改默认值； 默认IP地址为非本地（Non-Localhost）网络端口，通过启动参数“--bind-address”设置该值； 该端口用于接收HTTPS请求； 用于基于Tocken文件或客户端证书及HTTP Base的认证； 用于基于策略的授权； 默认不启动HTTPS安全访问控制。 2.3. 访问方式 Kubernetes REST API可参考https://kubernetes.io/docs/api-reference/v1.6/ 2.3.1. curl curl localhost:8080/api curl localhost:8080/api/v1/pods curl localhost:8080/api/v1/services curl localhost:8080/api/v1/replicationcontrollers 2.3.2. Kubectl Proxy Kubectl Proxy代理程序既能作为API Server的反向代理，也能作为普通客户端访问API Server的代理。通过master节点的8080端口来启动该代理程序。 kubectl proxy --port=8080 & 具体见kubectl proxy --help [root@node5 ~]# kubectl proxy --help To proxy all of the kubernetes api and nothing else, use: kubectl proxy --api-prefix=/ To proxy only part of the kubernetes api and also some static files: kubectl proxy --www=/my/files --www-prefix=/static/ --api-prefix=/api/ The above lets you 'curl localhost:8001/api/v1/pods'. To proxy the entire kubernetes api at a different root, use: kubectl proxy --api-prefix=/custom/ The above lets you 'curl localhost:8001/custom/api/v1/pods' Usage: kubectl proxy [--port=PORT] [--www=static-dir] [--www-prefix=prefix] [--api-prefix=prefix] [flags] Examples: # Run a proxy to kubernetes apiserver on port 8011, serving static content from ./local/www/ $ kubectl proxy --port=8011 --www=./local/www/ # Run a proxy to kubernetes apiserver on an arbitrary local port. # The chosen port for the server will be output to stdout. $ kubectl proxy --port=0 # Run a proxy to kubernetes apiserver, changing the api prefix to k8s-api # This makes e.g. the pods api available at localhost:8011/k8s-api/v1/pods/ $ kubectl proxy --api-prefix=/k8s-api Flags: --accept-hosts=\"^localhost$,^127//.0//.0//.1$,^//[::1//]$\": Regular expression for hosts that the proxy should accept. --accept-paths=\"^/.*\": Regular expression for paths that the proxy should accept. --api-prefix=\"/\": Prefix to serve the proxied API under. --disable-filter[=false]: If true, disable request filtering in the proxy. This is dangerous, and can leave you vulnerable to XSRF attacks, when used with an accessible port. -p, --port=8001: The port on which to run the proxy. Set to 0 to pick a random port. --reject-methods=\"POST,PUT,PATCH\": Regular expression for HTTP methods that the proxy should reject. --reject-paths=\"^/api/.*/exec,^/api/.*/run\": Regular expression for paths that the proxy should reject. -u, --unix-socket=\"\": Unix socket on which to run the proxy. -w, --www=\"\": Also serve static files from the given directory under the specified prefix. -P, --www-prefix=\"/static/\": Prefix to serve static files under, if static file directory is specified. Global Flags: --alsologtostderr[=false]: log to standard error as well as files --api-version=\"\": The API version to use when talking to the server --certificate-authority=\"\": Path to a cert. file for the certificate authority. --client-certificate=\"\": Path to a client key file for TLS. --client-key=\"\": Path to a client key file for TLS. --cluster=\"\": The name of the kubeconfig cluster to use --context=\"\": The name of the kubeconfig context to use --insecure-skip-tls-verify[=false]: If true, the server's certificate will not be checked for validity. This will make your HTTPS connections insecure. --kubeconfig=\"\": Path to the kubeconfig file to use for CLI requests. --log-backtrace-at=:0: when logging hits line file:N, emit a stack trace --log-dir=\"\": If non-empty, write log files in this directory --log-flush-frequency=5s: Maximum number of seconds between log flushes --logtostderr[=true]: log to standard error instead of files --match-server-version[=false]: Require server version to match client version --namespace=\"\": If present, the namespace scope for this CLI request. --password=\"\": Password for basic authentication to the API server. -s, --server=\"\": The address and port of the Kubernetes API server --stderrthreshold=2: logs at or above this threshold go to stderr --token=\"\": Bearer token for authentication to the API server. --user=\"\": The name of the kubeconfig user to use --username=\"\": Username for basic authentication to the API server. --v=0: log level for V logs --vmodule=: comma-separated list of pattern=N settings for file-filtered logging 2.3.3. kubectl客户端 命令行工具kubectl客户端，通过命令行参数转换为对API Server的REST API调用，并将调用结果输出。 命令格式：kubectl [command] [options] 具体可参考k8s常用命令 2.3.4. 编程方式调用 使用场景： 1、运行在Pod里的用户进程调用kubernetes API,通常用来实现分布式集群搭建的目标。 2、开发基于kubernetes的管理平台，比如调用kubernetes API来完成Pod、Service、RC等资源对象的图形化创建和管理界面。可以使用kubernetes提供的Client Library。 具体可参考https://github.com/kubernetes/client-go。 3. 通过API Server访问Node、Pod和Service k8s API Server最主要的REST接口是资源对象的增删改查，另外还有一类特殊的REST接口—k8s Proxy API接口，这类接口的作用是代理REST请求，即kubernetes API Server把收到的REST请求转发到某个Node上的kubelet守护进程的REST端口上，由该kubelet进程负责响应。 3.1. Node相关接口 关于Node相关的接口的REST路径为：/api/v1/proxy/nodes/{name}，其中{name}为节点的名称或IP地址。 /api/v1/proxy/nodes/{name}/pods/ #列出指定节点内所有Pod的信息 /api/v1/proxy/nodes/{name}/stats/ #列出指定节点内物理资源的统计信息 /api/v1/prxoy/nodes/{name}/spec/ #列出指定节点的概要信息 这里获取的Pod信息来自Node而非etcd数据库，两者时间点可能存在偏差。如果在kubelet进程启动时加--enable-debugging-handles=true参数，那么kubernetes Proxy API还会增加以下接口： /api/v1/proxy/nodes/{name}/run #在节点上运行某个容器 /api/v1/proxy/nodes/{name}/exec #在节点上的某个容器中运行某条命令 /api/v1/proxy/nodes/{name}/attach #在节点上attach某个容器 /api/v1/proxy/nodes/{name}/portForward #实现节点上的Pod端口转发 /api/v1/proxy/nodes/{name}/logs #列出节点的各类日志信息 /api/v1/proxy/nodes/{name}/metrics #列出和该节点相关的Metrics信息 /api/v1/proxy/nodes/{name}/runningpods #列出节点内运行中的Pod信息 /api/v1/proxy/nodes/{name}/debug/pprof #列出节点内当前web服务的状态，包括CPU和内存的使用情况 3.2. Pod相关接口 /api/v1/proxy/namespaces/{namespace}/pods/{name}/{path:*} #访问pod的某个服务接口 /api/v1/proxy/namespaces/{namespace}/pods/{name} #访问Pod #以下写法不同，功能一样 /api/v1/namespaces/{namespace}/pods/{name}/proxy/{path:*} #访问pod的某个服务接口 /api/v1/namespaces/{namespace}/pods/{name}/proxy #访问Pod 3.3. Service相关接口 /api/v1/proxy/namespaces/{namespace}/services/{name} Pod的proxy接口的作用：在kubernetes集群之外访问某个pod容器的服务（HTTP服务），可以用Proxy API实现，这种场景多用于管理目的，比如逐一排查Service的Pod副本，检查哪些Pod的服务存在异常问题。 4. 集群功能模块之间的通信 kubernetes API Server作为集群的核心，负责集群各功能模块之间的通信，集群内各个功能模块通过API Server将信息存入etcd，当需要获取和操作这些数据时，通过API Server提供的REST接口（GET/LIST/WATCH方法）来实现，从而实现各模块之间的信息交互。 4.1. kubelet与API Server交互 每个Node节点上的kubelet定期就会调用API Server的REST接口报告自身状态，API Server接收这些信息后，将节点状态信息更新到etcd中。kubelet也通过API Server的Watch接口监听Pod信息，从而对Node机器上的POD进行管理。 监听信息 kubelet动作 新的POD副本被调度绑定到本节点 执行POD对应的容器的创建和启动逻辑 POD对象被删除 删除本节点上相应的POD容器 修改POD信息 修改本节点的POD容器 4.2. kube-controller-manager与API Server交互 kube-controller-manager中的Node Controller模块通过API Server提供的Watch接口，实时监控Node的信息，并做相应处理。 4.3. kube-scheduler与API Server交互 Scheduler通过API Server的Watch接口监听到新建Pod副本的信息后，它会检索所有符合该Pod要求的Node列表，开始执行Pod调度逻辑。调度成功后将Pod绑定到目标节点上。 4.4. 特别说明 为了缓解各模块对API Server的访问压力，各功能模块都采用缓存机制来缓存数据，各功能模块定时从API Server获取指定的资源对象信息（LIST/WATCH方法），然后将信息保存到本地缓存，功能模块在某些情况下不直接访问API Server，而是通过访问缓存数据来间接访问API Server。 参考《kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"principle/component/kubernetes-core-principle-controller-manager.html":{"url":"principle/component/kubernetes-core-principle-controller-manager.html","title":"Controller Manager","keywords":"","body":"1. Controller Manager简介2. Replication Controller2.1. Replication Controller的职责2.2. Replication Controller使用场景3. Node Controller4. ResourceQuota Controller5. Namespace Controller6. Endpoint Controller7. Service Controller1. Controller Manager简介 Controller Manager作为集群内部的管理控制中心，负责集群内的Node、Pod副本、服务端点（Endpoint）、命名空间（Namespace）、服务账号（ServiceAccount）、资源定额（ResourceQuota）的管理，当某个Node意外宕机时，Controller Manager会及时发现并执行自动化修复流程，确保集群始终处于预期的工作状态。 图片 - controller manager 每个Controller通过API Server提供的接口实时监控整个集群的每个资源对象的当前状态，当发生各种故障导致系统状态发生变化时，会尝试将系统状态修复到“期望状态”。 2. Replication Controller 为了区分，将资源对象Replication Controller简称RC,而本文中是指Controller Manager中的Replication Controller，称为副本控制器。副本控制器的作用即保证集群中一个RC所关联的Pod副本数始终保持预设值。 只有当Pod的重启策略是Always的时候（RestartPolicy=Always），副本控制器才会管理该Pod的操作（创建、销毁、重启等）。 RC中的Pod模板就像一个模具，模具制造出来的东西一旦离开模具，它们之间就再没关系了。一旦Pod被创建，无论模板如何变化，也不会影响到已经创建的Pod。 Pod可以通过修改label来脱离RC的管控，该方法可以用于将Pod从集群中迁移，数据修复等调试。 删除一个RC不会影响它所创建的Pod，如果要删除Pod需要将RC的副本数属性设置为0。 不要越过RC创建Pod，因为RC可以实现自动化控制Pod，提高容灾能力。 2.1. Replication Controller的职责 确保集群中有且仅有N个Pod实例，N是RC中定义的Pod副本数量。 通过调整RC中的spec.replicas属性值来实现系统扩容或缩容。 通过改变RC中的Pod模板来实现系统的滚动升级。 2.2. Replication Controller使用场景 使用场景 说明 使用命令 重新调度 当发生节点故障或Pod被意外终止运行时，可以重新调度保证集群中仍然运行指定的副本数。 弹性伸缩 通过手动或自动扩容代理修复副本控制器的spec.replicas属性，可以实现弹性伸缩。 kubectl scale 滚动更新 创建一个新的RC文件，通过kubectl 命令或API执行，则会新增一个新的副本同时删除旧的副本，当旧副本为0时，删除旧的RC。 kubectl rolling-update 滚动升级，具体可参考kubectl rolling-update --help,官方文档：https://kubernetes.io/docs/tasks/run-application/rolling-update-replication-controller/ 3. Node Controller kubelet在启动时会通过API Server注册自身的节点信息，并定时向API Server汇报状态信息，API Server接收到信息后将信息更新到etcd中。 Node Controller通过API Server实时获取Node的相关信息，实现管理和监控集群中的各个Node节点的相关控制功能。流程如下 图片 - Node Controller 1、Controller Manager在启动时如果设置了--cluster-cidr参数，那么为每个没有设置Spec.PodCIDR的Node节点生成一个CIDR地址，并用该CIDR地址设置节点的Spec.PodCIDR属性，防止不同的节点的CIDR地址发生冲突。 2、具体流程见以上流程图。 3、逐个读取节点信息，如果节点状态变成非“就绪”状态，则将节点加入待删除队列，否则将节点从该队列删除。 4. ResourceQuota Controller 资源配额管理确保指定的资源对象在任何时候都不会超量占用系统物理资源。 支持三个层次的资源配置管理： 1）容器级别：对CPU和Memory进行限制 2）Pod级别：对一个Pod内所有容器的可用资源进行限制 3）Namespace级别：包括 Pod数量 Replication Controller数量 Service数量 ResourceQuota数量 Secret数量 可持有的PV（Persistent Volume）数量 说明： k8s配额管理是通过Admission Control（准入控制）来控制的； Admission Control提供两种配额约束方式：LimitRanger和ResourceQuota； LimitRanger作用于Pod和Container； ResourceQuota作用于Namespace上，限定一个Namespace里的各类资源的使用总额。 ResourceQuota Controller流程图： 图片 - ResourceQuota Controller 5. Namespace Controller 用户通过API Server可以创建新的Namespace并保存在etcd中，Namespace Controller定时通过API Server读取这些Namespace信息。 如果Namespace被API标记为优雅删除（即设置删除期限，DeletionTimestamp）,则将该Namespace状态设置为“Terminating”,并保存到etcd中。同时Namespace Controller删除该Namespace下的ServiceAccount、RC、Pod等资源对象。 6. Endpoint Controller Service、Endpoint、Pod的关系： 图片 - Endpoint Controller Endpoints表示了一个Service对应的所有Pod副本的访问地址，而Endpoints Controller负责生成和维护所有Endpoints对象的控制器。它负责监听Service和对应的Pod副本的变化。 如果监测到Service被删除，则删除和该Service同名的Endpoints对象； 如果监测到新的Service被创建或修改，则根据该Service信息获得相关的Pod列表，然后创建或更新Service对应的Endpoints对象。 如果监测到Pod的事件，则更新它对应的Service的Endpoints对象。 kube-proxy进程获取每个Service的Endpoints，实现Service的负载均衡功能。 7. Service Controller Service Controller是属于kubernetes集群与外部的云平台之间的一个接口控制器。Service Controller监听Service变化，如果是一个LoadBalancer类型的Service，则确保外部的云平台上对该Service对应的LoadBalancer实例被相应地创建、删除及更新路由转发表。 参考《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"principle/component/kubernetes-core-principle-scheduler.html":{"url":"principle/component/kubernetes-core-principle-scheduler.html","title":"Scheduler","keywords":"","body":"1. Scheduler简介2. 调度流程3. 预选策略3.1. NoDiskConflict3.2. PodFitsResources3.3. PodSelectorMatches3.4. PodFitsHost3.5. CheckNodeLabelPresence3.6. CheckServiceAffinity3.7. PodFitsPorts4. 优选策略4.1. LeastRequestedPriority4.2. CalculateNodeLabelPriority4.3. BalancedResourceAllocation1. Scheduler简介 Scheduler负责Pod调度。在整个系统中起\"承上启下\"作用，承上：负责接收Controller Manager创建的新的Pod，为其选择一个合适的Node；启下：Node上的kubelet接管Pod的生命周期。 Scheduler： 1）通过调度算法为待调度Pod列表的每个Pod从Node列表中选择一个最适合的Node，并将信息写入etcd中 2）kubelet通过API Server监听到kubernetes Scheduler产生的Pod绑定信息，然后获取对应的Pod清单，下载Image，并启动容器。 图片 - scheduler 2. 调度流程 1、预选调度过程，即遍历所有目标Node,筛选出符合要求的候选节点，kubernetes内置了多种预选策略（xxx Predicates）供用户选择 2、确定最优节点，在第一步的基础上采用优选策略（xxx Priority）计算出每个候选节点的积分，取最高积分。 调度流程通过插件式加载的“调度算法提供者”（AlgorithmProvider）具体实现，一个调度算法提供者就是包括一组预选策略与一组优选策略的结构体。 3. 预选策略 说明：返回true表示该节点满足该Pod的调度条件；返回false表示该节点不满足该Pod的调度条件。 3.1. NoDiskConflict 判断备选Pod的数据卷是否与该Node上已存在Pod挂载的数据卷冲突，如果是则返回false，否则返回true。 3.2. PodFitsResources 判断备选节点的资源是否满足备选Pod的需求，即节点的剩余资源满不满足该Pod的资源使用。 计算备选Pod和节点中已用资源（该节点所有Pod的使用资源）的总和。 获取备选节点的状态信息，包括节点资源信息。 如果（备选Pod+节点已用资源>该节点总资源）则返回false，即剩余资源不满足该Pod使用；否则返回true。 3.3. PodSelectorMatches 判断节点是否包含备选Pod的标签选择器指定的标签，即通过标签来选择Node。 如果Pod中没有指定spec.nodeSelector，则返回true。 否则获得备选节点的标签信息，判断该节点的标签信息中是否包含该Pod的spec.nodeSelector中指定的标签，如果包含返回true，否则返回false。 3.4. PodFitsHost 判断备选Pod的spec.nodeName所指定的节点名称与备选节点名称是否一致，如果一致返回true，否则返回false。 3.5. CheckNodeLabelPresence 检查备选节点中是否有Scheduler配置的标签，如果有返回true，否则返回false。 3.6. CheckServiceAffinity 判断备选节点是否包含Scheduler配置的标签，如果有返回true，否则返回false。 3.7. PodFitsPorts 判断备选Pod所用的端口列表中的端口是否在备选节点中已被占用，如果被占用返回false，否则返回true。 4. 优选策略 4.1. LeastRequestedPriority 优先从备选节点列表中选择资源消耗最小的节点（CPU+内存）。 4.2. CalculateNodeLabelPriority 优先选择含有指定Label的节点。 4.3. BalancedResourceAllocation 优先从备选节点列表中选择各项资源使用率最均衡的节点。 参考《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"principle/component/kubernetes-core-principle-kubelet.html":{"url":"principle/component/kubernetes-core-principle-kubelet.html","title":"Kubelet","keywords":"","body":"1. kubelet简介2. 节点管理3. Pod管理4. 容器健康检查5. cAdvisor资源监控1. kubelet简介 在kubernetes集群中，每个Node节点都会启动kubelet进程，用来处理Master节点下发到本节点的任务，管理Pod和其中的容器。kubelet会在API Server上注册节点信息，定期向Master汇报节点资源使用情况，并通过cAdvisor监控容器和节点资源。可以把kubelet理解成【Server-Agent】架构中的agent，是Node上的pod管家。 更多kubelet配置参数信息可参考kubelet --help 2. 节点管理 节点通过设置kubelet的启动参数“--register-node”，来决定是否向API Server注册自己，默认为true。可以通过kubelet --help或者查看kubernetes源码【cmd/kubelet/app/server.go中】来查看该参数。 kubelet的配置文件 默认配置文件在/etc/kubernetes/kubelet中，其中 --api-servers：用来配置Master节点的IP和端口。 --kubeconfig：用来配置kubeconfig的路径，kubeconfig文件常用来指定证书。 --hostname-override：用来配置该节点在集群中显示的主机名。 --node-status-update-frequency：配置kubelet向Master心跳上报的频率，默认为10s。 3. Pod管理 kubelet有几种方式获取自身Node上所需要运行的Pod清单。但本文只讨论通过API Server监听etcd目录，同步Pod列表的方式。 kubelet通过API Server Client使用WatchAndList的方式监听etcd中/registry/nodes/${当前节点名称}和/registry/pods的目录，将获取的信息同步到本地缓存中。 kubelet监听etcd，执行对Pod的操作，对容器的操作则是通过Docker Client执行，例如启动删除容器等。 kubelet创建和修改Pod流程： 为该Pod创建一个数据目录。 从API Server读取该Pod清单。 为该Pod挂载外部卷（External Volume） 下载Pod用到的Secret。 检查运行的Pod，执行Pod中未完成的任务。 先创建一个Pause容器，该容器接管Pod的网络，再创建其他容器。 Pod中容器的处理流程： 1）比较容器hash值并做相应处理。 2）如果容器被终止了且没有指定重启策略，则不做任何处理。 3）调用Docker Client下载容器镜像，调用Docker Client运行容器。 4. 容器健康检查 Pod通过探针的方式来检查容器的健康状态，具体可参考Pod详解#Pod健康检查。 5. cAdvisor资源监控 kubelet通过cAdvisor获取本节点信息及容器的数据。cAdvisor为谷歌开源的容器资源分析工具，默认集成到kubernetes中。 cAdvisor自动采集CPU,内存，文件系统，网络使用情况，容器中运行的进程，默认端口为4194。可以通过Node IP+Port访问。 更多参考：http://github.com/google/cadvisor 参考《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"principle/flow/_index.html":{"url":"principle/flow/_index.html","title":"流程图","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"principle/flow/pod-flow.html":{"url":"principle/flow/pod-flow.html","title":"Pod创建流程","keywords":"","body":"Pod创建基本流程图Pod创建完整流程图Pod创建基本流程图 Pod创建完整流程图 图片来源：https://fuckcloudnative.io/posts/what-happens-when-k8s/ 参考: https://fuckcloudnative.io/posts/what-happens-when-k8s/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"principle/flow/pvc-flow.html":{"url":"principle/flow/pvc-flow.html","title":"PVC创建流程","keywords":"","body":"pvc流程详细流程图pvc流程 流程如下： 用户创建了一个包含 PVC 的 Pod，该 PVC 要求使用动态存储卷； Scheduler 根据 Pod 配置、节点状态、PV 配置等信息，把 Pod 调度到一个合适的 Worker 节点上； PV 控制器 watch 到该 Pod 使用的 PVC 处于 Pending 状态，于是调用 Volume Plugin（in-tree）创建存储卷，并创建 PV 对象（out-of-tree 由 External Provisioner 来处理）； AD 控制器发现 Pod 和 PVC 处于待挂接状态，于是调用 Volume Plugin 挂接存储设备到目标 Worker 节点上 在 Worker 节点上，Kubelet 中的 Volume Manager 等待存储设备挂接完成，并通过 Volume Plugin 将设备挂载到全局目录：/var/lib/kubelet/pods/[pod uid]/volumes/kubernetes.io~iscsi/[PVname]（以 iscsi 为例）； Kubelet 通过 Docker 启动 Pod 的 Containers，用 bind mount 方式将已挂载到本地全局目录的卷映射到容器中。 详细流程图 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"network/docker-network.html":{"url":"network/docker-network.html","title":"Docker网络","keywords":"","body":"1. Docker的网络基础1.1. Network Namespace1.1.1. 网络命名空间的实现1.1.2. 网络命名空间的操作2. Docker的网络实现2.1. 容器网络2.2. 4类网络模式3. Docker网络模式3.1. bridge桥接模式3.1.1. 外部网络访问Docker容器3.1.2. Docker容器访问外部网络3.1.3. 缺点3.2. host模式3.3. container模式3.4. none模式1. Docker的网络基础 1.1. Network Namespace 不同的网络命名空间中，协议栈是独立的，完全隔离，彼此之间无法通信。同一个网络命名空间有独立的路由表和独立的Iptables/Netfilter来提供包的转发、NAT、IP包过滤等功能。 1.1.1. 网络命名空间的实现 将与网络协议栈相关的全局变量变成一个Net Namespace变量的成员，然后在调用协议栈函数中加入一个Namepace参数。 1.1.2. 网络命名空间的操作 1、创建网络命名空间 ip netns add name 2、在命名空间内执行命令 ip netns exec name command 3、进入命名空间 ip netns exec name bash 2. Docker的网络实现 2.1. 容器网络 Docker使用Linux桥接，在宿主机虚拟一个Docker容器网桥(docker0)，Docker启动一个容器时会根据Docker网桥的网段分配给容器一个IP地址，称为Container-IP，同时Docker网桥是每个容器的默认网关。因为在同一宿主机内的容器都接入同一个网桥，这样容器之间就能够通过容器的Container-IP直接通信。 Docker网桥是宿主机虚拟出来的，并不是真实存在的网络设备，外部网络是无法寻址到的，这也意味着外部网络无法通过直接Container-IP访问到容器。如果容器希望外部访问能够访问到，可以通过映射容器端口到宿主主机（端口映射），即docker run创建容器时候通过 -p 或 -P 参数来启用，访问容器的时候就通过[宿主机IP]:[容器端口]访问容器。 图片 - 这里写图片描述 2.2. 4类网络模式 Docker网络模式 配置 说明 host模式 --net=host 容器和宿主机共享Network namespace。 container模式 --net=container:NAME_or_ID 容器和另外一个容器共享Network namespace。 kubernetes中的pod就是多个容器共享一个Network namespace。 none模式 --net=none 容器有独立的Network namespace，但并没有对其进行任何网络设置，如分配veth pair 和网桥连接，配置IP等。 bridge模式 --net=bridge（默认为该模式） 桥接模式 3. Docker网络模式 3.1. bridge桥接模式 在bridge模式下，Docker可以使用独立的网络栈。实现方式是父进程在创建子进程的时候通过传入CLONE_NEWNET的参数创建出一个网络命名空间。 实现步骤： Docker Daemon首次启动时会创建一个虚拟网桥docker0，地址通常为172.x.x.x开头，在私有的网络空间中给这个网络分配一个子网。 由Docker创建处理的每个容器，都会创建一个虚拟以太设备对（veth pair），一端关联到网桥，另一端使用Namespace技术映射到容器内的eth0设备，然后从网桥的地址段内给eth0接口分配一个IP地址。 图片 - 这里写图片描述 一般情况，宿主机IP与docker0 IP、容器IP是不同的IP段，默认情况，外部看不到docker0和容器IP，对于外部来说相当于docker0和容器的IP为内网IP。 3.1.1. 外部网络访问Docker容器 外部访问docker容器可以通过端口映射(NAT)的方式，Docker使用NAT的方式将容器内部的服务与宿主机的某个端口port_1绑定。 外部访问容器的流程如下： 外界网络通过宿主机的IP和映射的端口port_1访问。 当宿主机收到此类请求，会通过DNAT将请求的目标IP即宿主机IP和目标端口即映射端口port_1替换成容器的IP和容器的端口port_0。 由于宿主机上可以识别容器IP，所以宿主机将请求发给veth pair。 veth pair将请求发送给容器内部的eth0，由容器内部的服务进行处理。 3.1.2. Docker容器访问外部网络 docker容器访问外部网络的流程： docker容器向外部目标IP和目标端口port_2发起请求，请求报文中的源IP为容器IP。 请求通过容器内部的eth0到veth pair的另一端docker0网桥。 docker0网桥通过数据报转发功能将请求转发到宿主机的eth0。 宿主机处理请求时通过SNAT将请求中的源IP换成宿主机eth0的IP。 处理后的报文通过请求的目标IP发送到外部网络。 3.1.3. 缺点 使用NAT的方式可能会带来性能的问题，影响网络传输效率。 3.2. host模式 host模式并没有给容器创建一个隔离的网络环境，而是和宿主机共用一个网络命名空间，容器使用宿主机的eth0和外界进行通信，同样容器也共用宿主机的端口资源，即分配端口可能存在与宿主机已分配的端口冲突的问题。 实现的方式即父进程在创建子进程的时候不传入CLONE_NEWNET的参数，从而和宿主机共享一个网络空间。 host模式没有通过NAT的方式进行转发因此性能上相对较好，但是不存在网络隔离性，可能产生端口冲突的问题。 3.3. container模式 container模式即docker容器可以使用其他容器的网络命名空间，即和其他容器处于同一个网络命名空间。 步骤： 查找其他容器的网络命名空间。 新创建的容器的网络命名空间使用其他容器的网络命名空间。 通过和其他容器共享网络命名空间的方式，可以让不同的容器之间处于相同的网络命名空间，可以直接通过localhost的方式进行通信，简化了强关联的多个容器之间的通信问题。 k8s中的pod的概念就是通过一组容器共享一个网络命名空间来达到pod内部的不同容器可以直接通过localhost的方式进行通信。 3.4. none模式 none模式即不为容器创建任何的网络环境，用户可以根据自己的需要手动去创建不同的网络定制配置。 参考： 《Docker源码分析》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"network/kubernetes-network.html":{"url":"network/kubernetes-network.html","title":"K8S网络","keywords":"","body":"1. kubernetes网络模型1.1. 基础原则1.2. k8s对集群的网络要求1.3. 网络架构图1.4. k8s集群IP概念汇总2. kubernetes的网络实现2.1. Pod网络2.2. Pod内部容器之间的通信2.3. Pod之间的通信2.3.1. 同Node的Pod之间的通信2.3.2. 不同Node的Pod之间的通信2.4. Service网络3. 开源的网络组件3.1. Flannel1. kubernetes网络模型 1.1. 基础原则 每个Pod都拥有一个独立的IP地址，而且假定所有Pod都在一个可以直接连通的、扁平的网络空间中，不管是否运行在同一Node上都可以通过Pod的IP来访问。 k8s中Pod的IP是最小粒度IP。同一个Pod内所有的容器共享一个网络堆栈，该模型称为IP-per-Pod模型。 Pod由docker0实际分配的IP，Pod内部看到的IP地址和端口与外部保持一致。同一个Pod内的不同容器共享网络，可以通过localhost来访问对方的端口，类似同一个VM内的不同进程。 IP-per-Pod模型从端口分配、域名解析、服务发现、负载均衡、应用配置等角度看，Pod可以看作是一台独立的VM或物理机。 1.2. k8s对集群的网络要求 所有容器都可以不用NAT的方式同别的容器通信。 所有节点都可以在不同NAT的方式下同所有容器通信，反之亦然。 容器的地址和别人看到的地址是同一个地址。 以上的集群网络要求可以通过第三方开源方案实现，例如flannel。 1.3. 网络架构图 图片 - 这里写图片描述 1.4. k8s集群IP概念汇总 由集群外部到集群内部： IP类型 说明 Proxy-IP 代理层公网地址IP，外部访问应用的网关服务器。[实际需要关注的IP] Service-IP Service的固定虚拟IP，Service-IP是内部，外部无法寻址到。 Node-IP 容器宿主机的主机IP。 Container-Bridge-IP 容器网桥（docker0）IP，容器的网络都需要通过容器网桥转发。 Pod-IP Pod的IP，等效于Pod中网络容器的Container-IP。 Container-IP 容器的IP，容器的网络是个隔离的网络空间。 2. kubernetes的网络实现 k8s网络场景 容器与容器之间的直接通信。 Pod与Pod之间的通信。 Pod到Service之间的通信。 集群外部与内部组件之间的通信。 2.1. Pod网络 Pod作为kubernetes的最小调度单元，Pod是容器的集合，是一个逻辑概念，Pod包含的容器都运行在同一个宿主机上，这些容器将拥有同样的网络空间，容器之间能够互相通信，它们能够在本地访问其它容器的端口。 实际上Pod都包含一个网络容器，它不做任何事情，只是用来接管Pod的网络，业务容器通过加入网络容器的网络从而实现网络共享。Pod网络本质上还是容器网络，所以Pod-IP就是网络容器的Container-IP。 一般将容器云平台的网络模型打造成一个扁平化网络平面，在这个网络平面内，Pod作为一个网络单元同Kubernetes Node的网络处于同一层级。 2.2. Pod内部容器之间的通信 同一个Pod之间的不同容器因为共享同一个网络命名空间，所以可以直接通过localhost直接通信。 2.3. Pod之间的通信 2.3.1. 同Node的Pod之间的通信 同一个Node内，不同的Pod都有一个全局IP，可以直接通过Pod的IP进行通信。Pod地址和docker0在同一个网段。 在pause容器启动之前，会创建一个虚拟以太网接口对（veth pair），该接口对一端连着容器内部的eth0 ，一端连着容器外部的vethxxx，vethxxx会绑定到容器运行时配置使用的网桥bridge0上，从该网络的IP段中分配IP给容器的eth0。 当同节点上的Pod-A发包给Pod-B时，包传送路线如下： pod-a的eth0—>pod-a的vethxxx—>bridge0—>pod-b的vethxxx—>pod-b的eth0 因为相同节点的bridge0是相通的，因此可以通过bridge0来完成不同pod直接的通信，但是不同节点的bridge0是不通的，因此不同节点的pod之间的通信需要将不同节点的bridge0给连接起来。 2.3.2. 不同Node的Pod之间的通信 不同的Node之间，Node的IP相当于外网IP，可以直接访问，而Node内的docker0和Pod的IP则是内网IP，无法直接跨Node访问。需要通过Node的网卡进行转发。 所以不同Node之间的通信需要达到两个条件： 对整个集群中的Pod-IP分配进行规划，不能有冲突（可以通过第三方开源工具来管理，例如flannel）。 将Node-IP与该Node上的Pod-IP关联起来，通过Node-IP再转发到Pod-IP。 不同节点的Pod之间的通信需要将不同节点的bridge0给连接起来。连接不同节点的bridge0的方式有好几种，主要有overlay和underlay，或常规的三层路由。 不同节点的bridge0需要不同的IP段，保证Pod IP分配不会冲突，节点的物理网卡eth0也要和该节点的网桥bridge0连接。因此，节点a上的pod-a发包给节点b上的pod-b，路线如下： 节点a上的pod-a的eth0—>pod-a的vethxxx—>节点a的bridge0—>节点a的eth0—> 节点b的eth0—>节点b的bridge0—>pod-b的vethxxx—>pod-b的eth0 图片 - 这里写图片描述 1. Pod间实现通信 例如：Pod1和Pod2（同主机），Pod1和Pod3(跨主机)能够通信 实现：因为Pod的Pod-IP是Docker网桥分配的，Pod-IP是同Node下全局唯一的。所以将不同Kubernetes Node的 Docker网桥配置成不同的IP网段即可。 2. Node与Pod间实现通信 例如：Node1和Pod1/ Pod2(同主机)，Pod3(跨主机)能够通信 实现：在容器集群中创建一个覆盖网络(Overlay Network)，联通各个节点，目前可以通过第三方网络插件来创建覆盖网络，比如Flannel和Open vSwitch等。 不同节点间的Pod访问也可以通过calico形成的Pod IP的路由表来解决。 2.4. Service网络 Service的就是在Pod之间起到服务代理的作用，对外表现为一个单一访问接口，将请求转发给Pod，Service的网络转发是Kubernetes实现服务编排的关键一环。Service都会生成一个虚拟IP，称为Service-IP， Kuberenetes Porxy组件负责实现Service-IP路由和转发，在容器覆盖网络之上又实现了虚拟转发网络。 Kubernetes Porxy实现了以下功能： 转发访问Service的Service-IP的请求到Endpoints(即Pod-IP)。 监控Service和Endpoints的变化，实时刷新转发规则。 负载均衡能力。 3. 开源的网络组件 3.1. Flannel 具体参考Flannel介绍 参考《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"network/pod-dns.html":{"url":"network/pod-dns.html","title":"Pod的DNS策略","keywords":"","body":"1. Pod的DNS策略2. Pod DNS的配置3. 自定义DNS服务3.1. 配置继承节点的DNS解析3.2. 配置CoreDNS3.3. 配置存根域和上游域名服务器4. 调试DNS问题4.1. 查看Coredns服务是否正常4.2. 查看/etc/resolv.conf4.3. nslookup查看解析报错1. Pod的DNS策略 可以在pod中定义dnsPolicy字段来设置dns的策略。 \"Default\": Pod 从运行所在的节点继承名称解析配置。就是该Pod的DNS配置会跟宿主机完全一致。 \"ClusterFirst\": 如果没有配置，即为默认的DNS策略。预先把kube-dns（或CoreDNS）的信息当作预设参数写入到该Pod内的DNS配置。与配置的集群域后缀不匹配的任何 DNS 查询（例如 \"www.kubernetes.io\"） 都会由 DNS 服务器转发到上游名称服务器。 \"ClusterFirstWithHostNet\": 对于以 hostNetwork 方式运行的 Pod，应将其 DNS 策略显式设置为 \"ClusterFirstWithHostNet\"。否则，以 hostNetwork 方式和 \"ClusterFirst\" 策略运行的 Pod 将会做出回退至 \"Default\" 策略的行为。 \"None\": 此设置允许 Pod 忽略 Kubernetes 环境中的 DNS 设置。Pod 会使用其 dnsConfig 字段所提供的 DNS 设置。 2. Pod DNS的配置 当 Pod 的 dnsPolicy 设置为 \"None\" 时，必须指定 dnsConfig 字段。 dnsConfig 字段中属性： nameservers：将用作于 Pod 的 DNS 服务器的 IP 地址列表。 最多可以指定 3 个 IP 地址。例如 coredns的Cluster IP。 searches：用于在 Pod 中查找主机名的 DNS 搜索域的列表。此属性是可选的。 options：可选的对象列表，其中每个对象可能具有 name 属性（必需）和 value 属性（可选）。 示例： apiVersion: v1 kind: Pod metadata: namespace: default name: dns-example spec: containers: - name: test image: nginx dnsPolicy: \"None\" dnsConfig: nameservers: - 1.2.3.4 searches: - ns1.svc.cluster-domain.example - my.dns.search.suffix options: - name: ndots value: \"2\" - name: edns0 通过以上配置，容器内的/etc/resolv.conf文件内容为： kubectl exec -it dns-example -- cat /etc/resolv.conf nameserver 1.2.3.4 search ns1.svc.cluster-domain.example my.dns.search.suffix options ndots:2 edns0 3. 自定义DNS服务 默认一般使用coredns来作为k8s的dns服务器。默认使用deployment的方式来运行coredns，会创建一个名为kube-dns的service，并用ClusterIP（默认为10.96.0.10）来作为集群内的pod的nameserver。 kubelet 使用 --cluster-dns= 标志将 DNS 解析器的信息传递给每个容器。使用 --cluster-domain= 标志配置本地域名。 可查看默认配置： # cat /var/lib/kubelet/config.yaml ... clusterDNS: - 10.96.0.10 clusterDomain: cluster.local 总结： 当没有给pod设置任何dns策略时，则默认使用ClusterFirst的策略，即nameserver的IP为coredns的ClusterIP。通过coredns来解析服务。 3.1. 配置继承节点的DNS解析 如果 Pod 的 dnsPolicy 设置为 default，则它将从 Pod 运行所在节点继承名称解析配置。 使用 kubelet 的 --resolv-conf 标志设置为宿主机的/etc/resolv.conf文件。 3.2. 配置CoreDNS 配置 apiVersion: v1 kind: ConfigMap metadata: name: coredns namespace: kube-system data: Corefi: | .:53 { errors health { lameduck 5s } ready kubernetes cluster.local in-addr.arpa ip6.arpa { pods insecure fallthrough in-addr.arpa ip6.arpa ttl 30 } prometheus :9153 forward . /etc/resolv.conf { max_concurrent 1000 } cache 30 loop reload loadbalance } 配置说明： Corefile 配置包括以下 CoreDNS 插件： errors：错误记录到标准输出。 health：在 http://localhost:8080/health 处提供 CoreDNS 的健康报告。 在这个扩展语法中，lameduck 会使此进程不健康，等待 5 秒后进程被关闭。 ready：在端口 8181 上提供的一个 HTTP 端点， 当所有能够表达自身就绪的插件都已就绪时，在此端点返回 200 OK。 kubernetes：CoreDNS 将基于服务和 Pod 的 IP 来应答 DNS 查询。 你可以在 CoreDNS 网站找到有关此插件的更多细节。 你可以使用 ttl 来定制响应的 TTL。默认值是 5 秒钟。TTL 的最小值可以是 0 秒钟， 最大值为 3600 秒。将 TTL 设置为 0 可以禁止对 DNS 记录进行缓存。 pods insecure 选项是为了与 kube-dns 向后兼容。 你可以使用 pods verified 选项，该选项使得仅在相同名字空间中存在具有匹配 IP 的 Pod 时才返回 A 记录。 如果你不使用 Pod 记录，则可以使用 pods disabled 选项。 prometheus：CoreDNS 的度量指标值以 Prometheus 格式（也称为 OpenMetrics）在 http://localhost:9153/metrics 上提供。 forward: 不在 Kubernetes 集群域内的任何查询都将转发到预定义的解析器 (/etc/resolv.conf)。 cache：启用前端缓存。 loop：检测简单的转发环，如果发现死循环，则中止 CoreDNS 进程。 reload：允许自动重新加载已更改的 Corefile。 编辑 ConfigMap 配置后，请等待两分钟，以使更改生效。 loadbalance：这是一个轮转式 DNS 负载均衡器， 它在应答中随机分配 A、AAAA 和 MX 记录的顺序。 3.3. 配置存根域和上游域名服务器 CoreDNS 能够使用 forward 插件配置存根域和上游域名服务器。 示例： 在 \"10.150.0.1\" 处运行了 Consul 域服务器， 且所有 Consul 名称都带有后缀 .consul.local。 consul.local:53 { errors cache 30 forward . 10.150.0.1 } 要显式强制所有非集群 DNS 查找通过特定的域名服务器（位于 172.16.0.1），可将 forward 指向该域名服务器，而不是 /etc/resolv.conf。 forward . 172.16.0.1 完整示例； apiVersion: v1 kind: ConfigMap metadata: name: coredns namespace: kube-system data: Corefile: | .:53 { errors health kubernetes cluster.local in-addr.arpa ip6.arpa { pods insecure fallthrough in-addr.arpa ip6.arpa } prometheus :9153 forward . 172.16.0.1 cache 30 loop reload loadbalance } consul.local:53 { errors cache 30 forward . 10.150.0.1 } 4. 调试DNS问题 创建一个调试的pod apiVersion: v1 kind: Pod metadata: name: dnsutils namespace: default spec: containers: - name: dnsutils image: registry.k8s.io/e2e-test-images/jessie-dnsutils:1.3 command: - sleep - \"infinity\" imagePullPolicy: IfNotPresent restartPolicy: Always 部署调试pod 4.1. 查看Coredns服务是否正常 kubectl get svc --namespace=kube-system NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE ... kube-dns ClusterIP 10.0.0.10 53/UDP,53/TCP 1h ... 4.2. 查看/etc/resolv.conf 查看容器内dns配置是否符合预期。 kubectl exec -ti dnsutils -- cat /etc/resolv.conf 4.3. nslookup查看解析报错 kubectl exec -i -t dnsutils -- nslookup kubernetes.default Server: 10.0.0.10 Address 1: 10.0.0.10 Name: kubernetes.default Address 1: 10.0.0.1 参考： Service 与 Pod 的 DNS | Kubernetes 自定义 DNS 服务 | Kubernetes 调试 DNS 问题 | Kubernetes 使用 CoreDNS 进行服务发现 | Kubernetes 自动扩缩集群 DNS 服务 | Kubernetes resolv.conf(5) - Linux manual page dns/specification.md at master · kubernetes/dns · GitHub deployment/CoreDNS-k8s_version.md at master · coredns/deployment · GitHub forward Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"network/flannel/_index.html":{"url":"network/flannel/_index.html","title":"网络插件","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"network/flannel/flannel-introduction.html":{"url":"network/flannel/flannel-introduction.html","title":"Flannel介绍","keywords":"","body":"1. flannel是什么（what）1.1. 概述1.2. 补充知识点1、覆盖网络[overlay network]2、路由2. 为什么使用flannel（why）3. 如何实现flannel（how）3.1. flannel原理图3.2. 实现说明1、UDP封装2、为docker分配不同的IP段3、路由规则3.3. flannel的安装与配置1、安装2、配置3、初始化flannel的etcd配置1. flannel是什么（what） 1.1. 概述 Flannel是CoreOS团队针对Kubernetes设计的一个网络规划服务，简单来说，它的功能是让集群中的不同节点主机创建的Docker容器都具有全集群唯一的虚拟IP地址。 Flannel官网：https://github.com/coreos/flannel 1.2. 补充知识点 1、覆盖网络[overlay network] 运行在一个网上的网（应用层网络），并不依靠ip地址来传递消息，而是采用一种映射机制，把ip地址和identifiers做映射来资源定位。 2、路由 互联网是由路由器连接的网络组合而成，路由器按照路由表、路由协议等机制实现对数据包正确地转发，从而到达目标主机。路由器根据数据包中目标主机的IP地址和路由控制表比较得出下一个接收数据的路由器。 1）静态路由：事先设置好路由器和主机中的路由表信息。 2）动态路由：让路由协议在运行中自动修改并设置路由表信息。 图片 - 动态路由 2. 为什么使用flannel（why） 在默认的Docker配置中，每个节点上的Docker服务会分别负责所在节点容器的IP分配。这样导致的一个问题是，不同节点上容器可能获得相同的内外IP地址。 Flannel的设计目的就是为集群中的所有节点重新规划IP地址的使用规则，从而使得不同节点上的容器能够获得“同属一个内网”且”不重复的”IP地址，并让属于不同节点上的容器能够直接通过内网IP通信。 3. 如何实现flannel（how） Flannel实质上是一种“覆盖网络(overlay network)”，也就是将TCP数据包装在另一种网络包里面进行路由转发和通信，目前已经支持UDP、VxLAN、AWS VPC和GCE路由等数据转发方式，默认的节点间数据通信方式是UDP转发。 3.1. flannel原理图 图片 - flannel 数据从源容器中发出后，经由所在主机的docker0虚拟网卡转发到flannel0虚拟网卡，这是个P2P的虚拟网卡，flanneld服务监听在网卡的另外一端。 Flannel通过Etcd服务维护了一张节点间的路由表。 源主机的flanneld服务将原本的数据内容UDP封装后根据自己的路由表投递给目的节点的flanneld服务，数据到达以后被解包，然后直 接进入目的节点的flannel0虚拟网卡，然后被转发到目的主机的docker0虚拟网卡，最后就像本机容器通信一下的有docker0路由到达目标容 器。 3.2. 实现说明 1、UDP封装 原始数据是在起始节点的Flannel服务上进行UDP封装的，投递到目的节点后就被另一端的Flannel服务还原成了原始的数据包，两边的Docker服务都感觉不到这个过程的存在。 UDP的数据内容部分其实是另一个ICMP（也就是ping命令）的数据包。 图片 - UDP封装 2、为docker分配不同的IP段 Flannel通过Etcd分配了每个节点可用的IP地址段后，偷偷的修改了Docker的启动参数。 图片 - docker启动参数 注意其中的“--bip=172.17.18.1/24”这个参数，它限制了所在节点容器获得的IP范围。 这个IP范围是由Flannel自动分配的，由Flannel通过保存在Etcd服务中的记录确保它们不会重复。 3、路由规则 1）数据发送节点的路由表 图片 - 数据发送节点路由表 2）数据接收节点的路由表 图片 - 数据接收节点路由表 例如现在有一个数据包要从IP为172.17.18.2的容器发到IP为172.17.46.2的容器。根据数据发送节点的路由表，它只与 172.17.0.0/16匹配这条记录匹配，因此数据从docker0出来以后就被投递到了flannel0。同理在目标节点，由于投递的地址是一个容 器，因此目的地址一定会落在docker0对于的172.17.46.0/24这个记录上，自然的被投递到了docker0网卡。 3.3. flannel的安装与配置 1、安装 wget http:///flannel/flannel-0.2.0-10.el7.x86_64.rpm yum localinstall -y flannel-0.2.0-10.el7.x86_64.rpm 2、配置 vi /etc/sysconfig/flanneld # Flanneld configuration options # etcd url location. Point this to the server where etcd runs FLANNEL_ETCD=\"http://127.0.0.1:4001\" # etcd config key. This is the configuration key that flannel queries # For address range assignment FLANNEL_ETCD_KEY=\"/xxx/flannel/product/network\" # Any additional options that you want to pass FLANNEL_OPTIONS=\" -iface=eth0\" 3、初始化flannel的etcd配置 etcdctl set /xxx/flannel/network/config '{ \"Network\": \"10.0.0.0/16\", \"Backend\": { \"Type\": \"vxlan\" } }' Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"network/cni/_index.html":{"url":"network/cni/_index.html","title":"CNI","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"network/cni/cni.html":{"url":"network/cni/cni.html","title":"CNI接口介绍","keywords":"","body":"1. CNI（Container Network Interface）2. CNI配置文件格式2.1. 主配置的字段2.2. 插件配置字段2.3. 配置文件示例3. CNI插件3.1. 安装插件3.2. 插件分类4. CNI插件接口4.1. ADD接口：添加容器网络4.2. DEL接口：删除容器网络4.3. CHECK接口：检查容器网络4.4. VERSION接口：输出CNI的版本1. CNI（Container Network Interface） CNI（Container Network Interface）即容器网络接口，通过约定统一的容器网络接口，从而kubelet可以通过这个标准的API来调用不同的网络插件实现不同的网络功能。 kubelet启动参数--network-plugin=cni来指定CNI插件，kubelet从--cni-conf-dir （默认是 /etc/cni/net.d） 读取文件并使用 该文件中的 CNI 配置来设置各个 Pod 的网络。 CNI 配置文件必须与 CNI 规约 匹配，并且配置所引用的所有所需的 CNI 插件都应存在于 --cni-bin-dir（默认是 /opt/cni/bin）下。如果有多个CNI配置文件，kubelet 将会使用按文件名的字典顺序排列 的第一个作为配置文件。 CNI规范定义： 网络配置文件的格式 容器runtime与CNI插件的通信协议 基于提供的配置执行网络插件的步骤 网络插件调用其他功能插件的步骤 插件返回给runtime结果的数据格式 2. CNI配置文件格式 CNI配置文件的格式为JSON格式，配置文件的默认路径：/etc/cni/net.d。插件二进制默认的路径为：/opt/cni/bin。 2.1. 主配置的字段 cniVersion (string)：CNI规范使用的版本，例如版本为0.4.0。 name (string)：目标网络的名称。 disableCheck (boolean)：关闭CHECK操作。 plugins (list)：CNI插件列表及插件配置。 2.2. 插件配置字段 根据不同的插件，插件配置所需的字段不同。 必选字段： type (string)：节点上插件二进制的名称，比如bridge，sriov，macvlan等。 可选字段： capabilities (dictionary) ipMasq (boolean)：为目标网络配上Outbound Masquerade(地址伪装)，即：由容器内部通过网关向外发送数据包时，对数据包的源IP地址进行修改。 当我们的容器以宿主机作为网关时，这个参数是必须要设置的。否则，从容器内部发出的数据包就没有办法通过网关路由到其他网段。因为容器内部的IP地址无法被目标网段识别，所以这些数据包最终会被丢弃掉。 ipam (dictionary)：IPAM(IP Adderss Management)即IP地址管理，提供了一系列方法用于对IP和路由进行管理。它对应的是由CNI提供的一组标准IPAM插件，比如像host-local，dhcp，static等。比如文中用到的bridge插件，会调用我们所指定的IPAM插件，实现对网络设备IP地址的分配和管理。**如果是自己开发的ipam插件，则相关的入参可以自己定义和实现。 以下以host-local为例说明。 type：指定所用IPAM插件的名称，在我们的例子里，用的是host-local。 subnet：为目标网络分配网段，包括网络ID和子网掩码，以CIDR形式标记。在我们的例子里为10.15.10.0/24，也就是目标网段为10.15.10.0，子网掩码为255.255.255.0。 routes：用于指定路由规则，插件会为我们在容器的路由表里生成相应的规则。其中，dst表示希望到达的目标网段，以CIDR形式标记。gw对应网关的IP地址，也就是要到达目标网段所要经过的“next hop(下一跳)”。如果省略gw的话，那么插件会自动帮我们选择默认网关。在我们的例子里，gw选择的是默认网关，而dst为0.0.0.0/0则代表“任何网络”，表示数据包将通过默认网关发往任何网络。实际上，这对应的是一条默认路由规则，即：当所有其他路由规则都不匹配时，将选择该路由。 rangeStart：允许分配的IP地址范围的起始值 rangeEnd：允许分配的IP地址范围的结束值 gateway：为网关（也就是我们将要在宿主机上创建的bridge）指定的IP地址。如果省略的话，那么插件会自动从允许分配的IP地址范围内选择起始值作为网关的IP地址。 dns (dictionary, optional)：dns配置 nameservers (list of strings, optional) domain (string, optional) search (list of strings, optional) options (list of strings, optional) 2.3. 配置文件示例 $ mkdir -p /etc/cni/net.d $ cat >/etc/cni/net.d/10-mynet.conf 3. CNI插件 3.1. 安装插件 安装CNI二进制插件，插件下载地：https://github.com/containernetworking/plugins/releases # 下载二进制 wget https://github.com/containernetworking/plugins/releases/download/v1.1.0/cni-plugins-linux-amd64-v1.1.0.tgz # 解压文件 tar -zvxf cni-plugins-linux-amd64-v1.1.0.tgz -C /opt/cni/bin/ # 查看解压文件 # ll -h 总用量 63M -rwxr-xr-x 1 root root 3.7M 2月 24 01:01 bandwidth -rwxr-xr-x 1 root root 4.1M 2月 24 01:01 bridge -rwxr-xr-x 1 root root 9.3M 2月 24 01:01 dhcp -rwxr-xr-x 1 root root 4.2M 2月 24 01:01 firewall -rwxr-xr-x 1 root root 3.7M 2月 24 01:01 host-device -rwxr-xr-x 1 root root 3.1M 2月 24 01:01 host-local -rwxr-xr-x 1 root root 3.8M 2月 24 01:01 ipvlan -rwxr-xr-x 1 root root 3.2M 2月 24 01:01 loopback -rwxr-xr-x 1 root root 3.8M 2月 24 01:01 macvlan -rwxr-xr-x 1 root root 3.6M 2月 24 01:01 portmap -rwxr-xr-x 1 root root 4.0M 2月 24 01:01 ptp -rwxr-xr-x 1 root root 3.4M 2月 24 01:01 sbr -rwxr-xr-x 1 root root 2.7M 2月 24 01:01 static -rwxr-xr-x 1 root root 3.3M 2月 24 01:01 tuning -rwxr-xr-x 1 root root 3.8M 2月 24 01:01 vlan -rwxr-xr-x 1 root root 3.4M 2月 24 01:01 vrf 3.2. 插件分类 参考：https://www.cni.dev/plugins/current/ 分类 插件 说明 main bridge Creates a bridge, adds the host and the container to it ipvlan Adds an ipvlan interface in the container macvlan Creates a new MAC address, forwards all traffic to that to the container ptp Creates a veth pair host-device Moves an already-existing device into a container vlan Creates a vlan interface off a master IPAM dhcp Runs a daemon on the host to make DHCP requests on behalf of a container host-local Maintains a local database of allocated IPs static Allocates static IPv4/IPv6 addresses to containers meta tuning Changes sysctl parameters of an existing interface portmap An iptables-based portmapping plugin. Maps ports from the host’s address space to the container bandwidth Allows bandwidth-limiting through use of traffic control tbf (ingress/egress) sbr A plugin that configures source based routing for an interface (from which it is chained) firewall A firewall plugin which uses iptables or firewalld to add rules to allow traffic to/from the container 4. CNI插件接口 具体可参考：https://github.com/containernetworking/cni/blob/master/SPEC.md#cni-operations CNI定义的接口操作有： ADD：添加容器网络，在容器启动时调用。 DEL：删除容器网络，在容器删除时调用。 CHECK：检查容器网络是否正常。 VERSION：显示插件版本。 这些操作通过CNI_COMMAND环境变量来传递给CNI插件二进制。 其中环境变量包括： CNI_COMMAND：命令操作，包括 ADD, DEL, CHECK, or VERSION。 CNI_CONTAINERID:容器的ID，有runtime分配，不为空。 CNI_NETNS:容器的网络命名空间，命名空间路径，例如：/run/netns/[nsname] CNI_IFNAME:容器内的网卡名称。 CNI_ARGS:其他参数。 CNI_PATH:CNI插件二进制的路径。 4.1. ADD接口：添加容器网络 在容器的网络命名空间CNI_NETNS中创建CNI_IFNAME网卡设备，或者调整网卡配置。 必选参数： CNI_COMMAND CNI_CONTAINERID CNI_NETNS CNI_IFNAME 可选参数： CNI_ARGS CNI_PATH 4.2. DEL接口：删除容器网络 删除容器网络命名空间CNI_NETNS中的容器网卡CNI_IFNAME，或者撤销ADD修改操作。 必选参数： CNI_COMMAND CNI_CONTAINERID CNI_IFNAME 可选参数： CNI_NETNS CNI_ARGS CNI_PATH 4.3. CHECK接口：检查容器网络 4.4. VERSION接口：输出CNI的版本 参考： https://www.cni.dev/docs/spec/ https://github.com/containernetworking/cni https://github.com/containernetworking/cni/blob/spec-v0.4.0/SPEC.md https://kubernetes.io/zh/docs/concepts/extend-kubernetes/compute-storage-net/network-plugins/ https://github.com/containernetworking/plugins/tree/master/plugins https://www.cni.dev/plugins/current/ https://cloud.tencent.com/developer/news/600713 配置CNI插件 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"network/cni/macvlan.html":{"url":"network/cni/macvlan.html","title":"Macvlan介绍","keywords":"","body":"1. 简介2. 四种工作模式2.1. VEPA (Virtual Ethernet Port Aggregator)2.2. Bridge mode2.3. Private mode2.4. Passthru mode1. 简介 macvlan可以看做是物理接口eth（父接口）的子接口，每个macvlan都拥有独立的mac地址，可以被绑定IP作为正常的网卡接口使用。通过这个特性，可以实现在一个物理网络设备绑定多个IP，每个IP拥有独立的mac地址。该特性经常被应用在容器虚拟化中（容器可以配置macvlan的网络，将macvlan interface移动到容器的namespace中）。 示意图： 2. 四种工作模式 2.1. VEPA (Virtual Ethernet Port Aggregator) VEPA为默认的工作模式，该模式下，所有macvlan发出的流量都会经过父接口，不管目的地是否与该macvlan共用一个父接口。 2.2. Bridge mode 该bridge模式类似于传统的网桥模式，拥有相同父接口的macvlan可以直接进行通信，不需要将数据发给父接口转发。该模式下不需要交换机支持hairpin模式，性能比VEPA模式好。另外相对于传统的网桥模式，该模式不需要学习mac地址，不需要STP，使得其性能比传统的网桥性能好得多。但是如果父接口down掉，则所有子接口也会down，同时无法通信。 2.3. Private mode 该模式是VEPA模式的增强版， 2.4. Passthru mode . 待完善 参考： https://backreference.org/2014/03/20/some-notes-on-macvlanmacvtap/ https://github.com/containernetworking/plugins/tree/master/plugins/main/macvlan https://github.com/containernetworking/plugins/blob/master/plugins/main/macvlan/macvlan.go http://wikibon.org/wiki/v/Edge_Virtual_Bridging Linux 虚拟网卡技术：Macvlan http://hicu.be/bridge-vs-macvlan http://hicu.be/docker-networking-macvlan-bridge-mode-configuration Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"storage/volume/_index.html":{"url":"storage/volume/_index.html","title":"存储卷概念","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"storage/volume/volume.html":{"url":"storage/volume/volume.html","title":"Volume","keywords":"","body":"1. volume概述2. volume类型2.1. emptyDir2.2. hostPath2.2.1. 同步文件变化到容器内2.3. configMap2.4. cephfs2.5. nfs2.6. persistentVolumeClaim1. volume概述 容器上的文件生命周期同容器的生命周期一致，即容器挂掉之后，容器将会以最初镜像中的文件系统内容启动，之前容器运行时产生的文件将会丢失。 Pod的volume的生命周期同Pod的生命周期一致，当Pod被删除的时候，对应的volume才会被删除。即Pod中的容器重启时，之前的文件仍可以保存。 容器中的进程看到的是由其 Docker 镜像和卷组成的文件系统视图。 Pod volume的使用方式 Pod 中的每个容器都必须独立指定每个卷的挂载位置，需要给Pod配置volume相关参数。 Pod的volume关键字段如下： spec.volumes：提供怎样的数据卷 spec.containers.volumeMounts：挂载到容器的什么路径 2. volume类型 2.1. emptyDir 1、特点 会创建emptyDir对应的目录，默认为空（如果该目录原来有文件也会被重置为空） Pod中的不同容器可以在目录中读写相同文件（即Pod中的不同容器可以通过该方式来共享文件） 当Pod被删除，emptyDir 中的数据将被永久删除，如果只是Pod挂掉该数据还会保留 2、使用场景 不同容器之间共享文件（例如日志采集等） 暂存空间，例如用于基于磁盘的合并排序 用作长时间计算崩溃恢复时的检查点 3、示例 apiVersion: v1 kind: Pod metadata: name: test-pd spec: containers: - image: k8s.gcr.io/test-webserver name: test-container volumeMounts: - mountPath: /cache name: cache-volume volumes: - name: cache-volume emptyDir: {} 2.2. hostPath 1、特点 会将宿主机的目录或文件挂载到Pod中 2、使用场景 运行需要访问 Docker 内部的容器；使用 /var/lib/docker 的 hostPath 在容器中运行 cAdvisor；使用 /dev/cgroups 的 hostPath 其他使用到宿主机文件的场景 hostPath的type字段 值 行为 空字符串（默认）用于向后兼容，这意味着在挂载 hostPath 卷之前不会执行任何检查。 DirectoryOrCreate 如果在给定的路径上没有任何东西存在，那么将根据需要在那里创建一个空目录，权限设置为 0755，与 Kubelet 具有相同的组和所有权。 Directory 给定的路径下必须存在目录 FileOrCreate 如果在给定的路径上没有任何东西存在，那么会根据需要创建一个空文件，权限设置为 0644，与 Kubelet 具有相同的组和所有权。 File 给定的路径下必须存在文件 Socket 给定的路径下必须存在 UNIX 套接字 CharDevice 给定的路径下必须存在字符设备 BlockDevice 给定的路径下必须存在块设备 注意事项 由于每个节点上的文件都不同，具有相同配置的 pod 在不同节点上的行为可能会有所不同 当 Kubernetes 按照计划添加资源感知调度时，将无法考虑 hostPath 使用的资源 在底层主机上创建的文件或目录只能由 root 写入。您需要在特权容器中以 root 身份运行进程，或修改主机上的文件权限以便写入 hostPath 卷 3、示例 apiVersion: v1 kind: Pod metadata: name: test-pd spec: containers: - image: k8s.gcr.io/test-webserver name: test-container volumeMounts: - mountPath: /test-pd name: test-volume volumes: - name: test-volume hostPath: # directory location on host path: /data # this field is optional type: Directory 2.2.1. 同步文件变化到容器内 hostPath的挂载方式可以挂载目录和文件两种格式，如果使用文件挂载的方式，通过简单的vi等命令修改宿主机的文件，并不会实时同步到容器内的映射文件。而需要对容器进行重启的操作才可以把文件的修改内容同步到文件中，但生产的容器一般不建议执行重启的操作。因此我们可以通过以下的方式来避免这个问题的发生。 以上文件不同步的本质原因是容器在初次挂载的时候使用了宿主机的文件的inode number进行标识，而vi等操作会导致文件的inode number发生变化，所以当宿主机文件的inode number发生变化，容器内并不会发生变化，为了保持文件内容一致，则需要保持修改文件的同时文件的inode number不变。那么我们可以使用 cat 或echo 命令覆盖文件的内容则inode number不会发生变化。 示例： containers: volumeMounts: - mountPath: /etc/hosts name: hosts readOnly: true volumes: - hostPath: path: /etc/hosts type: FileOrCreate name: hosts 例如，以上的案例是通过挂载宿主机的/etc/hosts文件来映射到容器，如果想修改宿主机的hosts文件来同步容器内的hosts文件，可以通过以下的方式: # 查看文件的inode ls -i /etc/hosts 39324780 /etc/hosts # 追加记录 echo \"1.1.1.1 xxx.com\" >> /etc/hosts # 替换内容 sed 's/1.1.1.1/2.2.2.2/g' /etc/hosts > temp.txt cat temp.txt > /etc/hosts # 查看宿主机和容器内的inode号都没有发生变化 # crictl exec -it 20891de31a4a6 sh /var/www/html # ls -i /etc/hosts 39324780 /etc/hosts 2.3. configMap configMap提供了一种给Pod注入配置文件的方式，配置文件内容存储在configMap对象中，如果Pod使用configMap作为volume的类型，需要先创建configMap的对象。 示例 apiVersion: v1 kind: Pod metadata: name: configmap-pod spec: containers: - name: test image: busybox volumeMounts: - name: config-vol mountPath: /etc/config volumes: - name: config-vol configMap: name: log-config items: - key: log_level path: log_level 2.4. cephfs cephfs的方式将Pod的存储挂载到ceph集群中，通过外部存储的方式持久化Pod的数据（即当Pod被删除数据仍可以存储在ceph集群中），前提是先部署和维护好一个ceph集群。 示例 apiVersion: v1 kind: Pod metadata: name: cephfs spec: containers: - name: cephfs-rw image: kubernetes/pause volumeMounts: - mountPath: \"/mnt/cephfs\" name: cephfs volumes: - name: cephfs cephfs: monitors: - 10.16.154.78:6789 - 10.16.154.82:6789 - 10.16.154.83:6789 # by default the path is /, but you can override and mount a specific path of the filesystem by using the path attribute # path: /some/path/in/side/cephfs user: admin secretFile: \"/etc/ceph/admin.secret\" readOnly: true 更多可参考 CephFS 示例。 2.5. nfs nfs的方式类似cephfs，即将Pod数据存储到NFS集群中，具体可参考NFS示例。 2.6. persistentVolumeClaim persistentVolumeClaim 卷用于将PersistentVolume挂载到容器中。PersistentVolumes 是在用户不知道特定云环境的细节的情况下“声明”持久化存储（例如 GCE PersistentDisk 或 iSCSI 卷）的一种方式。 参考文章： https://kubernetes.io/docs/concepts/storage/volumes/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"storage/volume/persistent-volume.html":{"url":"storage/volume/persistent-volume.html","title":"Persistent Volume","keywords":"","body":"1. PV概述2. PV和PVC的生命周期2.1. 配置（Provision）2.2. 绑定2.3. 使用2.4. 回收3. PV的类型4. PV的属性4.1. Capacity4.2. Volume Mode4.3. Access Modes4.4. Class4.5. Reclaim Policy4.6. Mount Options4.7. Phase1. PV概述 PersistentVolume（简称PV） 是 Volume 之类的卷插件，也是集群中的资源，但独立于Pod的生命周期（即不会因Pod删除而被删除），不归属于某个Namespace。 2. PV和PVC的生命周期 2.1. 配置（Provision） 有两种方式来配置 PV：静态或动态。 1、静态 手动创建PV，可供k8s集群中的对象消费。 2、动态 可以通过StorageClass和具体的Provisioner（例如nfs-client-provisioner）来动态地创建和删除PV。 2.2. 绑定 在动态配置的情况下，用户创建了特定的PVC，k8s会监听新的PVC，并寻找匹配的PV绑定。一旦绑定后，这种绑定是排他性的，PVC和PV的绑定是一对一的映射。 2.3. 使用 Pod 使用PVC作为卷。集群检查PVC以查找绑定的卷并为集群挂载该卷。用户通过在 Pod 的 volume 配置中包含 persistentVolumeClaim 来调度 Pod 并访问用户声明的 PV。 2.4. 回收 PV的回收策略可以设定PVC在释放后如何处理对应的Volume，目前有 Retained， Recycled和Deleted三种策略。 1、保留（Retain） 保留策略允许手动回收资源，当删除PVC的时候，PV仍然存在，可以通过以下步骤回收卷： 删除PV 手动清理外部存储的数据资源 手动删除或重新使用关联的存储资产 2、回收（Resycle） 该策略已废弃，推荐使用dynamic provisioning 回收策略会在 volume上执行基本擦除（rm -rf / thevolume / *），可被再次声明使用。 3、删除（Delete） 删除策略，当发生删除操作的时候，会从k8s集群中删除PV对象，并执行外部存储资源的删除操作（根据不同的provisioner定义的删除逻辑不同，有的是重命名）。 动态配置的卷继承其StorageClass的回收策略，默认为Delete，即当用户删除PVC的时候，会自动执行PV的删除策略。 如果要修改PV的回收策略，可执行以下命令： # Get pv kubectl get pv # Change policy to Retaion kubectl patch pv -p ‘{“spec”:{“persistentVolumeReclaimPolicy”:“Retain”}}’ 3. PV的类型 PersistentVolume 类型以插件形式实现。以下仅列部分常用类型： GCEPersistentDisk AWSElasticBlockStore NFS RBD (Ceph Block Device) CephFS Glusterfs 4. PV的属性 每个PV 配置中都包含一个 sepc规格字段和一个 status 卷状态字段。 apiVersion: v1 kind: PersistentVolume metadata: annotations: pv.kubernetes.io/provisioned-by: fuseim.pri/ifs creationTimestamp: 2018-07-12T06:46:48Z name: default-test-web-0-pvc-58cf5ec1-859f-11e8-bb61-005056b83985 resourceVersion: \"100163256\" selfLink: /api/v1/persistentvolumes/default-test-web-0-pvc-58cf5ec1-859f-11e8-bb61-005056b83985 uid: 59796ba3-859f-11e8-9c50-c81f66bcff65 spec: accessModes: - ReadWriteOnce capacity: storage: 2Gi volumeMode: Filesystem claimRef: apiVersion: v1 kind: PersistentVolumeClaim name: test-web-0 namespace: default resourceVersion: \"100163248\" uid: 58cf5ec1-859f-11e8-bb61-005056b83985 nfs: path: /data/nfs-storage/default-test-web-0-pvc-58cf5ec1-859f-11e8-bb61-005056b83985 server: 172.16.201.54 persistentVolumeReclaimPolicy: Delete storageClassName: managed-nfs-storage mountOptions: - hard - nfsvers=4.1 status: phase: Bound 4.1. Capacity 给PV设置特定的存储容量，更多 capacity 可参考Kubernetes 资源模型 。 4.2. Volume Mode volumeMode 的有效值可以是Filesystem或Block。如果未指定，volumeMode 将默认为Filesystem。 4.3. Access Modes 访问模式包括： ReadWriteOnce——该卷可以被单个节点以读/写模式挂载 ReadOnlyMany——该卷可以被多个节点以只读模式挂载 ReadWriteMany——该卷可以被多个节点以读/写模式挂载 在命令行中，访问模式缩写为： RWO - ReadWriteOnce ROX - ReadOnlyMany RWX - ReadWriteMany 一个卷一次只能使用一种访问模式挂载，即使它支持很多访问模式。 以下只列举部分常用插件： Volume 插件 ReadWriteOnce ReadOnlyMany ReadWriteMany AWSElasticBlockStore ✓ - - CephFS ✓ ✓ ✓ GCEPersistentDisk ✓ ✓ - Glusterfs ✓ ✓ ✓ HostPath ✓ - - NFS ✓ ✓ ✓ RBD ✓ ✓ - ... - 4.4. Class PV可以指定一个StorageClass来动态绑定PV和PVC，其中通过 storageClassName 属性来指定具体的StorageClass，如果没有指定该属性的PV，它只能绑定到不需要特定类的 PVC。 4.5. Reclaim Policy 回收策略包括： Retain（保留）——手动回收 Recycle（回收）——基本擦除（rm -rf /thevolume/*） Delete（删除）——关联的存储资产（例如 AWS EBS、GCE PD、Azure Disk 和 OpenStack Cinder 卷）将被删除 当前，只有 NFS 和 HostPath 支持回收策略。AWS EBS、GCE PD、Azure Disk 和 Cinder 卷支持删除策略。 4.6. Mount Options Kubernetes 管理员可以指定在节点上为挂载持久卷指定挂载选项。 注意：不是所有的持久化卷类型都支持挂载选项。 支持挂载选项常用的类型有： GCEPersistentDisk AWSElasticBlockStore AzureFile AzureDisk NFS RBD （Ceph Block Device） CephFS Cinder （OpenStack 卷存储） Glusterfs 4.7. Phase PV可以处于以下的某种状态： Available（可用）——一块空闲资源还没有被任何声明绑定 Bound（已绑定）——卷已经被声明绑定 Released（已释放）——声明被删除，但是资源还未被集群重新声明 Failed（失败）——该卷的自动回收失败 命令行会显示绑定到 PV 的 PVC 的名称。 参考文章： https://kubernetes.io/docs/concepts/storage/persistent-volumes/ https://kubernetes.io/docs/tasks/administer-cluster/change-pv-reclaim-policy/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"storage/volume/persistent-volume-claim.html":{"url":"storage/volume/persistent-volume-claim.html","title":"Persistent Volume Claim","keywords":"","body":"1. PVC概述2. PVC的属性2.1. accessModes2.2. volumeMode2.3. resources2.4. selector2.5. storageClassName3. 将PVC作为Volume1. PVC概述 PersistentVolumeClaim（简称PVC）是用户存储的请求，PVC消耗PV的资源，可以请求特定的大小和访问模式，需要指定归属于某个Namespace，在同一个Namespace的Pod才可以指定对应的PVC。 当需要不同性质的PV来满足存储需求时，可以使用StorageClass来实现。 每个 PVC 中都包含一个 spec 规格字段和一个 status 声明状态字段。 kind: PersistentVolumeClaim apiVersion: v1 metadata: name: myclaim spec: accessModes: - ReadWriteOnce volumeMode: Filesystem resources: requests: storage: 8Gi storageClassName: slow selector: matchLabels: release: \"stable\" matchExpressions: - {key: environment, operator: In, values: [dev]} 2. PVC的属性 2.1. accessModes 对应存储的访问模式，例如：ReadWriteOnce。 2.2. volumeMode 对应存储的数据卷模式，例如：Filesystem。 2.3. resources 声明可以请求特定数量的资源。相同的资源模型适用于Volume和PVC。 2.4. selector 声明label selector，只有标签与选择器匹配的卷可以绑定到声明。 matchLabels：volume 必须有具有该值的标签 matchExpressions：条件列表，通过条件表达式筛选匹配的卷。有效的运算符包括 In、NotIn、Exists 和 DoesNotExist。 2.5. storageClassName 通过storageClassName参数来指定使用对应名字的StorageClass，只有所请求的类与 PVC 具有相同 storageClassName 的 PV 才能绑定到 PVC。 PVC可以不指定storageClassName，或者将该值设置为空，如果打开了准入控制插件，并且指定一个默认的 StorageClass，则PVC会使用默认的StorageClass，否则就绑定到没有StorageClass的 PV上。 之前使用注解 volume.beta.kubernetes.io/storage-class 而不是 storageClassName 属性。这个注解仍然有效，但是在未来的 Kubernetes 版本中不会支持。 3. 将PVC作为Volume 将PVC作为Pod的Volume，PVC与Pod需要在同一个命名空间下，其实Pod的声明如下： kind: Pod apiVersion: v1 metadata: name: mypod spec: containers: - name: myfrontend image: dockerfile/nginx volumeMounts: - mountPath: \"/var/www/html\" name: mypd volumes: - name: mypd persistentVolumeClaim: # 使用PVC claimName: myclaim PersistentVolumes 绑定是唯一的，并且由于 PersistentVolumeClaims 是命名空间对象，因此只能在一个命名空间内挂载具有“多个”模式（ROX、RWX）的PVC。 参考文章： https://kubernetes.io/docs/concepts/storage/persistent-volumes/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"storage/volume/storage-class.html":{"url":"storage/volume/storage-class.html","title":"Storage Class","keywords":"","body":"1. StorageClass概述2. StorageClass的属性2.1. Provisioner（存储分配器）2.2. Reclaim Policy（回收策略）2.3. Mount Options（挂载选项）2.4. 参数1. StorageClass概述 StorageClass提供了一种描述存储类（class）的方法，不同的class可能会映射到不同的服务质量等级和备份策略或其他策略等。 StorageClass 对象中包含 provisioner、parameters 和 reclaimPolicy 字段，当需要动态分配 PersistentVolume 时会使用到。当创建 StorageClass 对象时，设置名称和其他参数，一旦创建了对象就不能再对其更新。也可以为没有申请绑定到特定 class 的 PVC 指定一个默认的 StorageClass 。 StorageClass对象文件 kind: StorageClass apiVersion: storage.k8s.io/v3 metadata: name: standard provisioner: kubernetes.io/aws-ebs parameters: type: gp2 reclaimPolicy: Retain mountOptions: - debug 2. StorageClass的属性 2.1. Provisioner（存储分配器） Storage class 有一个分配器（provisioner），用来决定使用哪个卷插件分配 PV，该字段必须指定。可以指定内部分配器，也可以指定外部分配器。外部分配器的代码地址为： kubernetes-incubator/external-storage，其中包括NFS和Ceph等。 2.2. Reclaim Policy（回收策略） 可以通过reclaimPolicy字段指定创建的Persistent Volume的回收策略，回收策略包括：Delete 或者 Retain，没有指定默认为Delete。 2.3. Mount Options（挂载选项） 由 storage class 动态创建的 Persistent Volume 将使用 class 中 mountOptions 字段指定的挂载选项。 2.4. 参数 Storage class 具有描述属于 storage class 卷的参数。取决于分配器，可以接受不同的参数。 当参数被省略时，会使用默认值。 例如以下使用Ceph RBD kind: StorageClass apiVersion: storage.k8s.io/v3 metadata: name: fast provisioner: kubernetes.io/rbd parameters: monitors: 30.36.353.305:6789 adminId: kube adminSecretName: ceph-secret adminSecretNamespace: kube-system pool: kube userId: kube userSecretName: ceph-secret-user fsType: ext4 imageFormat: \"2\" imageFeatures: \"layering\" 对应的参数说明 monitors：Ceph monitor，逗号分隔。该参数是必需的。 adminId：Ceph 客户端 ID，用于在池（ceph pool）中创建映像。 默认是 “admin”。 adminSecretNamespace：adminSecret 的 namespace。默认是 “default”。 adminSecret：adminId 的 Secret 名称。该参数是必需的。 提供的 secret 必须有值为 “kubernetes.io/rbd” 的 type 参数。 pool: Ceph RBD 池. 默认是 “rbd”。 userId：Ceph 客户端 ID，用于映射 RBD 镜像（RBD image）。默认与 adminId 相同。 userSecretName：用于映射 RBD 镜像的 userId 的 Ceph Secret 的名字。 它必须与 PVC 存在于相同的 namespace 中。该参数是必需的。 提供的 secret 必须具有值为 “kubernetes.io/rbd” 的 type 参数，例如以这样的方式创建： kubectl create secret generic ceph-secret --type=\"kubernetes.io/rbd\" \\ --from-literal=key='QVFEQ1pMdFhPUnQrSmhBQUFYaERWNHJsZ3BsMmNjcDR6RFZST0E9PQ==' \\ --namespace=kube-system fsType：Kubernetes 支持的 fsType。默认：\"ext4\"。 imageFormat：Ceph RBD 镜像格式，”1” 或者 “2”。默认值是 “1”。 imageFeatures：这个参数是可选的，只能在你将 imageFormat 设置为 “2” 才使用。 目前支持的功能只是 layering。 默认是 ““，没有功能打开。 参考文章： https://kubernetes.io/docs/concepts/storage/storage-classes/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"storage/volume/dynamic-provisioning.html":{"url":"storage/volume/dynamic-provisioning.html","title":"Dynamic Volume Provisioning","keywords":"","body":"Dynamic Volume Provisioning1. 开启Dynamic Provisioning2. 使用Dynamic Provisioning3. 默认的StorageClassDynamic Volume Provisioning Dynamic volume provisioning允许用户按需自动创建存储卷，这种方式可以让用户不需要关心存储的复杂性和差别，又可以选择不同的存储类型。 1. 开启Dynamic Provisioning 需要先提前创建StorageClass对象，StorageClass中定义了使用哪个provisioner，并且在provisioner被调用时传入哪些参数，具体可参考StorageClass介绍。 例如： 磁盘类存储 apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: slow provisioner: kubernetes.io/gce-pd parameters: type: pd-standard SSD类存储 apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: fast provisioner: kubernetes.io/gce-pd parameters: type: pd-ssd 2. 使用Dynamic Provisioning 创建一个PVC对象，并且在其中storageClassName字段指明需要用到的StorageClass的名称，例如： apiVersion: v1 kind: PersistentVolumeClaim metadata: name: claim1 spec: accessModes: - ReadWriteOnce storageClassName: fast resources: requests: storage: 30Gi 当使用到PVC的时候会自动创建对应的外部存储，当PVC被删除的时候，会自动销毁（或备份）外部存储。 3. 默认的StorageClass 当没有对应的StorageClass配置时，可以设定默认的StorageClass，需要执行以下操作： 在API Server开启DefaultStorageClass admission controller 。 设置默认的StorageClass对象。 可以通过添加storageclass.kubernetes.io/is-default-class注解的方式设置某个StorageClass为默认的StorageClass。当用户创建了一个PersistentVolumeClaim，但没有指定storageClassName的时候，会自动将该PVC的storageClassName指向默认的StorageClass。 参考文章： https://kubernetes.io/docs/concepts/storage/dynamic-provisioning/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"storage/csi/_index.html":{"url":"storage/csi/_index.html","title":"CSI","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"storage/csi/ceph/csi-cephfs-plugin.html":{"url":"storage/csi/ceph/csi-cephfs-plugin.html","title":"csi-cephfs-plugin","keywords":"","body":"1. 编译CSI CephFS plugin1.1. 编译二进制1.2. 编译Docker镜像2. 配置项2.1. 命令行参数2.2. volume参数2.3. provisionVolume2.3.1. 管理员密钥认证2.3.2. 普通用户密钥认证1. 编译CSI CephFS plugin CSI CephFS plugin用来提供CephFS存储卷和挂载存储卷，源码参考：https://github.com/ceph/ceph-csi 。 1.1. 编译二进制 $ make cephfsplugin 1.2. 编译Docker镜像 $ make image-cephfsplugin 2. 配置项 2.1. 命令行参数 Option Default value Description --endpoint unix://tmp/csi.sock CSI endpoint, must be a UNIX socket --drivername csi-cephfsplugin name of the driver (Kubernetes: provisioner field in StorageClass must correspond to this value) --nodeid empty This node’s ID --volumemounter empty default volume mounter. Available options are kernel and fuse. This is the mount method used if volume parameters don’t specify otherwise. If left unspecified, the driver will first probe for ceph-fuse in system’s path and will choose Ceph kernel client if probing failed. 2.2. volume参数 Parameter Required Description monitors yes Comma separated list of Ceph monitors (e.g. 192.168.100.1:6789,192.168.100.2:6789,192.168.100.3:6789) mounter no Mount method to be used for this volume. Available options are kernel for Ceph kernel client and fuse for Ceph FUSE driver. Defaults to “default mounter”, see command line arguments. provisionVolume yes Mode of operation. BOOL value. If true, a new CephFS volume will be provisioned. If false, an existing CephFS will be used. pool for provisionVolume=true Ceph pool into which the volume shall be created rootPath for provisionVolume=false Root path of an existing CephFS volume csiProvisionerSecretName, csiNodeStageSecretName for Kubernetes name of the Kubernetes Secret object containing Ceph client credentials. Both parameters should have the same value csiProvisionerSecretNamespace, csiNodeStageSecretNamespace for Kubernetes namespaces of the above Secret objects 2.3. provisionVolume 2.3.1. 管理员密钥认证 当provisionVolume=true时，必要的管理员认证参数如下： adminID: ID of an admin client adminKey: key of the admin client 2.3.2. 普通用户密钥认证 当provisionVolume=false时，必要的用户认证参数如下： userID: ID of a user client userKey: key of a user client 参考文章： https://github.com/ceph/ceph-csi/blob/master/docs/deploy-cephfs.md Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"storage/csi/ceph/deploy-csi-cephfs.html":{"url":"storage/csi/ceph/deploy-csi-cephfs.html","title":"部署csi-cephfs","keywords":"","body":"0. 说明涉及镜像1. 部署RBAC1.1. csi-attacher-rbac.yaml1.2. csi-provisioner-rbac.yaml1.3. csi-nodeplugin-rbac.yaml2. 部署CSI sidecar containers2.1. csi-cephfsplugin-provisioner.yaml2.2. csi-cephfsplugin-attacher.yaml3. 部署CSI-CephFS-driver(plugin)3.1. csi-cephfsplugin.yaml4. 确认部署结果0. 说明 要求Kubernetes的版本在1.11及以上，k8s集群必须允许特权Pod（privileged pods），即apiserver和kubelet需要设置--allow-privileged为true。节点的Docker daemon需要允许挂载共享卷。 涉及镜像 quay.io/k8scsi/csi-provisioner:v0.3.0 quay.io/k8scsi/csi-attacher:v0.3.0 quay.io/k8scsi/driver-registrar:v0.3.0 quay.io/cephcsi/cephfsplugin:v0.3.0 1. 部署RBAC 部署service accounts, cluster roles 和 cluster role bindings，这些可供RBD和CephFS CSI plugins共同使用，他们拥有相同的权限。 $ kubectl create -f csi-attacher-rbac.yaml $ kubectl create -f csi-provisioner-rbac.yaml $ kubectl create -f csi-nodeplugin-rbac.yaml 1.1. csi-attacher-rbac.yaml apiVersion: v1 kind: ServiceAccount metadata: name: csi-attacher --- kind: ClusterRole apiVersion: rbac.authorization.k8s.io/v1 metadata: name: external-attacher-runner rules: - apiGroups: [\"\"] resources: [\"events\"] verbs: [\"get\", \"list\", \"watch\", \"update\"] - apiGroups: [\"\"] resources: [\"persistentvolumes\"] verbs: [\"get\", \"list\", \"watch\", \"update\"] - apiGroups: [\"\"] resources: [\"nodes\"] verbs: [\"get\", \"list\", \"watch\"] - apiGroups: [\"storage.k8s.io\"] resources: [\"volumeattachments\"] verbs: [\"get\", \"list\", \"watch\", \"update\"] --- kind: ClusterRoleBinding apiVersion: rbac.authorization.k8s.io/v1 metadata: name: csi-attacher-role subjects: - kind: ServiceAccount name: csi-attacher namespace: default roleRef: kind: ClusterRole name: external-attacher-runner apiGroup: rbac.authorization.k8s.io 1.2. csi-provisioner-rbac.yaml apiVersion: v1 kind: ServiceAccount metadata: name: csi-provisioner --- kind: ClusterRole apiVersion: rbac.authorization.k8s.io/v1 metadata: name: external-provisioner-runner rules: - apiGroups: [\"\"] resources: [\"secrets\"] verbs: [\"get\", \"list\"] - apiGroups: [\"\"] resources: [\"persistentvolumes\"] verbs: [\"get\", \"list\", \"watch\", \"create\", \"delete\"] - apiGroups: [\"\"] resources: [\"persistentvolumeclaims\"] verbs: [\"get\", \"list\", \"watch\", \"update\"] - apiGroups: [\"storage.k8s.io\"] resources: [\"storageclasses\"] verbs: [\"get\", \"list\", \"watch\"] - apiGroups: [\"\"] resources: [\"events\"] verbs: [\"list\", \"watch\", \"create\", \"update\", \"patch\"] --- kind: ClusterRoleBinding apiVersion: rbac.authorization.k8s.io/v1 metadata: name: csi-provisioner-role subjects: - kind: ServiceAccount name: csi-provisioner namespace: default roleRef: kind: ClusterRole name: external-provisioner-runner apiGroup: rbac.authorization.k8s.io 1.3. csi-nodeplugin-rbac.yaml apiVersion: v1 kind: ServiceAccount metadata: name: csi-nodeplugin --- kind: ClusterRole apiVersion: rbac.authorization.k8s.io/v1 metadata: name: csi-nodeplugin rules: - apiGroups: [\"\"] resources: [\"nodes\"] verbs: [\"get\", \"list\", \"update\"] - apiGroups: [\"\"] resources: [\"namespaces\"] verbs: [\"get\", \"list\"] - apiGroups: [\"\"] resources: [\"persistentvolumes\"] verbs: [\"get\", \"list\", \"watch\", \"update\"] - apiGroups: [\"storage.k8s.io\"] resources: [\"volumeattachments\"] verbs: [\"get\", \"list\", \"watch\", \"update\"] --- kind: ClusterRoleBinding apiVersion: rbac.authorization.k8s.io/v1 metadata: name: csi-nodeplugin subjects: - kind: ServiceAccount name: csi-nodeplugin namespace: default roleRef: kind: ClusterRole name: csi-nodeplugin apiGroup: rbac.authorization.k8s.io 2. 部署CSI sidecar containers 通过StatefulSet的方式部署external-attacher和external-provisioner供CSI CephFS使用。 $ kubectl create -f csi-cephfsplugin-attacher.yaml $ kubectl create -f csi-cephfsplugin-provisioner.yaml 2.1. csi-cephfsplugin-provisioner.yaml kind: Service apiVersion: v1 metadata: name: csi-cephfsplugin-provisioner labels: app: csi-cephfsplugin-provisioner spec: selector: app: csi-cephfsplugin-provisioner ports: - name: dummy port: 12345 --- kind: StatefulSet apiVersion: apps/v1beta1 metadata: name: csi-cephfsplugin-provisioner spec: serviceName: \"csi-cephfsplugin-provisioner\" replicas: 1 template: metadata: labels: app: csi-cephfsplugin-provisioner spec: serviceAccount: csi-provisioner containers: - name: csi-provisioner image: quay.io/k8scsi/csi-provisioner:v0.3.0 args: - \"--provisioner=csi-cephfsplugin\" - \"--csi-address=$(ADDRESS)\" - \"--v=5\" env: - name: ADDRESS value: /var/lib/kubelet/plugins/csi-cephfsplugin/csi.sock imagePullPolicy: \"IfNotPresent\" volumeMounts: - name: socket-dir mountPath: /var/lib/kubelet/plugins/csi-cephfsplugin volumes: - name: socket-dir hostPath: path: /var/lib/kubelet/plugins/csi-cephfsplugin type: DirectoryOrCreate 2.2. csi-cephfsplugin-attacher.yaml kind: Service apiVersion: v1 metadata: name: csi-cephfsplugin-attacher labels: app: csi-cephfsplugin-attacher spec: selector: app: csi-cephfsplugin-attacher ports: - name: dummy port: 12345 --- kind: StatefulSet apiVersion: apps/v1beta1 metadata: name: csi-cephfsplugin-attacher spec: serviceName: \"csi-cephfsplugin-attacher\" replicas: 1 template: metadata: labels: app: csi-cephfsplugin-attacher spec: serviceAccount: csi-attacher containers: - name: csi-cephfsplugin-attacher image: quay.io/k8scsi/csi-attacher:v0.3.0 args: - \"--v=5\" - \"--csi-address=$(ADDRESS)\" env: - name: ADDRESS value: /var/lib/kubelet/plugins/csi-cephfsplugin/csi.sock imagePullPolicy: \"IfNotPresent\" volumeMounts: - name: socket-dir mountPath: /var/lib/kubelet/plugins/csi-cephfsplugin volumes: - name: socket-dir hostPath: path: /var/lib/kubelet/plugins/csi-cephfsplugin type: DirectoryOrCreate 3. 部署CSI-CephFS-driver(plugin) csi-cephfs-plugin 的作用类似nfs-client，部署在所有node节点上，执行ceph的挂载等相关任务。 通过DaemonSet的方式部署，其中包括两个容器：CSI driver-registrar 和 CSI CephFS driver。 $ kubectl create -f csi-cephfsplugin.yaml 3.1. csi-cephfsplugin.yaml kind: DaemonSet apiVersion: apps/v1beta2 metadata: name: csi-cephfsplugin spec: selector: matchLabels: app: csi-cephfsplugin template: metadata: labels: app: csi-cephfsplugin spec: serviceAccount: csi-nodeplugin hostNetwork: true # to use e.g. Rook orchestrated cluster, and mons' FQDN is # resolved through k8s service, set dns policy to cluster first dnsPolicy: ClusterFirstWithHostNet containers: - name: driver-registrar image: quay.io/k8scsi/driver-registrar:v0.3.0 args: - \"--v=5\" - \"--csi-address=$(ADDRESS)\" - \"--kubelet-registration-path=$(DRIVER_REG_SOCK_PATH)\" env: - name: ADDRESS value: /var/lib/kubelet/plugins/csi-cephfsplugin/csi.sock - name: DRIVER_REG_SOCK_PATH value: /var/lib/kubelet/plugins/csi-cephfsplugin/csi.sock - name: KUBE_NODE_NAME valueFrom: fieldRef: fieldPath: spec.nodeName volumeMounts: - name: socket-dir mountPath: /var/lib/kubelet/plugins/csi-cephfsplugin - name: registration-dir mountPath: /registration - name: csi-cephfsplugin securityContext: privileged: true capabilities: add: [\"SYS_ADMIN\"] allowPrivilegeEscalation: true image: quay.io/cephcsi/cephfsplugin:v0.3.0 args : - \"--nodeid=$(NODE_ID)\" - \"--endpoint=$(CSI_ENDPOINT)\" - \"--v=5\" - \"--drivername=csi-cephfsplugin\" env: - name: NODE_ID valueFrom: fieldRef: fieldPath: spec.nodeName - name: CSI_ENDPOINT value: unix://var/lib/kubelet/plugins/csi-cephfsplugin/csi.sock imagePullPolicy: \"IfNotPresent\" volumeMounts: - name: plugin-dir mountPath: /var/lib/kubelet/plugins/csi-cephfsplugin - name: pods-mount-dir mountPath: /var/lib/kubelet/pods mountPropagation: \"Bidirectional\" - mountPath: /sys name: host-sys - name: lib-modules mountPath: /lib/modules readOnly: true - name: host-dev mountPath: /dev volumes: - name: plugin-dir hostPath: path: /var/lib/kubelet/plugins/csi-cephfsplugin type: DirectoryOrCreate - name: registration-dir hostPath: path: /var/lib/kubelet/plugins/ type: Directory - name: pods-mount-dir hostPath: path: /var/lib/kubelet/pods type: Directory - name: socket-dir hostPath: path: /var/lib/kubelet/plugins/csi-cephfsplugin type: DirectoryOrCreate - name: host-sys hostPath: path: /sys - name: lib-modules hostPath: path: /lib/modules - name: host-dev hostPath: path: /dev 4. 确认部署结果 $ kubectl get all NAME READY STATUS RESTARTS AGE pod/csi-cephfsplugin-attacher-0 1/1 Running 0 26s pod/csi-cephfsplugin-provisioner-0 1/1 Running 0 25s pod/csi-cephfsplugin-rljcv 2/2 Running 0 24s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/csi-cephfsplugin-attacher ClusterIP 10.104.116.218 12345/TCP 27s service/csi-cephfsplugin-provisioner ClusterIP 10.101.78.75 12345/TCP 26s ... 参考文档： https://github.com/ceph/ceph-csi https://github.com/ceph/ceph-csi/blob/master/docs/deploy-cephfs.md https://github.com/ceph/ceph-csi/tree/master/deploy/cephfs/kubernetes Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"storage/csi/provisioner/cephfs-provisioner.html":{"url":"storage/csi/provisioner/cephfs-provisioner.html","title":"部署cephfs-provisioner","keywords":"","body":"1. 安装cephfs客户端2. 部署RBAC2.1. ClusterRole2.2. ClusterRoleBinding2.3. Role2.4. RoleBinding2.5. ServiceAccount3. 部署 cephfs-provisioner4. 部署storageclass5. 部署statefulset6. 日志6.1. cephfs-provisoner 执行日志6.2. debug 日志1. 安装cephfs客户端 所有node节点安装cephfs客户端，主要用来和ceph集群挂载使用。 yum install -y ceph-common 2. 部署RBAC 2.1. ClusterRole kind: ClusterRole apiVersion: rbac.authorization.k8s.io/v1 metadata: name: cephfs-provisioner namespace: cephfs rules: - apiGroups: [\"\"] resources: [\"persistentvolumes\"] verbs: [\"get\", \"list\", \"watch\", \"create\", \"delete\"] - apiGroups: [\"\"] resources: [\"persistentvolumeclaims\"] verbs: [\"get\", \"list\", \"watch\", \"update\"] - apiGroups: [\"storage.k8s.io\"] resources: [\"storageclasses\"] verbs: [\"get\", \"list\", \"watch\"] - apiGroups: [\"\"] resources: [\"events\"] verbs: [\"create\", \"update\", \"patch\"] - apiGroups: [\"\"] resources: [\"services\"] resourceNames: [\"kube-dns\",\"coredns\"] verbs: [\"list\", \"get\"] 2.2. ClusterRoleBinding kind: ClusterRoleBinding apiVersion: rbac.authorization.k8s.io/v1 metadata: name: cephfs-provisioner subjects: - kind: ServiceAccount name: cephfs-provisioner namespace: cephfs roleRef: kind: ClusterRole name: cephfs-provisioner apiGroup: rbac.authorization.k8s.io 2.3. Role apiVersion: rbac.authorization.k8s.io/v1 kind: Role metadata: name: cephfs-provisioner namespace: cephfs rules: - apiGroups: [\"\"] resources: [\"secrets\"] verbs: [\"create\", \"get\", \"delete\"] - apiGroups: [\"\"] resources: [\"endpoints\"] verbs: [\"get\", \"list\", \"watch\", \"create\", \"update\", \"patch\"] 2.4. RoleBinding apiVersion: rbac.authorization.k8s.io/v1 kind: RoleBinding metadata: name: cephfs-provisioner namespace: cephfs roleRef: apiGroup: rbac.authorization.k8s.io kind: Role name: cephfs-provisioner subjects: - kind: ServiceAccount name: cephfs-provisioner 2.5. ServiceAccount apiVersion: v1 kind: ServiceAccount metadata: name: cephfs-provisioner namespace: cephfs 3. 部署 cephfs-provisioner apiVersion: extensions/v1beta1 kind: Deployment metadata: name: cephfs-provisioner namespace: cephfs spec: replicas: 1 strategy: type: Recreate template: metadata: labels: app: cephfs-provisioner spec: containers: - name: cephfs-provisioner image: \"quay.io/external_storage/cephfs-provisioner:latest\" resources: limits: cpu: 500m memory: 512Mi requests: cpu: 100m memory: 64Mi env: - name: PROVISIONER_NAME # 与storageclass的provisioner参数相同 value: ceph.com/cephfs - name: PROVISIONER_SECRET_NAMESPACE # 与rbac的namespace相同 value: cephfs command: - \"/usr/local/bin/cephfs-provisioner\" args: - \"-id=cephfs-provisioner-1\" serviceAccount: cephfs-provisioner 4. 部署storageclass apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: cephfs-provisioner-sc provisioner: ceph.com/cephfs volumeBindingMode: WaitForFirstConsumer parameters: monitors: 192.168.27.43:6789,192.168.27.44:6789,192.168.27.45:6789 adminId: admin adminSecretName: csi-cephfs-secret adminSecretNamespace: \"kube-csi\" claimRoot: /pvc-volumes 5. 部署statefulset apiVersion: apps/v1 kind: StatefulSet metadata: name: cephfs-provisioner-nginx spec: serviceName: \"nginx\" replicas: 1 selector: matchLabels: app: nginx template: metadata: labels: app: nginx spec: containers: - name: nginx image: nginx:latest #nginx的镜像 imagePullPolicy: IfNotPresent volumeMounts: - mountPath: \"/mnt\" #容器里面的挂载目录，该目录挂载到NFS的共享目录上 name: test volumeClaimTemplates: - metadata: name: test spec: accessModes: [ \"ReadWriteOnce\" ] resources: requests: storage: 2Gi storageClassName: cephfs-provisioner-sc 6. 日志 6.1. cephfs-provisoner 执行日志 I0327 07:18:19.742239 1 controller.go:987] provision \"default/test-cephfs-ngx-wait-22-0\" class \"cephfs-provisioner-sc\": started I0327 07:18:19.745239 1 event.go:221] Event(v1.ObjectReference{Kind:\"PersistentVolumeClaim\", Namespace:\"default\", Name:\"test-cephfs-ngx-wait-22-0\", UID:\"7f6b60d5-5060-11e9-9a9c-c81f66bcff65\", APIVersion:\"v1\", ResourceVersion:\"347214256\", FieldPath:\"\"}): type: 'Normal' reason: 'Provisioning' External provisioner is provisioning volume for claim \"default/test-cephfs-ngx-wait-22-0\" I0327 07:18:23.281277 1 cephfs-provisioner.go:222] successfully created CephFS share &CephFSPersistentVolumeSource{Monitors:[192.168.27.43:6789 192.168.27.44:6789 192.168.27.45:6789],Path:/pvc-volumes/kubernetes/kubernetes-dynamic-pvc-7f7cb62f-5060-11e9-85c0-0adb8ef08100,User:kubernetes-dynamic-user-7f7cb69f-5060-11e9-85c0-0adb8ef08100,SecretFile:,SecretRef:&SecretReference{Name:ceph-kubernetes-dynamic-user-7f7cb69f-5060-11e9-85c0-0adb8ef08100-secret,Namespace:default,},ReadOnly:false,} I0327 07:18:23.281371 1 controller.go:1087] provision \"default/test-cephfs-ngx-wait-22-0\" class \"cephfs-provisioner-sc\": volume \"pvc-7f6b60d5-5060-11e9-9a9c-c81f66bcff65\" provisioned I0327 07:18:23.281415 1 controller.go:1101] provision \"default/test-cephfs-ngx-wait-22-0\" class \"cephfs-provisioner-sc\": trying to save persistentvvolume \"pvc-7f6b60d5-5060-11e9-9a9c-c81f66bcff65\" I0327 07:18:23.284621 1 controller.go:1108] provision \"default/test-cephfs-ngx-wait-22-0\" class \"cephfs-provisioner-sc\": persistentvolume \"pvc-7f6b60d5-5060-11e9-9a9c-c81f66bcff65\" saved I0327 07:18:23.284723 1 controller.go:1149] provision \"default/test-cephfs-ngx-wait-22-0\" class \"cephfs-provisioner-sc\": succeeded I0327 07:18:23.284810 1 event.go:221] Event(v1.ObjectReference{Kind:\"PersistentVolumeClaim\", Namespace:\"default\", Name:\"test-cephfs-ngx-wait-22-0\", UID:\"7f6b60d5-5060-11e9-9a9c-c81f66bcff65\", APIVersion:\"v1\", ResourceVersion:\"347214256\", FieldPath:\"\"}): type: 'Normal' reason: 'ProvisioningSucceeded' Successfully provisioned volume pvc-7f6b60d5-5060-11e9-9a9c-c81f66bcff65 6.2. debug 日志 I0327 08:08:11.789608 1 controller.go:987] provision \"default/test-cephfs-ngx-wait-44-0\" class \"cephfs-sc-wait\": started I0327 08:08:11.793258 1 event.go:221] Event(v1.ObjectReference{Kind:\"PersistentVolumeClaim\", Namespace:\"default\", Name:\"test-cephfs-ngx-wait-44-0\", UID:\"81846859-5067-11e9-9a9c-c81f66bcff65\", APIVersion:\"v1\", ResourceVersion:\"347237916\", FieldPath:\"\"}): type: 'Normal' reason: 'Provisioning' External provisioner is provisioning volume for claim \"default/test-cephfs-ngx-wait-44-0\" E0327 08:08:12.164705 1 cephfs-provisioner.go:158] failed to provision share \"kubernetes-dynamic-pvc-76ecdc5a-5067-11e9-9421-2a1b1be1aeef\" for \"kubernetes-dynamic-user-76ecdcee-5067-11e9-9421-2a1b1be1aeef\", err: exit status 1, output: Traceback (most recent call last): File \"/usr/local/bin/cephfs_provisioner\", line 364, in main() File \"/usr/local/bin/cephfs_provisioner\", line 358, in main print cephfs.create_share(share, user, size=size) File \"/usr/local/bin/cephfs_provisioner\", line 228, in create_share volume = self.volume_client.create_volume(volume_path, size=size, namespace_isolated=not self.ceph_namespace_isolation_disabled) File \"/usr/local/bin/cephfs_provisioner\", line 112, in volume_client self._volume_client.connect(None) File \"/lib/python2.7/site-packages/ceph_volume_client.py\", line 458, in connect self.rados.connect() File \"rados.pyx\", line 895, in rados.Rados.connect (/home/jenkins-build/build/workspace/ceph-build/ARCH/x86_64/AVAILABLE_ARCH/x86_64/AVAILABLE_DIST/centos7/DIST/centos7/MACHINE_SIZE/huge/release/13.2.1/rpm/el7/BUILD/ceph-13.2.1/build/src/pybind/rados/pyrex/rados.c:9815) rados.IOError: [errno 5] error connecting to the cluster W0327 08:08:12.164908 1 controller.go:746] Retrying syncing claim \"default/test-cephfs-ngx-wait-44-0\" because failures 2 参考 https://github.com/kubernetes-incubator/external-storage/tree/master/ceph/cephfs/deploy Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"storage/csi/flexvolume.html":{"url":"storage/csi/flexvolume.html","title":"FlexVolume介绍","keywords":"","body":"1. FlexVolume介绍2. 使用方式3. FlexVolume接口3.1. init3.2. attach3.3. detach3.4. waitforattach3.5. isattached3.6. mountdevice3.7. unmountdevice3.8. mount3.9. unmount3.10. 插件输出4. 示例4.1. pod的yaml文件内容4.2. 插件脚本1. FlexVolume介绍 Flexvolume提供了一种扩展k8s存储插件的方式，用户可以自定义自己的存储插件。类似的功能的实现还有CSI的方式。Flexvolume在k8s 1.8+以上版本提供GA功能版本。 2. 使用方式 在每个node节点安装存储插件二进制，该二进制实现flexvolume的相关接口，默认存储插件的存放路径为/usr/libexec/kubernetes/kubelet-plugins/volume/exec//。 其中vendor~driver的名字需要和pod中flexVolume.driver的字段名字匹配，该字段名字通过/替换~。 例如： path:/usr/libexec/kubernetes/kubelet-plugins/volume/exec/foo~cifs/cifs pod中flexVolume.driver:foo/cifs 3. FlexVolume接口 节点上的存储插件需要实现以下的接口。 3.1. init init 3.2. attach attach 3.3. detach detach 3.4. waitforattach waitforattach 3.5. isattached isattached 3.6. mountdevice mountdevice 3.7. unmountdevice unmountdevice 3.8. mount mount 3.9. unmount unmount 3.10. 插件输出 { \"status\": \"\", \"message\": \"\", \"device\": \"\" \"volumeName\": \"\" \"attached\": \"capabilities\": { \"attach\": } } 4. 示例 4.1. pod的yaml文件内容 nginx-nfs.yaml 相关参数为flexVolume.driver等。 apiVersion: v1 kind: Pod metadata: name: nginx-nfs namespace: default spec: containers: - name: nginx-nfs image: nginx volumeMounts: - name: test mountPath: /data ports: - containerPort: 80 volumes: - name: test flexVolume: driver: \"k8s/nfs\" fsType: \"nfs\" options: server: \"172.16.0.25\" share: \"dws_nas_scratch\" 4.2. 插件脚本 nfs脚本实现了flexvolume的接口。 /usr/libexec/kubernetes/kubelet-plugins/volume/exec/k8s~nfs/nfs。 #!/bin/bash # Copyright 2015 The Kubernetes Authors. # # Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Notes: # - Please install \"jq\" package before using this driver. usage() { err \"Invalid usage. Usage: \" err \"\\t$0 init\" err \"\\t$0 mount \" err \"\\t$0 unmount \" exit 1 } err() { echo -ne $* 1>&2 } log() { echo -ne $* >&1 } ismounted() { MOUNT=`findmnt -n ${MNTPATH} 2>/dev/null | cut -d' ' -f1` if [ \"${MOUNT}\" == \"${MNTPATH}\" ]; then echo \"1\" else echo \"0\" fi } domount() { MNTPATH=$1 NFS_SERVER=$(echo $2 | jq -r '.server') SHARE=$(echo $2 | jq -r '.share') if [ $(ismounted) -eq 1 ] ; then log '{\"status\": \"Success\"}' exit 0 fi mkdir -p ${MNTPATH} &> /dev/null mount -t nfs ${NFS_SERVER}:/${SHARE} ${MNTPATH} &> /dev/null if [ $? -ne 0 ]; then err \"{ \\\"status\\\": \\\"Failure\\\", \\\"message\\\": \\\"Failed to mount ${NFS_SERVER}:${SHARE} at ${MNTPATH}\\\"}\" exit 1 fi log '{\"status\": \"Success\"}' exit 0 } unmount() { MNTPATH=$1 if [ $(ismounted) -eq 0 ] ; then log '{\"status\": \"Success\"}' exit 0 fi umount ${MNTPATH} &> /dev/null if [ $? -ne 0 ]; then err \"{ \\\"status\\\": \\\"Failed\\\", \\\"message\\\": \\\"Failed to unmount volume at ${MNTPATH}\\\"}\" exit 1 fi log '{\"status\": \"Success\"}' exit 0 } op=$1 if ! command -v jq >/dev/null 2>&1; then err \"{ \\\"status\\\": \\\"Failure\\\", \\\"message\\\": \\\"'jq' binary not found. Please install jq package before using this driver\\\"}\" exit 1 fi if [ \"$op\" = \"init\" ]; then log '{\"status\": \"Success\", \"capabilities\": {\"attach\": false}}' exit 0 fi if [ $# -lt 2 ]; then usage fi shift case \"$op\" in mount) domount $* ;; unmount) unmount $* ;; *) log '{\"status\": \"Not supported\"}' exit 0 esac exit 1 参考： https://github.com/kubernetes/community/blob/master/contributors/devel/sig-storage/flexvolume.md https://github.com/kubernetes/examples/tree/master/staging/volumes/flexvolume https://github.com/kubernetes/examples/blob/master/staging/volumes/flexvolume/nginx-nfs.yaml https://github.com/kubernetes/examples/blob/master/staging/volumes/flexvolume/nfs Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"resource/resource-quota.html":{"url":"resource/resource-quota.html","title":"资源配额","keywords":"","body":"资源配额（ResourceQuota）1. 启动资源配额2. 计算资源配额3. 存储资源配额4. 对象数量的配额5. 限额的作用域6. request和limit7. 查看和设置配额8. 配额和集群容量资源配额（ResourceQuota） ResourceQuota对象用来定义某个命名空间下所有资源的使用限额，其实包括： 计算资源的配额 存储资源的配额 对象数量的配额 如果集群的总容量小于命名空间的配额总额，可能会产生资源竞争。这时会按照先到先得来处理。 资源竞争和配额的更新都不会影响已经创建好的资源。 1. 启动资源配额 Kubernetes 的众多发行版本默认开启了资源配额的支持。当在apiserver的--admission-control配置中添加ResourceQuota参数后，便启用了。 当一个命名空间中含有ResourceQuota对象时，资源配额将强制执行。 2. 计算资源配额 可以在给定的命名空间中限制可以请求的计算资源（compute resources）的总量。 资源名称 描述 cpu 非终止态的所有pod, cpu请求总量不能超出此值。 limits.cpu 非终止态的所有pod， cpu限制总量不能超出此值。 limits.memory 非终止态的所有pod, 内存限制总量不能超出此值。 memory 非终止态的所有pod, 内存请求总量不能超出此值。 requests.cpu 非终止态的所有pod, cpu请求总量不能超出此值。 requests.memory 非终止态的所有pod, 内存请求总量不能超出此值。 3. 存储资源配额 可以在给定的命名空间中限制可以请求的存储资源（storage resources）的总量。 资源名称 描述 requests.storage 所有PVC, 存储请求总量不能超出此值。 persistentvolumeclaims 命名空间中可以存在的PVC（persistent volume claims）总数。 .storageclass.storage.k8s.io/requests.storage 和该存储类关联的所有PVC, 存储请求总和不能超出此值。 .storageclass.storage.k8s.io/persistentvolumeclaims 和该存储类关联的所有PVC，命名空间中可以存在的PVC（persistent volume claims）总数。 4. 对象数量的配额 资源名称 描述 congfigmaps 命名空间中可以存在的配置映射的总数。 persistentvolumeclaims 命名空间中可以存在的PVC总数。 pods 命名空间中可以存在的非终止态的pod总数。如果一个pod的status.phase 是 Failed, Succeeded, 则该pod处于终止态。 replicationcontrollers 命名空间中可以存在的rc总数。 resourcequotas 命名空间中可以存在的资源配额（resource quotas）总数。 services 命名空间中可以存在的服务总数量。 services.loadbalancers 命名空间中可以存在的服务的负载均衡的总数量。 services.nodeports 命名空间中可以存在的服务的主机接口的总数量。 secrets 命名空间中可以存在的secrets的总数量。 例如：可以定义pod的限额来避免某用户消耗过多的Pod IPs。 5. 限额的作用域 作用域 描述 Terminating 匹配 spec.activeDeadlineSeconds >= 0 的pod NotTerminating 匹配 spec.activeDeadlineSeconds is nil 的pod BestEffort 匹配具有最佳服务质量的pod NotBestEffort 匹配具有非最佳服务质量的pod 6. request和limit 当分配计算资源时，每个容器可以为cpu或者内存指定一个请求值和一个限度值。可以配置限额值来限制它们中的任何一个值。 如果指定了requests.cpu 或者 requests.memory的限额值，那么就要求传入的每一个容器显式的指定这些资源的请求。如果指定了limits.cpu或者limits.memory，那么就要求传入的每一个容器显式的指定这些资源的限度。 7. 查看和设置配额 # 创建namespace $ kubectl create namespace myspace # 创建resourcequota $ cat compute-resources.yaml apiVersion: v1 kind: ResourceQuota metadata: name: compute-resources spec: hard: pods: \"4\" requests.cpu: \"1\" requests.memory: 1Gi limits.cpu: \"2\" limits.memory: 2Gi EOF $ kubectl create -f ./compute-resources.yaml --namespace=myspace # 查询resourcequota $ kubectl get quota --namespace=myspace NAME AGE compute-resources 30s # 查询resourcequota的详细信息 $ kubectl describe quota compute-resources --namespace=myspace Name: compute-resources Namespace: myspace Resource Used Hard -------- ---- ---- limits.cpu 0 2 limits.memory 0 2Gi pods 0 4 requests.cpu 0 1 requests.memory 0 1Gi 8. 配额和集群容量 资源配额对象与集群容量无关，它们以绝对单位表示。即增加节点的资源并不会增加已经配置的namespace的资源。 参考文章： https://kubernetes.io/docs/concepts/policy/resource-quotas/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"resource/limit-range.html":{"url":"resource/limit-range.html","title":"Pod限额","keywords":"","body":"Pod限额（LimitRange）1. 为namespace配置CPU和内存的默认值1.1. namespace的内存默认值1.2. namespace的CPU默认值1.3 说明2. 为namespace配置CPU和内存的最大最小值2.1. 内存的最大最小值2.2. CPU的最大最小值2.3. 说明Pod限额（LimitRange） ResourceQuota对象是限制某个namespace下所有Pod(容器)的资源限额 LimitRange对象是限制某个namespace单个Pod(容器)的资源限额 LimitRange对象用来定义某个命名空间下某种资源对象的使用限额，其中资源对象包括：Pod、Container、PersistentVolumeClaim。 1. 为namespace配置CPU和内存的默认值 如果在一个拥有默认内存或CPU限额的命名空间中创建一个容器，并且这个容器未指定它自己的内存或CPU的limit， 它会被分配这个默认的内存或CPU的limit。既没有设置pod的limit和request才会分配默认的内存或CPU的request。 1.1. namespace的内存默认值 # 创建namespace $ kubectl create namespace default-mem-example # 创建LimitRange $ cat memory-defaults.yaml apiVersion: v1 kind: LimitRange metadata: name: mem-limit-range spec: limits: - default: memory: 512Mi defaultRequest: memory: 256Mi type: Container $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/memory-defaults.yaml --namespace=default-mem-example # 创建Pod,未指定内存的limit和request $ cat memory-defaults-pod.yaml apiVersion: v1 kind: Pod metadata: name: default-mem-demo spec: containers: - name: default-mem-demo-ctr image: nginx $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/memory-defaults-pod.yaml --namespace=default-mem-example # 查看Pod $ kubectl get pod default-mem-demo --output=yaml --namespace=default-mem-example containers: - image: nginx imagePullPolicy: Always name: default-mem-demo-ctr resources: limits: memory: 512Mi requests: memory: 256Mi 1.2. namespace的CPU默认值 # 创建namespace $ kubectl create namespace default-cpu-example # 创建LimitRange $ cat cpu-defaults.yaml apiVersion: v1 kind: LimitRange metadata: name: cpu-limit-range spec: limits: - default: cpu: 1 defaultRequest: cpu: 0.5 type: Container $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/cpu-defaults.yaml --namespace=default-cpu-example # 创建Pod，未指定CPU的limit和request $ cat cpu-defaults-pod.yaml apiVersion: v1 kind: Pod metadata: name: default-cpu-demo spec: containers: - name: default-cpu-demo-ctr image: nginx $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/cpu-defaults-pod.yaml --namespace=default-cpu-example # 查看Pod $ kubectl get pod default-cpu-demo --output=yaml --namespace=default-cpu-example containers: - image: nginx imagePullPolicy: Always name: default-cpu-demo-ctr resources: limits: cpu: \"1\" requests: cpu: 500m 1.3 说明 如果没有指定pod的request和limit，则创建的pod会使用LimitRange对象定义的默认值（request和limit） 如果指定pod的limit但未指定request，则创建的pod的request值会取limit的值，而不会取LimitRange对象定义的request默认值。 如果指定pod的request但未指定limit，则创建的pod的limit值会取LimitRange对象定义的limit默认值。 默认Limit和request的动机 如果命名空间具有资源配额（ResourceQuota）, 它为内存限额（CPU限额）设置默认值是有意义的。 以下是资源配额对命名空间施加的两个限制： 在命名空间运行的每一个容器必须有它自己的内存限额（CPU限额）。 在命名空间中所有的容器使用的内存总量（CPU总量）不能超出指定的限额。 如果一个容器没有指定它自己的内存限额（CPU限额），它将被赋予默认的限额值，然后它才可以在被配额限制的命名空间中运行。 2. 为namespace配置CPU和内存的最大最小值 2.1. 内存的最大最小值 创建LimitRange # 创建namespace $ kubectl create namespace constraints-mem-example # 创建LimitRange $ cat memory-constraints.yaml apiVersion: v1 kind: LimitRange metadata: name: mem-min-max-demo-lr spec: limits: - max: memory: 1Gi min: memory: 500Mi type: Container $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/memory-constraints.yaml --namespace=constraints-mem-example # 查看LimitRange $ kubectl get limitrange cpu-min-max-demo --namespace=constraints-mem-example --output=yaml ... limits: - default: memory: 1Gi defaultRequest: memory: 1Gi max: memory: 1Gi min: memory: 500Mi type: Container ... # LimitRange设置了最大最小值，但没有设置默认值，也会被自动设置默认值。 创建符合要求的Pod # 创建符合要求的Pod $ cat memory-constraints-pod.yaml apiVersion: v1 kind: Pod metadata: name: constraints-mem-demo spec: containers: - name: constraints-mem-demo-ctr image: nginx resources: limits: memory: \"800Mi\" requests: memory: \"600Mi\" $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/memory-constraints-pod.yaml --namespace=constraints-mem-example # 查看Pod $ kubectl get pod constraints-mem-demo --output=yaml --namespace=constraints-mem-example ... resources: limits: memory: 800Mi requests: memory: 600Mi ... 创建超过最大内存limit的pod $ cat memory-constraints-pod-2.yaml apiVersion: v1 kind: Pod metadata: name: constraints-mem-demo-2 spec: containers: - name: constraints-mem-demo-2-ctr image: nginx resources: limits: memory: \"1.5Gi\" # 超过最大值 1Gi requests: memory: \"800Mi\" $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/memory-constraints-pod-2.yaml --namespace=constraints-mem-example # Pod创建失败，因为容器指定的limit过大 Error from server (Forbidden): error when creating \"docs/tasks/administer-cluster/memory-constraints-pod-2.yaml\": pods \"constraints-mem-demo-2\" is forbidden: maximum memory usage per Container is 1Gi, but limit is 1536Mi. 创建小于最小内存request的Pod $ cat memory-constraints-pod-3.yaml apiVersion: v1 kind: Pod metadata: name: constraints-mem-demo-3 spec: containers: - name: constraints-mem-demo-3-ctr image: nginx resources: limits: memory: \"800Mi\" requests: memory: \"100Mi\" # 小于最小值500Mi $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/memory-constraints-pod-3.yaml --namespace=constraints-mem-example # Pod创建失败，因为容器指定的内存request过小 Error from server (Forbidden): error when creating \"docs/tasks/administer-cluster/memory-constraints-pod-3.yaml\": pods \"constraints-mem-demo-3\" is forbidden: minimum memory usage per Container is 500Mi, but request is 100Mi. 创建没有指定任何内存limit和request的pod $ cat memory-constraints-pod-4.yaml apiVersion: v1 kind: Pod metadata: name: constraints-mem-demo-4 spec: containers: - name: constraints-mem-demo-4-ctr image: nginx $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/memory-constraints-pod-4.yaml --namespace=constraints-mem-example # 查看Pod $ kubectl get pod constraints-mem-demo-4 --namespace=constraints-mem-example --output=yaml ... resources: limits: memory: 1Gi requests: memory: 1Gi ... 容器没有指定自己的 CPU 请求和限制，所以它将从 LimitRange 获取默认的 CPU 请求和限制值。 2.2. CPU的最大最小值 创建LimitRange # 创建namespace $ kubectl create namespace constraints-cpu-example # 创建LimitRange $ cat cpu-constraints.yaml apiVersion: v1 kind: LimitRange metadata: name: cpu-min-max-demo-lr spec: limits: - max: cpu: \"800m\" min: cpu: \"200m\" type: Container $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/cpu-constraints.yaml --namespace=constraints-cpu-example # 查看LimitRange $ kubectl get limitrange cpu-min-max-demo-lr --output=yaml --namespace=constraints-cpu-example ... limits: - default: cpu: 800m defaultRequest: cpu: 800m max: cpu: 800m min: cpu: 200m type: Container ... 创建符合要求的Pod $ cat cpu-constraints-pod.yaml apiVersion: v1 kind: Pod metadata: name: constraints-cpu-demo spec: containers: - name: constraints-cpu-demo-ctr image: nginx resources: limits: cpu: \"800m\" requests: cpu: \"500m\" $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/cpu-constraints-pod.yaml --namespace=constraints-cpu-example # 查看Pod $ kubectl get pod constraints-cpu-demo --output=yaml --namespace=constraints-cpu-example ... resources: limits: cpu: 800m requests: cpu: 500m ... 创建超过最大CPU limit的Pod $ cat cpu-constraints-pod-2.yaml apiVersion: v1 kind: Pod metadata: name: constraints-cpu-demo-2 spec: containers: - name: constraints-cpu-demo-2-ctr image: nginx resources: limits: cpu: \"1.5\" requests: cpu: \"500m\" $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/cpu-constraints-pod-2.yaml --namespace=constraints-cpu-example # Pod创建失败，因为容器指定的CPU limit过大 Error from server (Forbidden): error when creating \"docs/tasks/administer-cluster/cpu-constraints-pod-2.yaml\": pods \"constraints-cpu-demo-2\" is forbidden: maximum cpu usage per Container is 800m, but limit is 1500m. 创建小于最小CPU request的Pod $ cat cpu-constraints-pod-3.yaml apiVersion: v1 kind: Pod metadata: name: constraints-cpu-demo-4 spec: containers: - name: constraints-cpu-demo-4-ctr image: nginx resources: limits: cpu: \"800m\" requests: cpu: \"100m\" $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/cpu-constraints-pod-3.yaml --namespace=constraints-cpu-example # Pod创建失败，因为容器指定的CPU request过小 Error from server (Forbidden): error when creating \"docs/tasks/administer-cluster/cpu-constraints-pod-3.yaml\": pods \"constraints-cpu-demo-4\" is forbidden: minimum cpu usage per Container is 200m, but request is 100m. 创建没有指定任何CPU limit和request的pod $ cat cpu-constraints-pod-4.yaml apiVersion: v1 kind: Pod metadata: name: constraints-cpu-demo-4 spec: containers: - name: constraints-cpu-demo-4-ctr image: vish/stress $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/cpu-constraints-pod-4.yaml --namespace=constraints-cpu-example # 查看Pod kubectl get pod constraints-cpu-demo-4 --namespace=constraints-cpu-example --output=yaml ... resources: limits: cpu: 800m requests: cpu: 800m ... 容器没有指定自己的 CPU 请求和限制，所以它将从 LimitRange 获取默认的 CPU 请求和限制值。 2.3. 说明 LimitRange 在 namespace 中施加的最小和最大内存（CPU）限制只有在创建和更新 Pod 时才会被应用。改变 LimitRange 不会对之前创建的 Pod 造成影响。 Kubernetes 都会执行下列步骤： 如果容器没有指定自己的内存（CPU）请求（request）和限制（limit），系统将会为其分配默认值。 验证容器的内存（CPU）请求大于等于最小值。 验证容器的内存（CPU）限制小于等于最大值。 参考文章： https://kubernetes.io/docs/tasks/administer-cluster/manage-resources/memory-default-namespace/ https://kubernetes.io/docs/tasks/administer-cluster/manage-resources/cpu-default-namespace/ https://kubernetes.io/docs/tasks/administer-cluster/manage-resources/memory-constraint-namespace/ https://kubernetes.io/docs/tasks/administer-cluster/manage-resources/cpu-constraint-namespace/ https://kubernetes.io/docs/tasks/administer-cluster/manage-resources/quota-memory-cpu-namespace/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"resource/quality-of-service.html":{"url":"resource/quality-of-service.html","title":"资源服务质量","keywords":"","body":"Resource Quality of Service1. 资源QoS简介2. Requests and Limits2.1. 可压缩的资源2.2. 不可压缩的资源3. QoS 级别3.1. Guaranteed3.2. Burstable3.3. Best-EffortResource Quality of Service 1. 资源QoS简介 request值表示容器保证可被分配到资源。limit表示容器可允许使用的最大资源。Pod级别的request和limit是其所有容器的request和limit之和。 2. Requests and Limits Pod可以指定request和limit资源。其中0 Node Allocatable & request 。调度是基于request而不是limit，即如果Pod被成功调度，那么可以保证Pod分配到指定的 request的资源。Pod使用的资源能否超过指定的limit值取决于该资源是否可被压缩。 2.1. 可压缩的资源 目前只支持CPU pod可以保证获得它们请求的CPU数量，它们可能会也可能不会获得额外的CPU时间(取决于正在运行的其他作业)。因为目前CPU隔离是在容器级别而不是pod级别。 2.2. 不可压缩的资源 目前只支持内存 pod将获得它们请求的内存数量，如果超过了它们的内存请求，它们可能会被杀死(如果其他一些pod需要内存)，但如果pod消耗的内存小于请求的内存，那么它们将不会被杀死(除非在系统任务或守护进程需要更多内存的情况下)。 3. QoS 级别 在机器资源超卖的情况下（limit的总量大于机器的资源容量），即CPU或内存耗尽，将不得不杀死部分不重要的容器。因此对容器分成了3个QoS的级别：Guaranteed,Burstable, Best-Effort，三个级别的优先级依次递减。 当CPU资源无法满足，pod不会被杀死可能被短暂控制。 内存是不可压缩的资源，当内存耗尽的情况下，会依次杀死优先级低的容器。Guaranteed的级别最高，不会被杀死，除非容器使用量超过limit限值或者资源耗尽，已经没有更低级别的容器可驱逐。 3.1. Guaranteed 所有的容器的limit值和request值被配置且两者相等（如果只配置limit没有request，则request取值于limit）。 例如： # 示例1 containers: name: foo resources: limits: cpu: 10m memory: 1Gi name: bar resources: limits: cpu: 100m memory: 100Mi # 示例2 containers: name: foo resources: limits: cpu: 10m memory: 1Gi requests: cpu: 10m memory: 1Gi name: bar resources: limits: cpu: 100m memory: 100Mi requests: cpu: 100m memory: 100Mi 3.2. Burstable 如果一个或多个容器的limit和request值被配置且两者不相等。 例如： # 示例1 containers: name: foo resources: limits: cpu: 10m memory: 1Gi requests: cpu: 10m memory: 1Gi name: bar # 示例2 containers: name: foo resources: limits: memory: 1Gi name: bar resources: limits: cpu: 100m # 示例3 containers: name: foo resources: requests: cpu: 10m memory: 1Gi name: bar 3.3. Best-Effort 所有的容器的limit和request值都没有配置。 例如： containers: name: foo resources: name: bar resources: 参考文章： https://github.com/kubernetes/community/blob/master/contributors/design-proposals/node/resource-qos.md Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"resource/lxcfs/lxcfs.html":{"url":"resource/lxcfs/lxcfs.html","title":"Lxcfs资源视图隔离","keywords":"","body":"1. 资源视图隔离2. Lxcfs简介3. Lxcfs原理4. 使用方式4.1. 安装lxcfs4.2. 运行lxcfs4.3. 挂载容器内/proc下的文件目录4.4. 验证容器内CPU和内存5. 使用k8s集群部署5.1. lxcfs-image5.2. daemonset5.3. lxcfs-admission-webhook1. 资源视图隔离 容器中的执行top、free等命令展示出来的CPU，内存等信息是从/proc目录中的相关文件里读取出来的。而容器并没有对/proc，/sys等文件系统做隔离，因此容器中读取出来的CPU和内存的信息是宿主机的信息，与容器实际分配和限制的资源量不同。 /proc/cpuinfo /proc/diskstats /proc/meminfo /proc/stat /proc/swaps /proc/uptime 为了实现让容器内部的资源视图更像虚拟机，使得应用程序可以拿到真实的CPU和内存信息，就需要通过文件挂载的方式将cgroup的真实的容器资源信息挂载到容器内/proc下的文件，使得容器内执行top、free等命令时可以拿到真实的CPU和内存信息。 2. Lxcfs简介 lxcfs是一个FUSE文件系统，使得Linux容器的文件系统更像虚拟机。lxcfs是一个常驻进程运行在宿主机上，从而来自动维护宿主机cgroup中容器的真实资源信息与容器内/proc下文件的映射关系。 lxcfs的命令信息如下： #/usr/local/bin/lxcfs -h Usage: lxcfs [-f|-d] -u -l -n [-p pidfile] mountpoint -f running foreground by default; -d enable debug output -l use loadavg -u no swap Default pidfile is /run/lxcfs.pid lxcfs -h lxcfs的源码：https://github.com/lxc/lxcfs 3. Lxcfs原理 lxcfs实现的基本原理是通过文件挂载的方式，把cgroup中容器相关的信息读取出来，存储到lxcfs相关的目录下，并将相关目录映射到容器内的/proc目录下，从而使得容器内执行top,free等命令时拿到的/proc下的数据是真实的cgroup分配给容器的CPU和内存数据。 原理图 图片 - lxcfs 映射目录 类别 容器内目录 宿主机lxcfs目录 cpu /proc/cpuinfo /var/lib/lxcfs/{container_id}/proc/cpuinfo 内存 /proc/meminfo /var/lib/lxcfs/{container_id}/proc/meminfo /proc/diskstats /var/lib/lxcfs/{container_id}/proc/diskstats /proc/stat /var/lib/lxcfs/{container_id}/proc/stat /proc/swaps /var/lib/lxcfs/{container_id}/proc/swaps /proc/uptime /var/lib/lxcfs/{container_id}/proc/uptime /proc/loadavg /var/lib/lxcfs/{container_id}/proc/loadavg /sys/devices/system/cpu/online /var/lib/lxcfs/{container_id}/sys/devices/system/cpu/online 4. 使用方式 4.1. 安装lxcfs 环境准备 yum install -y fuse fuse-lib fuse-devel 源码编译安装 git clone git://github.com/lxc/lxcfs cd lxcfs ./bootstrap.sh ./configure make make install 或者通过rpm包安装 wget https://copr-be.cloud.fedoraproject.org/results/ganto/lxc3/epel-7-x86_64/01041891-lxcfs/lxcfs-3.1.2-0.2.el7.x86_64.rpm; rpm -ivh lxcfs-3.1.2-0.2.el7.x86_64.rpm --force --nodeps 查看是否安装成功 lxcfs -h 4.2. 运行lxcfs 运行lxcfs主要执行两条命令。 sudo mkdir -p /var/lib/lxcfs sudo lxcfs /var/lib/lxcfs 可以通过systemd运行。 lxcfs.service文件： cat > /usr/lib/systemd/system/lxcfs.service 运行命令 systemctl daemon-reload && systemctl enable lxcfs && systemctl start lxcfs && systemctl status lxcfs 4.3. 挂载容器内/proc下的文件目录 docker run -it --rm -m 256m --cpus 2 \\ -v /var/lib/lxcfs/proc/cpuinfo:/proc/cpuinfo:rw \\ -v /var/lib/lxcfs/proc/diskstats:/proc/diskstats:rw \\ -v /var/lib/lxcfs/proc/meminfo:/proc/meminfo:rw \\ -v /var/lib/lxcfs/proc/stat:/proc/stat:rw \\ -v /var/lib/lxcfs/proc/swaps:/proc/swaps:rw \\ -v /var/lib/lxcfs/proc/uptime:/proc/uptime:rw \\ nginx:latest /bin/sh 4.4. 验证容器内CPU和内存 # cpu grep -c processor /proc/cpuinfo cat /proc/cpuinfo # memory free -g cat /proc/meminfo 5. 使用k8s集群部署 使用k8s集群部署与systemd部署方式同理，需要解决2个问题： 在每个node节点上部署lxcfs常驻进程，lxcfs需要通过镜像来运行，可以通过daemonset来部署。 实现将lxcfs维护的目录自动挂载到pod内的/proc目录。 具体可参考：https://github.com/denverdino/lxcfs-admission-webhook 5.1. lxcfs-image Dockerfile FROM centos:7 as build RUN yum -y update RUN yum -y install fuse-devel pam-devel wget install gcc automake autoconf libtool make ENV LXCFS_VERSION 3.1.2 RUN wget https://linuxcontainers.org/downloads/lxcfs/lxcfs-$LXCFS_VERSION.tar.gz && \\ mkdir /lxcfs && tar xzvf lxcfs-$LXCFS_VERSION.tar.gz -C /lxcfs --strip-components=1 && \\ cd /lxcfs && ./configure && make FROM centos:7 STOPSIGNAL SIGINT COPY --from=build /lxcfs/lxcfs /usr/local/bin/lxcfs COPY --from=build /lxcfs/.libs/liblxcfs.so /usr/local/lib/lxcfs/liblxcfs.so COPY --from=build /lxcfs/lxcfs /lxcfs/lxcfs COPY --from=build /lxcfs/.libs/liblxcfs.so /lxcfs/liblxcfs.so COPY --from=build /usr/lib64/libfuse.so.2.9.2 /usr/lib64/libfuse.so.2.9.2 COPY --from=build /usr/lib64/libulockmgr.so.1.0.1 /usr/lib64/libulockmgr.so.1.0.1 RUN ln -s /usr/lib64/libfuse.so.2.9.2 /usr/lib64/libfuse.so.2 && \\ ln -s /usr/lib64/libulockmgr.so.1.0.1 /usr/lib64/libulockmgr.so.1 COPY start.sh / CMD [\"/start.sh\"] star.sh #!/bin/bash # Cleanup nsenter -m/proc/1/ns/mnt fusermount -u /var/lib/lxcfs 2> /dev/null || true nsenter -m/proc/1/ns/mnt [ -L /etc/mtab ] || \\ sed -i \"/^lxcfs \\/var\\/lib\\/lxcfs fuse.lxcfs/d\" /etc/mtab # Prepare mkdir -p /usr/local/lib/lxcfs /var/lib/lxcfs # Update lxcfs cp -f /lxcfs/lxcfs /usr/local/bin/lxcfs cp -f /lxcfs/liblxcfs.so /usr/local/lib/lxcfs/liblxcfs.so # Mount exec nsenter -m/proc/1/ns/mnt /usr/local/bin/lxcfs /var/lib/lxcfs/ 5.2. daemonset lxcfs-daemonset.yaml apiVersion: apps/v1 kind: DaemonSet metadata: name: lxcfs labels: app: lxcfs spec: selector: matchLabels: app: lxcfs template: metadata: labels: app: lxcfs spec: hostPID: true tolerations: - key: node-role.kubernetes.io/master effect: NoSchedule containers: - name: lxcfs image: registry.cn-hangzhou.aliyuncs.com/denverdino/lxcfs:3.1.2 imagePullPolicy: Always securityContext: privileged: true volumeMounts: - name: cgroup mountPath: /sys/fs/cgroup - name: lxcfs mountPath: /var/lib/lxcfs mountPropagation: Bidirectional - name: usr-local mountPath: /usr/local volumes: - name: cgroup hostPath: path: /sys/fs/cgroup - name: usr-local hostPath: path: /usr/local - name: lxcfs hostPath: path: /var/lib/lxcfs type: DirectoryOrCreate 5.3. lxcfs-admission-webhook lxcfs-admission-webhook实现了一个动态的准入webhook，更准确的讲是实现了一个修改性质的webhook，即监听pod的创建，然后对pod执行patch的操作，从而将lxcfs与容器内的目录映射关系植入到pod创建的yaml中从而实现自动挂载。 deployment apiVersion: apps/v1 kind: Deployment metadata: name: lxcfs-admission-webhook-deployment labels: app: lxcfs-admission-webhook spec: replicas: 1 selector: matchLabels: app: lxcfs-admission-webhook template: metadata: labels: app: lxcfs-admission-webhook spec: containers: - name: lxcfs-admission-webhook image: registry.cn-hangzhou.aliyuncs.com/denverdino/lxcfs-admission-webhook:v1 imagePullPolicy: IfNotPresent args: - -tlsCertFile=/etc/webhook/certs/cert.pem - -tlsKeyFile=/etc/webhook/certs/key.pem - -alsologtostderr - -v=4 - 2>&1 volumeMounts: - name: webhook-certs mountPath: /etc/webhook/certs readOnly: true volumes: - name: webhook-certs secret: secretName: lxcfs-admission-webhook-certs 具体部署参考:install.sh #!/bin/bash ./deployment/webhook-create-signed-cert.sh kubectl get secret lxcfs-admission-webhook-certs kubectl create -f deployment/deployment.yaml kubectl create -f deployment/service.yaml cat ./deployment/mutatingwebhook.yaml | ./deployment/webhook-patch-ca-bundle.sh > ./deployment/mutatingwebhook-ca-bundle.yaml kubectl create -f deployment/mutatingwebhook-ca-bundle.yaml 执行命令 /deployment/install.sh 参考： https://github.com/lxc/lxcfs https://linuxcontainers.org/lxcfs/ https://github.com/denverdino/lxcfs-admission-webhook https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/kubernetes-troubleshooting.html":{"url":"operation/kubernetes-troubleshooting.html","title":"kubernetes集群问题排查","keywords":"","body":"1. 查看系统Event事件1.1. Pod1.2. NODE1.3. RC1.4. NAMESPACE1.5. Service2. 查看容器日志3. 查看k8s服务日志3.1. journalctl3.2. 日志文件4. 常见问题4.1. Pod状态一直为Pending4.2. Pod创建后不断重启1. 查看系统Event事件 kubectl describe pod --namespace= 该命令可以显示Pod创建时的配置定义、状态等信息和最近的Event事件，事件信息可用于排错。例如当Pod状态为Pending，可通过查看Event事件确认原因，一般原因有几种： 没有可用的Node可调度 开启了资源配额管理并且当前Pod的目标节点上恰好没有可用的资源 正在下载镜像（镜像拉取耗时太久）或镜像下载失败。 kubectl describe还可以查看其它k8s对象：NODE,RC,Service,Namespace,Secrets。 1.1. Pod kubectl describe pod --namespace= 以下是容器的启动命令非阻塞式导致容器挂掉，被k8s频繁重启所产生的事件。 kubectl describe pod --namespace= Events: FirstSeen LastSeen Count From SubobjectPath Reason Message ───────── ──────── ───── ──── ───────────── ────── ─────── 7m 7m 1 {scheduler } Scheduled Successfully assigned yangsc-1-0-0-index0 to 10.8.216.19 7m 7m 1 {kubelet 10.8.216.19} containers{infra} Pulled Container image \"gcr.io/kube-system/pause:0.8.0\" already present on machine 7m 7m 1 {kubelet 10.8.216.19} containers{infra} Created Created with docker id 84f133c324d0 7m 7m 1 {kubelet 10.8.216.19} containers{infra} Started Started with docker id 84f133c324d0 7m 7m 1 {kubelet 10.8.216.19} containers{yangsc0} Started Started with docker id 3f9f82abb145 7m 7m 1 {kubelet 10.8.216.19} containers{yangsc0} Created Created with docker id 3f9f82abb145 7m 7m 1 {kubelet 10.8.216.19} containers{yangsc0} Created Created with docker id fb112e4002f4 7m 7m 1 {kubelet 10.8.216.19} containers{yangsc0} Started Started with docker id fb112e4002f4 6m 6m 1 {kubelet 10.8.216.19} containers{yangsc0} Created Created with docker id 613b119d4474 6m 6m 1 {kubelet 10.8.216.19} containers{yangsc0} Started Started with docker id 613b119d4474 6m 6m 1 {kubelet 10.8.216.19} containers{yangsc0} Created Created with docker id 25cb68d1fd3d 6m 6m 1 {kubelet 10.8.216.19} containers{yangsc0} Started Started with docker id 25cb68d1fd3d 5m 5m 1 {kubelet 10.8.216.19} containers{yangsc0} Started Started with docker id 7d9ee8610b28 5m 5m 1 {kubelet 10.8.216.19} containers{yangsc0} Created Created with docker id 7d9ee8610b28 3m 3m 1 {kubelet 10.8.216.19} containers{yangsc0} Started Started with docker id 88b9e8d582dd 3m 3m 1 {kubelet 10.8.216.19} containers{yangsc0} Created Created with docker id 88b9e8d582dd 7m 1m 7 {kubelet 10.8.216.19} containers{yangsc0} Pulling Pulling image \"gcr.io/test/tcp-hello:1.0.0\" 1m 1m 1 {kubelet 10.8.216.19} containers{yangsc0} Started Started with docker id 089abff050e7 1m 1m 1 {kubelet 10.8.216.19} containers{yangsc0} Created Created with docker id 089abff050e7 7m 1m 7 {kubelet 10.8.216.19} containers{yangsc0} Pulled Successfully pulled image \"gcr.io/test/tcp-hello:1.0.0\" 6m 7s 34 {kubelet 10.8.216.19} containers{yangsc0} Backoff Back-off restarting failed docker container 1.2. NODE kubectl describe node 10.8.216.20 [root@FC-43745A-10 ~]# kubectl describe node 10.8.216.20 Name: 10.8.216.20 Labels: kubernetes.io/hostname=10.8.216.20,namespace/bcs-cc=true,namespace/myview=true CreationTimestamp: Mon, 17 Apr 2017 11:32:52 +0800 Phase: Conditions: Type Status LastHeartbeatTime LastTransitionTime Reason Message ──── ────── ───────────────── ────────────────── ────── ─────── Ready True Fri, 18 Aug 2017 09:38:33 +0800 Tue, 02 May 2017 17:40:58 +0800 KubeletReady kubelet is posting ready status OutOfDisk False Fri, 18 Aug 2017 09:38:33 +0800 Mon, 17 Apr 2017 11:31:27 +0800 KubeletHasSufficientDisk kubelet has sufficient disk space available Addresses: 10.8.216.20,10.8.216.20 Capacity: cpu: 32 memory: 67323039744 pods: 40 System Info: Machine ID: 723bafc7f6764022972b3eae1ce6b198 System UUID: 4C4C4544-0042-4210-8044-C3C04F595631 Boot ID: da01f2e3-987a-425a-9ca7-1caaec35d1e5 Kernel Version: 3.10.0-327.28.3.el7.x86_64 OS Image: CentOS Linux 7 (Core) Container Runtime Version: docker://1.13.1 Kubelet Version: v1.1.1-xxx2-13.1+79c90c68bfb72f-dirty Kube-Proxy Version: v1.1.1-xxx2-13.1+79c90c68bfb72f-dirty ExternalID: 10.8.216.20 Non-terminated Pods: (6 in total) Namespace Name CPU Requests CPU Limits Memory Requests Memory Limits ───────── ──── ──────────── ────────── ─────────────── ───────────── bcs-cc bcs-cc-api-0-0-1364-index0 1 (3%) 1 (3%) 4294967296 (6%) 4294967296 (6%) bcs-cc bcs-cc-api-0-0-1444-index0 1 (3%) 1 (3%) 4294967296 (6%) 4294967296 (6%) fw fw-demo2-0-0-1519-index0 1 (3%) 1 (3%) 4294967296 (6%) 4294967296 (6%) myview myview-api-0-0-1362-index0 1 (3%) 1 (3%) 4294967296 (6%) 4294967296 (6%) myview myview-api-0-0-1442-index0 1 (3%) 1 (3%) 4294967296 (6%) 4294967296 (6%) qa-ts-dna ts-dna-console3-0-0-1434-index0 1 (3%) 1 (3%) 4294967296 (6%) 4294967296 (6%) Allocated resources: (Total limits may be over 100%, i.e., overcommitted. More info: http://releases.k8s.io/HEAD/docs/user-guide/compute-resources.md) CPU Requests CPU Limits Memory Requests Memory Limits ──────────── ────────── ─────────────── ───────────── 6 (18%) 6 (18%) 25769803776 (38%) 25769803776 (38%) No events. 1.3. RC kubectl describe rc mytest-1-0-0 --namespace=test [root@FC-43745A-10 ~]# kubectl describe rc mytest-1-0-0 --namespace=test Name: mytest-1-0-0 Namespace: test Image(s): gcr.io/test/mywebcalculator:1.0.1 Selector: app=mytest,appVersion=1.0.0 Labels: app=mytest,appVersion=1.0.0,env=ts,zone=inner Replicas: 1 current / 1 desired Pods Status: 1 Running / 0 Waiting / 0 Succeeded / 0 Failed No volumes. Events: FirstSeen LastSeen Count From SubobjectPath Reason Message ───────── ──────── ───── ──── ───────────── ────── ─────── 20h 19h 9 {replication-controller } FailedCreate Error creating: Pod \"mytest-1-0-0-index0\" is forbidden: limited to 10 pods 20h 17h 7 {replication-controller } FailedCreate Error creating: pods \"mytest-1-0-0-index0\" already exists 20h 17h 4 {replication-controller } SuccessfulCreate Created pod: mytest-1-0-0-index0 1.4. NAMESPACE kubectl describe namespace test [root@FC-43745A-10 ~]# kubectl describe namespace test Name: test Labels: Status: Active Resource Quotas Resource Used Hard --- --- --- cpu 5 20 memory 1342177280 53687091200 persistentvolumeclaims 0 10 pods 4 10 replicationcontrollers 8 20 resourcequotas 1 1 secrets 3 10 services 8 20 No resource limits. 1.5. Service kubectl describe service xxx-containers-1-1-0 --namespace=test [root@FC-43745A-10 ~]# kubectl describe service xxx-containers-1-1-0 --namespace=test Name: xxx-containers-1-1-0 Namespace: test Labels: app=xxx-containers,appVersion=1.1.0,env=ts,zone=inner Selector: app=xxx-containers,appVersion=1.1.0 Type: ClusterIP IP: 10.254.46.42 Port: port-dna-tcp-35913 35913/TCP Endpoints: 10.0.92.17:35913 Port: port-l7-tcp-8080 8080/TCP Endpoints: 10.0.92.17:8080 Session Affinity: None No events. 2. 查看容器日志 1、查看指定pod的日志 kubectl logs kubectl logs -f #类似tail -f的方式查看 2、查看上一个pod的日志 kubectl logs -p 3、查看指定pod中指定容器的日志 kubectl logs -c 4、kubectl logs --help [root@node5 ~]# kubectl logs --help Print the logs for a container in a pod. If the pod has only one container, the container name is optional. Usage: kubectl logs [-f] [-p] POD [-c CONTAINER] [flags] Aliases: logs, log Examples: # Return snapshot logs from pod nginx with only one container $ kubectl logs nginx # Return snapshot of previous terminated ruby container logs from pod web-1 $ kubectl logs -p -c ruby web-1 # Begin streaming the logs of the ruby container in pod web-1 $ kubectl logs -f -c ruby web-1 # Display only the most recent 20 lines of output in pod nginx $ kubectl logs --tail=20 nginx # Show all logs from pod nginx written in the last hour $ kubectl logs --since=1h nginx 3. 查看k8s服务日志 3.1. journalctl 在Linux系统上systemd系统来管理kubernetes服务，并且journal系统会接管服务程序的输出日志，可以通过systemctl status 或journalctl -u -f来查看kubernetes服务的日志。 其中kubernetes组件包括： k8s组件 涉及日志内容 备注 kube-apiserver kube-controller-manager Pod扩容相关或RC相关 kube-scheduler Pod扩容相关或RC相关 kubelet Pod生命周期相关：创建、停止等 etcd 3.2. 日志文件 也可以通过指定日志存放目录来保存和查看日志 --logtostderr=false：不输出到stderr --log-dir=/var/log/kubernetes:日志的存放目录 --alsologtostderr=false:设置为true表示日志输出到文件也输出到stderr --v=0:glog的日志级别 --vmodule=gfs=2,test=4：glog基于模块的详细日志级别 4. 常见问题 4.1. Pod状态一直为Pending kubectl describe --namespace= 查看该POD的事件。 正在下载镜像但拉取不下来（镜像拉取耗时太久）[一般都是该原因] 没有可用的Node可调度 开启了资源配额管理并且当前Pod的目标节点上恰好没有可用的资源 解决方法： 查看该POD所在宿主机与镜像仓库之间的网络是否有问题，可以手动拉取镜像 删除POD实例，让POD调度到别的宿主机上 4.2. Pod创建后不断重启 kubectl get pods中Pod状态一会running，一会不是，且RESTARTS次数不断增加。 一般原因为容器启动命令不是阻塞式命令，导致容器运行后马上退出。 非阻塞式命令： 本身CMD指定的命令就是非阻塞式命令 将服务启动方式设置为后台运行 解决方法： 1、将命令改为阻塞式命令（前台运行），例如：zkServer.sh start-foreground 2、java运行程序的启动脚本将 nohup xxx &的nobup和&去掉，例如： nohup JAVA_HOME/bin/java JAVA_OPTS -cp $CLASSPATH com.cnc.open.processor.Main & 改为： JAVA_HOME/bin/java JAVA_OPTS -cp $CLASSPATH com.cnc.open.processor.Main 文章参考《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/kubectl/_index.html":{"url":"operation/kubectl/_index.html","title":"kubectl工具","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/kubectl/install-kubectl.html":{"url":"operation/kubectl/install-kubectl.html","title":"kubectl安装与配置","keywords":"","body":"1. kubectl的安装2. 配置k8s集群环境2.1. 命令行方式2.1.1 非安全方式2.1.2 安全方式2.1.3 查询当前配置环境2.2. 添加配置文件的方式3. kubectl config4. shell自动补齐1. kubectl的安装 curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/linux/amd64/kubectl && chmod +x kubectl && sudo mv kubectl /usr/local/bin/ 安装指定版本的kubectl，例如：v1.9.0 curl -LO https://storage.googleapis.com/kubernetes-release/release/v1.9.0/bin/linux/amd64/kubectl && chmod +x kubectl && sudo mv kubectl /usr/local/bin/ 2. 配置k8s集群环境 2.1. 命令行方式 2.1.1 非安全方式 kubectl config set-cluster k8s --server=http:// kubectl config set-context --cluster=k8s --namespace= kubectl config use-context 2.1.2 安全方式 kubectl config set-cluster k8s --server=https:// --insecure-skip-tls-verify=true kubectl config set-credentials k8s-user --username= --password= kubectl config set-context --cluster=k8s --user=k8s-user --namespace= kubectl config use-context 2.1.3 查询当前配置环境 [root@test ]# kubectl cluster-info Kubernetes master is running at http://192.168.10.3:8081 2.2. 添加配置文件的方式 当没有指定--kubeconfig参数和$KUBECONFIG的环境变量的时候，会默认读取${HOME}/.kube/config。 因此创建${HOME}/.kube/config文件，并在`${HOME}/.kube/ssl目录下创建ca.pem、cert.pem、key.pem文件。 内容如下： apiVersion: v1 kind: Config clusters: - name: local cluster: certificate-authority: ./ssl/ca.pem server: https://192.168.10.3:6443 users: - name: kubelet user: client-certificate: ./ssl/cert.pem client-key: ./ssl/key.pem contexts: - context: cluster: local user: kubelet name: kubelet-cluster.local current-context: kubelet-cluster.local 3. kubectl config kubectl config命令说明 $ kubectl config --help Modify kubeconfig files using subcommands like \"kubectl config set current-context my-context\" The loading order follows these rules: 1. If the --kubeconfig flag is set, then only that file is loaded. The flag may only be set once and no merging takes place. 2. If $KUBECONFIG environment variable is set, then it is used a list of paths (normal path delimitting rules for your system). These paths are merged. When a value is modified, it is modified in the file that defines the stanza. When a value is created, it is created in the first file that exists. If no files in the chain exist, then it creates the last file in the list. 3. Otherwise, ${HOME}/.kube/config is used and no merging takes place. Available Commands: current-context Displays the current-context delete-cluster Delete the specified cluster from the kubeconfig delete-context Delete the specified context from the kubeconfig get-clusters Display clusters defined in the kubeconfig get-contexts Describe one or many contexts rename-context Renames a context from the kubeconfig file. set Sets an individual value in a kubeconfig file set-cluster Sets a cluster entry in kubeconfig set-context Sets a context entry in kubeconfig set-credentials Sets a user entry in kubeconfig unset Unsets an individual value in a kubeconfig file use-context Sets the current-context in a kubeconfig file view Display merged kubeconfig settings or a specified kubeconfig file Usage: kubectl config SUBCOMMAND [options] Use \"kubectl --help\" for more information about a given command. Use \"kubectl options\" for a list of global command-line options (applies to all commands). 4. shell自动补齐 source > ~/.bashrc 如果出现以下报错 # kubectl自动补齐失败 kubectl _get_comp_words_by_ref : command not found 解决方法： yum install bash-completion -y source /etc/profile.d/bash_completion.sh 参考文章： https://kubernetes.io/docs/tasks/tools/install-kubectl/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/kubectl/kubectl-commands.html":{"url":"operation/kubectl/kubectl-commands.html","title":"kubectl命令说明","keywords":"","body":"1. kubectl命令介绍2. 操作的常用资源对象3. kubectl命令分类[command]3.1 增3.2 删3.3 改3.4 查4. Pod相关命令4.1 查询Pod4.2 进入Pod4.3 删除Pod4.4 日志查看5. 常用命令5.1. Node隔离与恢复5.2. kubectl label5.3. 升级镜像5.4. 调整资源值5.5. 调整readiness probe5.6. 调整tolerations属性5.7. 查看所有节点的IP5.8. 查看当前k8s组件leader节点5.9. 修改副本数5.10. 批量删除pod5.11. 各种查看命令5.12. 拷贝文件5.13. 强制删除namespace6. kubectl日志级别1. kubectl命令介绍 kubectl的命令语法 kubectl [command] [TYPE] [NAME] [flags] 其中command，TYPE，NAME，和flags分别是： command: 指定要在一个或多个资源进行操作，例如create，get，describe，delete。 TYPE：指定资源类型。资源类型区分大小写，您可以指定单数，复数或缩写形式。例如，以下命令产生相同的输出： kubectl get pod pod1 kubectl get pods pod1 kubectl get po pod1 NAME：指定资源的名称。名称区分大小写。如果省略名称，则会显示所有资源的详细信息,比如$ kubectl get pods。 按类型和名称指定多种资源： * 要分组资源，如果它们都是相同的类型：`TYPE1 name1 name2 name`. 例: `$ kubectl get pod example-pod1 example-pod2` * 要分别指定多种资源类型: `TYPE1/name1 TYPE1/name2 TYPE2/name3 TYPE/name`. 例: `$ kubectl get pod/example-pod1 replicationcontroller/example-rc1` flags：指定可选标志。例如，您可以使用-s或--serverflags来指定Kubernetes API服务器的地址和端口。 更多命令介绍： [root@node5 ~]# kubectl kubectl controls the Kubernetes cluster manager. Find more information at https://github.com/kubernetes/kubernetes. Basic Commands (Beginner): create Create a resource from a file or from stdin. expose Take a replication controller, service, deployment or pod and expose it as a new Kubernetes Service run Run a particular image on the cluster set Set specific features on objects run-container Run a particular image on the cluster. This command is deprecated, use \"run\" instead Basic Commands (Intermediate): get Display one or many resources explain Documentation of resources edit Edit a resource on the server delete Delete resources by filenames, stdin, resources and names, or by resources and label selector Deploy Commands: rollout Manage the rollout of a resource rolling-update Perform a rolling update of the given ReplicationController scale Set a new size for a Deployment, ReplicaSet, Replication Controller, or Job autoscale Auto-scale a Deployment, ReplicaSet, or ReplicationController Cluster Management Commands: certificate Modify certificate resources. cluster-info Display cluster info top Display Resource (CPU/Memory/Storage) usage. cordon Mark node as unschedulable uncordon Mark node as schedulable drain Drain node in preparation for maintenance taint Update the taints on one or more nodes Troubleshooting and Debugging Commands: describe Show details of a specific resource or group of resources logs Print the logs for a container in a pod attach Attach to a running container exec Execute a command in a container port-forward Forward one or more local ports to a pod proxy Run a proxy to the Kubernetes API server cp Copy files and directories to and from containers. auth Inspect authorization Advanced Commands: apply Apply a configuration to a resource by filename or stdin patch Update field(s) of a resource using strategic merge patch replace Replace a resource by filename or stdin convert Convert config files between different API versions Settings Commands: label Update the labels on a resource annotate Update the annotations on a resource completion Output shell completion code for the specified shell (bash or zsh) Other Commands: api-versions Print the supported API versions on the server, in the form of \"group/version\" config Modify kubeconfig files help Help about any command plugin Runs a command-line plugin version Print the client and server version information Use \"kubectl --help\" for more information about a given command. Use \"kubectl options\" for a list of global command-line options (applies to all commands). 2. 操作的常用资源对象 Node Podes Replication Controllers Services Namespace Deployment StatefulSet 具体对象类型及缩写： * all * certificatesigningrequests (aka 'csr') * clusterrolebindings * clusterroles * componentstatuses (aka 'cs') * configmaps (aka 'cm') * controllerrevisions * cronjobs * customresourcedefinition (aka 'crd') * daemonsets (aka 'ds') * deployments (aka 'deploy') * endpoints (aka 'ep') * events (aka 'ev') * horizontalpodautoscalers (aka 'hpa') * ingresses (aka 'ing') * jobs * limitranges (aka 'limits') * namespaces (aka 'ns') * networkpolicies (aka 'netpol') * nodes (aka 'no') * persistentvolumeclaims (aka 'pvc') * persistentvolumes (aka 'pv') * poddisruptionbudgets (aka 'pdb') * podpreset * pods (aka 'po') * podsecuritypolicies (aka 'psp') * podtemplates * replicasets (aka 'rs') * replicationcontrollers (aka 'rc') * resourcequotas (aka 'quota') * rolebindings * roles * secrets * serviceaccounts (aka 'sa') * services (aka 'svc') * statefulsets (aka 'sts') * storageclasses (aka 'sc') 3. kubectl命令分类[command] 3.1 增 1）create:[Create a resource by filename or stdin] 2）run:[ Run a particular image on the cluster] 3）apply:[Apply a configuration to a resource by filename or stdin] 4）proxy:[Run a proxy to the Kubernetes API server ] 3.2 删 1）delete:[Delete resources ] 3.3 改 1）scale:[Set a new size for a Replication Controller] 2）exec:[Execute a command in a container] 3）attach:[Attach to a running container] 4）patch:[Update field(s) of a resource by stdin] 5）edit:[Edit a resource on the server] 6） label:[Update the labels on a resource] 7）annotate:[Auto-scale a replication controller] 8）replace:[Replace a resource by filename or stdin] 9）config:[config modifies kubeconfig files] 3.4 查 1）get:[Display one or many resources] 2）describe:[Show details of a specific resource or group of resources] 3）log:[Print the logs for a container in a pod] 4）cluster-info:[Display cluster info] 5） version:[Print the client and server version information] 6）api-versions:[Print the supported API versions] 4. Pod相关命令 4.1 查询Pod kubectl get pod -o wide --namespace= 4.2 进入Pod kubectl exec -it /bin/bash --namespace= # 进入Pod中指定容器 kubectl exec -it -c /bin/bash --namespace= 4.3 删除Pod kubectl delete pod --namespace= # 强制删除Pod，当Pod一直处于Terminating状态 kubectl delete pod --namespace= --force --grace-period=0 # 删除某个namespace下某个类型的所有对象 kubectl delete deploy --all --namespace=test 4.4 日志查看 $ 查看运行容器日志 kubectl logs --namespace= $ 查看上一个挂掉的容器日志 kubectl logs -p --namespace= 5. 常用命令 5.1. Node隔离与恢复 说明：Node设置隔离之后，原先运行在该Node上的Pod不受影响，后续的Pod不会调度到被隔离的Node上。 1. Node隔离 # cordon命令 kubectl cordon # 或者 kubectl patch node -p '{\"spec\":{\"unschedulable\":true}}' 2. Node恢复 # uncordon kubectl uncordon # 或者 kubectl patch node -p '{\"spec\":{\"unschedulable\":false}}' 5.2. kubectl label 1. 固定Pod到指定机器 kubectl label node namespace/=true 2. 取消Pod固定机器 kubectl label node namespace/- 5.3. 升级镜像 # 升级镜像 kubectl set image deployment/nginx nginx=nginx:1.15.12 -n nginx # 查看滚动升级情况 kubectl rollout status deployment/nginx -n nginx 5.4. 调整资源值 # 调整指定容器的资源值 kubectl set resources sts nginx-0 -c=agent --limits=memory=512Mi -n nginx 5.5. 调整readiness probe # 批量查看readiness probe timeoutSeconds kubectl get statefulset -o=jsonpath='{range .items[*]}{.metadata.name}{\"\\t\"}{.spec.template.spec.containers[0].readinessProbe.timeoutSeconds}{\"\\n\"}{end}' # 调整readiness probe timeoutSeconds参数 kubectl patch statefulset nginx-sts --type='json' -p='[{\"op\": \"replace\", \"path\": \"/spec/template/spec/containers/0/readinessProbe/timeoutSeconds\", \"value\":5}]' -n nginx 5.6. 调整tolerations属性 kubectl patch statefulset nginx-sts --patch '{\"spec\": {\"template\": {\"spec\": {\"tolerations\": [{\"effect\": \"NoSchedule\",\"key\": \"dedicated\",\"operator\": \"Equal\",\"value\": \"nginx\"}]}}}}' -n nginx 5.7. 查看所有节点的IP kubectl get nodes -o=jsonpath='{range .items[*]}{.metadata.name}{\"\\t\"}{.status.addresses[0].address}{\"\\n\"}{end}' 5.8. 查看当前k8s组件leader节点 当k8s集群高可用部署的时候，kube-controller-manager和kube-scheduler只能一个服务处于实际逻辑运行状态，通过参数--leader-elect=true来开启选举操作。以下提供查询leader节点的命令。 $ kubectl get endpoints kube-controller-manager --namespace=kube-system -o yaml apiVersion: v1 kind: Endpoints metadata: annotations: control-plane.alpha.kubernetes.io/leader: '{\"holderIdentity\":\"xxx.xxx.xxx.xxx_6537b938-7f5a-11e9-8487-00220d338975\",\"leaseDurationSeconds\":15,\"acquireTime\":\"2019-05-26T02:03:18Z\",\"renewTime\":\"2019-05-26T02:06:08Z\",\"leaderTransitions\":1}' creationTimestamp: \"2019-05-26T01:52:39Z\" name: kube-controller-manager namespace: kube-system resourceVersion: \"1965\" selfLink: /api/v1/namespaces/kube-system/endpoints/kube-controller-manager uid: f1755fc5-7f58-11e9-b4c4-00220d338975 以上表示\"holderIdentity\":\"xxx.xxx.xxx.xxx为kube-controller-manager的leader节点。 同理，可以通过以下命令查看kube-scheduler的leader节点。 kubectl get endpoints kube-scheduler --namespace=kube-system -o yaml 5.9. 修改副本数 kubectl scale deployment.v1.apps/nginx-deployment --replicas=10 5.10. 批量删除pod kubectl get po -n default |grep Evicted |awk '{print $1}' |xargs -I {} kubectl delete po {} -n default 5.11. 各种查看命令 # 不使用外部工具来输出解码后的 Secret kubectl get secret my-secret -o go-template='{{range $k,$v := .data}}{{\"### \"}}{{$k}}{{\"\\n\"}}{{$v|base64decode}}{{\"\\n\\n\"}}{{end}}' # 列出事件（Events），按时间戳排序 kubectl get events --sort-by=.metadata.creationTimestamp 5.12. 拷贝文件 从pod拷贝到本地 注意事项： pod的目录是workdir的相对路径，可以将文件拷贝到workdir下再拷贝出来 文件绝对路径前面不能加 / 文件目标位置不能为文件夹，必须为文件路径 kubectl cp -n -c : # 示例： # 将pod workdir下的prometheus.env.yaml文件拷贝到本地 kubectl cp -n prometheus -c prometheus prometheus-0:prometheus.env.yaml ./prometheus.env.yaml 从本地拷贝到pod 注意事项： 如果没有加路径，默认拷贝到pod内workdir路径。 kubectl cp -n -c : # 示例： kubectl cp ./prometheus.env.yaml -n prometheus -c prometheus prometheus-0:prometheus.env.yaml 5.13. 强制删除namespace 如果强制删除ns失败，可以使用以下命令删除，将以下的calico-system改为需要删除的namespace。 kubectl get namespaces calico-system -o json \\ | tr -d \"\\n\" | sed \"s/\\\"finalizers\\\": \\[[^]]\\+\\]/\\\"finalizers\\\": []/\" \\ | kubectl replace --raw /api/v1/namespaces/calico-system/finalize -f - 6. kubectl日志级别 Kubectl 日志输出详细程度是通过 -v 或者 --v 来控制的，参数后跟一个数字表示日志的级别。 Kubernetes 通用的日志习惯和相关的日志级别在 这里 有相应的描述。 详细程度 描述 --v=0 用于那些应该 始终 对运维人员可见的信息，因为这些信息一般很有用。 --v=1 如果您不想要看到冗余信息，此值是一个合理的默认日志级别。 --v=2 输出有关服务的稳定状态的信息以及重要的日志消息，这些信息可能与系统中的重大变化有关。这是建议大多数系统设置的默认日志级别。 --v=3 包含有关系统状态变化的扩展信息。 --v=4 包含调试级别的冗余信息。 --v=5 跟踪级别的详细程度。 --v=6 显示所请求的资源。 --v=7 显示 HTTP 请求头。 --v=8 显示 HTTP 请求内容。 --v=9 显示 HTTP 请求内容而且不截断内容。 参考文章： https://kubernetes.io/docs/reference/kubectl/overview/ https://kubernetes.io/docs/tasks/access-application-cluster/configure-access-multiple-clusters/ https://kubernetes.io/zh/docs/reference/kubectl/cheatsheet/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/kubectl/kubectl-alias.html":{"url":"operation/kubectl/kubectl-alias.html","title":"kubectl命令别名","keywords":"","body":"1. kubectl-aliases2. 示例3. 安装1. kubectl-aliases kubectl-aliases开源工具是由脚本通过拼接各种kubectl相关元素组成的alias命令别名列表，其中命令别名拼接元素如下： base [system?] [operation] [resource] [flags] kubectl -n=kube-system get describe rm:delete logs exec apply pods deployment secret ingress node svc ns cm oyaml ojson owide all watch file l k=kubectl sys=--namespace kube-system commands: g=get d=describe rm=delete a:apply -f ex: exec -i -t lo: logs -f resources: po=pod dep=deployment ing=ingress svc=service cm=configmap sec=secret ns=namespace no=node flags: output format: oyaml, ojson, owide all: --all or --all-namespaces depending on the command sl: --show-labels w=-w/--watch value flags (should be at the end): f=-f/--filename l=-l/--selector 2. 示例 # 示例1 kd → kubectl describe # 示例2 kgdepallw → kubectl get deployment —all-namespaces —watch alias get示例： alias k='kubectl' alias kg='kubectl get' alias kgpo='kubectl get pods' alias kgpoojson='kubectl get pods -o=json' alias kgpon='kubectl get pods --namespace' alias ksysgpooyamll='kubectl --namespace=kube-system get pods -o=yaml -l' 3. 安装 # 将 .kubectl_aliases下载到 home 目录 cd ~ && wget https://raw.githubusercontent.com/ahmetb/kubectl-aliases/master/.kubectl_aliases # 将以下内容添加到 .bashrc中，并执行 source .bashrc [ -f ~/.kubectl_aliases ] && source ~/.kubectl_aliases function kubectl() { command kubectl $@; } # 如果需要提示别名的完整命令，则将以下内容添加到 .bashrc中，并执行 source .bashrc [ -f ~/.kubectl_aliases ] && source ~/.kubectl_aliases function kubectl() { echo \"+ kubectl $@\"; command kubectl $@; } 参考： https://ahmet.im/blog/kubectl-aliases/ https://github.com/ahmetb/kubectl-aliases Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/kubectl/kubectl-node-shell.html":{"url":"operation/kubectl/kubectl-node-shell.html","title":"kubectl进入node shell","keywords":"","body":"1. 安装krew node-shell1.1. 安装krew1.2. 安装node-shell2. 进入节点的shell2.1. 登录node2.2. 退出node3. 原理3.1. Pod.yaml本文介绍如何通过kubectl进入节点的shell环境。 1. 安装krew node-shell 1.1. 安装krew ( set -x; cd \"$(mktemp -d)\" && OS=\"$(uname | tr '[:upper:]' '[:lower:]')\" && ARCH=\"$(uname -m | sed -e 's/x86_64/amd64/' -e 's/\\(arm\\)\\(64\\)\\?.*/\\1\\2/' -e 's/aarch64$/arm64/')\" && KREW=\"krew-${OS}_${ARCH}\" && curl -fsSLO \"https://github.com/kubernetes-sigs/krew/releases/latest/download/${KREW}.tar.gz\" && tar zxvf \"${KREW}.tar.gz\" && ./\"${KREW}\" install krew ) 在~/.bashrc或~/.zshrc添加以下命令 export PATH=\"${KREW_ROOT:-$HOME/.krew}/bin:$PATH\" 1.2. 安装node-shell node-shell的代码参考：kubectl-node-shell/kubectl-node_shell at master · kvaps/kubectl-node-shell · GitHub kubectl krew install node-shell 示例： # kubectl krew install node-shell Updated the local copy of plugin index. Installing plugin: node-shell Installed plugin: node-shell \\ | Use this plugin: | kubectl node-shell | Documentation: | https://github.com/kvaps/kubectl-node-shell | Caveats: | \\ | | You need to be allowed to start privileged pods in the cluster | / / WARNING: You installed plugin \"node-shell\" from the krew-index plugin repository. These plugins are not audited for security by the Krew maintainers. Run them at your own risk. 2. 进入节点的shell 2.1. 登录node 创建一个临时的特权容器，登录容器即登录node shell。 kubectl node-shell 示例： # kubectl node-shell node1 spawning \"nsenter-9yqytp\" on \"node1\" If you don't see a command prompt, try pressing enter. groups: cannot find name for group ID 11 To run a command as administrator (user \"root\"), use \"sudo \". See \"man sudo_root\" for details. root@node1:/# 2.2. 退出node 退出容器，容器会被自动删除。 # exit logout pod default/nsenter-9yqytp terminated (Error) pod \"nsenter-9yqytp\" deleted 3. 原理 容器是弱隔离，共享节点的内核，通过cgroup和namespace来实现进程级别的隔离。那么通过在特权容器里执行nsenter的命令，则可以通过登录特权容器来实现登录node的shell环境。 创建一个特权容器，进入node shell的命令为： nsenter --target 1 --mount --uts --ipc --net --pid -- bash -l 进入 node shell 的权限： hostPID: true 共享 host 的 pid hostNetwork: true 共享 host 的网络 privileged: true: PSP 权限策略是 privileged, 即完全无限制。 3.1. Pod.yaml apiVersion: v1 kind: Pod metadata: labels: run: nsenter-9yqytp name: nsenter-9yqytp namespace: default spec: containers: - command: - nsenter - --target - \"1\" - --mount - --uts - --ipc - --net - --pid - -- - bash - -l image: docker.io/library/alpine imagePullPolicy: Always name: nsenter resources: limits: cpu: 100m memory: 256Mi requests: cpu: 100m memory: 256Mi securityContext: privileged: true stdin: true stdinOnce: true tty: true volumeMounts: - mountPath: /var/run/secrets/kubernetes.io/serviceaccount name: kube-api-access-4ktlf readOnly: true enableServiceLinks: true hostNetwork: true hostPID: true nodeName: node1 preemptionPolicy: PreemptLowerPriority priority: 0 restartPolicy: Never schedulerName: default-scheduler securityContext: {} serviceAccount: default serviceAccountName: default tolerations: - key: CriticalAddonsOnly operator: Exists - effect: NoExecute operator: Exists volumes: - name: kube-api-access-4ktlf projected: defaultMode: 420 sources: - serviceAccountToken: expirationSeconds: 3607 path: token - configMap: items: - key: ca.crt path: ca.crt name: kube-root-ca.crt - downwardAPI: items: - fieldRef: apiVersion: v1 fieldPath: metadata.namespace path: namespace 创建完容器后，直接登录容器即可登录节点的shell kubectl exec -it nsenter-9yqytp bash 参考： 如何通过 kubectl 进入 node shell - 东风微鸣技术博客 GitHub - kvaps/kubectl-node-shell: Exec into node via kubectl https://krew.sigs.k8s.io/docs/user-guide/setup/install/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/helm/_index.html":{"url":"operation/helm/_index.html","title":"helm工具","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/helm/helm-usage.html":{"url":"operation/helm/helm-usage.html","title":"helm的使用","keywords":"","body":"1. 安装helm2. 基本概念3. helm命令4. 常用命令4.1. helm search4.2. helm install/uninstall4.3. helm upgrade4.4. helm rollback4.5. helm repo4.6. helm pull5. 创建chart5.1. 初始化chart5.2. 调试模板1. 安装helm curl https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash 2. 基本概念 Helm是用来管理k8s集群上的软件包。 Chart:代表helm软件包 Repository：软件包的存放仓库 Release:运行在k8s上的一个发布实例。 3. helm命令 Usage: helm [command] Available Commands: completion generate autocompletion scripts for the specified shell create create a new chart with the given name dependency manage a chart's dependencies env helm client environment information get download extended information of a named release help Help about any command history fetch release history install install a chart lint examine a chart for possible issues list list releases package package a chart directory into a chart archive plugin install, list, or uninstall Helm plugins pull download a chart from a repository and (optionally) unpack it in local directory push push a chart to remote registry login to or logout from a registry repo add, list, remove, update, and index chart repositories rollback roll back a release to a previous revision search search for a keyword in charts show show information of a chart status display the status of the named release template locally render templates test run tests for a release uninstall uninstall a release upgrade upgrade a release verify verify that a chart at the given path has been signed and is valid version print the client version information 4. 常用命令 4.1. helm search helm search hub：从 Artifact Hub 中查找并列出 helm charts。支持模糊匹配。 helm search hub wordpress helm search repo：基于指定仓库进行搜索。 helm repo add brigade https://brigadecore.github.io/charts helm search repo brigade # 列出所有版本 helm search repo apisix -l 4.2. helm install/uninstall helm install # 示例 helm install happy-panda bitnami/wordpress - # uninstall helm uninstall RELEASE_NAME 安装自定义chart helm install -f values.yaml bitnami/wordpress --generate-name # 本地 chart 压缩包 helm install foo foo-0.1.1.tgz # 解压后的 chart 目录 helm install foo path/to/foo # 完整的 URL helm install foo https://example.com/charts/foo-1.2.3.tgz 4.3. helm upgrade helm upgrade happy-panda bitnami/wordpress 4.4. helm rollback helm rollback [REVISION] [flags] 4.5. helm repo helm repo add dev https://example.com/dev-charts helm repo list helm repo remove 4.6. helm pull 从仓库下载并（可选）在本地目录解压。 helm pull [chart URL | repo/chartname] helm pull [chart URL | repo/chartname] --version 5. 创建chart 5.1. 初始化chart helm create mychart 查看生成的文件目录： mychart |-- charts # 目录用于存放所依赖的子chart |-- Chart.yaml # 描述这个 Chart 的相关信息、包括名字、描述信息、版本等 |-- templates | |-- deployment.yaml | |-- _helpers.tpl # 模板助手文件，定义的值可在模板中使用 | |-- hpa.yaml | |-- ingress.yaml | |-- NOTES.txt # Chart 部署到集群后的一些信息，例如：如何使用、列出缺省值 | |-- serviceaccount.yaml | |-- service.yaml | `-- tests | `-- test-connection.yaml `-- values.yaml # 模板的值文件，这些值会在安装时应用到 GO 模板生成部署文件 移除默认模板文件，并添加自己的模板文件。 rm -rf mysubchart/templates/* 5.2. 调试模板 helm lint 是验证chart是否遵循最佳实践的首选工具。 helm template --debug 在本地测试渲染chart模板。 helm install --dry-run --debug：我们已经看到过这个技巧了，这是让服务器渲染模板的好方法，然后返回生成的清单文件。 helm get manifest: 这是查看安装在服务器上的模板的好方法。 参考： Helm | 安装Helm Helm | 使用Helm Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/node/_index.html":{"url":"operation/node/_index.html","title":"节点迁移","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/node/safely-drain-node.html":{"url":"operation/node/safely-drain-node.html","title":"安全迁移节点","keywords":"","body":"1. 迁移Pod1.1. 设置节点是否可调度1.2. 执行kubectl drain命令1.3. 特别说明2. kubectl drain 流程图3. TroubleShooting4. kubectl drain1. 迁移Pod 1.1. 设置节点是否可调度 确定需要迁移和被迁移的节点，将不允许被迁移的节点设置为不可调度。 # 查看节点 kubectl get nodes # 设置节点为不可调度 kubectl cordon # 设置节点为可调度 kubectl uncordon 1.2. 执行kubectl drain命令 # 驱逐节点的所有pod kubectl drain --force --ignore-daemonsets # 驱逐指定节点的pod kubectl drain --ignore-daemonsets --pod-selector=pod-template-hash=88964949c 示例： $ kubectl drain bjzw-prek8sredis-99-40 --force --ignore-daemonsets node \"bjzw-prek8sredis-99-40\" already cordoned WARNING: Deleting pods not managed by ReplicationController, ReplicaSet, Job, DaemonSet or StatefulSet: kube-proxy-bjzw-prek8sredis-99-40; Ignoring DaemonSet-managed pods: calicoopsmonitor-mfpqs, arachnia-agent-j56n8 pod \"pre-test-pro2-r-0-redis-2-8-19-1\" evicted pod \"pre-test-hwh1-r-8-redis-2-8-19-2\" evicted pod \"pre-eos-hdfs-vector-eos-hdfs-redis-2-8-19-0\" evicted 1.3. 特别说明 对于statefulset创建的Pod，kubectl drain的说明如下： kubectl drain操作会将相应节点上的旧Pod删除，并在可调度节点上面起一个对应的Pod。当旧Pod没有被正常删除的情况下，新Pod不会起来。例如：旧Pod一直处于Terminating状态。 对应的解决方式是通过重启相应节点的kubelet，或者强制删除该Pod。 示例： # 重启发生`Terminating`节点的kubelet systemctl restart kubelet # 强制删除`Terminating`状态的Pod kubectl delete pod --namespace= --force --grace-period=0 2. kubectl drain 流程图 3. TroubleShooting 1、存在不是通过ReplicationController, ReplicaSet, Job, DaemonSet 或者StatefulSet创建的Pod（即静态pod，通过文件方式创建的），所以需要设置强制执行的参数--force。 $ kubectl drain bjzw-prek8sredis-99-40 node \"bjzw-prek8sredis-99-40\" already cordoned error: unable to drain node \"bjzw-prek8sredis-99-40\", aborting command... There are pending nodes to be drained: bjzw-prek8sredis-99-40 error: DaemonSet-managed pods (use --ignore-daemonsets to ignore): calicoopsmonitor-mfpqs, arachnia-agent-j56n8; pods not managed by ReplicationController, ReplicaSet, Job, DaemonSet or StatefulSet (use --force to override): kube-proxy-bjzw-prek8sredis-99-40 2、存在DaemonSet方式管理的Pod，需要设置--ignore-daemonsets参数忽略报错。 $ kubectl drain bjzw-prek8sredis-99-40 --force node \"bjzw-prek8sredis-99-40\" already cordoned error: unable to drain node \"bjzw-prek8sredis-99-40\", aborting command... There are pending nodes to be drained: bjzw-prek8sredis-99-40 error: DaemonSet-managed pods (use --ignore-daemonsets to ignore): calicoopsmonitor-mfpqs, arachnia-agent-j56n8 4. kubectl drain $ kubectl drain --help Drain node in preparation for maintenance. The given node will be marked unschedulable to prevent new pods from arriving. 'drain' evicts the pods if the API server supports https://kubernetes.io/docs/concepts/workloads/pods/disruptions/ . Otherwise, it will use normal DELETE to delete the pods. The 'drain' evicts or deletes all pods except mirror pods (which cannot be deleted through the API server). If there are daemon set-managed pods, drain will not proceed without --ignore-daemonsets, and regardless it will not delete any daemon set-managed pods, because those pods would be immediately replaced by the daemon set controller, which ignores unschedulable markings. If there are any pods that are neither mirror pods nor managed by a replication controller, replica set, daemon set, stateful set, or job, then drain will not delete any pods unless you use --force. --force will also allow deletion to proceed if the managing resource of one or more pods is missing. 'drain' waits for graceful termination. You should not operate on the machine until the command completes. When you are ready to put the node back into service, use kubectl uncordon, which will make the node schedulable again. https://kubernetes.io/images/docs/kubectl_drain.svg Examples: # Drain node \"foo\", even if there are pods not managed by a replication controller, replica set, job, daemon set or stateful set on it kubectl drain foo --force # As above, but abort if there are pods not managed by a replication controller, replica set, job, daemon set or stateful set, and use a grace period of 15 minutes kubectl drain foo --grace-period=900 Options: --chunk-size=500: Return large lists in chunks rather than all at once. Pass 0 to disable. This flag is beta and may change in the future. --delete-emptydir-data=false: Continue even if there are pods using emptyDir (local data that will be deleted when the node is drained). --disable-eviction=false: Force drain to use delete, even if eviction is supported. This will bypass checking PodDisruptionBudgets, use with caution. --dry-run='none': Must be \"none\", \"server\", or \"client\". If client strategy, only print the object that would be sent, without sending it. If server strategy, submit server-side request without persisting the resource. --force=false: Continue even if there are pods not managed by a ReplicationController, ReplicaSet, Job, DaemonSet or StatefulSet. --grace-period=-1: Period of time in seconds given to each pod to terminate gracefully. If negative, the default value specified in the pod will be used. --ignore-daemonsets=false: Ignore DaemonSet-managed pods. --ignore-errors=false: Ignore errors occurred between drain nodes in group. --pod-selector='': Label selector to filter pods on the node -l, --selector='': Selector (label query) to filter on --skip-wait-for-delete-timeout=0: If pod DeletionTimestamp older than N seconds, skip waiting for the pod. Seconds must be greater than 0 to skip. --timeout=0s: The length of time to wait before giving up, zero means infinite Usage: kubectl drain NODE [options] Use \"kubectl options\" for a list of global command-line options (applies to all commands). 参考文档： https://kubernetes.io/docs/tasks/administer-cluster/safely-drain-node/ https://kubernetes.io/docs/tasks/run-application/configure-pdb/ https://kubernetes.io/docs/reference/generated/kubectl/kubectl-commands#drain Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/node/nodeselector-and-taint.html":{"url":"operation/node/nodeselector-and-taint.html","title":"指定Node调度与隔离","keywords":"","body":"1. NodeSelector1.1. 概念1.2. 使用方式1.2.1. 给Node打标签1.2.2. 给Pod设置NodeSelector1.3. 亲和性（Affinity）和反亲和性（Anti-affinity）2. Taint 和 Toleration2.1. 概念2.2. 使用方式2.2.1. kubectl taint2.2.2. 匹配规则：2.2.3. effect的类型2.3. 使用场景2.3.1. 专用节点2.3.2. 特殊硬件节点2.3.3. 基于taint驱逐1. NodeSelector 1.1. 概念 如果需要限制Pod到指定的Node上运行，则可以给Node打标签并给Pod配置NodeSelector。 1.2. 使用方式 1.2.1. 给Node打标签 # get node的name kubectl get nodes # 设置Label kubectl label nodes = # 例如 kubectl label nodes node-1 disktype=ssd # 查看Node的Label kubectl get nodes --show-labels # 删除Node的label kubectl label node - 1.2.2. 给Pod设置NodeSelector apiVersion: v1 kind: Pod metadata: name: nginx labels: env: test spec: containers: - name: nginx image: nginx imagePullPolicy: IfNotPresent nodeSelector: disktype: ssd # 对应Node的Label 1.3. 亲和性（Affinity）和反亲和性（Anti-affinity） 待补充 2. Taint 和 Toleration 2.1. 概念 nodeSelector可以通过打标签的形式让Pod被调度到指定的Node上，Taint则相反，它使节点能够排斥一类特定的Pod，除非Pod被指定了toleration的标签。（taint即污点，Node被打上污点；只有容忍[toleration]这些污点的Pod才可能被调度到该Node）。 2.2. 使用方式 2.2.1. kubectl taint # 给节点增加一个taint，它的key是，value是，effect是NoSchedule。 kubectl taint nodes =:NoSchedule 只有拥有和这个taint相匹配的toleration的pod才能够被分配到 node_name 这个节点。 例如，在 PodSpec 中定义 pod 的 toleration： tolerations: - key: \"key\" operator: \"Equal\" value: \"value\" effect: \"NoSchedule\" tolerations: - key: \"key\" operator: \"Exists\" effect: \"NoSchedule\" 2.2.2. 匹配规则： 一个 toleration 和一个 taint 相“匹配”是指它们有一样的 key 和 effect ，并且： 如果 operator 是 Exists （此时 toleration 不能指定 value） 如果 operator 是 Equal ，则它们的 value 应该相等 特殊情况： 如果一个 toleration 的 key 为空且 operator 为 Exists ，表示这个 toleration 与任意的 key 、 value 和 effect 都匹配，即这个 toleration 能容忍任意 taint。 tolerations: - operator: \"Exists\" 如果一个 toleration 的 effect 为空，则 key 值与之相同的相匹配 taint 的 effect 可以是任意值。 tolerations: - key: \"key\" operator: \"Exists\" 一个节点可以设置多个taint，一个pod也可以设置多个toleration。Kubernetes 处理多个 taint 和 toleration 的过程就像一个过滤器：从一个节点的所有 taint 开始遍历，过滤掉那些 pod 中存在与之相匹配的 toleration 的 taint。余下未被过滤的 taint 的 effect 值决定了 pod 是否会被分配到该节点，特别是以下情况： 如果未被过滤的 taint 中存在一个以上 effect 值为 NoSchedule 的 taint，则 Kubernetes 不会将 pod 分配到该节点。 如果未被过滤的 taint 中不存在 effect 值为 NoSchedule 的 taint，但是存在 effect 值为 PreferNoSchedule 的 taint，则 Kubernetes 会尝试将 pod 分配到该节点。 如果未被过滤的 taint 中存在一个以上 effect 值为 NoExecute 的 taint，则 Kubernetes 不会将 pod 分配到该节点（如果 pod 还未在节点上运行），或者将 pod 从该节点驱逐（如果 pod 已经在节点上运行）。 2.2.3. effect的类型 NoSchedule：只有拥有和这个 taint 相匹配的 toleration 的 pod 才能够被分配到这个节点。 PreferNoSchedule：系统会尽量避免将 pod 调度到存在其不能容忍 taint 的节点上，但这不是强制的。 NoExecute ：任何不能忍受这个 taint 的 pod 都会马上被驱逐，任何可以忍受这个 taint 的 pod 都不会被驱逐。Pod可指定属性 tolerationSeconds 的值，表示pod 还能继续在节点上运行的时间。 tolerations: - key: \"key1\" operator: \"Equal\" value: \"value1\" effect: \"NoExecute\" tolerationSeconds: 3600 2.3. 使用场景 2.3.1. 专用节点 kubectl taint nodes dedicated=:NoSchedule 先给Node添加taint，然后给Pod添加相对应的 toleration，则该Pod可调度到taint的Node，也可调度到其他节点。 如果想让Pod只调度某些节点且某些节点只接受对应的Pod，则需要在Node上添加Label（例如：dedicated=groupName），同时给Pod的nodeSelector添加对应的Label。 2.3.2. 特殊硬件节点 如果某些节点配置了特殊硬件（例如CPU），希望不使用这些特殊硬件的Pod不被调度该Node，以便保留必要资源。即可给Node设置taint和label，同时给Pod设置toleration和label来使得这些Node专门被指定Pod使用。 # kubectl taint kubectl taint nodes nodename special=true:NoSchedule # 或者 kubectl taint nodes nodename special=true:PreferNoSchedule 2.3.3. 基于taint驱逐 effect 值 NoExecute ，它会影响已经在节点上运行的 pod，即根据策略对Pod进行驱逐。 如果 pod 不能忍受effect 值为 NoExecute 的 taint，那么 pod 将马上被驱逐 如果 pod 能够忍受effect 值为 NoExecute 的 taint，但是在 toleration 定义中没有指定 tolerationSeconds，则 pod 还会一直在这个节点上运行。 如果 pod 能够忍受effect 值为 NoExecute 的 taint，而且指定了 tolerationSeconds，则 pod 还能在这个节点上继续运行这个指定的时间长度。 参考： https://kubernetes.io/docs/concepts/configuration/assign-pod-node/ https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/registry/_index.html":{"url":"operation/registry/_index.html","title":"镜像仓库","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/registry/config-private-registry.html":{"url":"operation/registry/config-private-registry.html","title":"配置私有的镜像仓库","keywords":"","body":"1. 镜像仓库的基本操作1.1. 登录镜像仓库1.2. 拉取镜像1.3. 推送镜像1.4. 重命名镜像2. docker.xxx.com镜像仓库2.1. 所有节点配置insecure-registries2.2. 所有节点配置/var/lib/kubelet/config.json2.3. 创建docker.xxx.com镜像的pod1. 镜像仓库的基本操作 1.1. 登录镜像仓库 docker login -u -p 1.2. 拉取镜像 docker pull https://registry.xxx.com/dev/nginx:latest 1.3. 推送镜像 docker push https://registry.xxx.com/dev/nginx:latest 1.4. 重命名镜像 docker tag 2. docker.xxx.com镜像仓库 使用docker.xxx.com镜像仓库。 2.1. 所有节点配置insecure-registries #cat /etc/docker/daemon.json { \"data-root\": \"/data/docker\", \"debug\": false, \"insecure-registries\": [ ... \"docker.xxx.com:8080\" ], ... } 2.2. 所有节点配置/var/lib/kubelet/config.json 具体参考：configuring-nodes-to-authenticate-to-a-private-registry 在某个节点登录docker.xxx.com:8080镜像仓库，会更新 $HOME/.docker/config.json 检查$HOME/.docker/config.json是否有该镜像仓库的auth信息。 #cat ~/.docker/config.json { \"auths\": { \"docker.xxx.com:8080\": { \"auth\": \"\" } }, \"HttpHeaders\": { \"User-Agent\": \"Docker-Client/18.09.9 (linux)\" } } 将$HOME/.docker/config.json拷贝到所有的Node节点上的/var/lib/kubelet/config.json。 # 获取所有节点的IP nodes=$(kubectl get nodes -o jsonpath='{range .items[*].status.addresses[?(@.type==\"ExternalIP\")]}{.address} {end}') # 拷贝到所有节点 for n in $nodes; do scp ~/.docker/config.json root@$n:/var/lib/kubelet/config.json; done 2.3. 创建docker.xxx.com镜像的pod 指定镜像为：docker.xxx.com:8080/public/2048:latest 完整pod.yaml apiVersion: apps/v1beta2 kind: Deployment metadata: annotations: deployment.kubernetes.io/revision: \"1\" generation: 1 labels: k8s-app: dockeroa-hub qcloud-app: dockeroa-hub name: dockeroa-hub namespace: test spec: progressDeadlineSeconds: 600 replicas: 3 revisionHistoryLimit: 10 selector: matchLabels: k8s-app: dockeroa-hub qcloud-app: dockeroa-hub strategy: rollingUpdate: maxSurge: 25% maxUnavailable: 25% type: RollingUpdate template: metadata: labels: k8s-app: dockeroa-hub qcloud-app: dockeroa-hub spec: containers: - image: docker.xxx.com:8080/public/2048:latest imagePullPolicy: Always name: game resources: limits: cpu: 500m memory: 1Gi requests: cpu: 250m memory: 256Mi terminationMessagePath: /dev/termination-log terminationMessagePolicy: File dnsPolicy: ClusterFirst restartPolicy: Always nodeName: 192.168.1.1 schedulerName: default-scheduler securityContext: {} terminationGracePeriodSeconds: 30 查看pod状态 #kgpoowide -n game NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES docker-oa-757bbbddb5-h6j7m 1/1 Running 0 14m 192.168.2.51 192.168.1.1 docker-oa-757bbbddb5-jp5dw 1/1 Running 0 14m 192.168.1.32 192.168.1.2 docker-oa-757bbbddb5-nlw9f 1/1 Running 0 14m 192.168.0.43 192.168.1.3 参考： https://kubernetes.io/docs/concepts/containers/images/#configuring-nodes-to-authenticate-to-a-private-registry Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/registry/ImagePullSecrets.html":{"url":"operation/registry/ImagePullSecrets.html","title":"拉取私有镜像","keywords":"","body":"1. 创建secret2. 添加ImagePullSecrets到serviceAccount3. 创建带有imagePullSecrets的pod4. 说明 本文介绍通过pod指定 ImagePullSecrets来拉取私有镜像仓库的镜像 1. 创建secret secret是namespace级别的，创建时候需要指定namespace。 kubectl create secret docker-registry --docker-server=DOCKER_REGISTRY_SERVER --docker-username=DOCKER_USER --docker-password=DOCKER_PASSWORD -n 2. 添加ImagePullSecrets到serviceAccount 可以通过将ImagePullSecrets到serviceAccount的方式来自动给pod添加imagePullSecrets参数值。 serviceAccount同样是namespace级别，只对该namespace生效。 #kubectl get secrets -n dev NAME TYPE DATA AGE docker.xxxx.com kubernetes.io/dockerconfigjson 1 6h23m 将ImagePullSecrets添加到serviceAccount对象中。 默认serviceAccount对象如下 #kubectl get serviceaccount default -n dev -o yaml apiVersion: v1 kind: ServiceAccount metadata: creationTimestamp: \"2020-02-27T03:30:38Z\" name: default namespace: dev resourceVersion: \"11651567\" selfLink: /api/v1/namespaces/dev/serviceaccounts/default uid: 85bcdd31-5911-11ea-9429-6c92bf3b7c33 secrets: - name: default-token-s7wfn 编辑或修改serviceAccount内容，增加imagePullSecrets字段。 imagePullSecrets: - name: docker.xxxx.com kubectl edit serviceaccount default -n dev 修改后内容为： apiVersion: v1 kind: ServiceAccount metadata: creationTimestamp: \"2020-02-27T03:30:38Z\" name: default namespace: dev resourceVersion: \"11651567\" selfLink: /api/v1/namespaces/dev/serviceaccounts/default uid: 85bcdd31-5911-11ea-9429-6c92bf3b7c33 secrets: - name: default-token-s7wfn imagePullSecrets: - name: docker.xxxx.com 其中imagePullSecrets字段是一个数组，可以配置多个镜像仓库的账号密码。 例如： apiVersion: v1 kind: ServiceAccount ... imagePullSecrets: - name: docker.xxxx.com - name: docker.test.xxxx.com 3. 创建带有imagePullSecrets的pod 如果已经执行了第二步操作，添加ImagePullSecrets到serviceAccount，则无需在pod中指定imagePullSecrets参数，默认会自动添加。 如果没有添加ImagePullSecrets到serviceAccount，则在pod中指定imagePullSecrets参数引用创建的镜像仓库的secret。 spec: imagePullSecrets: - name: docker.xxxx.com 4. 说明 由于secret和serviceaccount对象是对namespace级别生效，因此不同的namespace需要再次创建和更新这两个对象。该场景适合不同用户具有独立的镜像仓库的密码，可以通过该方式创建不同的镜像密码使用的secret来拉取不同的镜像部署。 参考： https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod https://kubernetes.io/docs/tasks/configure-pod-container/configure-service-account/#add-imagepullsecrets-to-a-service-account Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/access/_index.html":{"url":"operation/access/_index.html","title":"访问控制","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/access/rbac-auth.html":{"url":"operation/access/rbac-auth.html","title":"使用RBAC鉴权","keywords":"","body":"1. RBAC介绍2. API对象3. 权限3.1. 命名空间权限[Role]3.2. 集群级别权限[ClusterRole]3.3. 默认权限角色4. 用户4.1. ServiceAccount4.2. Secret5. 授权5.1. 授权命名空间权限[RoleBinding]5.2. 授权集群级别权限[ClusterRoleBinding] 本文基于https://kubernetes.io/zh-cn/docs/reference/access-authn-authz/rbac/ 整理。 1. RBAC介绍 基于角色的访问控制【Role-based access control (RBAC)】是一种基于组织中用户的角色来调节控制对 计算机或网络资源的访问的方法。 RBAC 鉴权机制使用 rbac.authorization.k8s.io API 组来驱动鉴权决定， 允许你通过 Kubernetes API 动态配置策略。 要启用 RBAC，在启动 API 服务器时将 --authorization-mode 参数设置为一个逗号分隔的列表并确保其中包含 RBAC。 kube-apiserver --authorization-mode=Example,RBAC -- -- 编写自定义CRD控制器或部署其他开源组件时，经常需要给组件配置RBAC权限。 理解RBAC权限体系，只需要理解以下三个概念对象即可： 【权限】Role：角色，它其实是一组规则，定义了一组对 Kubernetes API 对象的操作权限。 【用户】Subject：被作用者，既可以是“人”，也可以是“机器”，也可以是你在 Kubernetes 里定义的“用户”。 【授权】RoleBinding：定义了“被作用者”和“角色”的绑定关系。 一句话理解RBAC，就是将定义的权限与定义的用户之间的关系进行绑定，即授权某个用户某些权限。 快速授权脚本可以参考：https://github.com/huweihuang/kubeadm-scripts/tree/main/kubeconfig/token 2. API对象 角色（权限）---角色（权限）绑定---用户（subject） 集群级别范围 命名空间范围 权限 ClusterRole Role 授权 ClusterRoleBinding RoleBinding 用户 ServiceAccout 以下从权限、用户、授权三个概念进行说明。完成一套授权逻辑，主要分为三个步骤 创建权限，即创建Role或ClusterRole对象。 创建用户，即创建ServiceAccount对象。 分配权限，即创建RoleBinding或ClusterRoleBinding对象。 3. 权限 RBAC 的 Role 或 ClusterRole 中包含一组代表相关权限的规则。 这些权限是纯粹累加的（不存在拒绝某操作的规则）。 3.1. 命名空间权限[Role] Role是针对指定namespace的权限，即创建的时候需要指定namespace。 示例： apiVersion: rbac.authorization.k8s.io/v1 kind: Role metadata: namespace: default name: pod-reader rules: - apiGroups: [\"\"] # \"\" 标明 core API 组 resources: [\"pods\"] verbs: [\"get\", \"watch\", \"list\"] 3.2. 集群级别权限[ClusterRole] ClusterRole用于指定集群内的资源： 集群范围资源（比如节点（Node）） 非资源端点（比如 /healthz） 跨名字空间访问的名字空间作用域的资源（如 访问所有namespace下的Pod） 示例： apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: # \"namespace\" 被忽略，因为 ClusterRoles 不受名字空间限制 name: secret-reader rules: - apiGroups: [\"\"] # 在 HTTP 层面，用来访问 Secret 资源的名称为 \"secrets\" resources: [\"secrets\"] verbs: [\"get\", \"watch\", \"list\"] 3.3. 默认权限角色 在 Kubernetes 中已经内置了很多个为系统保留的 ClusterRole，它们的名字都以system:开头。可以通过 kubectl get clusterroles 查看到它们。 超级用户（Super-User）角色（cluster-admin）、 使用 ClusterRoleBinding 在集群范围内完成授权的角色（cluster-status）、 以及使用 RoleBinding 在特定名字空间中授予的角色（admin、edit、view）。 默认 ClusterRole 默认 ClusterRoleBinding 描述 cluster-admin system:masters 组 允许超级用户在平台上的任何资源上执行所有操作。 当在 ClusterRoleBinding 中使用时，可以授权对集群中以及所有名字空间中的全部资源进行完全控制。 当在 RoleBinding 中使用时，可以授权控制角色绑定所在名字空间中的所有资源，包括名字空间本身。 admin 无 允许管理员访问权限，旨在使用 RoleBinding 在名字空间内执行授权。如果在 RoleBinding 中使用，则可授予对名字空间中的大多数资源的读/写权限， 包括创建角色和角色绑定的能力。 此角色不允许对资源配额或者名字空间本身进行写操作。 此角色也不允许对 Kubernetes v1.22+ 创建的 Endpoints 进行写操作。 更多信息参阅 “Endpoints 写权限”小节。 edit 无 允许对名字空间的大多数对象进行读/写操作。此角色不允许查看或者修改角色或者角色绑定。 不过，此角色可以访问 Secret，以名字空间中任何 ServiceAccount 的身份运行 Pod， 所以可以用来了解名字空间内所有服务账户的 API 访问级别。 此角色也不允许对 Kubernetes v1.22+ 创建的 Endpoints 进行写操作。 更多信息参阅 “Endpoints 写操作”小节。 view 无 允许对名字空间的大多数对象有只读权限。 它不允许查看角色或角色绑定。此角色不允许查看 Secrets，因为读取 Secret 的内容意味着可以访问名字空间中 ServiceAccount 的凭据信息，进而允许利用名字空间中任何 ServiceAccount 的身份访问 API（这是一种特权提升）。 4. 用户 4.1. ServiceAccount 创建指定namespace的ServiceAccount对象。ServiceAccount可以在pod中被使用。 apiVersion: v1 kind: ServiceAccount metadata: namespace: name: 4.2. Secret 创建secret，绑定serviceaccount，会自动生成token。 k8s 1.24后的版本不再自动生成secret，绑定后当删除ServiceAccount时会自动删除secret apiVersion: v1 kind: Secret metadata: name: namespace: annotations: kubernetes.io/service-account.name: \"ServiceAccountName\" type: kubernetes.io/service-account-token 5. 授权 5.1. 授权命名空间权限[RoleBinding] RoleBinding角色绑定（Role Binding）是将角色中定义的权限赋予一个或者一组用户。 它包含若干 主体（用户、组或服务账户）的列表和对这些主体所获得的角色的引用。 RoleBinding 在指定的名字空间中执行授权，而 ClusterRoleBinding 在集群范围执行授权。 字段说明： subjects：表示权限所授予的用户，包括ServiceAccount，Group，User。 roleRef：表示权限对应的角色，包括Role，ClusterRole。 示例： kind: RoleBinding apiVersion: rbac.authorization.k8s.io/v1 metadata: name: ${USER}-rolebinding namespace: ${NAMESPACE} subjects: - kind: ServiceAccount name: ${ServiceAccountName} namespace: ${ServiceAccountNS} roleRef: kind: ClusterRole name: ${ROLE} apiGroup: rbac.authorization.k8s.io 可以给指定命名空间下的serviceaccount授权其他命名空间的权限。只需要新增RoleBinding在预授权的命名空间下即可。可以理解为可以给一个用户分配多个命名空间的权限。 #!/bin/bash set -e # 给已存在的用户USER 添加其他NAMESPACE的权限 USER=$1 NAMESPACE=$2 ROLE=$3 ROLE=${ROLE:-edit} ServiceAccountName=\"${USER}-user\" ServiceAccountNS=\"kubernetes-dashboard\" cat 5.2. 授权集群级别权限[ClusterRoleBinding] 要跨整个集群完成访问权限的授予，可以使用一个 ClusterRoleBinding。 示例： apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: ${USER} subjects: - kind: ServiceAccount name: ${USER} namespace: ${NAMESPACE} roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: ${ROLE} 参考： 使用 RBAC 鉴权 | Kubernetes 用户认证 | Kubernetes k8s serviceaccount创建后没有生成对应的secret Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/deployment/_index.html":{"url":"operation/deployment/_index.html","title":"版本发布","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"operation/deployment/canary-deployment.html":{"url":"operation/deployment/canary-deployment.html","title":"金丝雀发布","keywords":"","body":"Deployment配置金丝雀发布1. 设置发版节奏2. 升级版本并暂停3. 查看升级状态4. 恢复继续发版5. 回滚发布5.1. 回滚上一个版本5.2. 查看历史版本5.3. 回滚指定版本Deployment配置金丝雀发布 金丝雀发布是指控制更新过程中的滚动节奏，通过“暂停”（pause）或“继续”（resume）更新发布操作。通过一小部分的版本发布实例来观察新版本是否有异常，如果没有异常则依次发布剩余的实例。 1. 设置发版节奏 主要是两个字段的设置： maxSurge：最大发版实例数，可以创建的超出期望 Pod 个数的 Pod 数量。可以是百分比或者是数字。 maxUnavailable：最大不可用实例数。可以是百分比或数字。 # 例如将maxSurge设置为1，maxUnavailable设置为0 $ kubectl patch deployment myapp-deploy -p '{\"spec\": {\"strategy\": {\"rollingUpdate\": {\"maxSurge\": 1, \"maxUnavailable\": 0}}}}' 2. 升级版本并暂停 kubectl set image deployment myapp-deploy myapp=kubernetes/myapp:v3 && \\ kubectl rollout pause deployments myapp-deploy 3. 查看升级状态 $ kubectl rollout status deployment myapp-deploy Waiting for deployment \"myapp-deploy\" rollout to finish: 1 out of 3 new replicas have been updated... 4. 恢复继续发版 观察灰度的实例的流量是否正常，如果正常则继续发版，如果不正常则回滚之前的升级。 $ kubectl rollout resume deployments myapp-deploy 5. 回滚发布 5.1. 回滚上一个版本 kubectl rollout undo deployments myapp-deploy 5.2. 查看历史版本 $ kubectl rollout history deployment myapp-deploy deployment.apps/myapp-deploy REVISION CHANGE-CAUSE 3 5 6 5.3. 回滚指定版本 kubectl rollout undo deployment myapp-deploy --to-revision 3 参考： https://kubernetes.io/zh-cn/docs/concepts/workloads/controllers/deployment/ https://kubernetes.io/zh-cn/docs/concepts/cluster-administration/manage-deployment/#canary-deployments https://kubernetes.renkeju.com/chapter_5/5.3.4.Canary_release.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"develop/client-go.html":{"url":"develop/client-go.html","title":"client-go的使用及源码分析","keywords":"","body":"1. client-go简介1.1 client-go说明1.2 示例代码1.3 运行结果2. client-go源码分析2.1 kubeconfig2.2 rest.config2.3 clientset2.3.1 NewForConfig2.3.2 clientset的结构体2.3.3 clientset.Interface2.4 CoreV1Client2.4.1 corev1.NewForConfig2.4.2 CoreV1Client结构体2.4.3 CoreV1Interface2.4.4 PodsGetter2.5 RESTClient2.5.1 rest.RESTClientFor2.5.2 NewRESTClient2.5.3 RESTClient结构体2.5.4 RESTClient.Interface2.6 总结3. client-go对k8s资源的调用3.1 deployment3.2 service3.3 ingress3.4 replicaSet3.5 pod3.6 statefulset1. client-go简介 1.1 client-go说明 ​ client-go是一个调用kubernetes集群资源对象API的客户端，即通过client-go实现对kubernetes集群中资源对象（包括deployment、service、ingress、replicaSet、pod、namespace、node等）的增删改查等操作。大部分对kubernetes进行前置API封装的二次开发都通过client-go这个第三方包来实现。 ​ client-go官方文档：https://github.com/kubernetes/client-go 1.2 示例代码 git clone https://github.com/huweihuang/client-go.git cd client-go #保证本地HOME目录有配置kubernetes集群的配置文件 go run client-go.go client-go.go package main import ( \"flag\" \"fmt\" \"os\" \"path/filepath\" \"time\" metav1 \"k8s.io/apimachinery/pkg/apis/meta/v1\" \"k8s.io/client-go/kubernetes\" \"k8s.io/client-go/tools/clientcmd\" ) func main() { var kubeconfig *string if home := homeDir(); home != \"\" { kubeconfig = flag.String(\"kubeconfig\", filepath.Join(home, \".kube\", \"config\"), \"(optional) absolute path to the kubeconfig file\") } else { kubeconfig = flag.String(\"kubeconfig\", \"\", \"absolute path to the kubeconfig file\") } flag.Parse() // uses the current context in kubeconfig config, err := clientcmd.BuildConfigFromFlags(\"\", *kubeconfig) if err != nil { panic(err.Error()) } // creates the clientset clientset, err := kubernetes.NewForConfig(config) if err != nil { panic(err.Error()) } for { pods, err := clientset.CoreV1().Pods(\"\").List(metav1.ListOptions{}) if err != nil { panic(err.Error()) } fmt.Printf(\"There are %d pods in the cluster\\n\", len(pods.Items)) time.Sleep(10 * time.Second) } } func homeDir() string { if h := os.Getenv(\"HOME\"); h != \"\" { return h } return os.Getenv(\"USERPROFILE\") // windows } 1.3 运行结果 ➜ go run client-go.go There are 9 pods in the cluster There are 7 pods in the cluster There are 7 pods in the cluster There are 7 pods in the cluster There are 7 pods in the cluster 2. client-go源码分析 client-go源码：https://github.com/kubernetes/client-go client-go源码目录结构 The kubernetes package contains the clientset to access Kubernetes API. The discovery package is used to discover APIs supported by a Kubernetes API server. The dynamic package contains a dynamic client that can perform generic operations on arbitrary Kubernetes API objects. The transport package is used to set up auth and start a connection. The tools/cache package is useful for writing controllers. 2.1 kubeconfig kubeconfig = flag.String(\"kubeconfig\", filepath.Join(home, \".kube\", \"config\"), \"(optional) absolute path to the kubeconfig file\") 获取kubernetes配置文件kubeconfig的绝对路径。一般路径为$HOME/.kube/config。该文件主要用来配置本地连接的kubernetes集群。 config内容如下： apiVersion: v1 clusters: - cluster: server: http://:8080 name: k8s contexts: - context: cluster: k8s namespace: default user: \"\" name: default current-context: default kind: Config preferences: {} users: [] 2.2 rest.config 通过参数（master的url或者kubeconfig路径）和BuildConfigFromFlags方法来获取rest.Config对象，一般是通过参数kubeconfig的路径。 config, err := clientcmd.BuildConfigFromFlags(\"\", *kubeconfig) BuildConfigFromFlags函数源码 k8s.io/client-go/tools/clientcmd/client_config.go // BuildConfigFromFlags is a helper function that builds configs from a master // url or a kubeconfig filepath. These are passed in as command line flags for cluster // components. Warnings should reflect this usage. If neither masterUrl or kubeconfigPath // are passed in we fallback to inClusterConfig. If inClusterConfig fails, we fallback // to the default config. func BuildConfigFromFlags(masterUrl, kubeconfigPath string) (*restclient.Config, error) { if kubeconfigPath == \"\" && masterUrl == \"\" { glog.Warningf(\"Neither --kubeconfig nor --master was specified. Using the inClusterConfig. This might not work.\") kubeconfig, err := restclient.InClusterConfig() if err == nil { return kubeconfig, nil } glog.Warning(\"error creating inClusterConfig, falling back to default config: \", err) } return NewNonInteractiveDeferredLoadingClientConfig( &ClientConfigLoadingRules{ExplicitPath: kubeconfigPath}, &ConfigOverrides{ClusterInfo: clientcmdapi.Cluster{Server: masterUrl}}).ClientConfig() } 2.3 clientset 通过*rest.Config参数和NewForConfig方法来获取clientset对象，clientset是多个client的集合，每个client可能包含不同版本的方法调用。 clientset, err := kubernetes.NewForConfig(config) 2.3.1 NewForConfig NewForConfig函数就是初始化clientset中的每个client。 k8s.io/client-go/kubernetes/clientset.go // NewForConfig creates a new Clientset for the given config. func NewForConfig(c *rest.Config) (*Clientset, error) { configShallowCopy := *c ... var cs Clientset cs.appsV1beta1, err = appsv1beta1.NewForConfig(&configShallowCopy) ... cs.coreV1, err = corev1.NewForConfig(&configShallowCopy) ... } 2.3.2 clientset的结构体 k8s.io/client-go/kubernetes/clientset.go // Clientset contains the clients for groups. Each group has exactly one // version included in a Clientset. type Clientset struct { *discovery.DiscoveryClient admissionregistrationV1alpha1 *admissionregistrationv1alpha1.AdmissionregistrationV1alpha1Client appsV1beta1 *appsv1beta1.AppsV1beta1Client appsV1beta2 *appsv1beta2.AppsV1beta2Client authenticationV1 *authenticationv1.AuthenticationV1Client authenticationV1beta1 *authenticationv1beta1.AuthenticationV1beta1Client authorizationV1 *authorizationv1.AuthorizationV1Client authorizationV1beta1 *authorizationv1beta1.AuthorizationV1beta1Client autoscalingV1 *autoscalingv1.AutoscalingV1Client autoscalingV2beta1 *autoscalingv2beta1.AutoscalingV2beta1Client batchV1 *batchv1.BatchV1Client batchV1beta1 *batchv1beta1.BatchV1beta1Client batchV2alpha1 *batchv2alpha1.BatchV2alpha1Client certificatesV1beta1 *certificatesv1beta1.CertificatesV1beta1Client coreV1 *corev1.CoreV1Client extensionsV1beta1 *extensionsv1beta1.ExtensionsV1beta1Client networkingV1 *networkingv1.NetworkingV1Client policyV1beta1 *policyv1beta1.PolicyV1beta1Client rbacV1 *rbacv1.RbacV1Client rbacV1beta1 *rbacv1beta1.RbacV1beta1Client rbacV1alpha1 *rbacv1alpha1.RbacV1alpha1Client schedulingV1alpha1 *schedulingv1alpha1.SchedulingV1alpha1Client settingsV1alpha1 *settingsv1alpha1.SettingsV1alpha1Client storageV1beta1 *storagev1beta1.StorageV1beta1Client storageV1 *storagev1.StorageV1Client } 2.3.3 clientset.Interface clientset实现了以下的Interface，因此可以通过调用以下方法获得具体的client。例如： pods, err := clientset.CoreV1().Pods(\"\").List(metav1.ListOptions{}) clientset的方法集接口 k8s.io/client-go/kubernetes/clientset.go type Interface interface { Discovery() discovery.DiscoveryInterface AdmissionregistrationV1alpha1() admissionregistrationv1alpha1.AdmissionregistrationV1alpha1Interface // Deprecated: please explicitly pick a version if possible. Admissionregistration() admissionregistrationv1alpha1.AdmissionregistrationV1alpha1Interface AppsV1beta1() appsv1beta1.AppsV1beta1Interface AppsV1beta2() appsv1beta2.AppsV1beta2Interface // Deprecated: please explicitly pick a version if possible. Apps() appsv1beta2.AppsV1beta2Interface AuthenticationV1() authenticationv1.AuthenticationV1Interface // Deprecated: please explicitly pick a version if possible. Authentication() authenticationv1.AuthenticationV1Interface AuthenticationV1beta1() authenticationv1beta1.AuthenticationV1beta1Interface AuthorizationV1() authorizationv1.AuthorizationV1Interface // Deprecated: please explicitly pick a version if possible. Authorization() authorizationv1.AuthorizationV1Interface AuthorizationV1beta1() authorizationv1beta1.AuthorizationV1beta1Interface AutoscalingV1() autoscalingv1.AutoscalingV1Interface // Deprecated: please explicitly pick a version if possible. Autoscaling() autoscalingv1.AutoscalingV1Interface AutoscalingV2beta1() autoscalingv2beta1.AutoscalingV2beta1Interface BatchV1() batchv1.BatchV1Interface // Deprecated: please explicitly pick a version if possible. Batch() batchv1.BatchV1Interface BatchV1beta1() batchv1beta1.BatchV1beta1Interface BatchV2alpha1() batchv2alpha1.BatchV2alpha1Interface CertificatesV1beta1() certificatesv1beta1.CertificatesV1beta1Interface // Deprecated: please explicitly pick a version if possible. Certificates() certificatesv1beta1.CertificatesV1beta1Interface CoreV1() corev1.CoreV1Interface // Deprecated: please explicitly pick a version if possible. Core() corev1.CoreV1Interface ExtensionsV1beta1() extensionsv1beta1.ExtensionsV1beta1Interface // Deprecated: please explicitly pick a version if possible. Extensions() extensionsv1beta1.ExtensionsV1beta1Interface NetworkingV1() networkingv1.NetworkingV1Interface // Deprecated: please explicitly pick a version if possible. Networking() networkingv1.NetworkingV1Interface PolicyV1beta1() policyv1beta1.PolicyV1beta1Interface // Deprecated: please explicitly pick a version if possible. Policy() policyv1beta1.PolicyV1beta1Interface RbacV1() rbacv1.RbacV1Interface // Deprecated: please explicitly pick a version if possible. Rbac() rbacv1.RbacV1Interface RbacV1beta1() rbacv1beta1.RbacV1beta1Interface RbacV1alpha1() rbacv1alpha1.RbacV1alpha1Interface SchedulingV1alpha1() schedulingv1alpha1.SchedulingV1alpha1Interface // Deprecated: please explicitly pick a version if possible. Scheduling() schedulingv1alpha1.SchedulingV1alpha1Interface SettingsV1alpha1() settingsv1alpha1.SettingsV1alpha1Interface // Deprecated: please explicitly pick a version if possible. Settings() settingsv1alpha1.SettingsV1alpha1Interface StorageV1beta1() storagev1beta1.StorageV1beta1Interface StorageV1() storagev1.StorageV1Interface // Deprecated: please explicitly pick a version if possible. Storage() storagev1.StorageV1Interface } 2.4 CoreV1Client 我们以clientset中的CoreV1Client为例做分析。 通过传入的配置信息rest.Config初始化CoreV1Client对象。 k8s.io/client-go/kubernetes/clientset.go cs.coreV1, err = corev1.NewForConfig(&configShallowCopy) 2.4.1 corev1.NewForConfig k8s.io/client-go/kubernetes/typed/core/v1/core_client.go // NewForConfig creates a new CoreV1Client for the given config. func NewForConfig(c *rest.Config) (*CoreV1Client, error) { config := *c if err := setConfigDefaults(&config); err != nil { return nil, err } client, err := rest.RESTClientFor(&config) if err != nil { return nil, err } return &CoreV1Client{client}, nil } corev1.NewForConfig方法本质是调用了rest.RESTClientFor(&config)方法创建RESTClient对象，即CoreV1Client的本质就是一个RESTClient对象。 2.4.2 CoreV1Client结构体 以下是CoreV1Client结构体的定义： k8s.io/client-go/kubernetes/typed/core/v1/core_client.go // CoreV1Client is used to interact with features provided by the group. type CoreV1Client struct { restClient rest.Interface } CoreV1Client实现了CoreV1Interface的接口，即以下方法，从而对kubernetes的资源对象进行增删改查的操作。 k8s.io/client-go/kubernetes/typed/core/v1/core_client.go //CoreV1Client的方法 func (c *CoreV1Client) ComponentStatuses() ComponentStatusInterface {...} //ConfigMaps func (c *CoreV1Client) ConfigMaps(namespace string) ConfigMapInterface {...} //Endpoints func (c *CoreV1Client) Endpoints(namespace string) EndpointsInterface {...} func (c *CoreV1Client) Events(namespace string) EventInterface {...} func (c *CoreV1Client) LimitRanges(namespace string) LimitRangeInterface {...} //Namespaces func (c *CoreV1Client) Namespaces() NamespaceInterface {...} //Nodes func (c *CoreV1Client) Nodes() NodeInterface {...} func (c *CoreV1Client) PersistentVolumes() PersistentVolumeInterface {...} func (c *CoreV1Client) PersistentVolumeClaims(namespace string) PersistentVolumeClaimInterface {...} //Pods func (c *CoreV1Client) Pods(namespace string) PodInterface {...} func (c *CoreV1Client) PodTemplates(namespace string) PodTemplateInterface {...} //ReplicationControllers func (c *CoreV1Client) ReplicationControllers(namespace string) ReplicationControllerInterface {...} func (c *CoreV1Client) ResourceQuotas(namespace string) ResourceQuotaInterface {...} func (c *CoreV1Client) Secrets(namespace string) SecretInterface {...} //Services func (c *CoreV1Client) Services(namespace string) ServiceInterface {...} func (c *CoreV1Client) ServiceAccounts(namespace string) ServiceAccountInterface {...} 2.4.3 CoreV1Interface k8s.io/client-go/kubernetes/typed/core/v1/core_client.go type CoreV1Interface interface { RESTClient() rest.Interface ComponentStatusesGetter ConfigMapsGetter EndpointsGetter EventsGetter LimitRangesGetter NamespacesGetter NodesGetter PersistentVolumesGetter PersistentVolumeClaimsGetter PodsGetter PodTemplatesGetter ReplicationControllersGetter ResourceQuotasGetter SecretsGetter ServicesGetter ServiceAccountsGetter } CoreV1Interface中包含了各种kubernetes对象的调用接口，例如PodsGetter是对kubernetes中pod对象增删改查操作的接口。ServicesGetter是对service对象的操作的接口。 2.4.4 PodsGetter 以下我们以PodsGetter接口为例分析CoreV1Client对pod对象的增删改查接口调用。 示例中的代码如下： pods, err := clientset.CoreV1().Pods(\"\").List(metav1.ListOptions{}) CoreV1().Pods() k8s.io/client-go/kubernetes/typed/core/v1/core_client.go func (c *CoreV1Client) Pods(namespace string) PodInterface { return newPods(c, namespace) } newPods() k8s.io/client-go/kubernetes/typed/core/v1/pod.go // newPods returns a Pods func newPods(c *CoreV1Client, namespace string) *pods { return &pods{ client: c.RESTClient(), ns: namespace, } } CoreV1().Pods()的方法实际上是调用了newPods()的方法，创建了一个pods对象，pods对象继承了rest.Interface接口，即最终的实现本质是RESTClient的HTTP调用。 k8s.io/client-go/kubernetes/typed/core/v1/pod.go // pods implements PodInterface type pods struct { client rest.Interface ns string } pods对象实现了PodInterface接口。PodInterface定义了pods对象的增删改查等方法。 k8s.io/client-go/kubernetes/typed/core/v1/pod.go // PodInterface has methods to work with Pod resources. type PodInterface interface { Create(*v1.Pod) (*v1.Pod, error) Update(*v1.Pod) (*v1.Pod, error) UpdateStatus(*v1.Pod) (*v1.Pod, error) Delete(name string, options *meta_v1.DeleteOptions) error DeleteCollection(options *meta_v1.DeleteOptions, listOptions meta_v1.ListOptions) error Get(name string, options meta_v1.GetOptions) (*v1.Pod, error) List(opts meta_v1.ListOptions) (*v1.PodList, error) Watch(opts meta_v1.ListOptions) (watch.Interface, error) Patch(name string, pt types.PatchType, data []byte, subresources ...string) (result *v1.Pod, err error) PodExpansion } PodsGetter PodsGetter继承了PodInterface的接口。 k8s.io/client-go/kubernetes/typed/core/v1/pod.go // PodsGetter has a method to return a PodInterface. // A group's client should implement this interface. type PodsGetter interface { Pods(namespace string) PodInterface } Pods().List() pods.List()方法通过RESTClient的HTTP调用来实现对kubernetes的pod资源的获取。 k8s.io/client-go/kubernetes/typed/core/v1/pod.go // List takes label and field selectors, and returns the list of Pods that match those selectors. func (c *pods) List(opts meta_v1.ListOptions) (result *v1.PodList, err error) { result = &v1.PodList{} err = c.client.Get(). Namespace(c.ns). Resource(\"pods\"). VersionedParams(&opts, scheme.ParameterCodec). Do(). Into(result) return } 以上分析了clientset.CoreV1().Pods(\"\").List(metav1.ListOptions{})对pod资源获取的过程，最终是调用RESTClient的方法实现。 2.5 RESTClient 以下分析RESTClient的创建过程及作用。 RESTClient对象的创建同样是依赖传入的config信息。 k8s.io/client-go/kubernetes/typed/core/v1/core_client.go client, err := rest.RESTClientFor(&config) 2.5.1 rest.RESTClientFor k8s.io/client-go/rest/config.go // RESTClientFor returns a RESTClient that satisfies the requested attributes on a client Config // object. Note that a RESTClient may require fields that are optional when initializing a Client. // A RESTClient created by this method is generic - it expects to operate on an API that follows // the Kubernetes conventions, but may not be the Kubernetes API. func RESTClientFor(config *Config) (*RESTClient, error) { ... qps := config.QPS ... burst := config.Burst ... baseURL, versionedAPIPath, err := defaultServerUrlFor(config) ... transport, err := TransportFor(config) ... var httpClient *http.Client if transport != http.DefaultTransport { httpClient = &http.Client{Transport: transport} if config.Timeout > 0 { httpClient.Timeout = config.Timeout } } return NewRESTClient(baseURL, versionedAPIPath, config.ContentConfig, qps, burst, config.RateLimiter, httpClient) } RESTClientFor函数调用了NewRESTClient的初始化函数。 2.5.2 NewRESTClient k8s.io/client-go/rest/client.go // NewRESTClient creates a new RESTClient. This client performs generic REST functions // such as Get, Put, Post, and Delete on specified paths. Codec controls encoding and // decoding of responses from the server. func NewRESTClient(baseURL *url.URL, versionedAPIPath string, config ContentConfig, maxQPS float32, maxBurst int, rateLimiter flowcontrol.RateLimiter, client *http.Client) (*RESTClient, error) { base := *baseURL ... serializers, err := createSerializers(config) ... return &RESTClient{ base: &base, versionedAPIPath: versionedAPIPath, contentConfig: config, serializers: *serializers, createBackoffMgr: readExpBackoffConfig, Throttle: throttle, Client: client, }, nil } 2.5.3 RESTClient结构体 以下介绍RESTClient的结构体定义，RESTClient结构体中包含了http.Client，即本质上RESTClient就是一个http.Client的封装实现。 k8s.io/client-go/rest/client.go // RESTClient imposes common Kubernetes API conventions on a set of resource paths. // The baseURL is expected to point to an HTTP or HTTPS path that is the parent // of one or more resources. The server should return a decodable API resource // object, or an api.Status object which contains information about the reason for // any failure. // // Most consumers should use client.New() to get a Kubernetes API client. type RESTClient struct { // base is the root URL for all invocations of the client base *url.URL // versionedAPIPath is a path segment connecting the base URL to the resource root versionedAPIPath string // contentConfig is the information used to communicate with the server. contentConfig ContentConfig // serializers contain all serializers for underlying content type. serializers Serializers // creates BackoffManager that is passed to requests. createBackoffMgr func() BackoffManager // TODO extract this into a wrapper interface via the RESTClient interface in kubectl. Throttle flowcontrol.RateLimiter // Set specific behavior of the client. If not set http.DefaultClient will be used. Client *http.Client } 2.5.4 RESTClient.Interface RESTClient实现了以下的接口方法： k8s.io/client-go/rest/client.go // Interface captures the set of operations for generically interacting with Kubernetes REST apis. type Interface interface { GetRateLimiter() flowcontrol.RateLimiter Verb(verb string) *Request Post() *Request Put() *Request Patch(pt types.PatchType) *Request Get() *Request Delete() *Request APIVersion() schema.GroupVersion } 在调用HTTP方法（Post()，Put()，Get()，Delete() ）时，实际上调用了Verb(verb string)函数。 k8s.io/client-go/rest/client.go // Verb begins a request with a verb (GET, POST, PUT, DELETE). // // Example usage of RESTClient's request building interface: // c, err := NewRESTClient(...) // if err != nil { ... } // resp, err := c.Verb(\"GET\"). // Path(\"pods\"). // SelectorParam(\"labels\", \"area=staging\"). // Timeout(10*time.Second). // Do() // if err != nil { ... } // list, ok := resp.(*api.PodList) // func (c *RESTClient) Verb(verb string) *Request { backoff := c.createBackoffMgr() if c.Client == nil { return NewRequest(nil, verb, c.base, c.versionedAPIPath, c.contentConfig, c.serializers, backoff, c.Throttle) } return NewRequest(c.Client, verb, c.base, c.versionedAPIPath, c.contentConfig, c.serializers, backoff, c.Throttle) } Verb函数调用了NewRequest方法，最后调用Do()方法实现一个HTTP请求获取Result。 2.6 总结 client-go对kubernetes资源对象的调用，需要先获取kubernetes的配置信息，即$HOME/.kube/config。 整个调用的过程如下： kubeconfig→rest.config→clientset→具体的client(CoreV1Client)→具体的资源对象(pod)→RESTClient→http.Client→HTTP请求的发送及响应 通过clientset中不同的client和client中不同资源对象的方法实现对kubernetes中资源对象的增删改查等操作，常用的client有CoreV1Client、AppsV1beta1Client、ExtensionsV1beta1Client等。 3. client-go对k8s资源的调用 创建clientset //获取kubeconfig kubeconfig = flag.String(\"kubeconfig\", filepath.Join(home, \".kube\", \"config\"), \"(optional) absolute path to the kubeconfig file\") //创建config config, err := clientcmd.BuildConfigFromFlags(\"\", *kubeconfig) //创建clientset clientset, err := kubernetes.NewForConfig(config) //具体的资源调用见以下例子 3.1 deployment //声明deployment对象 var deployment *v1beta1.Deployment //构造deployment对象 //创建deployment deployment, err := clientset.AppsV1beta1().Deployments().Create() //更新deployment deployment, err := clientset.AppsV1beta1().Deployments().Update() //删除deployment err := clientset.AppsV1beta1().Deployments().Delete(, &meta_v1.DeleteOptions{}) //查询deployment deployment, err := clientset.AppsV1beta1().Deployments().Get(, meta_v1.GetOptions{}) //列出deployment deploymentList, err := clientset.AppsV1beta1().Deployments().List(&meta_v1.ListOptions{}) //watch deployment watchInterface, err := clientset.AppsV1beta1().Deployments().Watch(&meta_v1.ListOptions{}) 3.2 service //声明service对象 var service *v1.Service //构造service对象 //创建service service, err := clientset.CoreV1().Services().Create() //更新service service, err := clientset.CoreV1().Services().Update() //删除service err := clientset.CoreV1().Services().Delete(, &meta_v1.DeleteOptions{}) //查询service service, err := clientset.CoreV1().Services().Get(, meta_v1.GetOptions{}) //列出service serviceList, err := clientset.CoreV1().Services().List(&meta_v1.ListOptions{}) //watch service watchInterface, err := clientset.CoreV1().Services().Watch(&meta_v1.ListOptions{}) 3.3 ingress //声明ingress对象 var ingress *v1beta1.Ingress //构造ingress对象 //创建ingress ingress, err := clientset.ExtensionsV1beta1().Ingresses().Create() //更新ingress ingress, err := clientset.ExtensionsV1beta1().Ingresses().Update() //删除ingress err := clientset.ExtensionsV1beta1().Ingresses().Delete(, &meta_v1.DeleteOptions{}) //查询ingress ingress, err := clientset.ExtensionsV1beta1().Ingresses().Get(, meta_v1.GetOptions{}) //列出ingress ingressList, err := clientset.ExtensionsV1beta1().Ingresses().List(&meta_v1.ListOptions{}) //watch ingress watchInterface, err := clientset.ExtensionsV1beta1().Ingresses().Watch(&meta_v1.ListOptions{}) 3.4 replicaSet //声明replicaSet对象 var replicaSet *v1beta1.ReplicaSet //构造replicaSet对象 //创建replicaSet replicaSet, err := clientset.ExtensionsV1beta1().ReplicaSets().Create() //更新replicaSet replicaSet, err := clientset.ExtensionsV1beta1().ReplicaSets().Update() //删除replicaSet err := clientset.ExtensionsV1beta1().ReplicaSets().Delete(, &meta_v1.DeleteOptions{}) //查询replicaSet replicaSet, err := clientset.ExtensionsV1beta1().ReplicaSets().Get(, meta_v1.GetOptions{}) //列出replicaSet replicaSetList, err := clientset.ExtensionsV1beta1().ReplicaSets().List(&meta_v1.ListOptions{}) //watch replicaSet watchInterface, err := clientset.ExtensionsV1beta1().ReplicaSets().Watch(&meta_v1.ListOptions{}) 新版的kubernetes中一般通过deployment来创建replicaSet，再通过replicaSet来控制pod。 3.5 pod //声明pod对象 var pod *v1.Pod //创建pod pod, err := clientset.CoreV1().Pods().Create() //更新pod pod, err := clientset.CoreV1().Pods().Update() //删除pod err := clientset.CoreV1().Pods().Delete(, &meta_v1.DeleteOptions{}) //查询pod pod, err := clientset.CoreV1().Pods().Get(, meta_v1.GetOptions{}) //列出pod podList, err := clientset.CoreV1().Pods().List(&meta_v1.ListOptions{}) //watch pod watchInterface, err := clientset.CoreV1().Pods().Watch(&meta_v1.ListOptions{}) 3.6 statefulset //声明statefulset对象 var statefulset *v1.StatefulSet //创建statefulset statefulset, err := clientset.AppsV1().StatefulSets().Create() //更新statefulset statefulset, err := clientset.AppsV1().StatefulSets().Update() //删除statefulset err := clientset.AppsV1().StatefulSets().Delete(, &meta_v1.DeleteOptions{}) //查询statefulset statefulset, err := clientset.AppsV1().StatefulSets().Get(, meta_v1.GetOptions{}) //列出statefulset statefulsetList, err := clientset.AppsV1().StatefulSets().List(&meta_v1.ListOptions{}) //watch statefulset watchInterface, err := clientset.AppsV1().StatefulSets().Watch(&meta_v1.ListOptions{}) ​ 通过以上对kubernetes的资源对象的操作函数可以看出，每个资源对象都有增删改查等方法，基本调用逻辑类似。一般二次开发只需要创建deployment、service、ingress三个资源对象即可，pod对象由deployment包含的replicaSet来控制创建和删除。函数调用的入参一般只有NAMESPACE和kubernetesObject两个参数，部分操作有Options的参数。在创建前，需要对资源对象构造数据，可以理解为编辑一个资源对象的yaml文件，然后通过kubectl create -f xxx.yaml来创建对象。 参考文档: https://github.com/kubernetes/client-go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"develop/csi/_index.html":{"url":"develop/csi/_index.html","title":"CSI插件开发","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"develop/csi/nfs-client-provisioner.html":{"url":"develop/csi/nfs-client-provisioner.html","title":"nfs-client-provisioner源码分析","keywords":"","body":"1. Dynamic Provisioner1.1. Provisioner Interface1.2. VolumeOptions1.3. ProvisionController1.4. 开发provisioner的步骤2. NFS Client Provisioner2.1. Main函数2.1.1. 读取环境变量2.1.2. 获取clientset对象2.1.3. 构造nfsProvisioner对象2.1.4. 构建并运行ProvisionController2.2. Provision和Delete方法2.2.1. Provision方法2.2.2. Delete方法3. ProvisionController3.1. ProvisionController结构体3.1.1. 入参3.1.2. Controller和Informer3.1.3. workqueue3.1.4. 其他3.2. NewProvisionController方法3.2.1. 初始化默认值3.2.2. 初始化任务队列3.2.3. ListWatch3.2.4. ResourceEventHandlerFuncs3.2.5. 构造Store和Controller3.3. ProvisionController.Run方法3.3.1. prometheus数据收集3.3.2. Controller.Run3.3.3. Worker3.4. Operation3.4.1. provisionClaimOperation3.4.2. deleteVolumeOperation4. 总结 如果要开发一个Dynamic Provisioner，需要使用到the helper library。 1. Dynamic Provisioner 1.1. Provisioner Interface 开发Dynamic Provisioner需要实现Provisioner接口，该接口有两个方法，分别是： Provision：创建存储资源，并且返回一个PV对象。 Delete：移除对应的存储资源，但并没有删除PV对象。 Provisioner 接口源码如下： // Provisioner is an interface that creates templates for PersistentVolumes // and can create the volume as a new resource in the infrastructure provider. // It can also remove the volume it created from the underlying storage // provider. type Provisioner interface { // Provision creates a volume i.e. the storage asset and returns a PV object // for the volume Provision(VolumeOptions) (*v1.PersistentVolume, error) // Delete removes the storage asset that was created by Provision backing the // given PV. Does not delete the PV object itself. // // May return IgnoredError to indicate that the call has been ignored and no // action taken. Delete(*v1.PersistentVolume) error } 1.2. VolumeOptions Provisioner接口的Provision方法的入参是一个VolumeOptions对象。VolumeOptions对象包含了创建PV对象所需要的信息，例如：PV的回收策略，PV的名字，PV所对应的PVC对象以及PVC的StorageClass对象使用的参数等。 VolumeOptions 源码如下： // VolumeOptions contains option information about a volume // https://github.com/kubernetes/kubernetes/blob/release-1.4/pkg/volume/plugins.go type VolumeOptions struct { // Reclamation policy for a persistent volume PersistentVolumeReclaimPolicy v1.PersistentVolumeReclaimPolicy // PV.Name of the appropriate PersistentVolume. Used to generate cloud // volume name. PVName string // PV mount options. Not validated - mount of the PVs will simply fail if one is invalid. MountOptions []string // PVC is reference to the claim that lead to provisioning of a new PV. // Provisioners *must* create a PV that would be matched by this PVC, // i.e. with required capacity, accessMode, labels matching PVC.Selector and // so on. PVC *v1.PersistentVolumeClaim // Volume provisioning parameters from StorageClass Parameters map[string]string // Node selected by the scheduler for the volume. SelectedNode *v1.Node // Topology constraint parameter from StorageClass AllowedTopologies []v1.TopologySelectorTerm } 1.3. ProvisionController ProvisionController是一个给PVC提供PV的控制器，具体执行Provisioner接口的Provision和Delete的方法的所有逻辑。 1.4. 开发provisioner的步骤 写一个provisioner实现Provisioner接口（包含Provision和Delete的方法）。 通过该provisioner构建ProvisionController。 执行ProvisionController的Run方法。 2. NFS Client Provisioner nfs-client-provisioner是一个automatic provisioner，使用NFS作为存储，自动创建PV和对应的PVC，本身不提供NFS存储，需要外部先有一套NFS存储服务。 PV以 ${namespace}-${pvcName}-${pvName}的命名格式提供（在NFS服务器上） PV回收的时候以 archieved-${namespace}-${pvcName}-${pvName} 的命名格式（在NFS服务器上） 以下通过nfs-client-provisioner的源码分析来说明开发自定义provisioner整个过程。nfs-client-provisioner的主要代码都在provisioner.go的文件中。 nfs-client-provisioner源码地址：https://github.com/kubernetes-incubator/external-storage/tree/master/nfs-client 2.1. Main函数 2.1.1. 读取环境变量 源码如下： func main() { flag.Parse() flag.Set(\"logtostderr\", \"true\") server := os.Getenv(\"NFS_SERVER\") if server == \"\" { glog.Fatal(\"NFS_SERVER not set\") } path := os.Getenv(\"NFS_PATH\") if path == \"\" { glog.Fatal(\"NFS_PATH not set\") } provisionerName := os.Getenv(provisionerNameKey) if provisionerName == \"\" { glog.Fatalf(\"environment variable %s is not set! Please set it.\", provisionerNameKey) } ... } main函数先获取NFS_SERVER、NFS_PATH、PROVISIONER_NAME三个环境变量的值，因此在部署nfs-client-provisioner的时候，需要将这三个环境变量的值传入。 NFS_SERVER：NFS服务端的IP地址。 NFS_PATH：NFS服务端设置的共享目录 PROVISIONER_NAME：provisioner的名字，需要和StorageClass对象中的provisioner字段一致。 例如StorageClass对象的yaml文件如下： apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: managed-nfs-storage provisioner: fuseim.pri/ifs # or choose another name, must match deployment's env PROVISIONER_NAME' parameters: archiveOnDelete: \"false\" # When set to \"false\" your PVs will not be archived by the provisioner upon deletion of the PVC. 2.1.2. 获取clientset对象 源码如下： // Create an InClusterConfig and use it to create a client for the controller // to use to communicate with Kubernetes config, err := rest.InClusterConfig() if err != nil { glog.Fatalf(\"Failed to create config: %v\", err) } clientset, err := kubernetes.NewForConfig(config) if err != nil { glog.Fatalf(\"Failed to create client: %v\", err) } 通过读取对应的k8s的配置，创建clientset对象，用来执行k8s对应的API，其中主要包括对PV和PVC等对象的创建删除等操作。 2.1.3. 构造nfsProvisioner对象 源码如下： // The controller needs to know what the server version is because out-of-tree // provisioners aren't officially supported until 1.5 serverVersion, err := clientset.Discovery().ServerVersion() if err != nil { glog.Fatalf(\"Error getting server version: %v\", err) } clientNFSProvisioner := &nfsProvisioner{ client: clientset, server: server, path: path, } 通过clientset、server、path等值构造nfsProvisioner对象，同时还获取了k8s的版本信息，因为provisioners的功能在k8s 1.5及以上版本才支持。 nfsProvisioner类型定义如下： type nfsProvisioner struct { client kubernetes.Interface server string path string } var _ controller.Provisioner = &nfsProvisioner{} nfsProvisioner是一个自定义的provisioner，用来实现Provisioner的接口，其中的属性除了server、path这两个关于NFS相关的参数，还包含了client，主要用来调用k8s的API。 var _ controller.Provisioner = &nfsProvisioner{} 以上用法用来检测nfsProvisioner是否实现了Provisioner的接口。 2.1.4. 构建并运行ProvisionController 源码如下： // Start the provision controller which will dynamically provision efs NFS // PVs pc := controller.NewProvisionController(clientset, provisionerName, clientNFSProvisioner, serverVersion.GitVersion) pc.Run(wait.NeverStop) 通过nfsProvisioner构造ProvisionController对象并执行Run方法，ProvisionController实现了具体的PV和PVC的相关逻辑，Run方法以常驻进程的方式运行。 2.2. Provision和Delete方法 2.2.1. Provision方法 nfsProvisioner的Provision方法具体源码参考：https://github.com/kubernetes-incubator/external-storage/blob/master/nfs-client/cmd/nfs-client-provisioner/provisioner.go#L56 Provision方法用来创建存储资源，并且返回一个PV对象。其中入参是VolumeOptions，用来指定PV对象的相关属性。 1、构建PV和PVC的名称 func (p *nfsProvisioner) Provision(options controller.VolumeOptions) (*v1.PersistentVolume, error) { if options.PVC.Spec.Selector != nil { return nil, fmt.Errorf(\"claim Selector is not supported\") } glog.V(4).Infof(\"nfs provisioner: VolumeOptions %v\", options) pvcNamespace := options.PVC.Namespace pvcName := options.PVC.Name pvName := strings.Join([]string{pvcNamespace, pvcName, options.PVName}, \"-\") fullPath := filepath.Join(mountPath, pvName) glog.V(4).Infof(\"creating path %s\", fullPath) if err := os.MkdirAll(fullPath, 0777); err != nil { return nil, errors.New(\"unable to create directory to provision new pv: \" + err.Error()) } os.Chmod(fullPath, 0777) path := filepath.Join(p.path, pvName) ... } 通过VolumeOptions的入参，构建PV和PVC的名称，以及创建路径path。 2、构造PV对象 pv := &v1.PersistentVolume{ ObjectMeta: metav1.ObjectMeta{ Name: options.PVName, }, Spec: v1.PersistentVolumeSpec{ PersistentVolumeReclaimPolicy: options.PersistentVolumeReclaimPolicy, AccessModes: options.PVC.Spec.AccessModes, MountOptions: options.MountOptions, Capacity: v1.ResourceList{ v1.ResourceName(v1.ResourceStorage): options.PVC.Spec.Resources.Requests[v1.ResourceName(v1.ResourceStorage)], }, PersistentVolumeSource: v1.PersistentVolumeSource{ NFS: &v1.NFSVolumeSource{ Server: p.server, Path: path, ReadOnly: false, }, }, }, } return pv, nil 综上可以看出，Provision方法只是通过VolumeOptions参数来构建PV对象，并没有执行具体PV的创建或删除的操作。 不同类型的Provisioner的，一般是PersistentVolumeSource类型和参数不同，例如nfs-provisioner对应的PersistentVolumeSource为NFS，并且需要传入NFS相关的参数：Server，Path等。 2.2.2. Delete方法 nfsProvisioner的delete方法具体源码参考：https://github.com/kubernetes-incubator/external-storage/blob/master/nfs-client/cmd/nfs-client-provisioner/provisioner.go#L99 1、获取pvName和path等相关参数 func (p *nfsProvisioner) Delete(volume *v1.PersistentVolume) error { path := volume.Spec.PersistentVolumeSource.NFS.Path pvName := filepath.Base(path) oldPath := filepath.Join(mountPath, pvName) if _, err := os.Stat(oldPath); os.IsNotExist(err) { glog.Warningf(\"path %s does not exist, deletion skipped\", oldPath) return nil } ... } 通过path和pvName生成oldPath，其中oldPath是原先NFS服务器上pod对应的数据持久化存储路径。 2、获取archiveOnDelete参数并删除数据 // Get the storage class for this volume. storageClass, err := p.getClassForVolume(volume) if err != nil { return err } // Determine if the \"archiveOnDelete\" parameter exists. // If it exists and has a falsey value, delete the directory. // Otherwise, archive it. archiveOnDelete, exists := storageClass.Parameters[\"archiveOnDelete\"] if exists { archiveBool, err := strconv.ParseBool(archiveOnDelete) if err != nil { return err } if !archiveBool { return os.RemoveAll(oldPath) } } 如果storageClass对象中指定archiveOnDelete参数并且值为false，则会自动删除oldPath下的所有数据，即pod对应的数据持久化存储数据。 archiveOnDelete字面意思为删除时是否存档，false表示不存档，即删除数据，true表示存档，即重命名路径。 3、重命名旧数据路径 archivePath := filepath.Join(mountPath, \"archived-\"+pvName) glog.V(4).Infof(\"archiving path %s to %s\", oldPath, archivePath) return os.Rename(oldPath, archivePath) 如果storageClass对象中没有指定archiveOnDelete参数或者值为true，表明需要删除时存档，即将oldPath重命名，命名格式为oldPath前面增加archived-的前缀。 3. ProvisionController 3.1. ProvisionController结构体 源码具体参考：https://github.com/kubernetes-incubator/external-storage/blob/master/lib/controller/controller.go#L82 ProvisionController是一个给PVC提供PV的控制器，具体执行Provisioner接口的Provision和Delete的方法的所有逻辑。 3.1.1. 入参 // ProvisionController is a controller that provisions PersistentVolumes for // PersistentVolumeClaims. type ProvisionController struct { client kubernetes.Interface // The name of the provisioner for which this controller dynamically // provisions volumes. The value of annDynamicallyProvisioned and // annStorageProvisioner to set & watch for, respectively provisionerName string // The provisioner the controller will use to provision and delete volumes. // Presumably this implementer of Provisioner carries its own // volume-specific options and such that it needs in order to provision // volumes. provisioner Provisioner // Kubernetes cluster server version: // * 1.4: storage classes introduced as beta. Technically out-of-tree dynamic // provisioning is not officially supported, though it works // * 1.5: storage classes stay in beta. Out-of-tree dynamic provisioning is // officially supported // * 1.6: storage classes enter GA kubeVersion *utilversion.Version ... } client、provisionerName、provisioner、kubeVersion等属性作为NewProvisionController的入参。 client：clientset客户端，用来调用k8s的API。 provisionerName：provisioner的名字，需要和StorageClass对象中的provisioner字段一致。 provisioner：具体的provisioner的实现者，本文为nfsProvisioner。 kubeVersion：k8s的版本信息。 3.1.2. Controller和Informer type ProvisionController struct { ... claimInformer cache.SharedInformer claims cache.Store claimController cache.Controller volumeInformer cache.SharedInformer volumes cache.Store volumeController cache.Controller classInformer cache.SharedInformer classes cache.Store classController cache.Controller ... } ProvisionController结构体中包含了PV、PVC、StorageClass三个对象的Controller、Informer和Store，主要用来执行这三个对象的相关操作。 Controller：通用的控制框架 Informer：消息通知器 Store：通用的对象存储接口 3.1.3. workqueue type ProvisionController struct { ... claimQueue workqueue.RateLimitingInterface volumeQueue workqueue.RateLimitingInterface ... } claimQueue和volumeQueue分别是PV和PVC的任务队列。 3.1.4. 其他 // Identity of this controller, generated at creation time and not persisted // across restarts. Useful only for debugging, for seeing the source of // events. controller.provisioner may have its own, different notion of // identity which may/may not persist across restarts id string component string eventRecorder record.EventRecorder resyncPeriod time.Duration exponentialBackOffOnError bool threadiness int createProvisionedPVRetryCount int createProvisionedPVInterval time.Duration failedProvisionThreshold, failedDeleteThreshold int // The port for metrics server to serve on. metricsPort int32 // The IP address for metrics server to serve on. metricsAddress string // The path of metrics endpoint path. metricsPath string // Parameters of leaderelection.LeaderElectionConfig. leaseDuration, renewDeadline, retryPeriod time.Duration hasRun bool hasRunLock *sync.Mutex 3.2. NewProvisionController方法 源码地址：https://github.com/kubernetes-incubator/external-storage/blob/master/lib/controller/controller.go#L418 NewProvisionController方法主要用来构造ProvisionController。 3.2.1. 初始化默认值 // NewProvisionController creates a new provision controller using // the given configuration parameters and with private (non-shared) informers. func NewProvisionController( client kubernetes.Interface, provisionerName string, provisioner Provisioner, kubeVersion string, options ...func(*ProvisionController) error, ) *ProvisionController { ... controller := &ProvisionController{ client: client, provisionerName: provisionerName, provisioner: provisioner, kubeVersion: utilversion.MustParseSemantic(kubeVersion), id: id, component: component, eventRecorder: eventRecorder, resyncPeriod: DefaultResyncPeriod, exponentialBackOffOnError: DefaultExponentialBackOffOnError, threadiness: DefaultThreadiness, createProvisionedPVRetryCount: DefaultCreateProvisionedPVRetryCount, createProvisionedPVInterval: DefaultCreateProvisionedPVInterval, failedProvisionThreshold: DefaultFailedProvisionThreshold, failedDeleteThreshold: DefaultFailedDeleteThreshold, leaseDuration: DefaultLeaseDuration, renewDeadline: DefaultRenewDeadline, retryPeriod: DefaultRetryPeriod, metricsPort: DefaultMetricsPort, metricsAddress: DefaultMetricsAddress, metricsPath: DefaultMetricsPath, hasRun: false, hasRunLock: &sync.Mutex{}, } ... } 3.2.2. 初始化任务队列 ratelimiter := workqueue.NewMaxOfRateLimiter( workqueue.NewItemExponentialFailureRateLimiter(15*time.Second, 1000*time.Second), &workqueue.BucketRateLimiter{Limiter: rate.NewLimiter(rate.Limit(10), 100)}, ) if !controller.exponentialBackOffOnError { ratelimiter = workqueue.NewMaxOfRateLimiter( workqueue.NewItemExponentialFailureRateLimiter(15*time.Second, 15*time.Second), &workqueue.BucketRateLimiter{Limiter: rate.NewLimiter(rate.Limit(10), 100)}, ) } controller.claimQueue = workqueue.NewNamedRateLimitingQueue(ratelimiter, \"claims\") controller.volumeQueue = workqueue.NewNamedRateLimitingQueue(ratelimiter, \"volumes\") 3.2.3. ListWatch // PVC claimSource := &cache.ListWatch{ ListFunc: func(options metav1.ListOptions) (runtime.Object, error) { return client.CoreV1().PersistentVolumeClaims(v1.NamespaceAll).List(options) }, WatchFunc: func(options metav1.ListOptions) (watch.Interface, error) { return client.CoreV1().PersistentVolumeClaims(v1.NamespaceAll).Watch(options) }, } // PV volumeSource := &cache.ListWatch{ ListFunc: func(options metav1.ListOptions) (runtime.Object, error) { return client.CoreV1().PersistentVolumes().List(options) }, WatchFunc: func(options metav1.ListOptions) (watch.Interface, error) { return client.CoreV1().PersistentVolumes().Watch(options) }, } // StorageClass classSource = &cache.ListWatch{ ListFunc: func(options metav1.ListOptions) (runtime.Object, error) { return client.StorageV1().StorageClasses().List(options) }, WatchFunc: func(options metav1.ListOptions) (watch.Interface, error) { return client.StorageV1().StorageClasses().Watch(options) }, } list-watch机制是k8s中用来监听对象变化的核心机制，ListWatch包含ListFunc和WatchFunc两个函数，且不能为空，以上代码分别构造了PV、PVC、StorageClass三个对象的ListWatch结构体。该机制的实现在client-go的cache包中，具体参考：https://godoc.org/k8s.io/client-go/tools/cache。 更多ListWatch代码如下: 具体参考：https://github.com/kubernetes-incubator/external-storage/blob/89b0aaf6413b249b37834b124fc314ef7b8ee949/vendor/k8s.io/client-go/tools/cache/listwatch.go#L34 // ListerWatcher is any object that knows how to perform an initial list and start a watch on a resource. type ListerWatcher interface { // List should return a list type object; the Items field will be extracted, and the // ResourceVersion field will be used to start the watch in the right place. List(options metav1.ListOptions) (runtime.Object, error) // Watch should begin a watch at the specified version. Watch(options metav1.ListOptions) (watch.Interface, error) } // ListFunc knows how to list resources type ListFunc func(options metav1.ListOptions) (runtime.Object, error) // WatchFunc knows how to watch resources type WatchFunc func(options metav1.ListOptions) (watch.Interface, error) // ListWatch knows how to list and watch a set of apiserver resources. It satisfies the ListerWatcher interface. // It is a convenience function for users of NewReflector, etc. // ListFunc and WatchFunc must not be nil type ListWatch struct { ListFunc ListFunc WatchFunc WatchFunc // DisableChunking requests no chunking for this list watcher. DisableChunking bool } 3.2.4. ResourceEventHandlerFuncs // PVC claimHandler := cache.ResourceEventHandlerFuncs{ AddFunc: func(obj interface{}) { controller.enqueueWork(controller.claimQueue, obj) }, UpdateFunc: func(oldObj, newObj interface{}) { controller.enqueueWork(controller.claimQueue, newObj) }, DeleteFunc: func(obj interface{}) { controller.forgetWork(controller.claimQueue, obj) }, } // PV volumeHandler := cache.ResourceEventHandlerFuncs{ AddFunc: func(obj interface{}) { controller.enqueueWork(controller.volumeQueue, obj) }, UpdateFunc: func(oldObj, newObj interface{}) { controller.enqueueWork(controller.volumeQueue, newObj) }, DeleteFunc: func(obj interface{}) { controller.forgetWork(controller.volumeQueue, obj) }, } // StorageClass classHandler := cache.ResourceEventHandlerFuncs{ // We don't need an actual event handler for StorageClasses, // but we must pass a non-nil one to cache.NewInformer() AddFunc: nil, UpdateFunc: nil, DeleteFunc: nil, } ResourceEventHandlerFuncs是资源事件处理函数，主要用来对k8s资源对象增删改变化的事件进行消息通知，该函数实现了ResourceEventHandler的接口。具体代码逻辑在client-go的cache包中。 更多ResourceEventHandlerFuncs代码可参考： // ResourceEventHandler can handle notifications for events that happen to a // resource. The events are informational only, so you can't return an // error. // * OnAdd is called when an object is added. // * OnUpdate is called when an object is modified. Note that oldObj is the // last known state of the object-- it is possible that several changes // were combined together, so you can't use this to see every single // change. OnUpdate is also called when a re-list happens, and it will // get called even if nothing changed. This is useful for periodically // evaluating or syncing something. // * OnDelete will get the final state of the item if it is known, otherwise // it will get an object of type DeletedFinalStateUnknown. This can // happen if the watch is closed and misses the delete event and we don't // notice the deletion until the subsequent re-list. type ResourceEventHandler interface { OnAdd(obj interface{}) OnUpdate(oldObj, newObj interface{}) OnDelete(obj interface{}) } // ResourceEventHandlerFuncs is an adaptor to let you easily specify as many or // as few of the notification functions as you want while still implementing // ResourceEventHandler. type ResourceEventHandlerFuncs struct { AddFunc func(obj interface{}) UpdateFunc func(oldObj, newObj interface{}) DeleteFunc func(obj interface{}) } 3.2.5. 构造Store和Controller 1、PVC if controller.claimInformer != nil { controller.claimInformer.AddEventHandlerWithResyncPeriod(claimHandler, controller.resyncPeriod) controller.claims, controller.claimController = controller.claimInformer.GetStore(), controller.claimInformer.GetController() } else { controller.claims, controller.claimController = cache.NewInformer( claimSource, &v1.PersistentVolumeClaim{}, controller.resyncPeriod, claimHandler, ) } 2、PV if controller.volumeInformer != nil { controller.volumeInformer.AddEventHandlerWithResyncPeriod(volumeHandler, controller.resyncPeriod) controller.volumes, controller.volumeController = controller.volumeInformer.GetStore(), controller.volumeInformer.GetController() } else { controller.volumes, controller.volumeController = cache.NewInformer( volumeSource, &v1.PersistentVolume{}, controller.resyncPeriod, volumeHandler, ) } 3、StorageClass if controller.classInformer != nil { // no resource event handler needed for StorageClasses controller.classes, controller.classController = controller.classInformer.GetStore(), controller.classInformer.GetController() } else { controller.classes, controller.classController = cache.NewInformer( classSource, versionedClassType, controller.resyncPeriod, classHandler, ) } 通过cache.NewInformer的方法构造，入参是ListWatch结构体和ResourceEventHandlerFuncs函数等，返回值是Store和Controller。 通过以上各个部分的构造，最后返回一个具体的ProvisionController对象。 3.3. ProvisionController.Run方法 ProvisionController的Run方法是以常驻进程的方式运行，函数内部再运行其他的controller。 3.3.1. prometheus数据收集 // Run starts all of this controller's control loops func (ctrl *ProvisionController) Run(stopCh 0 { prometheus.MustRegister([]prometheus.Collector{ metrics.PersistentVolumeClaimProvisionTotal, metrics.PersistentVolumeClaimProvisionFailedTotal, metrics.PersistentVolumeClaimProvisionDurationSeconds, metrics.PersistentVolumeDeleteTotal, metrics.PersistentVolumeDeleteFailedTotal, metrics.PersistentVolumeDeleteDurationSeconds, }...) http.Handle(ctrl.metricsPath, promhttp.Handler()) address := net.JoinHostPort(ctrl.metricsAddress, strconv.FormatInt(int64(ctrl.metricsPort), 10)) glog.Infof(\"Starting metrics server at %s\\n\", address) go wait.Forever(func() { err := http.ListenAndServe(address, nil) if err != nil { glog.Errorf(\"Failed to listen on %s: %v\", address, err) } }, 5*time.Second) } ... } 3.3.2. Controller.Run // If a SharedInformer has been passed in, this controller should not // call Run again if ctrl.claimInformer == nil { go ctrl.claimController.Run(stopCh) } if ctrl.volumeInformer == nil { go ctrl.volumeController.Run(stopCh) } if ctrl.classInformer == nil { go ctrl.classController.Run(stopCh) } 运行消息通知器Informer。 3.3.3. Worker for i := 0; i runClaimWorker和runVolumeWorker分别为PVC和PV的worker，这两个的具体执行体分别是processNextClaimWorkItem和processNextVolumeWorkItem。 执行流程如下： PVC的函数调用流程 runClaimWorker→processNextClaimWorkItem→syncClaimHandler→syncClaim→provisionClaimOperation PV的函数调用流程 runVolumeWorker→processNextVolumeWorkItem→syncVolumeHandler→syncVolume→deleteVolumeOperation 可见最后执行的函数分别是provisionClaimOperation和deleteVolumeOperation。 3.4. Operation 3.4.1. provisionClaimOperation 1、provisionClaimOperation入参是PVC，通过PVC获得PV对象，并判断PV对象是否存在，如果存在则退出后续操作。 // provisionClaimOperation attempts to provision a volume for the given claim. // Returns error, which indicates whether provisioning should be retried // (requeue the claim) or not func (ctrl *ProvisionController) provisionClaimOperation(claim *v1.PersistentVolumeClaim) error { // Most code here is identical to that found in controller.go of kube's PV controller... claimClass := helper.GetPersistentVolumeClaimClass(claim) operation := fmt.Sprintf(\"provision %q class %q\", claimToClaimKey(claim), claimClass) glog.Infof(logOperation(operation, \"started\")) // A previous doProvisionClaim may just have finished while we were waiting for // the locks. Check that PV (with deterministic name) hasn't been provisioned // yet. pvName := ctrl.getProvisionedVolumeNameForClaim(claim) volume, err := ctrl.client.CoreV1().PersistentVolumes().Get(pvName, metav1.GetOptions{}) if err == nil && volume != nil { // Volume has been already provisioned, nothing to do. glog.Infof(logOperation(operation, \"persistentvolume %q already exists, skipping\", pvName)) return nil } ... } 2、获取StorageClass对象中的Provisioner和ReclaimPolicy参数，如果provisionerName和StorageClass对象中的provisioner字段不一致则报错并退出执行。 provisioner, parameters, err := ctrl.getStorageClassFields(claimClass) if err != nil { glog.Errorf(logOperation(operation, \"error getting claim's StorageClass's fields: %v\", err)) return nil } if provisioner != ctrl.provisionerName { // class.Provisioner has either changed since shouldProvision() or // annDynamicallyProvisioned contains different provisioner than // class.Provisioner. glog.Errorf(logOperation(operation, \"unknown provisioner %q requested in claim's StorageClass\", provisioner)) return nil } // Check if this provisioner can provision this claim. if err = ctrl.canProvision(claim); err != nil { ctrl.eventRecorder.Event(claim, v1.EventTypeWarning, \"ProvisioningFailed\", err.Error()) glog.Errorf(logOperation(operation, \"failed to provision volume: %v\", err)) return nil } reclaimPolicy := v1.PersistentVolumeReclaimDelete if ctrl.kubeVersion.AtLeast(utilversion.MustParseSemantic(\"v1.8.0\")) { reclaimPolicy, err = ctrl.fetchReclaimPolicy(claimClass) if err != nil { return err } } 3、执行具体的provisioner.Provision方法，构建PV对象，例如本文中的provisioner是nfs-provisioner。 options := VolumeOptions{ PersistentVolumeReclaimPolicy: reclaimPolicy, PVName: pvName, PVC: claim, MountOptions: mountOptions, Parameters: parameters, SelectedNode: selectedNode, AllowedTopologies: allowedTopologies, } ctrl.eventRecorder.Event(claim, v1.EventTypeNormal, \"Provisioning\", fmt.Sprintf(\"External provisioner is provisioning volume for claim %q\", claimToClaimKey(claim))) volume, err = ctrl.provisioner.Provision(options) if err != nil { if ierr, ok := err.(*IgnoredError); ok { // Provision ignored, do nothing and hope another provisioner will provision it. glog.Infof(logOperation(operation, \"volume provision ignored: %v\", ierr)) return nil } err = fmt.Errorf(\"failed to provision volume with StorageClass %q: %v\", claimClass, err) ctrl.eventRecorder.Event(claim, v1.EventTypeWarning, \"ProvisioningFailed\", err.Error()) return err } 4、创建k8s的PV对象。 // Try to create the PV object several times for i := 0; i 5、创建PV失败，清理存储资源。 if err != nil { // Save failed. Now we have a storage asset outside of Kubernetes, // but we don't have appropriate PV object for it. // Emit some event here and try to delete the storage asset several // times. ... for i := 0; i 如果创建成功，则打印成功的日志，并返回nil。 3.4.2. deleteVolumeOperation 1、deleteVolumeOperation入参是PV，先获得PV对象，并判断是否需要删除。 // deleteVolumeOperation attempts to delete the volume backing the given // volume. Returns error, which indicates whether deletion should be retried // (requeue the volume) or not func (ctrl *ProvisionController) deleteVolumeOperation(volume *v1.PersistentVolume) error { ... // This method may have been waiting for a volume lock for some time. // Our check does not have to be as sophisticated as PV controller's, we can // trust that the PV controller has set the PV to Released/Failed and it's // ours to delete newVolume, err := ctrl.client.CoreV1().PersistentVolumes().Get(volume.Name, metav1.GetOptions{}) if err != nil { return nil } if !ctrl.shouldDelete(newVolume) { glog.Infof(logOperation(operation, \"persistentvolume no longer needs deletion, skipping\")) return nil } ... } 2、调用具体的provisioner的Delete方法，例如，如果是nfs-provisioner，则是调用nfs-provisioner的Delete方法。 err = ctrl.provisioner.Delete(volume) if err != nil { if ierr, ok := err.(*IgnoredError); ok { // Delete ignored, do nothing and hope another provisioner will delete it. glog.Infof(logOperation(operation, \"volume deletion ignored: %v\", ierr)) return nil } // Delete failed, emit an event. glog.Errorf(logOperation(operation, \"volume deletion failed: %v\", err)) ctrl.eventRecorder.Event(volume, v1.EventTypeWarning, \"VolumeFailedDelete\", err.Error()) return err } 3、删除k8s中的PV对象。 // Delete the volume if err = ctrl.client.CoreV1().PersistentVolumes().Delete(volume.Name, nil); err != nil { // Oops, could not delete the volume and therefore the controller will // try to delete the volume again on next update. glog.Infof(logOperation(operation, \"failed to delete persistentvolume: %v\", err)) return err } 4. 总结 Provisioner接口包含Provision和Delete两个方法，自定义的provisioner需要实现这两个方法，这两个方法只是处理了跟存储类型相关的事项，并没有针对PV、PVC对象的增删等操作。 Provision方法主要用来构造PV对象，不同类型的Provisioner的，一般是PersistentVolumeSource类型和参数不同，例如nfs-provisioner对应的PersistentVolumeSource为NFS，并且需要传入NFS相关的参数：Server，Path等。 Delete方法主要针对对应的存储类型，做数据存档（备份）或删除的处理。 StorageClass对象需要单独创建，用来指定具体的provisioner来执行相关逻辑。 provisionClaimOperation和deleteVolumeOperation具体执行了k8s中PV对象的创建和删除操作，同时调用了具体provisioner的Provision和Delete两个方法来对存储数据做处理。 参考文章 https://github.com/kubernetes-incubator/external-storage/tree/master/docs/demo/hostpath-provisioner https://github.com/kubernetes-incubator/external-storage/tree/master/nfs-client https://github.com/kubernetes-incubator/external-storage/blob/master/lib/controller/controller.go https://github.com/kubernetes-incubator/external-storage/blob/master/lib/controller/volume.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"develop/csi/csi-provisioner.html":{"url":"develop/csi/csi-provisioner.html","title":"csi-provisioner源码分析","keywords":"","body":"1. Dynamic Provisioner1.1. Provisioner Interface1.2. 开发provisioner的步骤2. CSI Provisioner2.1. Main 函数2.1.1. 读取环境变量2.1.2. 获取clientset对象2.1.3. k8s版本校验2.1.4. 连接 csi socket2.1.5. 构造csi-Provisioner对象2.1.6. 运行ProvisionController2.2. Provision和Delete方法2.2.1. Provision方法2.2.2. Delete方法2.3. 总结3. csi-client3.1. 构造csi-client3.1.1. 构造grpcClient3.1.2. 构造csi-client3.2. csiClient.CreateVolume3.3. csiClient.DeleteVolume4. ProvisionController.Run 本文主要分析csi-provisioner的源码，关于开发一个Dynamic Provisioner，具体可参考nfs-client-provisioner的源码分析 1. Dynamic Provisioner 1.1. Provisioner Interface 开发Dynamic Provisioner需要实现Provisioner接口，该接口有两个方法，分别是： Provision：创建存储资源，并且返回一个PV对象。 Delete：移除对应的存储资源，但并没有删除PV对象。 1.2. 开发provisioner的步骤 写一个provisioner实现Provisioner接口（包含Provision和Delete的方法）。 通过该provisioner构建ProvisionController。 执行ProvisionController的Run方法。 2. CSI Provisioner CSI Provisioner的源码可参考：https://github.com/kubernetes-csi/external-provisioner。 2.1. Main 函数 2.1.1. 读取环境变量 源码如下： var ( provisioner = flag.String(\"provisioner\", \"\", \"Name of the provisioner. The provisioner will only provision volumes for claims that request a StorageClass with a provisioner field set equal to this name.\") master = flag.String(\"master\", \"\", \"Master URL to build a client config from. Either this or kubeconfig needs to be set if the provisioner is being run out of cluster.\") kubeconfig = flag.String(\"kubeconfig\", \"\", \"Absolute path to the kubeconfig file. Either this or master needs to be set if the provisioner is being run out of cluster.\") csiEndpoint = flag.String(\"csi-address\", \"/run/csi/socket\", \"The gRPC endpoint for Target CSI Volume\") connectionTimeout = flag.Duration(\"connection-timeout\", 10*time.Second, \"Timeout for waiting for CSI driver socket.\") volumeNamePrefix = flag.String(\"volume-name-prefix\", \"pvc\", \"Prefix to apply to the name of a created volume\") volumeNameUUIDLength = flag.Int(\"volume-name-uuid-length\", -1, \"Truncates generated UUID of a created volume to this length. Defaults behavior is to NOT truncate.\") showVersion = flag.Bool(\"version\", false, \"Show version.\") provisionController *controller.ProvisionController version = \"unknown\" ) func init() { var config *rest.Config var err error flag.Parse() flag.Set(\"logtostderr\", \"true\") if *showVersion { fmt.Println(os.Args[0], version) os.Exit(0) } glog.Infof(\"Version: %s\", version) ... } 通过init函数解析相关参数，其实provisioner指明为PVC提供PV的provisioner的名字，需要和StorageClass对象中的provisioner字段一致。 2.1.2. 获取clientset对象 源码如下： // get the KUBECONFIG from env if specified (useful for local/debug cluster) kubeconfigEnv := os.Getenv(\"KUBECONFIG\") if kubeconfigEnv != \"\" { glog.Infof(\"Found KUBECONFIG environment variable set, using that..\") kubeconfig = &kubeconfigEnv } if *master != \"\" || *kubeconfig != \"\" { glog.Infof(\"Either master or kubeconfig specified. building kube config from that..\") config, err = clientcmd.BuildConfigFromFlags(*master, *kubeconfig) } else { glog.Infof(\"Building kube configs for running in cluster...\") config, err = rest.InClusterConfig() } if err != nil { glog.Fatalf(\"Failed to create config: %v\", err) } clientset, err := kubernetes.NewForConfig(config) if err != nil { glog.Fatalf(\"Failed to create client: %v\", err) } // snapclientset.NewForConfig creates a new Clientset for VolumesnapshotV1alpha1Client snapClient, err := snapclientset.NewForConfig(config) if err != nil { glog.Fatalf(\"Failed to create snapshot client: %v\", err) } csiAPIClient, err := csiclientset.NewForConfig(config) if err != nil { glog.Fatalf(\"Failed to create CSI API client: %v\", err) } 通过读取对应的k8s的配置，创建clientset对象，用来执行k8s对应的API，其中主要包括对PV和PVC等对象的创建删除等操作。 2.1.3. k8s版本校验 // The controller needs to know what the server version is because out-of-tree // provisioners aren't officially supported until 1.5 serverVersion, err := clientset.Discovery().ServerVersion() if err != nil { glog.Fatalf(\"Error getting server version: %v\", err) } 获取了k8s的版本信息，因为provisioners的功能在k8s 1.5及以上版本才支持。 2.1.4. 连接 csi socket // Generate a unique ID for this provisioner timeStamp := time.Now().UnixNano() / int64(time.Millisecond) identity := strconv.FormatInt(timeStamp, 10) + \"-\" + strconv.Itoa(rand.Intn(10000)) + \"-\" + *provisioner // Provisioner will stay in Init until driver opens csi socket, once it's done // controller will exit this loop and proceed normally. socketDown := true grpcClient := &grpc.ClientConn{} for socketDown { grpcClient, err = ctrl.Connect(*csiEndpoint, *connectionTimeout) if err == nil { socketDown = false continue } time.Sleep(10 * time.Second) } 在Provisioner会停留在初始化状态，直到csi socket连接成功才正常运行。如果连接失败，会暂停10秒后重试，其中涉及以下2个参数： csiEndpoint：CSI Volume的gRPC地址，默认通过为/run/csi/socket。 connectionTimeout：连接CSI driver socket的超时时间，默认为10秒。 2.1.5. 构造csi-Provisioner对象 // Create the provisioner: it implements the Provisioner interface expected by // the controller csiProvisioner := ctrl.NewCSIProvisioner(clientset, csiAPIClient, *csiEndpoint, *connectionTimeout, identity, *volumeNamePrefix, *volumeNameUUIDLength, grpcClient, snapClient) provisionController = controller.NewProvisionController( clientset, *provisioner, csiProvisioner, serverVersion.GitVersion, ) 通过参数clientset,csiAPIClient, csiEndpoint, connectionTimeout, identity, volumeNamePrefix, volumeNameUUIDLength,grpcClient, snapClient构造csi-Provisioner对象。 通过csiProvisioner构造ProvisionController对象。 2.1.6. 运行ProvisionController func main() { provisionController.Run(wait.NeverStop) } ProvisionController实现了具体的PV和PVC的相关逻辑，Run方法以常驻进程的方式运行。 2.2. Provision和Delete方法 2.2.1. Provision方法 csiProvisioner的Provision方法具体源码参考：https://github.com/kubernetes-csi/external-provisioner/blob/master/pkg/controller/controller.go#L336 Provision方法用来创建存储资源，并且返回一个PV对象。其中入参是VolumeOptions，用来指定PV对象的相关属性。 1、构造PV相关属性 pvName, err := makeVolumeName(p.volumeNamePrefix, fmt.Sprintf(\"%s\", options.PVC.ObjectMeta.UID), p.volumeNameUUIDLength) if err != nil { return nil, err } 2、构造CSIPersistentVolumeSource相关属性 driverState, err := checkDriverState(p.grpcClient, p.timeout, needSnapshotSupport) if err != nil { return nil, err } ... // Resolve controller publish, node stage, node publish secret references controllerPublishSecretRef, err := getSecretReference(controllerPublishSecretNameKey, controllerPublishSecretNamespaceKey, options.Parameters, pvName, options.PVC) if err != nil { return nil, err } nodeStageSecretRef, err := getSecretReference(nodeStageSecretNameKey, nodeStageSecretNamespaceKey, options.Parameters, pvName, options.PVC) if err != nil { return nil, err } nodePublishSecretRef, err := getSecretReference(nodePublishSecretNameKey, nodePublishSecretNamespaceKey, options.Parameters, pvName, options.PVC) if err != nil { return nil, err } ... volumeAttributes := map[string]string{provisionerIDKey: p.identity} for k, v := range rep.Volume.Attributes { volumeAttributes[k] = v } ... fsType := \"\" for k, v := range options.Parameters { switch strings.ToLower(k) { case \"fstype\": fsType = v } } if len(fsType) == 0 { fsType = defaultFSType } 3、创建CSI CreateVolumeRequest // Create a CSI CreateVolumeRequest and Response req := csi.CreateVolumeRequest{ Name: pvName, Parameters: options.Parameters, VolumeCapabilities: volumeCaps, CapacityRange: &csi.CapacityRange{ RequiredBytes: int64(volSizeBytes), }, } ... glog.V(5).Infof(\"CreateVolumeRequest %+v\", req) rep := &csi.CreateVolumeResponse{} ... opts := wait.Backoff{Duration: backoffDuration, Factor: backoffFactor, Steps: backoffSteps} err = wait.ExponentialBackoff(opts, func() (bool, error) { ctx, cancel := context.WithTimeout(context.Background(), p.timeout) defer cancel() rep, err = p.csiClient.CreateVolume(ctx, &req) if err == nil { // CreateVolume has finished successfully return true, nil } if status, ok := status.FromError(err); ok { if status.Code() == codes.DeadlineExceeded { // CreateVolume timed out, give it another chance to complete glog.Warningf(\"CreateVolume timeout: %s has expired, operation will be retried\", p.timeout.String()) return false, nil } } // CreateVolume failed , no reason to retry, bailing from ExponentialBackoff return false, err }) if err != nil { return nil, err } if rep.Volume != nil { glog.V(3).Infof(\"create volume rep: %+v\", *rep.Volume) } respCap := rep.GetVolume().GetCapacityBytes() if respCap Provison方法核心功能是调用p.csiClient.CreateVolume(ctx, &req)。 4、构造PV对象 pv := &v1.PersistentVolume{ ObjectMeta: metav1.ObjectMeta{ Name: pvName, }, Spec: v1.PersistentVolumeSpec{ PersistentVolumeReclaimPolicy: options.PersistentVolumeReclaimPolicy, AccessModes: options.PVC.Spec.AccessModes, Capacity: v1.ResourceList{ v1.ResourceName(v1.ResourceStorage): bytesToGiQuantity(respCap), }, // TODO wait for CSI VolumeSource API PersistentVolumeSource: v1.PersistentVolumeSource{ CSI: &v1.CSIPersistentVolumeSource{ Driver: driverState.driverName, VolumeHandle: p.volumeIdToHandle(rep.Volume.Id), FSType: fsType, VolumeAttributes: volumeAttributes, ControllerPublishSecretRef: controllerPublishSecretRef, NodeStageSecretRef: nodeStageSecretRef, NodePublishSecretRef: nodePublishSecretRef, }, }, }, } if driverState.capabilities.Has(PluginCapability_ACCESSIBILITY_CONSTRAINTS) { pv.Spec.NodeAffinity = GenerateVolumeNodeAffinity(rep.Volume.AccessibleTopology) } glog.Infof(\"successfully created PV %+v\", pv.Spec.PersistentVolumeSource) return pv, nil Provision方法只是通过VolumeOptions参数来构建PV对象，并没有执行具体PV的创建或删除的操作。 不同类型的Provisioner的，一般是PersistentVolumeSource类型和参数不同，例如csi-provisioner对应的PersistentVolumeSource为CSI，并且需要传入CSI相关的参数： Driver VolumeHandle FSType VolumeAttributes ControllerPublishSecretRef NodeStageSecretRef NodePublishSecretRef 2.2.2. Delete方法 csiProvisioner的delete方法具体源码参考：https://github.com/kubernetes-csi/external-provisioner/blob/master/pkg/controller/controller.go#L606 func (p *csiProvisioner) Delete(volume *v1.PersistentVolume) error { if volume == nil || volume.Spec.CSI == nil { return fmt.Errorf(\"invalid CSI PV\") } volumeId := p.volumeHandleToId(volume.Spec.CSI.VolumeHandle) _, err := checkDriverState(p.grpcClient, p.timeout, false) if err != nil { return err } req := csi.DeleteVolumeRequest{ VolumeId: volumeId, } // get secrets if StorageClass specifies it storageClassName := volume.Spec.StorageClassName if len(storageClassName) != 0 { if storageClass, err := p.client.StorageV1().StorageClasses().Get(storageClassName, metav1.GetOptions{}); err == nil { // Resolve provision secret credentials. // No PVC is provided when resolving provision/delete secret names, since the PVC may or may not exist at delete time. provisionerSecretRef, err := getSecretReference(provisionerSecretNameKey, provisionerSecretNamespaceKey, storageClass.Parameters, volume.Name, nil) if err != nil { return err } credentials, err := getCredentials(p.client, provisionerSecretRef) if err != nil { return err } req.ControllerDeleteSecrets = credentials } } ctx, cancel := context.WithTimeout(context.Background(), p.timeout) defer cancel() _, err = p.csiClient.DeleteVolume(ctx, &req) return err } Delete方法主要是调用了p.csiClient.DeleteVolume(ctx, &req)方法。 2.3. 总结 csi provisioner实现了Provisioner接口，其中包含Provison和Delete两个方法: Provision：调用csiClient.CreateVolume方法，同时构造并返回PV对象。 Delete：调用csiClient.DeleteVolume方法。 csi provisioner的核心方法都调用了csi-client相关方法。 3. csi-client csi client的相关代码参考：https://github.com/container-storage-interface/spec/blob/master/lib/go/csi/v0/csi.pb.go 3.1. 构造csi-client 3.1.1. 构造grpcClient // Provisioner will stay in Init until driver opens csi socket, once it's done // controller will exit this loop and proceed normally. socketDown := true grpcClient := &grpc.ClientConn{} for socketDown { grpcClient, err = ctrl.Connect(*csiEndpoint, *connectionTimeout) if err == nil { socketDown = false continue } time.Sleep(10 * time.Second) } 通过连接csi socket，连接成功才构造可用的grpcClient。 3.1.2. 构造csi-client 通过grpcClient构造csi-client。 // Create the provisioner: it implements the Provisioner interface expected by // the controller csiProvisioner := ctrl.NewCSIProvisioner(clientset, csiAPIClient, *csiEndpoint, *connectionTimeout, identity, *volumeNamePrefix, *volumeNameUUIDLength, grpcClient, snapClient) NewCSIProvisioner // NewCSIProvisioner creates new CSI provisioner func NewCSIProvisioner(client kubernetes.Interface, csiAPIClient csiclientset.Interface, csiEndpoint string, connectionTimeout time.Duration, identity string, volumeNamePrefix string, volumeNameUUIDLength int, grpcClient *grpc.ClientConn, snapshotClient snapclientset.Interface) controller.Provisioner { csiClient := csi.NewControllerClient(grpcClient) provisioner := &csiProvisioner{ client: client, grpcClient: grpcClient, csiClient: csiClient, csiAPIClient: csiAPIClient, snapshotClient: snapshotClient, timeout: connectionTimeout, identity: identity, volumeNamePrefix: volumeNamePrefix, volumeNameUUIDLength: volumeNameUUIDLength, } return provisioner } NewControllerClient csiClient := csi.NewControllerClient(grpcClient) ... type controllerClient struct { cc *grpc.ClientConn } func NewControllerClient(cc *grpc.ClientConn) ControllerClient { return &controllerClient{cc} } 3.2. csiClient.CreateVolume csi provisoner中调用csiClient.CreateVolume代码如下： opts := wait.Backoff{Duration: backoffDuration, Factor: backoffFactor, Steps: backoffSteps} err = wait.ExponentialBackoff(opts, func() (bool, error) { ctx, cancel := context.WithTimeout(context.Background(), p.timeout) defer cancel() rep, err = p.csiClient.CreateVolume(ctx, &req) if err == nil { // CreateVolume has finished successfully return true, nil } if status, ok := status.FromError(err); ok { if status.Code() == codes.DeadlineExceeded { // CreateVolume timed out, give it another chance to complete glog.Warningf(\"CreateVolume timeout: %s has expired, operation will be retried\", p.timeout.String()) return false, nil } } // CreateVolume failed , no reason to retry, bailing from ExponentialBackoff return false, err }) CreateVolumeRequest的构造： // Create a CSI CreateVolumeRequest and Response req := csi.CreateVolumeRequest{ Name: pvName, Parameters: options.Parameters, VolumeCapabilities: volumeCaps, CapacityRange: &csi.CapacityRange{ RequiredBytes: int64(volSizeBytes), }, } ... req.VolumeContentSource = volumeContentSource ... req.AccessibilityRequirements = requirements ... req.ControllerCreateSecrets = provisionerCredentials 具体的Create实现方法如下： 其中csiClient是个接口类型 具体代码参考controllerClient.CreateVolume func (c *controllerClient) CreateVolume(ctx context.Context, in *CreateVolumeRequest, opts ...grpc.CallOption) (*CreateVolumeResponse, error) { out := new(CreateVolumeResponse) err := grpc.Invoke(ctx, \"/csi.v0.Controller/CreateVolume\", in, out, c.cc, opts...) if err != nil { return nil, err } return out, nil } 3.3. csiClient.DeleteVolume csi provisoner中调用csiClient.DeleteVolume代码如下： func (p *csiProvisioner) Delete(volume *v1.PersistentVolume) error { ... req := csi.DeleteVolumeRequest{ VolumeId: volumeId, } // get secrets if StorageClass specifies it ... ctx, cancel := context.WithTimeout(context.Background(), p.timeout) defer cancel() _, err = p.csiClient.DeleteVolume(ctx, &req) return err } DeleteVolumeRequest的构造： req := csi.DeleteVolumeRequest{ VolumeId: volumeId, } ... req.ControllerDeleteSecrets = credentials 将构造的DeleteVolumeRequest传给DeleteVolume方法。 具体的Delete实现方法如下： 具体代码参考：controllerClient.DeleteVolume func (c *controllerClient) DeleteVolume(ctx context.Context, in *DeleteVolumeRequest, opts ...grpc.CallOption) (*DeleteVolumeResponse, error) { out := new(DeleteVolumeResponse) err := grpc.Invoke(ctx, \"/csi.v0.Controller/DeleteVolume\", in, out, c.cc, opts...) if err != nil { return nil, err } return out, nil } 4. ProvisionController.Run 自定义的provisioner实现了Provisoner接口的Provision和Delete方法，这两个方法主要对后端存储做创建和删除操作，并没有对PV对象进行创建和删除操作。 PV对象的相关操作具体由ProvisionController中的provisionClaimOperation和deleteVolumeOperation具体执行，同时调用了具体provisioner的Provision和Delete两个方法来对存储数据做处理。 func main() { provisionController.Run(wait.NeverStop) } 这块代码逻辑可参考：nfs-client-provisioner 源码分析 参考文章： https://github.com/kubernetes-csi/external-provisioner https://github.com/container-storage-interface/spec https://github.com/kubernetes/community/blob/master/contributors/design-proposals/storage/container-storage-interface.md https://github.com/container-storage-interface/spec/blob/master/spec.md Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"develop/operator/_index.html":{"url":"develop/operator/_index.html","title":"operator开发","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"develop/operator/kubebuilder.html":{"url":"develop/operator/kubebuilder.html","title":"kubebuilder的使用","keywords":"","body":"1. kubebuilder1.1. 安装kubebuilder1.2. kubebuilder命令2. 操作步骤2.1. 初始化2.2. 创建项目2.3. 创建API3. troubleshooting3.1. controller-gen: No such file or directory1. kubebuilder 1.1. 安装kubebuilder # download kubebuilder and install locally. curl -L -o kubebuilder https://go.kubebuilder.io/dl/latest/$(go env GOOS)/$(go env GOARCH) chmod +x kubebuilder && mv kubebuilder /usr/local/bin/ 1.2. kubebuilder命令 Development kit for building Kubernetes extensions and tools. Provides libraries and tools to create new projects, APIs and controllers. Includes tools for packaging artifacts into an installer container. Typical project lifecycle: - initialize a project: kubebuilder init --domain example.com --license apache2 --owner \"The Kubernetes authors\" - create one or more a new resource APIs and add your code to them: kubebuilder create api --group --version --kind Create resource will prompt the user for if it should scaffold the Resource and / or Controller. To only scaffold a Controller for an existing Resource, select \"n\" for Resource. To only define the schema for a Resource without writing a Controller, select \"n\" for Controller. After the scaffold is written, api will run make on the project. Usage: kubebuilder [command] Available Commands: create Scaffold a Kubernetes API or webhook. edit This command will edit the project configuration help Help about any command init Initialize a new project version Print the kubebuilder version Flags: -h, --help help for kubebuilder Use \"kubebuilder [command] --help\" for more information about a command. 2. 操作步骤 2.1. 初始化 mkdir $GOPATH/src/github.com/huweihuang/operator-example cd $GOPATH/src/github.com/huweihuang/operator-example go mod init github.com/huweihuang/operator-example 2.2. 创建项目 # kubebuilder init --domain github.com --license apache2 --owner \"Hu Weihuang\" Writing scaffold for you to edit... Get controller runtime: $ go get sigs.k8s.io/controller-runtime@v0.5.0 Update go.mod: $ go mod tidy Running make: $ make go: creating new go.mod: module tmp go: finding sigs.k8s.io v0.2.5 go: finding sigs.k8s.io/controller-tools/cmd v0.2.5 go: finding sigs.k8s.io/controller-tools/cmd/controller-gen v0.2.5 /Users/weihuanghu/go/bin/controller-gen object:headerFile=\"hack/boilerplate.go.txt\" paths=\"./...\" go fmt ./... go vet ./... go build -o bin/manager main.go Next: define a resource with: $ kubebuilder create api 查看生成文件： ./ ├── Dockerfile ├── Makefile ├── PROJECT ├── bin │ └── manager ├── config │ ├── certmanager │ │ ├── certificate.yaml │ │ ├── kustomization.yaml │ │ └── kustomizeconfig.yaml │ ├── default │ │ ├── kustomization.yaml │ │ ├── manager_auth_proxy_patch.yaml │ │ ├── manager_webhook_patch.yaml │ │ └── webhookcainjection_patch.yaml │ ├── manager │ │ ├── kustomization.yaml │ │ └── manager.yaml │ ├── prometheus │ │ ├── kustomization.yaml │ │ └── monitor.yaml │ ├── rbac │ │ ├── auth_proxy_client_clusterrole.yaml │ │ ├── auth_proxy_role.yaml │ │ ├── auth_proxy_role_binding.yaml │ │ ├── auth_proxy_service.yaml │ │ ├── kustomization.yaml │ │ ├── leader_election_role.yaml │ │ ├── leader_election_role_binding.yaml │ │ └── role_binding.yaml │ └── webhook │ ├── kustomization.yaml │ ├── kustomizeconfig.yaml │ └── service.yaml ├── go.mod ├── go.sum ├── hack │ └── boilerplate.go.txt └── main.go 2.3. 创建API # kubebuilder create api --group webapp --version v1 --kind Guestbook Create Resource [y/n] y Create Controller [y/n] y Writing scaffold for you to edit... api/v1/guestbook_types.go controllers/guestbook_controller.go Running make: $ make go: creating new go.mod: module tmp go: finding sigs.k8s.io/controller-tools/cmd v0.2.5 go: finding sigs.k8s.io/controller-tools/cmd/controller-gen v0.2.5 go: finding sigs.k8s.io v0.2.5 /Users/weihuanghu/go/bin/controller-gen object:headerFile=\"hack/boilerplate.go.txt\" paths=\"./...\" go fmt ./... go vet ./... go build -o bin/manager main.go 查看创建文件 api └── v1 ├── groupversion_info.go ├── guestbook_types.go └── zz_generated.deepcopy.go controllers ├── guestbook_controller.go └── suite_test.go 查看api/v1/guestbook_types.go // GuestbookSpec defines the desired state of Guestbook type GuestbookSpec struct { // INSERT ADDITIONAL SPEC FIELDS - desired state of cluster // Important: Run \"make\" to regenerate code after modifying this file // Quantity of instances // +kubebuilder:validation:Minimum=1 // +kubebuilder:validation:Maximum=10 Size int32 `json:\"size\"` // Name of the ConfigMap for GuestbookSpec's configuration // +kubebuilder:validation:MaxLength=15 // +kubebuilder:validation:MinLength=1 ConfigMapName string `json:\"configMapName\"` // +kubebuilder:validation:Enum=Phone;Address;Name Type string `json:\"alias,omitempty\"` } // GuestbookStatus defines the observed state of Guestbook type GuestbookStatus struct { // INSERT ADDITIONAL STATUS FIELD - define observed state of cluster // Important: Run \"make\" to regenerate code after modifying this file // PodName of the active Guestbook node. Active string `json:\"active\"` // PodNames of the standby Guestbook nodes. Standby []string `json:\"standby\"` } // +kubebuilder:object:root=true // +kubebuilder:subresource:status // +kubebuilder:resource:scope=Cluster // Guestbook is the Schema for the guestbooks API type Guestbook struct { metav1.TypeMeta `json:\",inline\"` metav1.ObjectMeta `json:\"metadata,omitempty\"` Spec GuestbookSpec `json:\"spec,omitempty\"` Status GuestbookStatus `json:\"status,omitempty\"` } 3. troubleshooting 3.1. controller-gen: No such file or directory ➜ operator-example kubebuilder init --domain github.com --license apache2 --owner \"Hu Weihuang\" Writing scaffold for you to edit... Get controller runtime: $ go get sigs.k8s.io/controller-runtime@v0.5.0 Update go.mod: $ go mod tidy Running make: $ make go: creating new go.mod: module tmp go: finding sigs.k8s.io v0.2.5 go: finding sigs.k8s.io/controller-tools/cmd v0.2.5 go: finding sigs.k8s.io/controller-tools/cmd/controller-gen v0.2.5 /Users/weihuanghu/go:/Users/weihuanghu/k8spath/bin/controller-gen object:headerFile=\"hack/boilerplate.go.txt\" paths=\"./...\" /bin/sh: /Users/weihuanghu/go:/Users/weihuanghu/k8spath/bin/controller-gen: No such file or directory make: *** [generate] Error 127 2020/04/13 14:34:47 failed to initialize project: exit status 2 由于本地存在多个GOPATH的目录，而获取了非当前项目下的GOPATH目录，因此将当前项目所在的GOPATH目录export到GOPATH环境变量中，就可以解决。 export GOPATH=\"/path/to/gopath\" 参考： https://kubernetes.io/zh/docs/concepts/extend-kubernetes/operator/ https://github.com/kubernetes-sigs/kubebuilder https://book.kubebuilder.io/quick-start.html https://operatorhub.io/ https://devops.college/developing-kubernetes-operator-is-now-easy-with-operator-framework-d3194a7428ff Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"develop/operator/develop-operator.html":{"url":"develop/operator/develop-operator.html","title":"如何开发一个Operator","keywords":"","body":"1. 准备工具环境及创建项目2. 创建CRD对象3. 实现控制器逻辑4. 注册控制器开发一个k8s operator组件主要会用到以下几个仓库或工具： kubebuilder/operator-sdk：主要用于创建CRD对象。 controller-manager：主要用于实现operator的controller逻辑。 本文以kubebuilder的工具和controller-manager example为例。 1. 准备工具环境及创建项目 安装kubebuilder工具 curl -L -o kubebuilder https://go.kubebuilder.io/dl/latest/$(go env GOOS)/$(go env GOARCH) chmod +x kubebuilder && mv kubebuilder /usr/local/bin/ 创建operator项目目录 $ kubebuilder init --domain example.com --license apache2 --owner \"Your Name\" --repo github.com/example/my-operator 将 example.com 替换为你的域名，Your Name 替换为你的名字，github.com/example/my-operator 替换为你的 GitHub 仓库。 2. 创建CRD对象 $ kubebuilder create api --group mygroup --version v1alpha1 --kind MyResource 这将在 api/v1alpha1 文件夹中创建一个名为 myresource_types.go 的文件，其中定义了 MyResource 资源的规范和状态。 编辑 api/v1alpha1/myresource_types.go 文件，添加自定义资源的规范和状态字段。 3. 实现控制器逻辑 在 controllers/myresource_controller.go 文件中实现控制器逻辑。该部分是不同的CRD实现自定义逻辑的核心部分。 主要包含以下几个部分： 定义ReconcileMyResource结构体。 实现func (r *ReconcileMyResource) Reconcile(ctx context.Context, request reconcile.Request) (reconcile.Result, error)函数接口。 定义finalizer逻辑。 定义reconcile逻辑。 package controllers import ( \"context\" \"github.com/go-logr/logr\" \"sigs.k8s.io/controller-runtime/pkg/controller/controllerutil\" \"sigs.k8s.io/controller-runtime/pkg/controller/inject\" \"sigs.k8s.io/controller-runtime/pkg/reconcile\" myv1alpha1 \"github.com/example/my-operator/api/v1alpha1\" corev1 \"k8s.io/api/core/v1\" \"k8s.io/apimachinery/pkg/api/errors\" \"k8s.io/apimachinery/pkg/runtime\" \"sigs.k8s.io/controller-runtime/pkg/client\" \"sigs.k8s.io/controller-runtime/pkg/controller\" logf \"sigs.k8s.io/controller-runtime/pkg/log\" \"sigs.k8s.io/controller-runtime/pkg/log/zap\" \"sigs.k8s.io/controller-runtime/pkg/manager\" \"sigs.k8s.io/controller-runtime/pkg/reconcile\" \"sigs.k8s.io/controller-runtime/pkg/source\" ) var log = logf.Log.WithName(\"controller_myresource\") // Add creates a new MyResource Controller and adds it to the Manager. The Manager will set fields on the Controller // and Start it when the Manager is Started. func Add(mgr manager.Manager) error { return add(mgr, newReconciler(mgr)) } // newReconciler returns a new reconcile.Reconciler func newReconciler(mgr manager.Manager) reconcile.Reconciler { return &ReconcileMyResource{client: mgr.GetClient(), scheme: mgr.GetScheme()} } // blank assignment to verify that ReconcileMyResource implements reconcile.Reconciler var _ reconcile.Reconciler = &ReconcileMyResource{} // ReconcileMyResource reconciles a MyResource object type ReconcileMyResource struct { client client.Client scheme *runtime.Scheme } // Reconcile reads that state of the cluster for a MyResource object and makes changes based on the state read // and what is in the MyResource.Spec func (r *ReconcileMyResource) Reconcile(ctx context.Context, request reconcile.Request) (reconcile.Result, error) { reqLogger := log.WithValues(\"Namespace\", request.Namespace, \"Name\", request.Name) reqLogger.Info(\"Reconciling MyResource\") // Fetch the MyResource instance myresource := &myv1alpha1.MyResource{} err := r.client.Get(ctx, request.NamespacedName, myresource) if err != nil { if errors.IsNotFound(err) { // Request object not found, could have been deleted after reconcile request. // Owned objects are automatically garbage collected. For additional cleanup logic use finalizers. // Return and don't requeue return reconcile.Result{}, nil } // Error reading the object - requeue the request. return reconcile.Result{}, err } // Add finalizer logic here // Add reconcile logic here return reconcile.Result{}, nil } 4. 注册控制器 在 main.go 文件中注册控制器，主要包含以下几个步骤 ctrl.SetLogger(zap.New())：注册日志工具 ctrl.NewManager：创建一个manager，必要时可设置选主逻辑。 ctrl.NewControllerManagedBy(mgr)：预计mgr创建manager controller并注册Reconciler。 mgr.Start(ctrl.SetupSignalHandler()): 运行mgr。 func main() { // set logger ctrl.SetLogger(zap.New()) // Set up a Manager mgr, err := ctrl.NewManager(ctrl.GetConfigOrDie(), ctrl.Options{ Scheme: myv1alpha1.SchemeBuilder.Scheme, MetricsBindAddress: *metricsHost, Port: 9443, LeaderElection: *enableLeaderElection, LeaderElectionID: \"my-operator-lock\", }) if err != nil { setupLog.Error(err, \"unable to start manager\") os.Exit(1) } // in a real controller, we'd create a new scheme for this err = api.AddToScheme(mgr.GetScheme()) if err != nil { setupLog.Error(err, \"unable to add scheme\") os.Exit(1) } // Create a new Reconciler r := &ReconcileMyResource{ client: m.GetClient(), scheme: m.GetScheme(), } // Create a new Controller and register the Reconciler err = ctrl.NewControllerManagedBy(mgr). For(&myv1alpha1.MyResource{}). // 自定义crd Complete(r) // 注册Reconciler if err != nil { setupLog.Error(err, \"unable to create controller\") os.Exit(1) } err = ctrl.NewWebhookManagedBy(mgr). For(&api.ChaosPod{}). Complete() if err != nil { setupLog.Error(err, \"unable to create webhook\") os.Exit(1) } setupLog.Info(\"starting manager\") if err := mgr.Start(ctrl.SetupSignalHandler()); err != nil { setupLog.Error(err, \"problem running manager\") os.Exit(1) } } 参考： Implementing a controller - The Kubebuilder Book controller-runtime源码分析 | 李乾坤的博客 Kubebuilder - SDK for building Kubernetes APIs using CRDs operator-framework/operator-sdk: SDK for building Kubernetes applications. https://blog.hdls.me/16500403497126.html kubebuilder-cronjob_controller Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"develop/develop-k8s.html":{"url":"develop/develop-k8s.html","title":"k8s社区开发指南","keywords":"","body":"1. 社区说明1.1. Community membership1.2. 社区活动日历1.3. 加入k8s slack1.4. 特别兴趣小组（SIG）2. 编译k8s仓库2.1. 编译二进制2.1.1. 基于docker构建容器编译。2.1.2. 基于构建机环境编译2.2. 编译镜像3. 如何给k8s提交PR1. 社区说明 1.1. Community membership Role Responsibilities Requirements Defined by Member Active contributor in the community Sponsored by 2 reviewers and multiple contributions to the project Kubernetes GitHub org member Reviewer Review contributions from other members History of review and authorship in a subproject OWNERS file reviewer entry Approver Contributions acceptance approval Highly experienced active reviewer and contributor to a subproject OWNERS file approver entry Subproject owner Set direction and priorities for a subproject Demonstrated responsibility and excellent technical judgement for the subproject sigs.yaml subproject OWNERS file owners entry 1.2. 社区活动日历 Community Calendar | Kubernetes Contributors 1.3. 加入k8s slack 点击 https://communityinviter.com/apps/kubernetes/community 1.4. 特别兴趣小组（SIG） 列表： https://github.com/kubernetes/community/blob/master/sig-list.md 2. 编译k8s仓库 参考： Building Kubernetes https://github.com/kubernetes/community/blob/master/contributors/devel/development.md#building-kubernetes 2.1. 编译二进制 2.1.1. 基于docker构建容器编译。 该方式为官方镜像及二进制文件的构建方式。 构建镜像(大小：5.97GB)为： kube-build:build-8faa8d3cb7-5-v1.27.0-go1.20.6-bullseye.0 git clone https://github.com/kubernetes/kubernetes.git cd kubernetes build/run.sh make # 构建全部 #指定模块构建 build/run.sh make kubeadm 输出如下： # build/run.sh make +++ [0804 18:39:11] Verifying Prerequisites.... +++ [0804 18:39:16] Building Docker image kube-build:build-8faa8d3cb7-5-v1.27.0-go1.20.6-bullseye.0 +++ [0804 18:40:49] Creating data container kube-build-data-8faa8d3cb7-5-v1.27.0-go1.20.6-bullseye.0 +++ [0804 18:40:50] Syncing sources to container +++ [0804 18:40:58] Output from this container will be rsynced out upon completion. Set KUBE_RUN_COPY_OUTPUT=n to disable. +++ [0804 18:40:58] Running build command... go: downloading go.uber.org/automaxprocs v1.5.2 +++ [0804 18:41:04] Setting GOMAXPROCS: 8 Go version: go version go1.20.6 linux/amd64 +++ [0804 18:41:04] Building go targets for linux/amd64 k8s.io/kubernetes/cmd/kube-proxy (static) k8s.io/kubernetes/cmd/kube-apiserver (static) k8s.io/kubernetes/cmd/kube-controller-manager (static) k8s.io/kubernetes/cmd/kubelet (non-static) k8s.io/kubernetes/cmd/kubeadm (static) k8s.io/kubernetes/cmd/kube-scheduler (static) k8s.io/component-base/logs/kube-log-runner (static) k8s.io/kube-aggregator (static) k8s.io/apiextensions-apiserver (static) k8s.io/kubernetes/cluster/gce/gci/mounter (non-static) k8s.io/kubernetes/cmd/kubectl (static) k8s.io/kubernetes/cmd/kubectl-convert (static) github.com/onsi/ginkgo/v2/ginkgo (non-static) k8s.io/kubernetes/test/e2e/e2e.test (test) k8s.io/kubernetes/test/conformance/image/go-runner (non-static) k8s.io/kubernetes/cmd/kubemark (static) github.com/onsi/ginkgo/v2/ginkgo (non-static) k8s.io/kubernetes/test/e2e_node/e2e_node.test (test) Env for linux/amd64: GOOS=linux GOARCH=amd64 GOROOT=/usr/local/go CGO_ENABLED= CC= Coverage is disabled. Coverage is disabled. +++ [0804 18:48:17] Placing binaries +++ [0804 18:48:25] Syncing out of container 产物文件在_output目录上。 kubernetes/_output# tree . |-- dockerized | |-- bin | | `-- linux | | `-- amd64 | | |-- apiextensions-apiserver | | |-- e2e_node.test | | |-- e2e.test | | |-- ginkgo | | |-- go-runner | | |-- kubeadm | | |-- kube-aggregator | | |-- kube-apiserver | | |-- kube-controller-manager | | |-- kubectl | | |-- kubectl-convert | | |-- kubelet | | |-- kube-log-runner | | |-- kubemark | | |-- kube-proxy | | |-- kube-scheduler | | |-- mounter | | `-- ncpu | `-- go `-- images `-- kube-build:build-8faa8d3cb7-5-v1.27.0-go1.20.6-bullseye.0 |-- Dockerfile |-- localtime |-- rsyncd.password `-- rsyncd.sh 2.1.2. 基于构建机环境编译 git clone https://github.com/kubernetes/kubernetes.git cd kubernetes # 构建全部二进制 make # 构建指定二进制 make WHAT=cmd/kubeadm 输出如下： # make WHAT=cmd/kubeadm go version go1.20.6 linux/amd64 +++ [0804 19:30:55] Setting GOMAXPROCS: 8 +++ [0804 19:30:56] Building go targets for linux/amd64 k8s.io/kubernetes/cmd/kubeadm (static) 2.2. 编译镜像 git clone https://github.com/kubernetes/kubernetes cd kubernetes make quick-release 3. 如何给k8s提交PR 参考： https://github.com/kubernetes/community/blob/master/contributors/guide/pull-requests.md https://github.com/kubernetes/community/blob/master/contributors/guide/first-contribution.md Here is the bot commands documentation. testing guide 参考： https://github.com/kubernetes/community/ https://github.com/kubernetes/community/tree/master/contributors/guide https://github.com/kubernetes/community/blob/master/contributors/guide/first-contribution.md issues labeled as a good first issue Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"trouble-shooting/node/_index.html":{"url":"trouble-shooting/node/_index.html","title":"节点相关问题","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"trouble-shooting/node/keycreate-permission-denied.html":{"url":"trouble-shooting/node/keycreate-permission-denied.html","title":"keycreate permission denied","keywords":"","body":"问题描述解决办法问题描述 write /proc/self/attr/keycreate: permission denied 具体报错： kuberuntime_manager.go:758] createPodSandbox for pod \"ecc-hostpath-provisioner-8jbhf_kube-system(b8050fd3-4ffe-11eb-a82e-c6090b53405b)\" failed: rpc error: code = Unknown desc = failed to start sandbox container for pod \"ecc-hostpath-provisioner-8jbhf\": Error response from daemon: OCI runtime create failed: container_linux.go:349: starting container process caused \"process_linux.go:449: container init caused \\\"write /proc/self/attr/keycreate: permission denied\\\"\": unknown 解决办法 SELINUX未设置成disabled # 将SELINUX设置成disabled setenforce 0 # 临时生效 # 永久生效，但需重启，配合上述命令可以不用立即重启 sed -i \"s/SELINUX=enforcing/SELINUX=disabled/g\" /etc/selinux/config # 查看SELinux状态 $ /usr/sbin/sestatus -v SELinux status: disabled $ getenforce Disabled Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"trouble-shooting/node/cgroup-pid-error.html":{"url":"trouble-shooting/node/cgroup-pid-error.html","title":"Cgroup不支持pid资源","keywords":"","body":"问题描述原因分析解决方案问题描述 机器内核版本较低，kubelet启动异常，报错如下： Failed to start ContainerManager failed to initialize top level QOS containers: failed to update top level Burstable QOS cgroup : failed to set supported cgroup subsystems for cgroup [kubepods burstable]: Failed to find subsystem mount for required subsystem: pids 原因分析 低版本内核的cgroup不支持pids资源的功能， cat /proc/cgroups #subsys_name hierarchy num_cgroups enabled cpuset 5 6 1 cpu 2 76 1 cpuacct 2 76 1 memory 4 76 1 devices 10 76 1 freezer 7 6 1 net_cls 3 6 1 blkio 8 76 1 perf_event 9 6 1 hugetlb 6 6 1 正常机器的cgroup root@host:~# cat /proc/cgroups #subsys_name hierarchy num_cgroups enabled cpuset 5 17 1 cpu 7 80 1 cpuacct 7 80 1 memory 12 80 1 devices 10 80 1 freezer 2 17 1 net_cls 4 17 1 blkio 8 80 1 perf_event 6 17 1 hugetlb 11 17 1 pids 3 80 1 # 此处支持pids资源 oom 9 1 1 解决方案 1、升级内核版本，使得cgroup支持pids资源。 或者 2、将kubelet的启动参数添加 SupportPodPidsLimit=false,SupportNodePidsLimit=false vi /etc/systemd/system/kubelet.service # 添加 kubelet 启动参数 --feature-gates=... ,SupportPodPidsLimit=false,SupportNodePidsLimit=false \\ systemctl daemon-reload && systemctl restart kubelet.service 文档参考： Kubernetes 1.14 稳定性改进中的进程ID限制 https://blog.csdn.net/qq_38900565/article/details/100707025 https://adoyle.me/Today-I-Learned/k8s/k8s-deployment.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"trouble-shooting/node/cgroup-subsystem-not-mount.html":{"url":"trouble-shooting/node/cgroup-subsystem-not-mount.html","title":"Cgroup子系统无法挂载","keywords":"","body":"问题描述解决问题描述 内核版本： 5.4.56-200.el7.x86_64 docker报错 May 13 16:54:26 8b26d7a8 dockerd[44352]: time=\"2021-05-13T16:54:26.565235530+08:00\" level=warning msg=\"failed to load plugin io.containerd.snapshotter.v1.devmapper\" error=\"devmapper not configured\" May 13 16:54:26 8b26d7a8 dockerd[44352]: time=\"2021-05-13T16:54:26.565525512+08:00\" level=warning msg=\"could not use snapshotter devmapper in metadata plugin\" error=\"devmapper not configured\" May 13 16:54:26 8b26d7a8 dockerd[44352]: time=\"2021-05-13T16:54:26.574734345+08:00\" level=warning msg=\"Your kernel does not support CPU realtime scheduler\" May 13 16:54:26 8b26d7a8 dockerd[44352]: time=\"2021-05-13T16:54:26.574792864+08:00\" level=warning msg=\"Your kernel does not support cgroup blkio weight\" May 13 16:54:26 8b26d7a8 dockerd[44352]: time=\"2021-05-13T16:54:26.574800326+08:00\" level=warning msg=\"Your kernel does not support cgroup blkio weight_device\" kubelet报错 图片 - kubelet 解决 cgroup问题解决： 1、curl https://pi-ops.oss-cn-hangzhou.aliyuncs.com/scripts/cgroupfs-mount.sh | bash 2、重启设备即可解决 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"trouble-shooting/pod-evicted.html":{"url":"trouble-shooting/pod-evicted.html","title":"Pod驱逐","keywords":"","body":"问题描述原因1. 查看节点和该节点pod状态2. 查看对应节点kubelet的日志3. 查看磁盘相关信息4. 查看kubelet配置解决方案问题描述 节点Pod被驱逐 原因 1. 查看节点和该节点pod状态 查看节点状态为Ready，查看该节点的所有pod，发现存在被驱逐的pod和nvidia-device-plugin为pending root@host:~$ kgpoallowide |grep 192.168.1.1 department-56 173e397c-ea35-4aac-85d8-07106e55d7b7 0/1 Evicted 0 52d 192.168.1.1 kube-system nvidia-device-plugin-daemonset-d58d2 0/1 Pending 0 1s 192.168.1.1 2. 查看对应节点kubelet的日志 0905 15:42:13.182280 23506 eviction_manager.go:142] Failed to admit pod rdma-device-plugin-daemonset-8nwb8_kube-system(acc28a85-cfb0-11e9-9729-6c92bf5e2432) - node has conditions: [DiskPressure] I0905 15:42:14.827343 23506 kubelet.go:1836] SyncLoop (ADD, \"api\"): \"nvidia-device-plugin-daemonset-88sm6_kube-system(adbd9227-cfb0-11e9-9729-6c92bf5e2432)\" W0905 15:42:14.827372 23506 eviction_manager.go:142] Failed to admit pod nvidia-device-plugin-daemonset-88sm6_kube-system(adbd9227-cfb0-11e9-9729-6c92bf5e2432) - node has conditions: [DiskPressure] I0905 15:42:15.722378 23506 kubelet_node_status.go:607] Update capacity for nvidia.com/gpu-share to 0 I0905 15:42:16.692488 23506 kubelet.go:1852] SyncLoop (DELETE, \"api\"): \"rdma-device-plugin-daemonset-8nwb8_kube-system(acc28a85-cfb0-11e9-9729-6c92bf5e2432)\" W0905 15:42:16.698445 23506 status_manager.go:489] Failed to delete status for pod \"rdma-device-plugin-daemonset-8nwb8_kube-system(acc28a85-cfb0-11e9-9729-6c92bf5e2432)\": pod \"rdma-device-plugin-daemonset-8nwb8\" not found I0905 15:42:16.698490 23506 kubelet.go:1846] SyncLoop (REMOVE, \"api\"): \"rdma-device-plugin-daemonset-8nwb8_kube-system(acc28a85-cfb0-11e9-9729-6c92bf5e2432)\" I0905 15:42:16.699267 23506 kubelet.go:2040] Failed to delete pod \"rdma-device-plugin-daemonset-8nwb8_kube-system(acc28a85-cfb0-11e9-9729-6c92bf5e2432)\", err: pod not found W0905 15:42:16.777355 23506 eviction_manager.go:332] eviction manager: attempting to reclaim nodefs I0905 15:42:16.777384 23506 eviction_manager.go:346] eviction manager: must evict pod(s) to reclaim nodefs E0905 15:42:16.777390 23506 eviction_manager.go:357] eviction manager: eviction thresholds have been met, but no pods are active to evict 存在关于pod驱逐相关的日志，驱逐的原因为node has conditions: [DiskPressure]。 3. 查看磁盘相关信息 [root@host /]# df -h Filesystem Size Used Avail Use% Mounted on devtmpfs 126G 0 126G 0% /dev tmpfs 126G 0 126G 0% /dev/shm tmpfs 126G 27M 126G 1% /run tmpfs 126G 0 126G 0% /sys/fs/cgroup /dev/sda1 20G 19G 0 100% / # 根目录磁盘满 /dev/nvme1n1 3.0T 191G 2.8T 7% /data2 /dev/nvme0n1 3.0T 1.3T 1.7T 44% /data1 /dev/sda4 182G 95G 87G 53% /data /dev/sda3 20G 3.8G 15G 20% /usr/local tmpfs 26G 0 26G 0% /run/user/0 发现根目录的磁盘盘，接着查看哪些文件占用磁盘。 [root@host ~/kata]# du -sh ./* 1.0M ./log 944K ./netlink 6.6G ./kernel3 /var/log/下存在7G 的日志。清理相关日志和无用文件后，根目录恢复空间。 [root@host /data]# df -h Filesystem Size Used Avail Use% Mounted on devtmpfs 126G 0 126G 0% /dev tmpfs 126G 0 126G 0% /dev/shm tmpfs 126G 27M 126G 1% /run tmpfs 126G 0 126G 0% /sys/fs/cgroup /dev/sda1 20G 5.8G 13G 32% / # 根目录正常 /dev/nvme1n1 3.0T 191G 2.8T 7% /data2 查看节点pod状态，相关plugin的pod恢复正常。 root@host:~$ kgpoallowide |grep 192.168.1.1 kube-system nvidia-device-plugin-daemonset-h4pjc 1/1 Running 0 16m 192.168.1.1 192.168.1.1 kube-system rdma-device-plugin-daemonset-xlkbv 1/1 Running 0 16m 192.168.1.1 192.168.1.1 4. 查看kubelet配置 查看kubelet关于pod驱逐相关的参数配置，可见节点kubelet开启了驱逐机制，正常情况下该配置应该是关闭的。 ExecStart=/usr/local/bin/kubelet \\ ... --eviction-hard=nodefs.available 解决方案 总结以上原因为，kubelet开启了pod驱逐的机制，根目录的磁盘达到100%，pod被驱逐，且无法再正常创建在该节点。 解决方案如下： 1、关闭kubelet的驱逐机制。 2、清除根目录的文件，恢复根目录空间，并后续增加根目录的磁盘监控。 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"trouble-shooting/pod-image-error.html":{"url":"trouble-shooting/pod-image-error.html","title":"镜像拉取失败问题","keywords":"","body":"1. Pod状态为ErrImagePull或ImagePullBackOff2. 查看pod的事件2.1. http: server gave HTTP response to HTTPS client2.2. no basic auth credentials常见镜像拉取问题排查 1. Pod状态为ErrImagePull或ImagePullBackOff docker-hub-75d4dfb984-5hggg 0/1 ImagePullBackOff 0 14m 192.168.1.30 docker-hub-75d4dfb984-9r57b 0/1 ErrImagePull 0 53s 192.168.0.42 ErrImagePull：表示pod已经调度到node节点，kubelet调用docker去拉取镜像失败。 ImagePullBackOff：表示kubelet拉取镜像失败后，不断重试去拉取仍然失败。 2. 查看pod的事件 通过kubectl describe pod 命令查看pod事件，该事件的报错信息在kubelet或docker的日志中也会查看到。 2.1. http: server gave HTTP response to HTTPS client 如果遇到以下报错，尝试将该镜像仓库添加到docker可信任的镜像仓库配置中。 Error getting v2 registry: Get https://docker.com:8080/v2/: http: server gave HTTP response to HTTPS client\" 具体操作是修改/etc/docker/daemon.json的insecure-registries参数 #cat /etc/docker/daemon.json { ... \"insecure-registries\": [ ... \"docker.com:8080\" ], ... } 2.2. no basic auth credentials 如果遇到no basic auth credentials报错，说明kubelet调用docker接口去拉取镜像时，镜像仓库的认证信息失败。 Normal BackOff 18s kubelet, 192.168.1.1 Back-off pulling image \"docker.com:8080/public/2048:latest\" Warning Failed 18s kubelet, 192.168.1.1 Error: ImagePullBackOff Normal Pulling 5s (x2 over 18s) kubelet, 192.168.1.1 Pulling image \"docker.com:8080/public/2048:latest\" Warning Failed 5s (x2 over 18s) kubelet, 192.168.1.1 Failed to pull image \"docker.com:8080/public/2048:latest\": rpc error: code = Unknown desc = Error response from daemon: Get http://docker.com:8080/v2/public/2048/manifests/latest: no basic auth credentials Warning Failed 5s (x2 over 18s) kubelet, 192.168.1.1 Error: ErrImagePull 具体操作，在拉取镜像失败的节点上登录该镜像仓库，认证信息会更新到$HOME/.docker/config.json文件中。将该文件拷贝到/var/lib/kubelet/config.json中。 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"trouble-shooting/pvc-terminating.html":{"url":"trouble-shooting/pvc-terminating.html","title":"PVC Terminating","keywords":"","body":"问题描述解决方法问题描述 pvc terminating pvc在删除时，卡在terminating中。 解决方法 kubectl patch pvc {PVC_NAME} -p '{\"metadata\":{\"finalizers\":null}}' Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"code-analysis/code-analysis-notes.html":{"url":"code-analysis/code-analysis-notes.html","title":"Kubernetes源码分析笔记","keywords":"","body":"详见：Kubernetes源码分析笔记详见：Kubernetes源码分析笔记 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kubelet/_index.html":{"url":"code-analysis/kubelet/_index.html","title":"kubelet","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kubelet/NewKubeletCommand.html":{"url":"code-analysis/kubelet/NewKubeletCommand.html","title":"NewKubeletCommand","keywords":"","body":"kubelet源码分析（一）之 NewKubeletCommand1. Main 函数2. NewKubeletCommand2.1. 参数解析2.1.1. 初始化参数和配置2.1.2. 打印帮助信息和版本信息2.1.3. kubelet config2.1.4. dynamic kubelet config2.2. 初始化kubeletServer和kubeletDeps2.2.1. kubeletServer2.2.2. kubeletDeps2.2.3. docker shim2.3. AddFlags2.4. 运行kubelet3. Run3.1. 构造kubeDeps3.1.1. clientConfig3.1.2. kubeClient3.1.3. dynamicKubeClient3.1.4. eventClient3.1.5. heartbeatClient3.1.6. csiClient3.1.7. CAdvisorInterface3.1.8. ContainerManager3.1.9. oomAdjuster3.2. Health check3.3. RunKubelet4. RunKubelet4.1. CreateAndInitKubelet4.1.1. NewMainKubelet4.1.2. PodConfig4.2. startKubelet4.2.1. k.Run5. 总结kubelet源码分析（一）之 NewKubeletCommand 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析 https://github.com/kubernetes/kubernetes/tree/v1.12.0/cmd/kubelet 部分的代码。 本文主要分析 kubernetes/cmd/kubelet部分，该部分主要涉及kubelet的参数解析，及初始化和构造相关的依赖组件（主要在kubeDeps结构体中），并没有kubelet运行的详细逻辑，该部分位于kubernetes/pkg/kubelet模块，待后续文章分析。 kubelet的cmd代码目录结构如下： kubelet ├── app │ ├── auth.go │ ├── init_others.go │ ├── init_windows.go │ ├── options # 包括kubelet使用到的option │ │ ├── container_runtime.go │ │ ├── globalflags.go │ │ ├── globalflags_linux.go │ │ ├── globalflags_other.go │ │ ├── options.go # 包括KubeletFlags、AddFlags、AddKubeletConfigFlags等 │ │ ├── osflags_others.go │ │ └── osflags_windows.go │ ├── plugins.go │ ├── server.go # 包括NewKubeletCommand、Run、RunKubelet、CreateAndInitKubelet、startKubelet等 │ ├── server_linux.go │ └── server_unsupported.go └── kubelet.go # kubelet的main入口函数 1. Main 函数 kubelet的入口函数Main 函数，具体代码参考：https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kubelet/kubelet.go。 func main() { rand.Seed(time.Now().UTC().UnixNano()) command := app.NewKubeletCommand(server.SetupSignalHandler()) logs.InitLogs() defer logs.FlushLogs() if err := command.Execute(); err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } } kubelet代码主要采用了Cobra命令行框架，核心代码如下： // 初始化命令行 command := app.NewKubeletCommand(server.SetupSignalHandler()) // 执行Execute err := command.Execute() 2. NewKubeletCommand NewKubeletCommand基于参数创建了一个*cobra.Command对象。其中核心部分代码为参数解析部分和Run函数。 // NewKubeletCommand creates a *cobra.Command object with default parameters func NewKubeletCommand(stopCh 2.1. 参数解析 kubelet开启了DisableFlagParsing参数，没有使用Cobra框架中的默认参数解析，而是自定义参数解析。 2.1.1. 初始化参数和配置 初始化参数解析，初始化cleanFlagSet，kubeletFlags，kubeletConfig。 cleanFlagSet := pflag.NewFlagSet(componentKubelet, pflag.ContinueOnError) cleanFlagSet.SetNormalizeFunc(flag.WordSepNormalizeFunc) kubeletFlags := options.NewKubeletFlags() kubeletConfig, err := options.NewKubeletConfiguration() 2.1.2. 打印帮助信息和版本信息 如果输入非法参数则打印使用帮助信息。 // initial flag parse, since we disable cobra's flag parsing if err := cleanFlagSet.Parse(args); err != nil { cmd.Usage() glog.Fatal(err) } // check if there are non-flag arguments in the command line cmds := cleanFlagSet.Args() if len(cmds) > 0 { cmd.Usage() glog.Fatalf(\"unknown command: %s\", cmds[0]) } 遇到help和version参数则打印相关内容并退出。 // short-circuit on help help, err := cleanFlagSet.GetBool(\"help\") if err != nil { glog.Fatal(`\"help\" flag is non-bool, programmer error, please correct`) } if help { cmd.Help() return } // short-circuit on verflag verflag.PrintAndExitIfRequested() utilflag.PrintFlags(cleanFlagSet) 2.1.3. kubelet config 加载并校验kubelet config。其中包括校验初始化的kubeletFlags，并从kubeletFlags的KubeletConfigFile参数获取kubelet config的内容。 // set feature gates from initial flags-based config if err := utilfeature.DefaultFeatureGate.SetFromMap(kubeletConfig.FeatureGates); err != nil { glog.Fatal(err) } // validate the initial KubeletFlags if err := options.ValidateKubeletFlags(kubeletFlags); err != nil { glog.Fatal(err) } if kubeletFlags.ContainerRuntime == \"remote\" && cleanFlagSet.Changed(\"pod-infra-container-image\") { glog.Warning(\"Warning: For remote container runtime, --pod-infra-container-image is ignored in kubelet, which should be set in that remote runtime instead\") } // load kubelet config file, if provided if configFile := kubeletFlags.KubeletConfigFile; len(configFile) > 0 { kubeletConfig, err = loadConfigFile(configFile) if err != nil { glog.Fatal(err) } // We must enforce flag precedence by re-parsing the command line into the new object. // This is necessary to preserve backwards-compatibility across binary upgrades. // See issue #56171 for more details. if err := kubeletConfigFlagPrecedence(kubeletConfig, args); err != nil { glog.Fatal(err) } // update feature gates based on new config if err := utilfeature.DefaultFeatureGate.SetFromMap(kubeletConfig.FeatureGates); err != nil { glog.Fatal(err) } } // We always validate the local configuration (command line + config file). // This is the default \"last-known-good\" config for dynamic config, and must always remain valid. if err := kubeletconfigvalidation.ValidateKubeletConfiguration(kubeletConfig); err != nil { glog.Fatal(err) } 2.1.4. dynamic kubelet config 如果开启使用动态kubelet的配置，则由动态配置文件替换kubelet配置文件。 // use dynamic kubelet config, if enabled var kubeletConfigController *dynamickubeletconfig.Controller if dynamicConfigDir := kubeletFlags.DynamicConfigDir.Value(); len(dynamicConfigDir) > 0 { var dynamicKubeletConfig *kubeletconfiginternal.KubeletConfiguration dynamicKubeletConfig, kubeletConfigController, err = BootstrapKubeletConfigController(dynamicConfigDir, func(kc *kubeletconfiginternal.KubeletConfiguration) error { // Here, we enforce flag precedence inside the controller, prior to the controller's validation sequence, // so that we get a complete validation at the same point where we can decide to reject dynamic config. // This fixes the flag-precedence component of issue #63305. // See issue #56171 for general details on flag precedence. return kubeletConfigFlagPrecedence(kc, args) }) if err != nil { glog.Fatal(err) } // If we should just use our existing, local config, the controller will return a nil config if dynamicKubeletConfig != nil { kubeletConfig = dynamicKubeletConfig // Note: flag precedence was already enforced in the controller, prior to validation, // by our above transform function. Now we simply update feature gates from the new config. if err := utilfeature.DefaultFeatureGate.SetFromMap(kubeletConfig.FeatureGates); err != nil { glog.Fatal(err) } } } 总结：以上通过对各种特定参数的解析，最终生成kubeletFlags和kubeletConfig两个重要的参数对象，用来构造kubeletServer和其他需求。 2.2. 初始化kubeletServer和kubeletDeps 2.2.1. kubeletServer // construct a KubeletServer from kubeletFlags and kubeletConfig kubeletServer := &options.KubeletServer{ KubeletFlags: *kubeletFlags, KubeletConfiguration: *kubeletConfig, } 2.2.2. kubeletDeps // use kubeletServer to construct the default KubeletDeps kubeletDeps, err := UnsecuredDependencies(kubeletServer) if err != nil { glog.Fatal(err) } // add the kubelet config controller to kubeletDeps kubeletDeps.KubeletConfigController = kubeletConfigController 2.2.3. docker shim 如果开启了docker shim参数，则执行RunDockershim。 // start the experimental docker shim, if enabled if kubeletServer.KubeletFlags.ExperimentalDockershim { if err := RunDockershim(&kubeletServer.KubeletFlags, kubeletConfig, stopCh); err != nil { glog.Fatal(err) } return } 2.3. AddFlags // keep cleanFlagSet separate, so Cobra doesn't pollute it with the global flags kubeletFlags.AddFlags(cleanFlagSet) options.AddKubeletConfigFlags(cleanFlagSet, kubeletConfig) options.AddGlobalFlags(cleanFlagSet) cleanFlagSet.BoolP(\"help\", \"h\", false, fmt.Sprintf(\"help for %s\", cmd.Name())) // ugly, but necessary, because Cobra's default UsageFunc and HelpFunc pollute the flagset with global flags const usageFmt = \"Usage:\\n %s\\n\\nFlags:\\n%s\" cmd.SetUsageFunc(func(cmd *cobra.Command) error { fmt.Fprintf(cmd.OutOrStderr(), usageFmt, cmd.UseLine(), cleanFlagSet.FlagUsagesWrapped(2)) return nil }) cmd.SetHelpFunc(func(cmd *cobra.Command, args []string) { fmt.Fprintf(cmd.OutOrStdout(), \"%s\\n\\n\"+usageFmt, cmd.Long, cmd.UseLine(), cleanFlagSet.FlagUsagesWrapped(2)) }) 其中： AddFlags代码可参考：kubernetes/cmd/kubelet/app/options/options.go#L323 AddKubeletConfigFlags代码可参考：kubernetes/cmd/kubelet/app/options/options.go#L424 2.4. 运行kubelet 运行kubelet并且不退出。由Run函数进入后续的操作。 // run the kubelet glog.V(5).Infof(\"KubeletConfiguration: %#v\", kubeletServer.KubeletConfiguration) if err := Run(kubeletServer, kubeletDeps, stopCh); err != nil { glog.Fatal(err) } 3. Run // Run runs the specified KubeletServer with the given Dependencies. This should never exit. // The kubeDeps argument may be nil - if so, it is initialized from the settings on KubeletServer. // Otherwise, the caller is assumed to have set up the Dependencies object and a default one will // not be generated. func Run(s *options.KubeletServer, kubeDeps *kubelet.Dependencies, stopCh 当运行环境是Windows的时候，初始化操作，但是该操作为空，只是预留。具体执行run(s, kubeDeps, stopCh)函数。 3.1. 构造kubeDeps 3.1.1. clientConfig 创建clientConfig，该对象用来创建各种的kubeDeps属性中包含的client。 clientConfig, err := createAPIServerClientConfig(s) if err != nil { return fmt.Errorf(\"invalid kubeconfig: %v\", err) } 3.1.2. kubeClient kubeClient, err = clientset.NewForConfig(clientConfig) if err != nil { glog.Warningf(\"New kubeClient from clientConfig error: %v\", err) } else if kubeClient.CertificatesV1beta1() != nil && clientCertificateManager != nil { glog.V(2).Info(\"Starting client certificate rotation.\") clientCertificateManager.SetCertificateSigningRequestClient(kubeClient.CertificatesV1beta1().CertificateSigningRequests()) clientCertificateManager.Start() } 3.1.3. dynamicKubeClient dynamicKubeClient, err = dynamic.NewForConfig(clientConfig) if err != nil { glog.Warningf(\"Failed to initialize dynamic KubeClient: %v\", err) } 3.1.4. eventClient // make a separate client for events eventClientConfig := *clientConfig eventClientConfig.QPS = float32(s.EventRecordQPS) eventClientConfig.Burst = int(s.EventBurst) eventClient, err = v1core.NewForConfig(&eventClientConfig) if err != nil { glog.Warningf(\"Failed to create API Server client for Events: %v\", err) } 3.1.5. heartbeatClient // make a separate client for heartbeat with throttling disabled and a timeout attached heartbeatClientConfig := *clientConfig heartbeatClientConfig.Timeout = s.KubeletConfiguration.NodeStatusUpdateFrequency.Duration // if the NodeLease feature is enabled, the timeout is the minimum of the lease duration and status update frequency if utilfeature.DefaultFeatureGate.Enabled(features.NodeLease) { leaseTimeout := time.Duration(s.KubeletConfiguration.NodeLeaseDurationSeconds) * time.Second if heartbeatClientConfig.Timeout > leaseTimeout { heartbeatClientConfig.Timeout = leaseTimeout } } heartbeatClientConfig.QPS = float32(-1) heartbeatClient, err = clientset.NewForConfig(&heartbeatClientConfig) if err != nil { glog.Warningf(\"Failed to create API Server client for heartbeat: %v\", err) } 3.1.6. csiClient // csiClient works with CRDs that support json only clientConfig.ContentType = \"application/json\" csiClient, err := csiclientset.NewForConfig(clientConfig) if err != nil { glog.Warningf(\"Failed to create CSI API client: %v\", err) } client赋值 kubeDeps.KubeClient = kubeClient kubeDeps.DynamicKubeClient = dynamicKubeClient if heartbeatClient != nil { kubeDeps.HeartbeatClient = heartbeatClient kubeDeps.OnHeartbeatFailure = closeAllConns } if eventClient != nil { kubeDeps.EventClient = eventClient } kubeDeps.CSIClient = csiClient 3.1.7. CAdvisorInterface if kubeDeps.CAdvisorInterface == nil { imageFsInfoProvider := cadvisor.NewImageFsInfoProvider(s.ContainerRuntime, s.RemoteRuntimeEndpoint) kubeDeps.CAdvisorInterface, err = cadvisor.New(imageFsInfoProvider, s.RootDirectory, cadvisor.UsingLegacyCadvisorStats(s.ContainerRuntime, s.RemoteRuntimeEndpoint)) if err != nil { return err } } 3.1.8. ContainerManager if kubeDeps.ContainerManager == nil { if s.CgroupsPerQOS && s.CgroupRoot == \"\" { glog.Infof(\"--cgroups-per-qos enabled, but --cgroup-root was not specified. defaulting to /\") s.CgroupRoot = \"/\" } kubeReserved, err := parseResourceList(s.KubeReserved) if err != nil { return err } systemReserved, err := parseResourceList(s.SystemReserved) if err != nil { return err } var hardEvictionThresholds []evictionapi.Threshold // If the user requested to ignore eviction thresholds, then do not set valid values for hardEvictionThresholds here. if !s.ExperimentalNodeAllocatableIgnoreEvictionThreshold { hardEvictionThresholds, err = eviction.ParseThresholdConfig([]string{}, s.EvictionHard, nil, nil, nil) if err != nil { return err } } experimentalQOSReserved, err := cm.ParseQOSReserved(s.QOSReserved) if err != nil { return err } devicePluginEnabled := utilfeature.DefaultFeatureGate.Enabled(features.DevicePlugins) kubeDeps.ContainerManager, err = cm.NewContainerManager( kubeDeps.Mounter, kubeDeps.CAdvisorInterface, cm.NodeConfig{ RuntimeCgroupsName: s.RuntimeCgroups, SystemCgroupsName: s.SystemCgroups, KubeletCgroupsName: s.KubeletCgroups, ContainerRuntime: s.ContainerRuntime, CgroupsPerQOS: s.CgroupsPerQOS, CgroupRoot: s.CgroupRoot, CgroupDriver: s.CgroupDriver, KubeletRootDir: s.RootDirectory, ProtectKernelDefaults: s.ProtectKernelDefaults, NodeAllocatableConfig: cm.NodeAllocatableConfig{ KubeReservedCgroupName: s.KubeReservedCgroup, SystemReservedCgroupName: s.SystemReservedCgroup, EnforceNodeAllocatable: sets.NewString(s.EnforceNodeAllocatable...), KubeReserved: kubeReserved, SystemReserved: systemReserved, HardEvictionThresholds: hardEvictionThresholds, }, QOSReserved: *experimentalQOSReserved, ExperimentalCPUManagerPolicy: s.CPUManagerPolicy, ExperimentalCPUManagerReconcilePeriod: s.CPUManagerReconcilePeriod.Duration, ExperimentalPodPidsLimit: s.PodPidsLimit, EnforceCPULimits: s.CPUCFSQuota, CPUCFSQuotaPeriod: s.CPUCFSQuotaPeriod.Duration, }, s.FailSwapOn, devicePluginEnabled, kubeDeps.Recorder) if err != nil { return err } } 3.1.9. oomAdjuster // TODO(vmarmol): Do this through container config. oomAdjuster := kubeDeps.OOMAdjuster if err := oomAdjuster.ApplyOOMScoreAdj(0, int(s.OOMScoreAdj)); err != nil { glog.Warning(err) } 3.2. Health check if s.HealthzPort > 0 { healthz.DefaultHealthz() go wait.Until(func() { err := http.ListenAndServe(net.JoinHostPort(s.HealthzBindAddress, strconv.Itoa(int(s.HealthzPort))), nil) if err != nil { glog.Errorf(\"Starting health server failed: %v\", err) } }, 5*time.Second, wait.NeverStop) } 3.3. RunKubelet 通过各种赋值构造了完整的kubeDeps结构体，最后再执行RunKubelet转入后续的kubelet执行流程。 if err := RunKubelet(s, kubeDeps, s.RunOnce); err != nil { return err } 4. RunKubelet // RunKubelet is responsible for setting up and running a kubelet. It is used in three different applications: // 1 Integration tests // 2 Kubelet binary // 3 Standalone 'kubernetes' binary // Eventually, #2 will be replaced with instances of #3 func RunKubelet(kubeServer *options.KubeletServer, kubeDeps *kubelet.Dependencies, runOnce bool) error { ... k, err := CreateAndInitKubelet(&kubeServer.KubeletConfiguration, ... kubeServer.NodeStatusMaxImages) if err != nil { return fmt.Errorf(\"failed to create kubelet: %v\", err) } // NewMainKubelet should have set up a pod source config if one didn't exist // when the builder was run. This is just a precaution. if kubeDeps.PodConfig == nil { return fmt.Errorf(\"failed to create kubelet, pod source config was nil\") } podCfg := kubeDeps.PodConfig rlimit.RlimitNumFiles(uint64(kubeServer.MaxOpenFiles)) // process pods and exit. if runOnce { if _, err := k.RunOnce(podCfg.Updates()); err != nil { return fmt.Errorf(\"runonce failed: %v\", err) } glog.Infof(\"Started kubelet as runonce\") } else { startKubelet(k, podCfg, &kubeServer.KubeletConfiguration, kubeDeps, kubeServer.EnableServer) glog.Infof(\"Started kubelet\") } return nil } RunKubelet函数核心代码为执行了CreateAndInitKubelet和startKubelet两个函数的操作，以下对这两个函数进行分析。 4.1. CreateAndInitKubelet 通过传入kubeDeps调用CreateAndInitKubelet初始化Kubelet。 k, err := CreateAndInitKubelet(&kubeServer.KubeletConfiguration, kubeDeps, &kubeServer.ContainerRuntimeOptions, kubeServer.ContainerRuntime, kubeServer.RuntimeCgroups, kubeServer.HostnameOverride, kubeServer.NodeIP, kubeServer.ProviderID, kubeServer.CloudProvider, kubeServer.CertDirectory, kubeServer.RootDirectory, kubeServer.RegisterNode, kubeServer.RegisterWithTaints, kubeServer.AllowedUnsafeSysctls, kubeServer.RemoteRuntimeEndpoint, kubeServer.RemoteImageEndpoint, kubeServer.ExperimentalMounterPath, kubeServer.ExperimentalKernelMemcgNotification, kubeServer.ExperimentalCheckNodeCapabilitiesBeforeMount, kubeServer.ExperimentalNodeAllocatableIgnoreEvictionThreshold, kubeServer.MinimumGCAge, kubeServer.MaxPerPodContainerCount, kubeServer.MaxContainerCount, kubeServer.MasterServiceNamespace, kubeServer.RegisterSchedulable, kubeServer.NonMasqueradeCIDR, kubeServer.KeepTerminatedPodVolumes, kubeServer.NodeLabels, kubeServer.SeccompProfileRoot, kubeServer.BootstrapCheckpointPath, kubeServer.NodeStatusMaxImages) if err != nil { return fmt.Errorf(\"failed to create kubelet: %v\", err) } 4.1.1. NewMainKubelet CreateAndInitKubelet方法中执行的核心函数是NewMainKubelet，NewMainKubelet实例化一个kubelet对象，该部分的具体代码在kubernetes/pkg/kubelet中，具体参考：kubernetes/pkg/kubelet/kubelet.go#L325。 func CreateAndInitKubelet(kubeCfg *kubeletconfiginternal.KubeletConfiguration, ... nodeStatusMaxImages int32) (k kubelet.Bootstrap, err error) { // TODO: block until all sources have delivered at least one update to the channel, or break the sync loop // up into \"per source\" synchronizations k, err = kubelet.NewMainKubelet(kubeCfg, kubeDeps, crOptions, containerRuntime, runtimeCgroups, hostnameOverride, nodeIP, providerID, cloudProvider, certDirectory, rootDirectory, registerNode, registerWithTaints, allowedUnsafeSysctls, remoteRuntimeEndpoint, remoteImageEndpoint, experimentalMounterPath, experimentalKernelMemcgNotification, experimentalCheckNodeCapabilitiesBeforeMount, experimentalNodeAllocatableIgnoreEvictionThreshold, minimumGCAge, maxPerPodContainerCount, maxContainerCount, masterServiceNamespace, registerSchedulable, nonMasqueradeCIDR, keepTerminatedPodVolumes, nodeLabels, seccompProfileRoot, bootstrapCheckpointPath, nodeStatusMaxImages) if err != nil { return nil, err } k.BirthCry() k.StartGarbageCollection() return k, nil } 4.1.2. PodConfig if kubeDeps.PodConfig == nil { var err error kubeDeps.PodConfig, err = makePodSourceConfig(kubeCfg, kubeDeps, nodeName, bootstrapCheckpointPath) if err != nil { return nil, err } } NewMainKubelet-->PodConfig-->NewPodConfig-->kubetypes.PodUpdate。会生成一个podUpdate的channel来监听pod的变化，该channel会在k.Run(podCfg.Updates())中作为关键入参。 4.2. startKubelet // process pods and exit. if runOnce { if _, err := k.RunOnce(podCfg.Updates()); err != nil { return fmt.Errorf(\"runonce failed: %v\", err) } glog.Infof(\"Started kubelet as runonce\") } else { startKubelet(k, podCfg, &kubeServer.KubeletConfiguration, kubeDeps, kubeServer.EnableServer) glog.Infof(\"Started kubelet\") } 如果设置了只运行一次的参数，则执行k.RunOnce，否则执行核心函数startKubelet。具体实现如下： func startKubelet(k kubelet.Bootstrap, podCfg *config.PodConfig, kubeCfg *kubeletconfiginternal.KubeletConfiguration, kubeDeps *kubelet.Dependencies, enableServer bool) { // start the kubelet go wait.Until(func() { k.Run(podCfg.Updates()) }, 0, wait.NeverStop) // start the kubelet server if enableServer { go k.ListenAndServe(net.ParseIP(kubeCfg.Address), uint(kubeCfg.Port), kubeDeps.TLSOptions, kubeDeps.Auth, kubeCfg.EnableDebuggingHandlers, kubeCfg.EnableContentionProfiling) } if kubeCfg.ReadOnlyPort > 0 { go k.ListenAndServeReadOnly(net.ParseIP(kubeCfg.Address), uint(kubeCfg.ReadOnlyPort)) } } 4.2.1. k.Run // start the kubelet go wait.Until(func() { k.Run(podCfg.Updates()) }, 0, wait.NeverStop) 通过长驻进程的方式运行k.Run，不退出，将kubelet的运行逻辑引入kubernetes/pkg/kubelet/kubelet.go部分，kubernetes/pkg/kubelet部分的运行逻辑待后续文章分析。 5. 总结 kubelet采用Cobra命令行框架和pflag参数解析框架，和apiserver、scheduler、controller-manager形成统一的代码风格。 kubernetes/cmd/kubelet部分主要对运行参数进行定义和解析，初始化和构造相关的依赖组件（主要在kubeDeps结构体中），并没有kubelet运行的详细逻辑，该部分位于kubernetes/pkg/kubelet模块。 cmd部分调用流程如下：Main-->NewKubeletCommand-->Run(kubeletServer, kubeletDeps, stopCh)-->run(s *options.KubeletServer, kubeDeps ..., stopCh ...)--> RunKubelet(s, kubeDeps, s.RunOnce)-->startKubelet-->k.Run(podCfg.Updates())-->pkg/kubelet。 同时RunKubelet(s, kubeDeps, s.RunOnce)-->CreateAndInitKubelet-->kubelet.NewMainKubelet-->pkg/kubelet。 参考文章： https://github.com/kubernetes/kubernetes/tree/v1.12.0 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kubelet/NewMainKubelet.html":{"url":"code-analysis/kubelet/NewMainKubelet.html","title":"NewMainKubelet","keywords":"","body":"kubelet源码分析（二）之 NewMainKubelet1. NewMainKubelet1.1. PodConfig1.1.1. makePodSourceConfig1.1.2. NewPodConfig1.1.3. NewSourceApiserver1.2. Lister1.2.1. serviceLister1.2.2. nodeLister1.3. 各种Manager1.3.1. containerRefManager1.3.2. oomWatcher1.3.3. dnsConfigurer1.3.4. secretManager & configMapManager1.3.5. livenessManager1.3.6. podManager1.3.7. resourceAnalyzer1.3.8. containerGC1.3.9. imageManager1.3.10. statusManager1.3.11. probeManager1.3.12. tokenManager1.3.13. volumePluginMgr1.3.14. volumeManager1.3.15. evictionManager1.4. containerRuntime1.4.1. NewDockerService1.4.2. NewDockerServer1.4.3. DockerServer.Start1.5. podWorker1.5.1. PodWorkers接口2. 总结kubelet源码分析（二）之 NewMainKubelet 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析 https://github.com/kubernetes/kubernetes/tree/v1.12.0/pkg/kubelet 部分的代码。 本文主要分析kubelet中的NewMainKubelet部分。 1. NewMainKubelet NewMainKubelet主要用来初始化和构造一个kubelet结构体，kubelet结构体定义参考:https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/kubelet/kubelet.go#L888 // NewMainKubelet instantiates a new Kubelet object along with all the required internal modules. // No initialization of Kubelet and its modules should happen here. func NewMainKubelet(kubeCfg *kubeletconfiginternal.KubeletConfiguration, kubeDeps *Dependencies, crOptions *config.ContainerRuntimeOptions, containerRuntime string, runtimeCgroups string, hostnameOverride string, nodeIP string, providerID string, cloudProvider string, certDirectory string, rootDirectory string, registerNode bool, registerWithTaints []api.Taint, allowedUnsafeSysctls []string, remoteRuntimeEndpoint string, remoteImageEndpoint string, experimentalMounterPath string, experimentalKernelMemcgNotification bool, experimentalCheckNodeCapabilitiesBeforeMount bool, experimentalNodeAllocatableIgnoreEvictionThreshold bool, minimumGCAge metav1.Duration, maxPerPodContainerCount int32, maxContainerCount int32, masterServiceNamespace string, registerSchedulable bool, nonMasqueradeCIDR string, keepTerminatedPodVolumes bool, nodeLabels map[string]string, seccompProfileRoot string, bootstrapCheckpointPath string, nodeStatusMaxImages int32) (*Kubelet, error) { ... } 1.1. PodConfig 通过makePodSourceConfig生成Pod config。 if kubeDeps.PodConfig == nil { var err error kubeDeps.PodConfig, err = makePodSourceConfig(kubeCfg, kubeDeps, nodeName, bootstrapCheckpointPath) if err != nil { return nil, err } } 1.1.1. makePodSourceConfig // makePodSourceConfig creates a config.PodConfig from the given // KubeletConfiguration or returns an error. func makePodSourceConfig(kubeCfg *kubeletconfiginternal.KubeletConfiguration, kubeDeps *Dependencies, nodeName types.NodeName, bootstrapCheckpointPath string) (*config.PodConfig, error) { ... // source of all configuration cfg := config.NewPodConfig(config.PodConfigNotificationIncremental, kubeDeps.Recorder) // define file config source if kubeCfg.StaticPodPath != \"\" { glog.Infof(\"Adding pod path: %v\", kubeCfg.StaticPodPath) config.NewSourceFile(kubeCfg.StaticPodPath, nodeName, kubeCfg.FileCheckFrequency.Duration, cfg.Channel(kubetypes.FileSource)) } // define url config source if kubeCfg.StaticPodURL != \"\" { glog.Infof(\"Adding pod url %q with HTTP header %v\", kubeCfg.StaticPodURL, manifestURLHeader) config.NewSourceURL(kubeCfg.StaticPodURL, manifestURLHeader, nodeName, kubeCfg.HTTPCheckFrequency.Duration, cfg.Channel(kubetypes.HTTPSource)) } // Restore from the checkpoint path // NOTE: This MUST happen before creating the apiserver source // below, or the checkpoint would override the source of truth. ... if kubeDeps.KubeClient != nil { glog.Infof(\"Watching apiserver\") if updatechannel == nil { updatechannel = cfg.Channel(kubetypes.ApiserverSource) } config.NewSourceApiserver(kubeDeps.KubeClient, nodeName, updatechannel) } return cfg, nil } 1.1.2. NewPodConfig // NewPodConfig creates an object that can merge many configuration sources into a stream // of normalized updates to a pod configuration. func NewPodConfig(mode PodConfigNotificationMode, recorder record.EventRecorder) *PodConfig { updates := make(chan kubetypes.PodUpdate, 50) storage := newPodStorage(updates, mode, recorder) podConfig := &PodConfig{ pods: storage, mux: config.NewMux(storage), updates: updates, sources: sets.String{}, } return podConfig } 1.1.3. NewSourceApiserver // NewSourceApiserver creates a config source that watches and pulls from the apiserver. func NewSourceApiserver(c clientset.Interface, nodeName types.NodeName, updates chan 1.2. Lister serviceLister和nodeLister分别通过List-Watch机制监听service和node的列表变化。 1.2.1. serviceLister serviceIndexer := cache.NewIndexer(cache.MetaNamespaceKeyFunc, cache.Indexers{cache.NamespaceIndex: cache.MetaNamespaceIndexFunc}) if kubeDeps.KubeClient != nil { serviceLW := cache.NewListWatchFromClient(kubeDeps.KubeClient.CoreV1().RESTClient(), \"services\", metav1.NamespaceAll, fields.Everything()) r := cache.NewReflector(serviceLW, &v1.Service{}, serviceIndexer, 0) go r.Run(wait.NeverStop) } serviceLister := corelisters.NewServiceLister(serviceIndexer) 1.2.2. nodeLister nodeIndexer := cache.NewIndexer(cache.MetaNamespaceKeyFunc, cache.Indexers{}) if kubeDeps.KubeClient != nil { fieldSelector := fields.Set{api.ObjectNameField: string(nodeName)}.AsSelector() nodeLW := cache.NewListWatchFromClient(kubeDeps.KubeClient.CoreV1().RESTClient(), \"nodes\", metav1.NamespaceAll, fieldSelector) r := cache.NewReflector(nodeLW, &v1.Node{}, nodeIndexer, 0) go r.Run(wait.NeverStop) } nodeInfo := &predicates.CachedNodeInfo{NodeLister: corelisters.NewNodeLister(nodeIndexer)} 1.3. 各种Manager 1.3.1. containerRefManager containerRefManager := kubecontainer.NewRefManager() 1.3.2. oomWatcher oomWatcher := NewOOMWatcher(kubeDeps.CAdvisorInterface, kubeDeps.Recorder) 1.3.3. dnsConfigurer clusterDNS := make([]net.IP, 0, len(kubeCfg.ClusterDNS)) for _, ipEntry := range kubeCfg.ClusterDNS { ip := net.ParseIP(ipEntry) if ip == nil { glog.Warningf(\"Invalid clusterDNS ip '%q'\", ipEntry) } else { clusterDNS = append(clusterDNS, ip) } } ... dns.NewConfigurer(kubeDeps.Recorder, nodeRef, parsedNodeIP, clusterDNS, kubeCfg.ClusterDomain, kubeCfg.ResolverConfig), 1.3.4. secretManager & configMapManager var secretManager secret.Manager var configMapManager configmap.Manager switch kubeCfg.ConfigMapAndSecretChangeDetectionStrategy { case kubeletconfiginternal.WatchChangeDetectionStrategy: secretManager = secret.NewWatchingSecretManager(kubeDeps.KubeClient) configMapManager = configmap.NewWatchingConfigMapManager(kubeDeps.KubeClient) case kubeletconfiginternal.TTLCacheChangeDetectionStrategy: secretManager = secret.NewCachingSecretManager( kubeDeps.KubeClient, manager.GetObjectTTLFromNodeFunc(klet.GetNode)) configMapManager = configmap.NewCachingConfigMapManager( kubeDeps.KubeClient, manager.GetObjectTTLFromNodeFunc(klet.GetNode)) case kubeletconfiginternal.GetChangeDetectionStrategy: secretManager = secret.NewSimpleSecretManager(kubeDeps.KubeClient) configMapManager = configmap.NewSimpleConfigMapManager(kubeDeps.KubeClient) default: return nil, fmt.Errorf(\"unknown configmap and secret manager mode: %v\", kubeCfg.ConfigMapAndSecretChangeDetectionStrategy) } klet.secretManager = secretManager klet.configMapManager = configMapManager 1.3.5. livenessManager klet.livenessManager = proberesults.NewManager() 1.3.6. podManager // podManager is also responsible for keeping secretManager and configMapManager contents up-to-date. klet.podManager = kubepod.NewBasicPodManager(kubepod.NewBasicMirrorClient(klet.kubeClient), secretManager, configMapManager, checkpointManager) 1.3.7. resourceAnalyzer klet.resourceAnalyzer = serverstats.NewResourceAnalyzer(klet, kubeCfg.VolumeStatsAggPeriod.Duration) 1.3.8. containerGC // setup containerGC containerGC, err := kubecontainer.NewContainerGC(klet.containerRuntime, containerGCPolicy, klet.sourcesReady) if err != nil { return nil, err } klet.containerGC = containerGC klet.containerDeletor = newPodContainerDeletor(klet.containerRuntime, integer.IntMax(containerGCPolicy.MaxPerPodContainer, minDeadContainerInPod)) 1.3.9. imageManager // setup imageManager imageManager, err := images.NewImageGCManager(klet.containerRuntime, klet.StatsProvider, kubeDeps.Recorder, nodeRef, imageGCPolicy, crOptions.PodSandboxImage) if err != nil { return nil, fmt.Errorf(\"failed to initialize image manager: %v\", err) } klet.imageManager = imageManager 1.3.10. statusManager klet.statusManager = status.NewManager(klet.kubeClient, klet.podManager, klet) 1.3.11. probeManager klet.probeManager = prober.NewManager( klet.statusManager, klet.livenessManager, klet.runner, containerRefManager, kubeDeps.Recorder) 1.3.12. tokenManager tokenManager := token.NewManager(kubeDeps.KubeClient) 1.3.13. volumePluginMgr klet.volumePluginMgr, err = NewInitializedVolumePluginMgr(klet, secretManager, configMapManager, tokenManager, kubeDeps.VolumePlugins, kubeDeps.DynamicPluginProber) if err != nil { return nil, err } if klet.enablePluginsWatcher { klet.pluginWatcher = pluginwatcher.NewWatcher(klet.getPluginsDir()) } 1.3.14. volumeManager // setup volumeManager klet.volumeManager = volumemanager.NewVolumeManager( kubeCfg.EnableControllerAttachDetach, nodeName, klet.podManager, klet.statusManager, klet.kubeClient, klet.volumePluginMgr, klet.containerRuntime, kubeDeps.Mounter, klet.getPodsDir(), kubeDeps.Recorder, experimentalCheckNodeCapabilitiesBeforeMount, keepTerminatedPodVolumes) 1.3.15. evictionManager // setup eviction manager evictionManager, evictionAdmitHandler := eviction.NewManager(klet.resourceAnalyzer, evictionConfig, killPodNow(klet.podWorkers, kubeDeps.Recorder), klet.imageManager, klet.containerGC, kubeDeps.Recorder, nodeRef, klet.clock) klet.evictionManager = evictionManager klet.admitHandlers.AddPodAdmitHandler(evictionAdmitHandler) 1.4. containerRuntime 目前pod所使用的runtime只有docker和remote两种，rkt已经废弃。 if containerRuntime == \"rkt\" { glog.Fatalln(\"rktnetes has been deprecated in favor of rktlet. Please see https://github.com/kubernetes-incubator/rktlet for more information.\") } 当runtime是docker的时候，会执行docker相关操作。 switch containerRuntime { case kubetypes.DockerContainerRuntime: // Create and start the CRI shim running as a grpc server. ... // The unix socket for kubelet dockershim communication. ... // Create dockerLegacyService when the logging driver is not supported. ... case kubetypes.RemoteContainerRuntime: // No-op. break default: return nil, fmt.Errorf(\"unsupported CRI runtime: %q\", containerRuntime) } 1.4.1. NewDockerService // Create and start the CRI shim running as a grpc server. streamingConfig := getStreamingConfig(kubeCfg, kubeDeps, crOptions) ds, err := dockershim.NewDockerService(kubeDeps.DockerClientConfig, crOptions.PodSandboxImage, streamingConfig, &pluginSettings, runtimeCgroups, kubeCfg.CgroupDriver, crOptions.DockershimRootDirectory, !crOptions.RedirectContainerStreaming) if err != nil { return nil, err } if crOptions.RedirectContainerStreaming { klet.criHandler = ds } 1.4.2. NewDockerServer // The unix socket for kubelet dockershim communication. glog.V(5).Infof(\"RemoteRuntimeEndpoint: %q, RemoteImageEndpoint: %q\", remoteRuntimeEndpoint, remoteImageEndpoint) glog.V(2).Infof(\"Starting the GRPC server for the docker CRI shim.\") server := dockerremote.NewDockerServer(remoteRuntimeEndpoint, ds) if err := server.Start(); err != nil { return nil, err } 1.4.3. DockerServer.Start // Start starts the dockershim grpc server. func (s *DockerServer) Start() error { // Start the internal service. if err := s.service.Start(); err != nil { glog.Errorf(\"Unable to start docker service\") return err } glog.V(2).Infof(\"Start dockershim grpc server\") l, err := util.CreateListener(s.endpoint) if err != nil { return fmt.Errorf(\"failed to listen on %q: %v\", s.endpoint, err) } // Create the grpc server and register runtime and image services. s.server = grpc.NewServer( grpc.MaxRecvMsgSize(maxMsgSize), grpc.MaxSendMsgSize(maxMsgSize), ) runtimeapi.RegisterRuntimeServiceServer(s.server, s.service) runtimeapi.RegisterImageServiceServer(s.server, s.service) go func() { if err := s.server.Serve(l); err != nil { glog.Fatalf(\"Failed to serve connections: %v\", err) } }() return nil } 1.5. podWorker 构造podWorkers和workQueue。 klet.workQueue = queue.NewBasicWorkQueue(klet.clock) klet.podWorkers = newPodWorkers(klet.syncPod, kubeDeps.Recorder, klet.workQueue, klet.resyncInterval, backOffPeriod, klet.podCache) 1.5.1. PodWorkers接口 // PodWorkers is an abstract interface for testability. type PodWorkers interface { UpdatePod(options *UpdatePodOptions) ForgetNonExistingPodWorkers(desiredPods map[types.UID]empty) ForgetWorker(uid types.UID) } podWorker主要用来对pod相应事件进行处理和同步，包含以下三个方法：UpdatePod、ForgetNonExistingPodWorkers、ForgetWorker。 2. 总结 NewMainKubelet主要用来构造kubelet结构体，其中kubelet除了包含必要的配置和client（例如：kubeClient、csiClient等）外，最主要的包含各种manager来管理不同的任务。 核心的manager有以下几种： oomWatcher：监控pod内存是否发生OOM。 podManager：管理pod的生命周期，包括对pod的增删改查操作等。 containerGC：对死亡容器进行垃圾回收。 imageManager：对容器镜像进行垃圾回收。 statusManager：与apiserver同步pod状态，同时也作状态缓存。 volumeManager：对pod的volume进行attached/detached/mounted/unmounted操作。 evictionManager：保证节点稳定，必要时对pod进行驱逐（例如资源不足的情况下）。 NewMainKubelet还包含了serviceLister和nodeLister来监听service和node的列表变化。 kubelet使用到的containerRuntime目前主要是docker，其中rkt已废弃。NewMainKubelet启动了dockershim grpc server来执行docker相关操作。 构建了podWorker来对pod相关的更新逻辑进行处理。 参考文章： https://github.com/kubernetes/kubernetes/tree/v1.12.0 https://github.com/kubernetes/kubernetes/tree/v1.12.0/pkg/kubelet Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kubelet/startKubelet.html":{"url":"code-analysis/kubelet/startKubelet.html","title":"startKubelet","keywords":"","body":"kubelet源码分析（三）之 startKubelet1. startKubelet2. Kubelet.Run3. initializeModules3.1. setupDataDirs3.2. manager4. 运行各种manager4.1. volumeManager4.2. syncNodeStatus4.3. updateRuntimeUp4.4. syncNetworkUtil4.5. podKiller4.6. statusManager4.7. probeManager4.8. runtimeClassManager4.9. PodLifecycleEventGenerator4.10. syncLoop5. syncLoop6. 总结6.1. 基本流程6.2. Managerkubelet源码分析（三）之 startKubelet 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析startKubelet，其中主要是kubelet.Run部分，该部分的内容主要是初始化并运行一些manager。对于kubelet所包含的各种manager的执行逻辑和pod的生命周期管理逻辑待后续文章分析。 后续的文章主要会分类分析pkg/kubelet部分的代码实现。 kubelet的pkg代码目录结构： kubelet ├── apis # 定义一些相关接口 ├── cadvisor # cadvisor ├── cm # ContainerManager、cpu manger、cgroup manager ├── config ├── configmap # configmap manager ├── container # Runtime、ImageService ├── dockershim # docker的相关调用 ├── eviction # eviction manager ├── images # image manager ├── kubeletconfig ├── kuberuntime # 核心：kubeGenericRuntimeManager、runtime容器的相关操作 ├── lifecycle ├── mountpod ├── network # pod dns ├── nodelease ├── nodestatus # MachineInfo、节点相关信息 ├── pleg # PodLifecycleEventGenerator ├── pod # 核心：pod manager、mirror pod ├── preemption ├── qos # 资源服务质量，不过暂时内容很少 ├── remote # RemoteRuntimeService ├── server ├── stats # StatsProvider ├── status # status manager ├── types # PodUpdate、PodOperation ├── volumemanager # VolumeManager ├── kubelet.go # 核心: SyncHandler、kubelet的大部分操作 ├── kubelet_getters.go # 各种get操作，例如获取相关目录：getRootDir、getPodsDir、getPluginsDir ├── kubelet_network.go # ├── kubelet_network_linux.go ├── kubelet_node_status.go # registerWithAPIServer、initialNode、syncNodeStatus ├── kubelet_pods.go # 核心：pod的增删改查等相关操作、podKiller、 ├── kubelet_resources.go ├── kubelet_volumes.go # ListVolumesForPod、cleanupOrphanedPodDirs ├── oom_watcher.go # OOMWatcher ├── pod_container_deletor.go ├── pod_workers.go # 核心：PodWorkers、UpdatePodOptions、syncPodOptions、managePodLoop ├── runonce.go # RunOnce ├── runtime.go ... 1. startKubelet startKubelet的函数位于cmd/kubelet/app/server.go，启动并运行一个kubelet，运行kubelet的逻辑代码位于pkg/kubelet/kubelet.go。 主要内容如下： 运行一个kubelet，执行kubelet中各种manager的相关逻辑。 运行kubelet server启动监听服务。 此部分代码位于cmd/kubelet/app/server.go func startKubelet(k kubelet.Bootstrap, podCfg *config.PodConfig, kubeCfg *kubeletconfiginternal.KubeletConfiguration, kubeDeps *kubelet.Dependencies, enableServer bool) { // start the kubelet go wait.Until(func() { k.Run(podCfg.Updates()) }, 0, wait.NeverStop) // start the kubelet server if enableServer { go k.ListenAndServe(net.ParseIP(kubeCfg.Address), uint(kubeCfg.Port), kubeDeps.TLSOptions, kubeDeps.Auth, kubeCfg.EnableDebuggingHandlers, kubeCfg.EnableContentionProfiling) } if kubeCfg.ReadOnlyPort > 0 { go k.ListenAndServeReadOnly(net.ParseIP(kubeCfg.Address), uint(kubeCfg.ReadOnlyPort)) } } 2. Kubelet.Run Kubelet.Run方法主要将NewMainKubelet构造的各种manager运行起来，让各种manager执行相应的功能，大部分manager为常驻进程的方式运行。 Kubelet.Run完整代码如下： 此部分代码位于pkg/kubelet/kubelet.go // Run starts the kubelet reacting to config updates func (kl *Kubelet) Run(updates 以下对Kubelet.Run分段进行分析。 3. initializeModules initializeModules包含了imageManager、serverCertificateManager、oomWatcher和resourceAnalyzer。 主要流程如下： 创建文件系统目录，包括kubelet的root目录、pods的目录、plugins的目录和容器日志目录。 启动imageManager、serverCertificateManager、oomWatcher、resourceAnalyzer。 各种manager的说明如下： imageManager：负责镜像垃圾回收。 serverCertificateManager：负责处理证书。 oomWatcher：监控内存使用，是否发生内存耗尽。 resourceAnalyzer：监控资源使用情况。 完整代码如下： 此部分代码位于pkg/kubelet/kubelet.go // initializeModules will initialize internal modules that do not require the container runtime to be up. // Note that the modules here must not depend on modules that are not initialized here. func (kl *Kubelet) initializeModules() error { // Prometheus metrics. metrics.Register(kl.runtimeCache, collectors.NewVolumeStatsCollector(kl)) // Setup filesystem directories. if err := kl.setupDataDirs(); err != nil { return err } // If the container logs directory does not exist, create it. if _, err := os.Stat(ContainerLogsDir); err != nil { if err := kl.os.MkdirAll(ContainerLogsDir, 0755); err != nil { glog.Errorf(\"Failed to create directory %q: %v\", ContainerLogsDir, err) } } // Start the image manager. kl.imageManager.Start() // Start the certificate manager if it was enabled. if kl.serverCertificateManager != nil { kl.serverCertificateManager.Start() } // Start out of memory watcher. if err := kl.oomWatcher.Start(kl.nodeRef); err != nil { return fmt.Errorf(\"Failed to start OOM watcher %v\", err) } // Start resource analyzer kl.resourceAnalyzer.Start() return nil } 3.1. setupDataDirs initializeModules先创建相关目录。 具体目录如下： ContainerLogsDir：目录为/var/log/containers。 rootDirectory：由参数传入，一般为/var/lib/kubelet。 PodsDir：目录为{rootDirectory}/pods。 PluginsDir：目录为{rootDirectory}/plugins。 initializeModules中setupDataDirs的相关代码如下： // Setup filesystem directories. if err := kl.setupDataDirs(); err != nil { return err } // If the container logs directory does not exist, create it. if _, err := os.Stat(ContainerLogsDir); err != nil { if err := kl.os.MkdirAll(ContainerLogsDir, 0755); err != nil { glog.Errorf(\"Failed to create directory %q: %v\", ContainerLogsDir, err) } } setupDataDirs代码如下 // setupDataDirs creates: // 1. the root directory // 2. the pods directory // 3. the plugins directory func (kl *Kubelet) setupDataDirs() error { kl.rootDirectory = path.Clean(kl.rootDirectory) if err := os.MkdirAll(kl.getRootDir(), 0750); err != nil { return fmt.Errorf(\"error creating root directory: %v\", err) } if err := kl.mounter.MakeRShared(kl.getRootDir()); err != nil { return fmt.Errorf(\"error configuring root directory: %v\", err) } if err := os.MkdirAll(kl.getPodsDir(), 0750); err != nil { return fmt.Errorf(\"error creating pods directory: %v\", err) } if err := os.MkdirAll(kl.getPluginsDir(), 0750); err != nil { return fmt.Errorf(\"error creating plugins directory: %v\", err) } return nil } 3.2. manager initializeModules中的manager如下： // Start the image manager. kl.imageManager.Start() // Start the certificate manager if it was enabled. if kl.serverCertificateManager != nil { kl.serverCertificateManager.Start() } // Start out of memory watcher. if err := kl.oomWatcher.Start(kl.nodeRef); err != nil { return fmt.Errorf(\"Failed to start OOM watcher %v\", err) } // Start resource analyzer kl.resourceAnalyzer.Start() 4. 运行各种manager 4.1. volumeManager volumeManager主要运行一组异步循环，根据在此节点上安排的pod调整哪些volume需要attached/detached/mounted/unmounted。 // Start volume manager go kl.volumeManager.Run(kl.sourcesReady, wait.NeverStop) volumeManager.Run实现代码如下： func (vm *volumeManager) Run(sourcesReady config.SourcesReady, stopCh 4.2. syncNodeStatus syncNodeStatus通过goroutine的方式定期执行，它将节点的状态同步给master，必要的时候注册kubelet。 if kl.kubeClient != nil { // Start syncing node status immediately, this may set up things the runtime needs to run. go wait.Until(kl.syncNodeStatus, kl.nodeStatusUpdateFrequency, wait.NeverStop) go kl.fastStatusUpdateOnce() // start syncing lease if utilfeature.DefaultFeatureGate.Enabled(features.NodeLease) { go kl.nodeLeaseController.Run(wait.NeverStop) } } 4.3. updateRuntimeUp updateRuntimeUp调用容器运行时状态回调，在容器运行时首次启动时初始化运行时相关模块，如果状态检查失败则返回错误。 如果状态检查正常，在kubelet runtimeState中更新容器运行时的正常运行时间。 go wait.Until(kl.updateRuntimeUp, 5*time.Second, wait.NeverStop) 4.4. syncNetworkUtil 通过循环的方式同步iptables的规则，不过当前代码并没有执行任何操作。 // Start loop to sync iptables util rules if kl.makeIPTablesUtilChains { go wait.Until(kl.syncNetworkUtil, 1*time.Minute, wait.NeverStop) } 4.5. podKiller 但pod没有被podworker正确处理的时候，启动一个goroutine负责杀死pod。 // Start a goroutine responsible for killing pods (that are not properly // handled by pod workers). go wait.Until(kl.podKiller, 1*time.Second, wait.NeverStop) podKiller代码如下： 此部分代码位于pkg/kubelet/kubelet_pods.go // podKiller launches a goroutine to kill a pod received from the channel if // another goroutine isn't already in action. func (kl *Kubelet) podKiller() { killing := sets.NewString() // guard for the killing set lock := sync.Mutex{} for podPair := range kl.podKillingCh { runningPod := podPair.RunningPod apiPod := podPair.APIPod lock.Lock() exists := killing.Has(string(runningPod.ID)) if !exists { killing.Insert(string(runningPod.ID)) } lock.Unlock() if !exists { go func(apiPod *v1.Pod, runningPod *kubecontainer.Pod) { glog.V(2).Infof(\"Killing unwanted pod %q\", runningPod.Name) err := kl.killPod(apiPod, runningPod, nil, nil) if err != nil { glog.Errorf(\"Failed killing the pod %q: %v\", runningPod.Name, err) } lock.Lock() killing.Delete(string(runningPod.ID)) lock.Unlock() }(apiPod, runningPod) } } } 4.6. statusManager 使用apiserver同步pods状态; 也用作状态缓存。 // Start component sync loops. kl.statusManager.Start() statusManager.Start的实现代码如下： func (m *manager) Start() { // Don't start the status manager if we don't have a client. This will happen // on the master, where the kubelet is responsible for bootstrapping the pods // of the master components. if m.kubeClient == nil { glog.Infof(\"Kubernetes client is nil, not starting status manager.\") return } glog.Info(\"Starting to sync pod status with apiserver\") syncTicker := time.Tick(syncPeriod) // syncPod and syncBatch share the same go routine to avoid sync races. go wait.Forever(func() { select { case syncRequest := 4.7. probeManager 处理容器探针 kl.probeManager.Start() 4.8. runtimeClassManager // Start syncing RuntimeClasses if enabled. if kl.runtimeClassManager != nil { go kl.runtimeClassManager.Run(wait.NeverStop) } 4.9. PodLifecycleEventGenerator // Start the pod lifecycle event generator. kl.pleg.Start() PodLifecycleEventGenerator是一个pod生命周期时间生成器接口，具体如下： // PodLifecycleEventGenerator contains functions for generating pod life cycle events. type PodLifecycleEventGenerator interface { Start() Watch() chan *PodLifecycleEvent Healthy() (bool, error) } start方法具体实现如下： // Start spawns a goroutine to relist periodically. func (g *GenericPLEG) Start() { go wait.Until(g.relist, g.relistPeriod, wait.NeverStop) } 4.10. syncLoop 最后调用syncLoop来执行同步变化变更的循环。 kl.syncLoop(updates, kl) 5. syncLoop syncLoop是处理变化的循环。 它监听来自三种channel（file，apiserver和http）的更改。 对于看到的任何新更改，将针对所需状态和运行状态运行同步。 如果没有看到配置的变化，将在每个同步频率秒同步最后已知的所需状态。 // syncLoop is the main loop for processing changes. It watches for changes from // three channels (file, apiserver, and http) and creates a union of them. For // any new change seen, will run a sync against desired state and running state. If // no changes are seen to the configuration, will synchronize the last known desired // state every sync-frequency seconds. Never returns. func (kl *Kubelet) syncLoop(updates 其中调用了syncLoopIteration的函数来执行更具体的监控pod变化的循环。syncLoopIteration代码逻辑待后续单独分析。 6. 总结 6.1. 基本流程 Kubelet.Run主要流程如下： 初始化模块，其实就是运行imageManager、serverCertificateManager、oomWatcher、resourceAnalyzer。 运行各种manager，大部分以常驻goroutine的方式运行，其中包括volumeManager、statusManager等。 执行处理变更的循环函数syncLoop，对pod的生命周期进行管理。 syncLoop： syncLoop函数，对pod的生命周期进行管理，其中syncLoop调用了syncLoopIteration函数，该函数根据podUpdate的信息，针对不同的操作，由SyncHandler来执行pod的增删改查等生命周期的管理，其中的syncHandler包括HandlePodSyncs和HandlePodCleanups等。该部分逻辑待后续文章具体分析。 6.2. Manager 以下介绍kubelet运行时涉及到的manager的内容。 manager 说明 imageManager 负责镜像垃圾回收 serverCertificateManager 负责处理证书 oomWatcher 监控内存使用，是否发生内存耗尽即OOM resourceAnalyzer 监控资源使用情况 volumeManager 对pod执行attached/detached/mounted/unmounted操作 statusManager 使用apiserver同步pods状态; 也用作状态缓存 probeManager 处理容器探针 runtimeClassManager 同步RuntimeClasses podKiller 负责杀死pod 参考文章： https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kubelet/app/server.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/kubelet/kubelet.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kubelet/syncLoopIteration.html":{"url":"code-analysis/kubelet/syncLoopIteration.html","title":"syncLoopIteration","keywords":"","body":"kubelet源码分析（四）之 syncLoopIteration1. syncLoop2. syncLoopIteration2.1. configCh2.2. plegCh2.3. syncCh2.4. livenessManager.Update2.5. housekeepingCh3. SyncHandler3.1. HandlePodAdditions3.2. HandlePodUpdates3.3. HandlePodRemoves3.4. HandlePodReconcile3.5. HandlePodSyncs3.6. HandlePodCleanups4. dispatchWork5. PodWorkers.UpdatePod6. managePodLoop7. 总结kubelet源码分析（四）之 syncLoopIteration 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析kubelet中syncLoopIteration部分。syncLoopIteration通过几种channel来对不同类型的事件进行监听并做增删改查的处理。 1. syncLoop syncLoop是处理变更的循环。 它监听来自三种channel（file，apiserver和http）的更改。 对于看到的任何新更改，将针对所需状态和运行状态运行同步。 如果没有看到配置的变化，将在每个同步频率秒同步最后已知的所需状态。 此部分代码位于pkg/kubelet/kubelet.go // syncLoop is the main loop for processing changes. It watches for changes from // three channels (file, apiserver, and http) and creates a union of them. For // any new change seen, will run a sync against desired state and running state. If // no changes are seen to the configuration, will synchronize the last known desired // state every sync-frequency seconds. Never returns. func (kl *Kubelet) syncLoop(updates 其中调用了syncLoopIteration的函数来执行更具体的监控pod变化的循环。 2. syncLoopIteration syncLoopIteration主要通过几种channel来对不同类型的事件进行监听并处理。其中包括：configCh、plegCh、syncCh、houseKeepingCh、livenessManager.Updates()。 syncLoopIteration实际执行了pod的操作，此部分设置了几种不同的channel: configCh：将配置更改的pod分派给事件类型的相应处理程序回调。 plegCh：更新runtime缓存，同步pod。 syncCh：同步所有等待同步的pod。 houseKeepingCh：触发清理pod。 livenessManager.Updates()：对失败的pod或者liveness检查失败的pod进行sync操作。 syncLoopIteration部分代码位于pkg/kubelet/kubelet.go 2.1. configCh configCh将配置更改的pod分派给事件类型的相应处理程序回调，该部分主要通过SyncHandler对pod的不同事件进行增删改查等操作。 func (kl *Kubelet) syncLoopIteration(configCh 可以看出syncLoopIteration根据podUpdate的值来执行不同的pod操作，具体如下： ADD：HandlePodAdditions UPDATE：HandlePodUpdates REMOVE：HandlePodRemoves RECONCILE：HandlePodReconcile DELETE：HandlePodUpdates RESTORE：HandlePodAdditions podsToSync：HandlePodSyncs 其中执行pod的handler操作的是SyncHandler，该类型是一个接口，实现体为kubelet本身，具体见后续分析。 2.2. plegCh plegCh：更新runtime缓存，同步pod。此处调用了HandlePodSyncs的函数。 case e := 2.3. syncCh syncCh：同步所有等待同步的pod。此处调用了HandlePodSyncs的函数。 case 2.4. livenessManager.Update livenessManager.Updates()：对失败的pod或者liveness检查失败的pod进行sync操作。此处调用了HandlePodSyncs的函数。 case update := 2.5. housekeepingCh houseKeepingCh：触发清理pod。此处调用了HandlePodCleanups的函数。 case 3. SyncHandler SyncHandler是一个定义Pod的不同Handler的接口，具体是实现者是kubelet，该接口的方法主要在syncLoopIteration中调用，接口定义如下： // SyncHandler is an interface implemented by Kubelet, for testability type SyncHandler interface { HandlePodAdditions(pods []*v1.Pod) HandlePodUpdates(pods []*v1.Pod) HandlePodRemoves(pods []*v1.Pod) HandlePodReconcile(pods []*v1.Pod) HandlePodSyncs(pods []*v1.Pod) HandlePodCleanups() error } SyncHandler部分代码位于pkg/kubelet/kubelet.go 3.1. HandlePodAdditions HandlePodAdditions先根据pod创建时间对pod进行排序，然后遍历pod列表，来执行pod的相关操作。 // HandlePodAdditions is the callback in SyncHandler for pods being added from // a config source. func (kl *Kubelet) HandlePodAdditions(pods []*v1.Pod) { start := kl.clock.Now() sort.Sort(sliceutils.PodsByCreationTime(pods)) for _, pod := range pods { ... } } 将pod添加到pod manager中。 for _, pod := range pods { // Responsible for checking limits in resolv.conf if kl.dnsConfigurer != nil && kl.dnsConfigurer.ResolverConfig != \"\" { kl.dnsConfigurer.CheckLimitsForResolvConf() } existingPods := kl.podManager.GetPods() // Always add the pod to the pod manager. Kubelet relies on the pod // manager as the source of truth for the desired state. If a pod does // not exist in the pod manager, it means that it has been deleted in // the apiserver and no action (other than cleanup) is required. kl.podManager.AddPod(pod) ... } 如果是mirror pod，则对mirror pod进行处理。 if kubepod.IsMirrorPod(pod) { kl.handleMirrorPod(pod, start) continue } 如果当前pod的状态不是Terminated状态，则判断是否接受该pod，如果不接受则将pod状态改为Failed。 if !kl.podIsTerminated(pod) { // Only go through the admission process if the pod is not // terminated. // We failed pods that we rejected, so activePods include all admitted // pods that are alive. activePods := kl.filterOutTerminatedPods(existingPods) // Check if we can admit the pod; if not, reject it. if ok, reason, message := kl.canAdmitPod(activePods, pod); !ok { kl.rejectPod(pod, reason, message) continue } } 执行dispatchWork函数，该函数是syncHandler中调用到的核心函数，该函数在pod worker中启动一个异步循环，来分派pod的相关操作。该函数的具体操作待后续分析。 mirrorPod, _ := kl.podManager.GetMirrorPodByPod(pod) kl.dispatchWork(pod, kubetypes.SyncPodCreate, mirrorPod, start) 最后加pod添加到probe manager中。 kl.probeManager.AddPod(pod) 3.2. HandlePodUpdates HandlePodUpdates同样遍历pod列表，执行相应的操作。 // HandlePodUpdates is the callback in the SyncHandler interface for pods // being updated from a config source. func (kl *Kubelet) HandlePodUpdates(pods []*v1.Pod) { start := kl.clock.Now() for _, pod := range pods { ... } } 将pod更新到pod manager中。 for _, pod := range pods { // Responsible for checking limits in resolv.conf if kl.dnsConfigurer != nil && kl.dnsConfigurer.ResolverConfig != \"\" { kl.dnsConfigurer.CheckLimitsForResolvConf() } kl.podManager.UpdatePod(pod) ... } 如果是mirror pod，则对mirror pod进行处理。 if kubepod.IsMirrorPod(pod) { kl.handleMirrorPod(pod, start) continue } 执行dispatchWork函数。 // TODO: Evaluate if we need to validate and reject updates. mirrorPod, _ := kl.podManager.GetMirrorPodByPod(pod) kl.dispatchWork(pod, kubetypes.SyncPodUpdate, mirrorPod, start) 3.3. HandlePodRemoves HandlePodRemoves遍历pod列表。 // HandlePodRemoves is the callback in the SyncHandler interface for pods // being removed from a config source. func (kl *Kubelet) HandlePodRemoves(pods []*v1.Pod) { start := kl.clock.Now() for _, pod := range pods { ... } } 从pod manager中删除pod。 for _, pod := range pods { kl.podManager.DeletePod(pod) ... } 如果是mirror pod，则对mirror pod进行处理。 if kubepod.IsMirrorPod(pod) { kl.handleMirrorPod(pod, start) continue } 调用kubelet的deletePod函数来删除pod。 // Deletion is allowed to fail because the periodic cleanup routine // will trigger deletion again. if err := kl.deletePod(pod); err != nil { glog.V(2).Infof(\"Failed to delete pod %q, err: %v\", format.Pod(pod), err) } deletePod 函数将需要删除的pod加入podKillingCh的channel中，有podKiller监听这个channel去执行删除任务，实现如下： // deletePod deletes the pod from the internal state of the kubelet by: // 1. stopping the associated pod worker asynchronously // 2. signaling to kill the pod by sending on the podKillingCh channel // // deletePod returns an error if not all sources are ready or the pod is not // found in the runtime cache. func (kl *Kubelet) deletePod(pod *v1.Pod) error { if pod == nil { return fmt.Errorf(\"deletePod does not allow nil pod\") } if !kl.sourcesReady.AllReady() { // If the sources aren't ready, skip deletion, as we may accidentally delete pods // for sources that haven't reported yet. return fmt.Errorf(\"skipping delete because sources aren't ready yet\") } kl.podWorkers.ForgetWorker(pod.UID) // Runtime cache may not have been updated to with the pod, but it's okay // because the periodic cleanup routine will attempt to delete again later. runningPods, err := kl.runtimeCache.GetPods() if err != nil { return fmt.Errorf(\"error listing containers: %v\", err) } runningPod := kubecontainer.Pods(runningPods).FindPod(\"\", pod.UID) if runningPod.IsEmpty() { return fmt.Errorf(\"pod not found\") } podPair := kubecontainer.PodPair{APIPod: pod, RunningPod: &runningPod} kl.podKillingCh 从probe manager中移除pod。 kl.probeManager.RemovePod(pod) 3.4. HandlePodReconcile 遍历pod列表。 // HandlePodReconcile is the callback in the SyncHandler interface for pods // that should be reconciled. func (kl *Kubelet) HandlePodReconcile(pods []*v1.Pod) { start := kl.clock.Now() for _, pod := range pods { ... } } 将pod更新到pod manager中。 for _, pod := range pods { // Update the pod in pod manager, status manager will do periodically reconcile according // to the pod manager. kl.podManager.UpdatePod(pod) ... } 必要时调整pod的Ready状态，执行dispatchWork函数。 // Reconcile Pod \"Ready\" condition if necessary. Trigger sync pod for reconciliation. if status.NeedToReconcilePodReadiness(pod) { mirrorPod, _ := kl.podManager.GetMirrorPodByPod(pod) kl.dispatchWork(pod, kubetypes.SyncPodSync, mirrorPod, start) } 如果pod被设定为需要被驱逐的，则删除pod中的容器。 // After an evicted pod is synced, all dead containers in the pod can be removed. if eviction.PodIsEvicted(pod.Status) { if podStatus, err := kl.podCache.Get(pod.UID); err == nil { kl.containerDeletor.deleteContainersInPod(\"\", podStatus, true) } } 3.5. HandlePodSyncs HandlePodSyncs是syncHandler接口回调函数，调用dispatchWork，通过pod worker来执行任务。 // HandlePodSyncs is the callback in the syncHandler interface for pods // that should be dispatched to pod workers for sync. func (kl *Kubelet) HandlePodSyncs(pods []*v1.Pod) { start := kl.clock.Now() for _, pod := range pods { mirrorPod, _ := kl.podManager.GetMirrorPodByPod(pod) kl.dispatchWork(pod, kubetypes.SyncPodSync, mirrorPod, start) } } 3.6. HandlePodCleanups HandlePodCleanups主要用来执行pod的清理任务，其中包括terminating的pod，orphaned的pod等。 首先查看pod使用到的cgroup。 // HandlePodCleanups performs a series of cleanup work, including terminating // pod workers, killing unwanted pods, and removing orphaned volumes/pod // directories. // NOTE: This function is executed by the main sync loop, so it // should not contain any blocking calls. func (kl *Kubelet) HandlePodCleanups() error { // The kubelet lacks checkpointing, so we need to introspect the set of pods // in the cgroup tree prior to inspecting the set of pods in our pod manager. // this ensures our view of the cgroup tree does not mistakenly observe pods // that are added after the fact... var ( cgroupPods map[types.UID]cm.CgroupName err error ) if kl.cgroupsPerQOS { pcm := kl.containerManager.NewPodContainerManager() cgroupPods, err = pcm.GetAllPodsFromCgroups() if err != nil { return fmt.Errorf(\"failed to get list of pods that still exist on cgroup mounts: %v\", err) } } ... } 列出所有pod包括mirror pod。 allPods, mirrorPods := kl.podManager.GetPodsAndMirrorPods() // Pod phase progresses monotonically. Once a pod has reached a final state, // it should never leave regardless of the restart policy. The statuses // of such pods should not be changed, and there is no need to sync them. // TODO: the logic here does not handle two cases: // 1. If the containers were removed immediately after they died, kubelet // may fail to generate correct statuses, let alone filtering correctly. // 2. If kubelet restarted before writing the terminated status for a pod // to the apiserver, it could still restart the terminated pod (even // though the pod was not considered terminated by the apiserver). // These two conditions could be alleviated by checkpointing kubelet. activePods := kl.filterOutTerminatedPods(allPods) desiredPods := make(map[types.UID]empty) for _, pod := range activePods { desiredPods[pod.UID] = empty{} } pod worker停止不再存在的pod的任务，并从probe manager中清除pod。 // Stop the workers for no-longer existing pods. // TODO: is here the best place to forget pod workers? kl.podWorkers.ForgetNonExistingPodWorkers(desiredPods) kl.probeManager.CleanupPods(activePods) 将需要杀死的pod加入到podKillingCh的channel中，podKiller的任务会监听该channel并获取需要杀死的pod列表来执行杀死pod的操作。 runningPods, err := kl.runtimeCache.GetPods() if err != nil { glog.Errorf(\"Error listing containers: %#v\", err) return err } for _, pod := range runningPods { if _, found := desiredPods[pod.ID]; !found { kl.podKillingCh 当pod不再被绑定到该节点，移除podStatus，其中removeOrphanedPodStatuses最后调用的函数是statusManager的RemoveOrphanedStatuses方法。 kl.removeOrphanedPodStatuses(allPods, mirrorPods) 移除所有的orphaned volume。 // Remove any orphaned volumes. // Note that we pass all pods (including terminated pods) to the function, // so that we don't remove volumes associated with terminated but not yet // deleted pods. err = kl.cleanupOrphanedPodDirs(allPods, runningPods) if err != nil { // We want all cleanup tasks to be run even if one of them failed. So // we just log an error here and continue other cleanup tasks. // This also applies to the other clean up tasks. glog.Errorf(\"Failed cleaning up orphaned pod directories: %v\", err) } 移除mirror pod。 // Remove any orphaned mirror pods. kl.podManager.DeleteOrphanedMirrorPods() 删除不再运行的pod的cgroup。 // Remove any cgroups in the hierarchy for pods that are no longer running. if kl.cgroupsPerQOS { kl.cleanupOrphanedPodCgroups(cgroupPods, activePods) } 执行垃圾回收（GC）操作。 kl.backOff.GC() 4. dispatchWork dispatchWork通过pod worker启动一个异步的循环。 完整代码如下： // dispatchWork starts the asynchronous sync of the pod in a pod worker. // If the pod is terminated, dispatchWork func (kl *Kubelet) dispatchWork(pod *v1.Pod, syncType kubetypes.SyncPodType, mirrorPod *v1.Pod, start time.Time) { if kl.podIsTerminated(pod) { if pod.DeletionTimestamp != nil { // If the pod is in a terminated state, there is no pod worker to // handle the work item. Check if the DeletionTimestamp has been // set, and force a status update to trigger a pod deletion request // to the apiserver. kl.statusManager.TerminatePod(pod) } return } // Run the sync in an async worker. kl.podWorkers.UpdatePod(&UpdatePodOptions{ Pod: pod, MirrorPod: mirrorPod, UpdateType: syncType, OnCompleteFunc: func(err error) { if err != nil { metrics.PodWorkerLatency.WithLabelValues(syncType.String()).Observe(metrics.SinceInMicroseconds(start)) } }, }) // Note the number of containers for new pods. if syncType == kubetypes.SyncPodCreate { metrics.ContainersPerPodCount.Observe(float64(len(pod.Spec.Containers))) } } 以下分段进行分析： 如果pod的状态是处于Terminated状态，则执行statusManager的TerminatePod操作。 // dispatchWork starts the asynchronous sync of the pod in a pod worker. // If the pod is terminated, dispatchWork func (kl *Kubelet) dispatchWork(pod *v1.Pod, syncType kubetypes.SyncPodType, mirrorPod *v1.Pod, start time.Time) { if kl.podIsTerminated(pod) { if pod.DeletionTimestamp != nil { // If the pod is in a terminated state, there is no pod worker to // handle the work item. Check if the DeletionTimestamp has been // set, and force a status update to trigger a pod deletion request // to the apiserver. kl.statusManager.TerminatePod(pod) } return } ... } 执行pod worker的UpdatePod函数，该函数是pod worker的核心函数，来执行pod相关操作。具体逻辑待下文分析。 // Run the sync in an async worker. kl.podWorkers.UpdatePod(&UpdatePodOptions{ Pod: pod, MirrorPod: mirrorPod, UpdateType: syncType, OnCompleteFunc: func(err error) { if err != nil { metrics.PodWorkerLatency.WithLabelValues(syncType.String()).Observe(metrics.SinceInMicroseconds(start)) } }, }) 当创建类型是SyncPodCreate（即创建pod的时候），统计新pod中容器的数目。 // Note the number of containers for new pods. if syncType == kubetypes.SyncPodCreate { metrics.ContainersPerPodCount.Observe(float64(len(pod.Spec.Containers))) } 5. PodWorkers.UpdatePod PodWorkers是一个接口类型： // PodWorkers is an abstract interface for testability. type PodWorkers interface { UpdatePod(options *UpdatePodOptions) ForgetNonExistingPodWorkers(desiredPods map[types.UID]empty) ForgetWorker(uid types.UID) } 其中UpdatePod是一个核心方法，通过podUpdates的channel来传递需要处理的pod信息，对于新创建的pod每个pod都会由一个goroutine来执行managePodLoop。 此部分代码位于pkg/kubelet/pod_workers.go // Apply the new setting to the specified pod. // If the options provide an OnCompleteFunc, the function is invoked if the update is accepted. // Update requests are ignored if a kill pod request is pending. func (p *podWorkers) UpdatePod(options *UpdatePodOptions) { pod := options.Pod uid := pod.UID var podUpdates chan UpdatePodOptions var exists bool p.podLock.Lock() defer p.podLock.Unlock() if podUpdates, exists = p.podUpdates[uid]; !exists { // We need to have a buffer here, because checkForUpdates() method that // puts an update into channel is called from the same goroutine where // the channel is consumed. However, it is guaranteed that in such case // the channel is empty, so buffer of size 1 is enough. podUpdates = make(chan UpdatePodOptions, 1) p.podUpdates[uid] = podUpdates // Creating a new pod worker either means this is a new pod, or that the // kubelet just restarted. In either case the kubelet is willing to believe // the status of the pod for the first pod worker sync. See corresponding // comment in syncPod. go func() { defer runtime.HandleCrash() p.managePodLoop(podUpdates) }() } if !p.isWorking[pod.UID] { p.isWorking[pod.UID] = true podUpdates 6. managePodLoop managePodLoop通过读取podUpdateschannel的信息，执行syncPodFn函数，而syncPodFn函数在newPodWorkers的时候赋值了，即kubelet.syncPod。kubelet.syncPod具体代码逻辑待后续文章单独分析。 // newPodWorkers传入syncPod函数 klet.podWorkers = newPodWorkers(klet.syncPod, kubeDeps.Recorder, klet.workQueue, klet.resyncInterval, backOffPeriod, klet.podCache) newPodWorkers函数参考： func newPodWorkers(syncPodFn syncPodFnType, recorder record.EventRecorder, workQueue queue.WorkQueue, resyncInterval, backOffPeriod time.Duration, podCache kubecontainer.Cache) *podWorkers { return &podWorkers{ podUpdates: map[types.UID]chan UpdatePodOptions{}, isWorking: map[types.UID]bool{}, lastUndeliveredWorkUpdate: map[types.UID]UpdatePodOptions{}, syncPodFn: syncPodFn, // 构造传入klet.syncPod函数 recorder: recorder, workQueue: workQueue, resyncInterval: resyncInterval, backOffPeriod: backOffPeriod, podCache: podCache, } } managePodLoop函数参考： 此部分代码位于pkg/kubelet/pod_workers.go func (p *podWorkers) managePodLoop(podUpdates 7. 总结 syncLoopIteration基本流程如下： 通过几种channel来对不同类型的事件进行监听并处理。其中channel包括：configCh、plegCh、syncCh、houseKeepingCh、livenessManager.Updates()。 不同的SyncHandler执行不同的增删改查操作。 其中HandlePodAdditions、HandlePodUpdates、HandlePodReconcile、HandlePodSyncs都调用到了dispatchWork来执行pod的相关操作。HandlePodCleanups的pod清理任务，通过channel的方式加需要清理的pod给podKiller来清理。 dispatchWork调用podWorkers.UpdatePod执行异步操作。 podWorkers.UpdatePod中调用managePodLoop来执行pod相关操作循环。 channel类型及作用： configCh：将配置更改的pod分派给事件类型的相应处理程序回调。 plegCh：更新runtime缓存，同步pod。 syncCh：同步所有等待同步的pod。 houseKeepingCh：触发清理pod。 livenessManager.Updates()：对失败的pod或者liveness检查失败的pod进行sync操作。 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/kubelet/kubelet.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/kubelet/pod_workers.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kubelet/syncPod.html":{"url":"code-analysis/kubelet/syncPod.html","title":"syncPod","keywords":"","body":"kubelet源码分析（五）之 syncPod1. managePodLoop2. syncPod2.1. SyncPodKill2.2. SyncPodCreate2.3. Cgroups2.4. Mirror Pod2.5. makePodDataDirs2.6. mount volumes2.7. PullSecretsForPod2.8. containerRuntime.SyncPod3. Runtime.SyncPod3.1. computePodActions3.2. killPodWithSyncResult3.3. killContainer3.4. createPodSandbox3.5. start init container3.6. start containers4. startContainer4.1. pull image4.2. CreateContainer4.3. StartContainer4.4. execute post start hook5. 总结kubelet源码分析（五）之 syncPod 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析kubelet中syncPod的部分。 1. managePodLoop managePodLoop通过读取podUpdateschannel的信息，执行syncPodFn函数，而syncPodFn函数在newPodWorkers的时候赋值了，即kubelet.syncPod。 managePodLoop完整代码如下： 此部分代码位于pkg/kubelet/pod_workers.go func (p *podWorkers) managePodLoop(podUpdates 以下分析syncPod相关逻辑。 2. syncPod syncPod可以理解为是一个单个pod进行同步任务的事务脚本。其中入参是syncPodOptions，syncPodOptions记录了需要同步的pod的相关信息。具体定义如下： // syncPodOptions provides the arguments to a SyncPod operation. type syncPodOptions struct { // the mirror pod for the pod to sync, if it is a static pod mirrorPod *v1.Pod // pod to sync pod *v1.Pod // the type of update (create, update, sync) updateType kubetypes.SyncPodType // the current status podStatus *kubecontainer.PodStatus // if update type is kill, use the specified options to kill the pod. killPodOptions *KillPodOptions } syncPod主要执行以下的工作流： 如果是正在创建的pod，则记录pod worker的启动latency。 调用generateAPIPodStatus为pod提供v1.PodStatus信息。 如果pod是第一次运行，记录pod的启动latency。 更新status manager中的pod状态。 如果pod不应该被运行则杀死pod。 如果pod是一个static pod，并且没有对应的mirror pod，则创建一个mirror pod。 如果没有pod的数据目录则给pod创建对应的数据目录。 等待volume被attach或mount。 获取pod的secret数据。 调用container runtime的SyncPod函数，执行相关pod操作。 更新pod的ingress和egress的traffic limit。 当以上任务流中有任何的error，则return error。在下一次执行syncPod的任务流会被再次执行。对于错误信息会被记录到event中，方便debug。 以下对syncPod的执行过程进行分析。 syncPod的代码位于pkg/kubelet/kubelet.go 2.1. SyncPodKill 首先，获取syncPodOptions的pod信息。 func (kl *Kubelet) syncPod(o syncPodOptions) error { // pull out the required options pod := o.pod mirrorPod := o.mirrorPod podStatus := o.podStatus updateType := o.updateType ... } 如果pod是需要被杀死的，则执行killPod，会在指定的宽限期内杀死pod。 // if we want to kill a pod, do it now! if updateType == kubetypes.SyncPodKill { killPodOptions := o.killPodOptions if killPodOptions == nil || killPodOptions.PodStatusFunc == nil { return fmt.Errorf(\"kill pod options are required if update type is kill\") } apiPodStatus := killPodOptions.PodStatusFunc(pod, podStatus) kl.statusManager.SetPodStatus(pod, apiPodStatus) // we kill the pod with the specified grace period since this is a termination if err := kl.killPod(pod, nil, podStatus, killPodOptions.PodTerminationGracePeriodSecondsOverride); err != nil { kl.recorder.Eventf(pod, v1.EventTypeWarning, events.FailedToKillPod, \"error killing pod: %v\", err) // there was an error killing the pod, so we return that error directly utilruntime.HandleError(err) return err } return nil } 2.2. SyncPodCreate 如果pod是需要被创建的，则记录pod的启动latency，latency与pod在apiserver中第一次被记录相关。 // Latency measurements for the main workflow are relative to the // first time the pod was seen by the API server. var firstSeenTime time.Time if firstSeenTimeStr, ok := pod.Annotations[kubetypes.ConfigFirstSeenAnnotationKey]; ok { firstSeenTime = kubetypes.ConvertToTimestamp(firstSeenTimeStr).Get() } // Record pod worker start latency if being created // TODO: make pod workers record their own latencies if updateType == kubetypes.SyncPodCreate { if !firstSeenTime.IsZero() { // This is the first time we are syncing the pod. Record the latency // since kubelet first saw the pod if firstSeenTime is set. metrics.PodWorkerStartLatency.Observe(metrics.SinceInMicroseconds(firstSeenTime)) } else { glog.V(3).Infof(\"First seen time not recorded for pod %q\", pod.UID) } } 通过pod和pod status生成最终的api pod status并设置pod的IP。 // Generate final API pod status with pod and status manager status apiPodStatus := kl.generateAPIPodStatus(pod, podStatus) // The pod IP may be changed in generateAPIPodStatus if the pod is using host network. (See #24576) // TODO(random-liu): After writing pod spec into container labels, check whether pod is using host network, and // set pod IP to hostIP directly in runtime.GetPodStatus podStatus.IP = apiPodStatus.PodIP 记录pod到running状态的时间。 // Record the time it takes for the pod to become running. existingStatus, ok := kl.statusManager.GetPodStatus(pod.UID) if !ok || existingStatus.Phase == v1.PodPending && apiPodStatus.Phase == v1.PodRunning && !firstSeenTime.IsZero() { metrics.PodStartLatency.Observe(metrics.SinceInMicroseconds(firstSeenTime)) } 如果pod是不可运行的，则更新pod和container的状态和相应的原因。 runnable := kl.canRunPod(pod) if !runnable.Admit { // Pod is not runnable; update the Pod and Container statuses to why. apiPodStatus.Reason = runnable.Reason apiPodStatus.Message = runnable.Message // Waiting containers are not creating. const waitingReason = \"Blocked\" for _, cs := range apiPodStatus.InitContainerStatuses { if cs.State.Waiting != nil { cs.State.Waiting.Reason = waitingReason } } for _, cs := range apiPodStatus.ContainerStatuses { if cs.State.Waiting != nil { cs.State.Waiting.Reason = waitingReason } } } 并更新status manager中的状态信息，杀死不可运行的pod。 // Update status in the status manager kl.statusManager.SetPodStatus(pod, apiPodStatus) // Kill pod if it should not be running if !runnable.Admit || pod.DeletionTimestamp != nil || apiPodStatus.Phase == v1.PodFailed { var syncErr error if err := kl.killPod(pod, nil, podStatus, nil); err != nil { kl.recorder.Eventf(pod, v1.EventTypeWarning, events.FailedToKillPod, \"error killing pod: %v\", err) syncErr = fmt.Errorf(\"error killing pod: %v\", err) utilruntime.HandleError(syncErr) } else { if !runnable.Admit { // There was no error killing the pod, but the pod cannot be run. // Return an error to signal that the sync loop should back off. syncErr = fmt.Errorf(\"pod cannot be run: %s\", runnable.Message) } } return syncErr } 如果网络插件还没到Ready状态，则只有在使用host网络模式的情况下才启动pod。 // If the network plugin is not ready, only start the pod if it uses the host network if rs := kl.runtimeState.networkErrors(); len(rs) != 0 && !kubecontainer.IsHostNetworkPod(pod) { kl.recorder.Eventf(pod, v1.EventTypeWarning, events.NetworkNotReady, \"%s: %v\", NetworkNotReadyErrorMsg, rs) return fmt.Errorf(\"%s: %v\", NetworkNotReadyErrorMsg, rs) } 2.3. Cgroups 给pod创建Cgroups，如果cgroups-per-qos参数开启，则申请相应的资源。对于terminated的pod不需要创建或更新pod的Cgroups。 当重新启动kubelet并且启用cgroups-per-qos时，应该间歇性地终止所有pod的运行容器并在qos cgroup hierarchy下重新启动。 如果pod的cgroup已经存在或者pod第一次运行，不杀死pod中容器。 // Create Cgroups for the pod and apply resource parameters // to them if cgroups-per-qos flag is enabled. pcm := kl.containerManager.NewPodContainerManager() // If pod has already been terminated then we need not create // or update the pod's cgroup if !kl.podIsTerminated(pod) { // When the kubelet is restarted with the cgroups-per-qos // flag enabled, all the pod's running containers // should be killed intermittently and brought back up // under the qos cgroup hierarchy. // Check if this is the pod's first sync firstSync := true for _, containerStatus := range apiPodStatus.ContainerStatuses { if containerStatus.State.Running != nil { firstSync = false break } } // Don't kill containers in pod if pod's cgroups already // exists or the pod is running for the first time podKilled := false if !pcm.Exists(pod) && !firstSync { if err := kl.killPod(pod, nil, podStatus, nil); err == nil { podKilled = true } } ... 如果pod被杀死并且重启策略是Never，则不创建或更新对应的Cgroups，否则创建和更新pod的Cgroups。 // Create and Update pod's Cgroups // Don't create cgroups for run once pod if it was killed above // The current policy is not to restart the run once pods when // the kubelet is restarted with the new flag as run once pods are // expected to run only once and if the kubelet is restarted then // they are not expected to run again. // We don't create and apply updates to cgroup if its a run once pod and was killed above if !(podKilled && pod.Spec.RestartPolicy == v1.RestartPolicyNever) { if !pcm.Exists(pod) { if err := kl.containerManager.UpdateQOSCgroups(); err != nil { glog.V(2).Infof(\"Failed to update QoS cgroups while syncing pod: %v\", err) } if err := pcm.EnsureExists(pod); err != nil { kl.recorder.Eventf(pod, v1.EventTypeWarning, events.FailedToCreatePodContainer, \"unable to ensure pod container exists: %v\", err) return fmt.Errorf(\"failed to ensure that the pod: %v cgroups exist and are correctly applied: %v\", pod.UID, err) } } } 其中创建Cgroups是通过containerManager的UpdateQOSCgroups来执行。 if err := kl.containerManager.UpdateQOSCgroups(); err != nil { glog.V(2).Infof(\"Failed to update QoS cgroups while syncing pod: %v\", err) } 2.4. Mirror Pod 如果pod是一个static pod，没有对应的mirror pod，则创建一个mirror pod；如果存在mirror pod则删除再重建一个mirror pod。 // Create Mirror Pod for Static Pod if it doesn't already exist if kubepod.IsStaticPod(pod) { podFullName := kubecontainer.GetPodFullName(pod) deleted := false if mirrorPod != nil { if mirrorPod.DeletionTimestamp != nil || !kl.podManager.IsMirrorPodOf(mirrorPod, pod) { // The mirror pod is semantically different from the static pod. Remove // it. The mirror pod will get recreated later. glog.Warningf(\"Deleting mirror pod %q because it is outdated\", format.Pod(mirrorPod)) if err := kl.podManager.DeleteMirrorPod(podFullName); err != nil { glog.Errorf(\"Failed deleting mirror pod %q: %v\", format.Pod(mirrorPod), err) } else { deleted = true } } } if mirrorPod == nil || deleted { node, err := kl.GetNode() if err != nil || node.DeletionTimestamp != nil { glog.V(4).Infof(\"No need to create a mirror pod, since node %q has been removed from the cluster\", kl.nodeName) } else { glog.V(4).Infof(\"Creating a mirror pod for static pod %q\", format.Pod(pod)) if err := kl.podManager.CreateMirrorPod(pod); err != nil { glog.Errorf(\"Failed creating a mirror pod for %q: %v\", format.Pod(pod), err) } } } } 2.5. makePodDataDirs 给pod创建数据目录。 // Make data directories for the pod if err := kl.makePodDataDirs(pod); err != nil { kl.recorder.Eventf(pod, v1.EventTypeWarning, events.FailedToMakePodDataDirectories, \"error making pod data directories: %v\", err) glog.Errorf(\"Unable to make pod data directories for pod %q: %v\", format.Pod(pod), err) return err } 其中数据目录包括 PodDir：{kubelet.rootDirectory}/pods/podUID PodVolumesDir：{PodDir}/volumes PodPluginsDir：{PodDir}/plugins // makePodDataDirs creates the dirs for the pod datas. func (kl *Kubelet) makePodDataDirs(pod *v1.Pod) error { uid := pod.UID if err := os.MkdirAll(kl.getPodDir(uid), 0750); err != nil && !os.IsExist(err) { return err } if err := os.MkdirAll(kl.getPodVolumesDir(uid), 0750); err != nil && !os.IsExist(err) { return err } if err := os.MkdirAll(kl.getPodPluginsDir(uid), 0750); err != nil && !os.IsExist(err) { return err } return nil } 2.6. mount volumes 对非terminated状态的pod挂载volume。 // Volume manager will not mount volumes for terminated pods if !kl.podIsTerminated(pod) { // Wait for volumes to attach/mount if err := kl.volumeManager.WaitForAttachAndMount(pod); err != nil { kl.recorder.Eventf(pod, v1.EventTypeWarning, events.FailedMountVolume, \"Unable to mount volumes for pod %q: %v\", format.Pod(pod), err) glog.Errorf(\"Unable to mount volumes for pod %q: %v; skipping pod\", format.Pod(pod), err) return err } } 2.7. PullSecretsForPod 获取pod的secret数据。 // Fetch the pull secrets for the pod pullSecrets := kl.getPullSecretsForPod(pod) getPullSecretsForPod具体实现函数如下： // getPullSecretsForPod inspects the Pod and retrieves the referenced pull // secrets. func (kl *Kubelet) getPullSecretsForPod(pod *v1.Pod) []v1.Secret { pullSecrets := []v1.Secret{} for _, secretRef := range pod.Spec.ImagePullSecrets { secret, err := kl.secretManager.GetSecret(pod.Namespace, secretRef.Name) if err != nil { glog.Warningf(\"Unable to retrieve pull secret %s/%s for %s/%s due to %v. The image pull may not succeed.\", pod.Namespace, secretRef.Name, pod.Namespace, pod.Name, err) continue } pullSecrets = append(pullSecrets, *secret) } return pullSecrets } 2.8. containerRuntime.SyncPod 调用container runtime的SyncPod函数，执行相关pod操作，由此kubelet.syncPod的操作逻辑转入containerRuntime.SyncPod函数中。 // Call the container runtime's SyncPod callback result := kl.containerRuntime.SyncPod(pod, apiPodStatus, podStatus, pullSecrets, kl.backOff) kl.reasonCache.Update(pod.UID, result) if err := result.Error(); err != nil { // Do not return error if the only failures were pods in backoff for _, r := range result.SyncResults { if r.Error != kubecontainer.ErrCrashLoopBackOff && r.Error != images.ErrImagePullBackOff { // Do not record an event here, as we keep all event logging for sync pod failures // local to container runtime so we get better errors return err } } return nil } 3. Runtime.SyncPod SyncPod主要执行sync操作使得运行的pod达到期望状态的pod。主要执行以下操作： 计算sandbox和container的变化。 必要的时候杀死pod。 杀死所有不需要运行的container。 必要时创建sandbox。 创建init container。 创建正常的container。 Runtime.SyncPod部分代码位于pkg/kubelet/kuberuntime/kuberuntime_manager.go 3.1. computePodActions 计算sandbox和container的变化。 // Step 1: Compute sandbox and container changes. podContainerChanges := m.computePodActions(pod, podStatus) glog.V(3).Infof(\"computePodActions got %+v for pod %q\", podContainerChanges, format.Pod(pod)) if podContainerChanges.CreateSandbox { ref, err := ref.GetReference(legacyscheme.Scheme, pod) if err != nil { glog.Errorf(\"Couldn't make a ref to pod %q: '%v'\", format.Pod(pod), err) } if podContainerChanges.SandboxID != \"\" { m.recorder.Eventf(ref, v1.EventTypeNormal, events.SandboxChanged, \"Pod sandbox changed, it will be killed and re-created.\") } else { glog.V(4).Infof(\"SyncPod received new pod %q, will create a sandbox for it\", format.Pod(pod)) } } 3.2. killPodWithSyncResult 必要的时候杀死pod。 // Step 2: Kill the pod if the sandbox has changed. if podContainerChanges.KillPod { if !podContainerChanges.CreateSandbox { glog.V(4).Infof(\"Stopping PodSandbox for %q because all other containers are dead.\", format.Pod(pod)) } else { glog.V(4).Infof(\"Stopping PodSandbox for %q, will start new one\", format.Pod(pod)) } killResult := m.killPodWithSyncResult(pod, kubecontainer.ConvertPodStatusToRunningPod(m.runtimeName, podStatus), nil) result.AddPodSyncResult(killResult) if killResult.Error() != nil { glog.Errorf(\"killPodWithSyncResult failed: %v\", killResult.Error()) return } if podContainerChanges.CreateSandbox { m.purgeInitContainers(pod, podStatus) } } 3.3. killContainer 杀死所有不需要运行的container。 // Step 3: kill any running containers in this pod which are not to keep. for containerID, containerInfo := range podContainerChanges.ContainersToKill { glog.V(3).Infof(\"Killing unwanted container %q(id=%q) for pod %q\", containerInfo.name, containerID, format.Pod(pod)) killContainerResult := kubecontainer.NewSyncResult(kubecontainer.KillContainer, containerInfo.name) result.AddSyncResult(killContainerResult) if err := m.killContainer(pod, containerID, containerInfo.name, containerInfo.message, nil); err != nil { killContainerResult.Fail(kubecontainer.ErrKillContainer, err.Error()) glog.Errorf(\"killContainer %q(id=%q) for pod %q failed: %v\", containerInfo.name, containerID, format.Pod(pod), err) return } } 3.4. createPodSandbox 必要时创建sandbox。 // Step 4: Create a sandbox for the pod if necessary. ... glog.V(4).Infof(\"Creating sandbox for pod %q\", format.Pod(pod)) createSandboxResult := kubecontainer.NewSyncResult(kubecontainer.CreatePodSandbox, format.Pod(pod)) result.AddSyncResult(createSandboxResult) podSandboxID, msg, err = m.createPodSandbox(pod, podContainerChanges.Attempt) if err != nil { createSandboxResult.Fail(kubecontainer.ErrCreatePodSandbox, msg) glog.Errorf(\"createPodSandbox for pod %q failed: %v\", format.Pod(pod), err) ref, referr := ref.GetReference(legacyscheme.Scheme, pod) if referr != nil { glog.Errorf(\"Couldn't make a ref to pod %q: '%v'\", format.Pod(pod), referr) } m.recorder.Eventf(ref, v1.EventTypeWarning, events.FailedCreatePodSandBox, \"Failed create pod sandbox: %v\", err) return } glog.V(4).Infof(\"Created PodSandbox %q for pod %q\", podSandboxID, format.Pod(pod)) 3.5. start init container 创建init container。 // Step 5: start the init container. if container := podContainerChanges.NextInitContainerToStart; container != nil { // Start the next init container. startContainerResult := kubecontainer.NewSyncResult(kubecontainer.StartContainer, container.Name) result.AddSyncResult(startContainerResult) isInBackOff, msg, err := m.doBackOff(pod, container, podStatus, backOff) if isInBackOff { startContainerResult.Fail(err, msg) glog.V(4).Infof(\"Backing Off restarting init container %+v in pod %v\", container, format.Pod(pod)) return } glog.V(4).Infof(\"Creating init container %+v in pod %v\", container, format.Pod(pod)) if msg, err := m.startContainer(podSandboxID, podSandboxConfig, container, pod, podStatus, pullSecrets, podIP, kubecontainer.ContainerTypeInit); err != nil { startContainerResult.Fail(err, msg) utilruntime.HandleError(fmt.Errorf(\"init container start failed: %v: %s\", err, msg)) return } // Successfully started the container; clear the entry in the failure glog.V(4).Infof(\"Completed init container %q for pod %q\", container.Name, format.Pod(pod)) } 3.6. start containers 创建正常的container。 // Step 6: start containers in podContainerChanges.ContainersToStart. for _, idx := range podContainerChanges.ContainersToStart { container := &pod.Spec.Containers[idx] startContainerResult := kubecontainer.NewSyncResult(kubecontainer.StartContainer, container.Name) result.AddSyncResult(startContainerResult) isInBackOff, msg, err := m.doBackOff(pod, container, podStatus, backOff) if isInBackOff { startContainerResult.Fail(err, msg) glog.V(4).Infof(\"Backing Off restarting container %+v in pod %v\", container, format.Pod(pod)) continue } glog.V(4).Infof(\"Creating container %+v in pod %v\", container, format.Pod(pod)) // 通过startContainer来运行容器 if msg, err := m.startContainer(podSandboxID, podSandboxConfig, container, pod, podStatus, pullSecrets, podIP, kubecontainer.ContainerTypeRegular); err != nil { startContainerResult.Fail(err, msg) // known errors that are logged in other places are logged at higher levels here to avoid // repetitive log spam switch { case err == images.ErrImagePullBackOff: glog.V(3).Infof(\"container start failed: %v: %s\", err, msg) default: utilruntime.HandleError(fmt.Errorf(\"container start failed: %v: %s\", err, msg)) } continue } } 4. startContainer startContainer启动一个容器并返回是否成功。 主要包括以下几个步骤： 拉取镜像 创建容器 启动容器 运行post start lifecycle hooks(如果有设置此项) startContainer完整代码如下： startContainer部分代码位于pkg/kubelet/kuberuntime/kuberuntime_container.go // startContainer starts a container and returns a message indicates why it is failed on error. // It starts the container through the following steps: // * pull the image // * create the container // * start the container // * run the post start lifecycle hooks (if applicable) func (m *kubeGenericRuntimeManager) startContainer(podSandboxID string, podSandboxConfig *runtimeapi.PodSandboxConfig, container *v1.Container, pod *v1.Pod, podStatus *kubecontainer.PodStatus, pullSecrets []v1.Secret, podIP string, containerType kubecontainer.ContainerType) (string, error) { // Step 1: pull the image. imageRef, msg, err := m.imagePuller.EnsureImageExists(pod, container, pullSecrets) if err != nil { m.recordContainerEvent(pod, container, \"\", v1.EventTypeWarning, events.FailedToCreateContainer, \"Error: %v\", grpc.ErrorDesc(err)) return msg, err } // Step 2: create the container. ref, err := kubecontainer.GenerateContainerRef(pod, container) if err != nil { glog.Errorf(\"Can't make a ref to pod %q, container %v: %v\", format.Pod(pod), container.Name, err) } glog.V(4).Infof(\"Generating ref for container %s: %#v\", container.Name, ref) // For a new container, the RestartCount should be 0 restartCount := 0 containerStatus := podStatus.FindContainerStatusByName(container.Name) if containerStatus != nil { restartCount = containerStatus.RestartCount + 1 } containerConfig, cleanupAction, err := m.generateContainerConfig(container, pod, restartCount, podIP, imageRef, containerType) if cleanupAction != nil { defer cleanupAction() } if err != nil { m.recordContainerEvent(pod, container, \"\", v1.EventTypeWarning, events.FailedToCreateContainer, \"Error: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), ErrCreateContainerConfig } containerID, err := m.runtimeService.CreateContainer(podSandboxID, containerConfig, podSandboxConfig) if err != nil { m.recordContainerEvent(pod, container, containerID, v1.EventTypeWarning, events.FailedToCreateContainer, \"Error: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), ErrCreateContainer } err = m.internalLifecycle.PreStartContainer(pod, container, containerID) if err != nil { m.recordContainerEvent(pod, container, containerID, v1.EventTypeWarning, events.FailedToStartContainer, \"Internal PreStartContainer hook failed: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), ErrPreStartHook } m.recordContainerEvent(pod, container, containerID, v1.EventTypeNormal, events.CreatedContainer, \"Created container\") if ref != nil { m.containerRefManager.SetRef(kubecontainer.ContainerID{ Type: m.runtimeName, ID: containerID, }, ref) } // Step 3: start the container. err = m.runtimeService.StartContainer(containerID) if err != nil { m.recordContainerEvent(pod, container, containerID, v1.EventTypeWarning, events.FailedToStartContainer, \"Error: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), kubecontainer.ErrRunContainer } m.recordContainerEvent(pod, container, containerID, v1.EventTypeNormal, events.StartedContainer, \"Started container\") // Symlink container logs to the legacy container log location for cluster logging // support. // TODO(random-liu): Remove this after cluster logging supports CRI container log path. containerMeta := containerConfig.GetMetadata() sandboxMeta := podSandboxConfig.GetMetadata() legacySymlink := legacyLogSymlink(containerID, containerMeta.Name, sandboxMeta.Name, sandboxMeta.Namespace) containerLog := filepath.Join(podSandboxConfig.LogDirectory, containerConfig.LogPath) // only create legacy symlink if containerLog path exists (or the error is not IsNotExist). // Because if containerLog path does not exist, only dandling legacySymlink is created. // This dangling legacySymlink is later removed by container gc, so it does not make sense // to create it in the first place. it happens when journald logging driver is used with docker. if _, err := m.osInterface.Stat(containerLog); !os.IsNotExist(err) { if err := m.osInterface.Symlink(containerLog, legacySymlink); err != nil { glog.Errorf(\"Failed to create legacy symbolic link %q to container %q log %q: %v\", legacySymlink, containerID, containerLog, err) } } // Step 4: execute the post start hook. if container.Lifecycle != nil && container.Lifecycle.PostStart != nil { kubeContainerID := kubecontainer.ContainerID{ Type: m.runtimeName, ID: containerID, } msg, handlerErr := m.runner.Run(kubeContainerID, pod, container, container.Lifecycle.PostStart) if handlerErr != nil { m.recordContainerEvent(pod, container, kubeContainerID.ID, v1.EventTypeWarning, events.FailedPostStartHook, msg) if err := m.killContainer(pod, kubeContainerID, container.Name, \"FailedPostStartHook\", nil); err != nil { glog.Errorf(\"Failed to kill container %q(id=%q) in pod %q: %v, %v\", container.Name, kubeContainerID.String(), format.Pod(pod), ErrPostStartHook, err) } return msg, fmt.Errorf(\"%s: %v\", ErrPostStartHook, handlerErr) } } return \"\", nil } 以下对startContainer分段分析： 4.1. pull image 通过EnsureImageExists方法拉取拉取指定pod容器的镜像，并返回镜像信息和错误。 // Step 1: pull the image. imageRef, msg, err := m.imagePuller.EnsureImageExists(pod, container, pullSecrets) if err != nil { m.recordContainerEvent(pod, container, \"\", v1.EventTypeWarning, events.FailedToCreateContainer, \"Error: %v\", grpc.ErrorDesc(err)) return msg, err } 4.2. CreateContainer 首先生成container的*v1.ObjectReference对象，该对象包括container的相关信息。 // Step 2: create the container. ref, err := kubecontainer.GenerateContainerRef(pod, container) if err != nil { glog.Errorf(\"Can't make a ref to pod %q, container %v: %v\", format.Pod(pod), container.Name, err) } glog.V(4).Infof(\"Generating ref for container %s: %#v\", container.Name, ref) 统计container的重启次数，新的容器默认重启次数为0。 // For a new container, the RestartCount should be 0 restartCount := 0 containerStatus := podStatus.FindContainerStatusByName(container.Name) if containerStatus != nil { restartCount = containerStatus.RestartCount + 1 } 生成container的配置。 containerConfig, cleanupAction, err := m.generateContainerConfig(container, pod, restartCount, podIP, imageRef, containerType) if cleanupAction != nil { defer cleanupAction() } if err != nil { m.recordContainerEvent(pod, container, \"\", v1.EventTypeWarning, events.FailedToCreateContainer, \"Error: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), ErrCreateContainerConfig } 调用runtimeService，执行CreateContainer的操作。 containerID, err := m.runtimeService.CreateContainer(podSandboxID, containerConfig, podSandboxConfig) if err != nil { m.recordContainerEvent(pod, container, containerID, v1.EventTypeWarning, events.FailedToCreateContainer, \"Error: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), ErrCreateContainer } err = m.internalLifecycle.PreStartContainer(pod, container, containerID) if err != nil { m.recordContainerEvent(pod, container, containerID, v1.EventTypeWarning, events.FailedToStartContainer, \"Internal PreStartContainer hook failed: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), ErrPreStartHook } m.recordContainerEvent(pod, container, containerID, v1.EventTypeNormal, events.CreatedContainer, \"Created container\") if ref != nil { m.containerRefManager.SetRef(kubecontainer.ContainerID{ Type: m.runtimeName, ID: containerID, }, ref) } 4.3. StartContainer 执行runtimeService的StartContainer方法，来启动容器。 // Step 3: start the container. err = m.runtimeService.StartContainer(containerID) if err != nil { m.recordContainerEvent(pod, container, containerID, v1.EventTypeWarning, events.FailedToStartContainer, \"Error: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), kubecontainer.ErrRunContainer } m.recordContainerEvent(pod, container, containerID, v1.EventTypeNormal, events.StartedContainer, \"Started container\") // Symlink container logs to the legacy container log location for cluster logging // support. // TODO(random-liu): Remove this after cluster logging supports CRI container log path. containerMeta := containerConfig.GetMetadata() sandboxMeta := podSandboxConfig.GetMetadata() legacySymlink := legacyLogSymlink(containerID, containerMeta.Name, sandboxMeta.Name, sandboxMeta.Namespace) containerLog := filepath.Join(podSandboxConfig.LogDirectory, containerConfig.LogPath) // only create legacy symlink if containerLog path exists (or the error is not IsNotExist). // Because if containerLog path does not exist, only dandling legacySymlink is created. // This dangling legacySymlink is later removed by container gc, so it does not make sense // to create it in the first place. it happens when journald logging driver is used with docker. if _, err := m.osInterface.Stat(containerLog); !os.IsNotExist(err) { if err := m.osInterface.Symlink(containerLog, legacySymlink); err != nil { glog.Errorf(\"Failed to create legacy symbolic link %q to container %q log %q: %v\", legacySymlink, containerID, containerLog, err) } } 4.4. execute post start hook 如果有指定Lifecycle.PostStart，则执行PostStart操作，PostStart如果执行失败，则容器会根据重启的规则进行重启。 // Step 4: execute the post start hook. if container.Lifecycle != nil && container.Lifecycle.PostStart != nil { kubeContainerID := kubecontainer.ContainerID{ Type: m.runtimeName, ID: containerID, } msg, handlerErr := m.runner.Run(kubeContainerID, pod, container, container.Lifecycle.PostStart) if handlerErr != nil { m.recordContainerEvent(pod, container, kubeContainerID.ID, v1.EventTypeWarning, events.FailedPostStartHook, msg) if err := m.killContainer(pod, kubeContainerID, container.Name, \"FailedPostStartHook\", nil); err != nil { glog.Errorf(\"Failed to kill container %q(id=%q) in pod %q: %v, %v\", container.Name, kubeContainerID.String(), format.Pod(pod), ErrPostStartHook, err) } return msg, fmt.Errorf(\"%s: %v\", ErrPostStartHook, handlerErr) } } 5. 总结 kubelet的工作是管理pod在Node上的生命周期（包括增删改查），kubelet通过各种类型的manager异步工作各自执行各自的任务，其中使用到了多种的channel来控制状态信号变化的传递，例如比较重要的channel有podUpdates ，来传递pod的变化情况。 创建pod的调用逻辑 syncLoopIteration-->kubetypes.ADD-->HandlePodAdditions(u.Pods)-->dispatchWork(pod, kubetypes.SyncPodCreate, mirrorPod, start)-->podWorkers.UpdatePod-->managePodLoop(podUpdates)-->syncPod(o syncPodOptions)-->containerRuntime.SyncPod-->startContainer 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/kubelet/kubelet.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/kubelet/pod_workers.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/kubelet/kuberuntime/kuberuntime_container.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-controller-manager/_index.html":{"url":"code-analysis/kube-controller-manager/_index.html","title":"kube-controller-manager","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-controller-manager/NewControllerManagerCommand.html":{"url":"code-analysis/kube-controller-manager/NewControllerManagerCommand.html","title":"NewControllerManagerCommand","keywords":"","body":"kube-controller-manager源码分析（一）之 NewControllerManagerCommand1. Main函数2. NewControllerManagerCommand2.1. NewKubeControllerManagerOptions2.2. AddFlagSet3. Run3.1. CreateControllerContext3.2. NewControllerInitializers3.3. StartControllers3.4. InformerFactory.Start3.4.1. SharedInformerFactory3.4.2. sharedInformerFactory.Start3.4.3. sharedIndexInformer.Run4. initFn(ctx)4.1. startStatefulSetController4.2. startDeploymentController5. 总结kube-controller-manager源码分析（一）之 NewControllerManagerCommand 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析https://github.com/kubernetes/kubernetes/tree/v1.12.0/cmd/kube-controller-manager 部分的代码。 本文主要分析 kubernetes/cmd/kube-controller-manager部分，该部分主要涉及各种类型的controller的参数解析，及初始化，例如 deployment controller 和statefulset controller。并没有具体controller运行的详细逻辑，该部分位于kubernetes/pkg/controller模块，待后续文章分析。 kube-controller-manager的cmd部分代码目录结构如下： kube-controller-manager ├── app │ ├── apps.go # 包含:startDeploymentController、startReplicaSetController、startStatefulSetController、startDaemonSetController │ ├── autoscaling.go # startHPAController │ ├── batch.go # startJobController、startCronJobController │ ├── bootstrap.go │ ├── certificates.go │ ├── cloudproviders.go │ ├── config │ │ └── config.go # config: controller manager执行的上下文 │ ├── controllermanager.go # 包含:NewControllerManagerCommand、Run、NewControllerInitializers、StartControllers等 │ ├── core.go # startServiceController、startNodeIpamController、startPersistentVolumeBinderController、startNamespaceController等 │ ├── options # 包含不同controller的option参数 │ │ ├── attachdetachcontroller.go │ │ ├── csrsigningcontroller.go │ │ ├── daemonsetcontroller.go # DaemonSetControllerOptions │ │ ├── deploymentcontroller.go # DeploymentControllerOptions │ │ ├── deprecatedcontroller.go │ │ ├── endpointcontroller.go │ │ ├── garbagecollectorcontroller.go │ │ ├── hpacontroller.go │ │ ├── jobcontroller.go │ │ ├── namespacecontroller.go # NamespaceControllerOptions │ │ ├── nodeipamcontroller.go │ │ ├── nodelifecyclecontroller.go │ │ ├── options.go # KubeControllerManagerOptions、NewKubeControllerManagerOptions │ │ ├── persistentvolumebindercontroller.go │ │ ├── podgccontroller.go │ │ ├── replicasetcontroller.go # ReplicaSetControllerOptions │ │ ├── replicationcontroller.go │ │ ├── resourcequotacontroller.go │ │ ├── serviceaccountcontroller.go │ │ └── ttlafterfinishedcontroller.go └── controller-manager.go # main入口函数 1. Main函数 kube-controller-manager的入口函数Main函数，仍然是采用统一的代码风格，使用Cobra命令行框架。 func main() { rand.Seed(time.Now().UTC().UnixNano()) command := app.NewControllerManagerCommand() // TODO: once we switch everything over to Cobra commands, we can go back to calling // utilflag.InitFlags() (by removing its pflag.Parse() call). For now, we have to set the // normalize func and add the go flag set by hand. pflag.CommandLine.SetNormalizeFunc(utilflag.WordSepNormalizeFunc) pflag.CommandLine.AddGoFlagSet(goflag.CommandLine) // utilflag.InitFlags() logs.InitLogs() defer logs.FlushLogs() if err := command.Execute(); err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } } 核心代码： // 初始化命令行结构体 command := app.NewControllerManagerCommand() // 执行Execute err := command.Execute() 2. NewControllerManagerCommand 该部分代码位于：kubernetes/cmd/kube-controller-manager/app/controllermanager.go // NewControllerManagerCommand creates a *cobra.Command object with default parameters func NewControllerManagerCommand() *cobra.Command { ... cmd := &cobra.Command{ Use: \"kube-controller-manager\", Long: `The Kubernetes controller manager is a daemon that embeds the core control loops shipped with Kubernetes. In applications of robotics and automation, a control loop is a non-terminating loop that regulates the state of the system. In Kubernetes, a controller is a control loop that watches the shared state of the cluster through the apiserver and makes changes attempting to move the current state towards the desired state. Examples of controllers that ship with Kubernetes today are the replication controller, endpoints controller, namespace controller, and serviceaccounts controller.`, Run: func(cmd *cobra.Command, args []string) { verflag.PrintAndExitIfRequested() utilflag.PrintFlags(cmd.Flags()) c, err := s.Config(KnownControllers(), ControllersDisabledByDefault.List()) if err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } if err := Run(c.Complete(), wait.NeverStop); err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } }, } ... } 构建一个*cobra.Command对象，然后执行Run函数。 2.1. NewKubeControllerManagerOptions s, err := options.NewKubeControllerManagerOptions() if err != nil { glog.Fatalf(\"unable to initialize command options: %v\", err) } 初始化controllerManager的参数，其中主要包括了各种controller的option，例如DeploymentControllerOptions: // DeploymentControllerOptions holds the DeploymentController options. type DeploymentControllerOptions struct { ConcurrentDeploymentSyncs int32 DeploymentControllerSyncPeriod metav1.Duration } 具体代码如下： // NewKubeControllerManagerOptions creates a new KubeControllerManagerOptions with a default config. func NewKubeControllerManagerOptions() (*KubeControllerManagerOptions, error) { componentConfig, err := NewDefaultComponentConfig(ports.InsecureKubeControllerManagerPort) if err != nil { return nil, err } s := KubeControllerManagerOptions{ Generic: cmoptions.NewGenericControllerManagerConfigurationOptions(componentConfig.Generic), KubeCloudShared: cmoptions.NewKubeCloudSharedOptions(componentConfig.KubeCloudShared), AttachDetachController: &AttachDetachControllerOptions{ ReconcilerSyncLoopPeriod: componentConfig.AttachDetachController.ReconcilerSyncLoopPeriod, }, CSRSigningController: &CSRSigningControllerOptions{ ClusterSigningCertFile: componentConfig.CSRSigningController.ClusterSigningCertFile, ClusterSigningKeyFile: componentConfig.CSRSigningController.ClusterSigningKeyFile, ClusterSigningDuration: componentConfig.CSRSigningController.ClusterSigningDuration, }, DaemonSetController: &DaemonSetControllerOptions{ ConcurrentDaemonSetSyncs: componentConfig.DaemonSetController.ConcurrentDaemonSetSyncs, }, DeploymentController: &DeploymentControllerOptions{ ConcurrentDeploymentSyncs: componentConfig.DeploymentController.ConcurrentDeploymentSyncs, DeploymentControllerSyncPeriod: componentConfig.DeploymentController.DeploymentControllerSyncPeriod, }, DeprecatedFlags: &DeprecatedControllerOptions{ RegisterRetryCount: componentConfig.DeprecatedController.RegisterRetryCount, }, EndpointController: &EndpointControllerOptions{ ConcurrentEndpointSyncs: componentConfig.EndpointController.ConcurrentEndpointSyncs, }, GarbageCollectorController: &GarbageCollectorControllerOptions{ ConcurrentGCSyncs: componentConfig.GarbageCollectorController.ConcurrentGCSyncs, EnableGarbageCollector: componentConfig.GarbageCollectorController.EnableGarbageCollector, }, HPAController: &HPAControllerOptions{ HorizontalPodAutoscalerSyncPeriod: componentConfig.HPAController.HorizontalPodAutoscalerSyncPeriod, HorizontalPodAutoscalerUpscaleForbiddenWindow: componentConfig.HPAController.HorizontalPodAutoscalerUpscaleForbiddenWindow, HorizontalPodAutoscalerDownscaleForbiddenWindow: componentConfig.HPAController.HorizontalPodAutoscalerDownscaleForbiddenWindow, HorizontalPodAutoscalerDownscaleStabilizationWindow: componentConfig.HPAController.HorizontalPodAutoscalerDownscaleStabilizationWindow, HorizontalPodAutoscalerCPUInitializationPeriod: componentConfig.HPAController.HorizontalPodAutoscalerCPUInitializationPeriod, HorizontalPodAutoscalerInitialReadinessDelay: componentConfig.HPAController.HorizontalPodAutoscalerInitialReadinessDelay, HorizontalPodAutoscalerTolerance: componentConfig.HPAController.HorizontalPodAutoscalerTolerance, HorizontalPodAutoscalerUseRESTClients: componentConfig.HPAController.HorizontalPodAutoscalerUseRESTClients, }, JobController: &JobControllerOptions{ ConcurrentJobSyncs: componentConfig.JobController.ConcurrentJobSyncs, }, NamespaceController: &NamespaceControllerOptions{ NamespaceSyncPeriod: componentConfig.NamespaceController.NamespaceSyncPeriod, ConcurrentNamespaceSyncs: componentConfig.NamespaceController.ConcurrentNamespaceSyncs, }, NodeIPAMController: &NodeIPAMControllerOptions{ NodeCIDRMaskSize: componentConfig.NodeIPAMController.NodeCIDRMaskSize, }, NodeLifecycleController: &NodeLifecycleControllerOptions{ EnableTaintManager: componentConfig.NodeLifecycleController.EnableTaintManager, NodeMonitorGracePeriod: componentConfig.NodeLifecycleController.NodeMonitorGracePeriod, NodeStartupGracePeriod: componentConfig.NodeLifecycleController.NodeStartupGracePeriod, PodEvictionTimeout: componentConfig.NodeLifecycleController.PodEvictionTimeout, }, PersistentVolumeBinderController: &PersistentVolumeBinderControllerOptions{ PVClaimBinderSyncPeriod: componentConfig.PersistentVolumeBinderController.PVClaimBinderSyncPeriod, VolumeConfiguration: componentConfig.PersistentVolumeBinderController.VolumeConfiguration, }, PodGCController: &PodGCControllerOptions{ TerminatedPodGCThreshold: componentConfig.PodGCController.TerminatedPodGCThreshold, }, ReplicaSetController: &ReplicaSetControllerOptions{ ConcurrentRSSyncs: componentConfig.ReplicaSetController.ConcurrentRSSyncs, }, ReplicationController: &ReplicationControllerOptions{ ConcurrentRCSyncs: componentConfig.ReplicationController.ConcurrentRCSyncs, }, ResourceQuotaController: &ResourceQuotaControllerOptions{ ResourceQuotaSyncPeriod: componentConfig.ResourceQuotaController.ResourceQuotaSyncPeriod, ConcurrentResourceQuotaSyncs: componentConfig.ResourceQuotaController.ConcurrentResourceQuotaSyncs, }, SAController: &SAControllerOptions{ ConcurrentSATokenSyncs: componentConfig.SAController.ConcurrentSATokenSyncs, }, ServiceController: &cmoptions.ServiceControllerOptions{ ConcurrentServiceSyncs: componentConfig.ServiceController.ConcurrentServiceSyncs, }, TTLAfterFinishedController: &TTLAfterFinishedControllerOptions{ ConcurrentTTLSyncs: componentConfig.TTLAfterFinishedController.ConcurrentTTLSyncs, }, SecureServing: apiserveroptions.NewSecureServingOptions().WithLoopback(), InsecureServing: (&apiserveroptions.DeprecatedInsecureServingOptions{ BindAddress: net.ParseIP(componentConfig.Generic.Address), BindPort: int(componentConfig.Generic.Port), BindNetwork: \"tcp\", }).WithLoopback(), Authentication: apiserveroptions.NewDelegatingAuthenticationOptions(), Authorization: apiserveroptions.NewDelegatingAuthorizationOptions(), } s.Authentication.RemoteKubeConfigFileOptional = true s.Authorization.RemoteKubeConfigFileOptional = true s.Authorization.AlwaysAllowPaths = []string{\"/healthz\"} s.SecureServing.ServerCert.CertDirectory = \"/var/run/kubernetes\" s.SecureServing.ServerCert.PairName = \"kube-controller-manager\" s.SecureServing.BindPort = ports.KubeControllerManagerPort gcIgnoredResources := make([]kubectrlmgrconfig.GroupResource, 0, len(garbagecollector.DefaultIgnoredResources())) for r := range garbagecollector.DefaultIgnoredResources() { gcIgnoredResources = append(gcIgnoredResources, kubectrlmgrconfig.GroupResource{Group: r.Group, Resource: r.Resource}) } s.GarbageCollectorController.GCIgnoredResources = gcIgnoredResources return &s, nil } 2.2. AddFlagSet 添加参数及帮助函数。 fs := cmd.Flags() namedFlagSets := s.Flags(KnownControllers(), ControllersDisabledByDefault.List()) for _, f := range namedFlagSets.FlagSets { fs.AddFlagSet(f) } usageFmt := \"Usage:\\n %s\\n\" cols, _, _ := apiserverflag.TerminalSize(cmd.OutOrStdout()) cmd.SetUsageFunc(func(cmd *cobra.Command) error { fmt.Fprintf(cmd.OutOrStderr(), usageFmt, cmd.UseLine()) apiserverflag.PrintSections(cmd.OutOrStderr(), namedFlagSets, cols) return nil }) cmd.SetHelpFunc(func(cmd *cobra.Command, args []string) { fmt.Fprintf(cmd.OutOrStdout(), \"%s\\n\\n\"+usageFmt, cmd.Long, cmd.UseLine()) apiserverflag.PrintSections(cmd.OutOrStdout(), namedFlagSets, cols) }) 3. Run 此部分的代码位于cmd/kube-controller-manager/app/controllermanager.go 基于KubeControllerManagerOptions运行controllerManager，不退出。 // Run runs the KubeControllerManagerOptions. This should never exit. func Run(c *config.CompletedConfig, stopCh Run函数涉及的核心代码如下： // 创建controller的context controllerContext, err := CreateControllerContext(c, rootClientBuilder, clientBuilder, ctx.Done()) // 启动各种controller err := StartControllers(controllerContext, saTokenControllerInitFunc, NewControllerInitializers(controllerContext.LoopMode), unsecuredMux) 其中StartControllers中的入参NewControllerInitializers初始化了各种controller。 3.1. CreateControllerContext CreateControllerContext构建了各种controller所需的资源的上下文，各种controller在启动时，入参为该context，具体参考initFn(ctx)。 // CreateControllerContext creates a context struct containing references to resources needed by the // controllers such as the cloud provider and clientBuilder. rootClientBuilder is only used for // the shared-informers client and token controller. func CreateControllerContext(s *config.CompletedConfig, rootClientBuilder, clientBuilder controller.ControllerClientBuilder, stop 核心代码为NewSharedInformerFactory。 // 创建SharedInformerFactory sharedInformers := informers.NewSharedInformerFactory(versionedClient, ResyncPeriod(s)()) // 赋值给ControllerContext ctx := ControllerContext{ InformerFactory: sharedInformers, } SharedInformerFactory提供了公共的k8s对象的informers。 // SharedInformerFactory provides shared informers for resources in all known // API group versions. type SharedInformerFactory interface { internalinterfaces.SharedInformerFactory ForResource(resource schema.GroupVersionResource) (GenericInformer, error) WaitForCacheSync(stopCh 3.2. NewControllerInitializers NewControllerInitializers定义了各种controller的类型和其对于的启动函数，例如deployment``、statefulset、replicaset、replicationcontroller、namespace等。 // NewControllerInitializers is a public map of named controller groups (you can start more than one in an init func) // paired to their InitFunc. This allows for structured downstream composition and subdivision. func NewControllerInitializers(loopMode ControllerLoopMode) map[string]InitFunc { controllers := map[string]InitFunc{} controllers[\"endpoint\"] = startEndpointController controllers[\"replicationcontroller\"] = startReplicationController controllers[\"podgc\"] = startPodGCController controllers[\"resourcequota\"] = startResourceQuotaController controllers[\"namespace\"] = startNamespaceController controllers[\"serviceaccount\"] = startServiceAccountController controllers[\"garbagecollector\"] = startGarbageCollectorController controllers[\"daemonset\"] = startDaemonSetController controllers[\"job\"] = startJobController controllers[\"deployment\"] = startDeploymentController controllers[\"replicaset\"] = startReplicaSetController controllers[\"horizontalpodautoscaling\"] = startHPAController controllers[\"disruption\"] = startDisruptionController controllers[\"statefulset\"] = startStatefulSetController controllers[\"cronjob\"] = startCronJobController controllers[\"csrsigning\"] = startCSRSigningController controllers[\"csrapproving\"] = startCSRApprovingController controllers[\"csrcleaner\"] = startCSRCleanerController controllers[\"ttl\"] = startTTLController controllers[\"bootstrapsigner\"] = startBootstrapSignerController controllers[\"tokencleaner\"] = startTokenCleanerController controllers[\"nodeipam\"] = startNodeIpamController if loopMode == IncludeCloudLoops { controllers[\"service\"] = startServiceController controllers[\"route\"] = startRouteController // TODO: volume controller into the IncludeCloudLoops only set. // TODO: Separate cluster in cloud check from node lifecycle controller. } controllers[\"nodelifecycle\"] = startNodeLifecycleController controllers[\"persistentvolume-binder\"] = startPersistentVolumeBinderController controllers[\"attachdetach\"] = startAttachDetachController controllers[\"persistentvolume-expander\"] = startVolumeExpandController controllers[\"clusterrole-aggregation\"] = startClusterRoleAggregrationController controllers[\"pvc-protection\"] = startPVCProtectionController controllers[\"pv-protection\"] = startPVProtectionController controllers[\"ttl-after-finished\"] = startTTLAfterFinishedController return controllers } 3.3. StartControllers func StartControllers(ctx ControllerContext, startSATokenController InitFunc, controllers map[string]InitFunc, unsecuredMux *mux.PathRecorderMux) error { ... for controllerName, initFn := range controllers { if !ctx.IsControllerEnabled(controllerName) { glog.Warningf(\"%q is disabled\", controllerName) continue } time.Sleep(wait.Jitter(ctx.ComponentConfig.Generic.ControllerStartInterval.Duration, ControllerStartJitter)) glog.V(1).Infof(\"Starting %q\", controllerName) debugHandler, started, err := initFn(ctx) if err != nil { glog.Errorf(\"Error starting %q\", controllerName) return err } if !started { glog.Warningf(\"Skipping %q\", controllerName) continue } if debugHandler != nil && unsecuredMux != nil { basePath := \"/debug/controllers/\" + controllerName unsecuredMux.UnlistedHandle(basePath, http.StripPrefix(basePath, debugHandler)) unsecuredMux.UnlistedHandlePrefix(basePath+\"/\", http.StripPrefix(basePath, debugHandler)) } glog.Infof(\"Started %q\", controllerName) } return nil } 核心代码： for controllerName, initFn := range controllers { debugHandler, started, err := initFn(ctx) } 启动各种controller，controller的启动函数在NewControllerInitializers中定义了，例如： // deployment controllers[\"deployment\"] = startDeploymentController // statefulset controllers[\"statefulset\"] = startStatefulSetController 3.4. InformerFactory.Start InformerFactory实际上是SharedInformerFactory，具体的实现逻辑在client-go中的informer的实现机制。 controllerContext.InformerFactory.Start(controllerContext.Stop) close(controllerContext.InformersStarted) 3.4.1. SharedInformerFactory SharedInformerFactory是一个informer工厂的接口定义。 // SharedInformerFactory a small interface to allow for adding an informer without an import cycle type SharedInformerFactory interface { Start(stopCh 3.4.2. sharedInformerFactory.Start Start方法初始化各种类型的informer // Start initializes all requested informers. func (f *sharedInformerFactory) Start(stopCh 3.4.3. sharedIndexInformer.Run sharedIndexInformer.Run具体运行了sharedIndexInformer的实现逻辑，该部分待后续对informer机制做专题分析。 func (s *sharedIndexInformer) Run(stopCh 4. initFn(ctx) initFn实际调用的就是各种类型的controller，代码位于kubernetes/cmd/kube-controller-manager/app/apps.go，本文以startStatefulSetController和startDeploymentController为例，controller中实际调用的函数逻辑位于kubernetes/pkg/controller中，待后续分析。 4.1. startStatefulSetController func startStatefulSetController(ctx ControllerContext) (http.Handler, bool, error) { if !ctx.AvailableResources[schema.GroupVersionResource{Group: \"apps\", Version: \"v1\", Resource: \"statefulsets\"}] { return nil, false, nil } go statefulset.NewStatefulSetController( ctx.InformerFactory.Core().V1().Pods(), ctx.InformerFactory.Apps().V1().StatefulSets(), ctx.InformerFactory.Core().V1().PersistentVolumeClaims(), ctx.InformerFactory.Apps().V1().ControllerRevisions(), ctx.ClientBuilder.ClientOrDie(\"statefulset-controller\"), ).Run(1, ctx.Stop) return nil, true, nil } 其中使用到了InformerFactory，包含了Pods、StatefulSets、PersistentVolumeClaims、ControllerRevisions的informer。 startStatefulSetController主要调用的函数为NewStatefulSetController和对应的Run函数。 4.2. startDeploymentController func startDeploymentController(ctx ControllerContext) (http.Handler, bool, error) { if !ctx.AvailableResources[schema.GroupVersionResource{Group: \"apps\", Version: \"v1\", Resource: \"deployments\"}] { return nil, false, nil } dc, err := deployment.NewDeploymentController( ctx.InformerFactory.Apps().V1().Deployments(), ctx.InformerFactory.Apps().V1().ReplicaSets(), ctx.InformerFactory.Core().V1().Pods(), ctx.ClientBuilder.ClientOrDie(\"deployment-controller\"), ) if err != nil { return nil, true, fmt.Errorf(\"error creating Deployment controller: %v\", err) } go dc.Run(int(ctx.ComponentConfig.DeploymentController.ConcurrentDeploymentSyncs), ctx.Stop) return nil, true, nil } startDeploymentController主要调用的函数为NewDeploymentController和对应的Run函数。该部分逻辑在kubernetes/pkg/controller中。 5. 总结 Kube-controller-manager的代码风格仍然是Cobra命令行框架。通过构造ControllerManagerCommand，然后执行command.Execute()函数。基本的流程就是构造option，添加Flags，执行Run函数。 cmd部分的调用流程如下：Main-->NewControllerManagerCommand--> Run(c.Complete(), wait.NeverStop)-->StartControllers-->initFn(ctx)-->startDeploymentController/startStatefulSetController-->sts.NewStatefulSetController.Run/dc.NewDeploymentController.Run-->pkg/controller。 其中CreateControllerContext函数用来创建各类型controller所需要使用的context，NewControllerInitializers初始化了各种类型的controller，其中就包括DeploymentController和StatefulSetController等。 基本流程如下： 构造controller manager option，并转化为Config对象，执行Run函数。 基于Config对象创建ControllerContext，其中包含InformerFactory。 基于ControllerContext运行各种controller，各种controller的定义在NewControllerInitializers中。 执行InformerFactory.Start。 每种controller都会构造自身的结构体并执行对应的Run函数。 参考： https://github.com/kubernetes/kubernetes/tree/v1.12.0/cmd/kube-controller-manager https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-controller-manager/controller-manager.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-controller-manager/app/controllermanager.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-controller-manager/app/apps.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-controller-manager/deployment-controller.html":{"url":"code-analysis/kube-controller-manager/deployment-controller.html","title":"DeploymentController","keywords":"","body":"kube-controller-manager源码分析（二）之 DeploymentController1. startDeploymentController2. NewDeploymentController2.1. eventBroadcaster2.2. rsControl2.3. Informer().AddEventHandler2.4. Informer.Lister()2.5. Informer().HasSynced2.6. syncHandler3. DeploymentController.Run3.1. WaitForCacheSync3.2. dc.worker4. syncDeployment4.1. Get deployment4.2. getReplicaSetsForDeployment4.3. getPodMapForDeployment4.4. checkPausedConditions4.5. isScalingEvent4.6. rolloutRecreate4.7. rolloutRolling5. 总结kube-controller-manager源码分析（二）之 DeploymentController 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要以deployment controller为例，分析该类controller的运行逻辑。此部分代码主要为位于pkg/controller/deployment。pkg/controller部分的代码包括了各种类型的controller的具体实现。 controller manager的pkg部分代码目录结构如下： controller # 主要包含各种controller的具体实现 ├── apis ├── bootstrap ├── certificates ├── client_builder.go ├── cloud ├── clusterroleaggregation ├── controller_ref_manager.go ├── controller_utils.go # WaitForCacheSync ├── cronjob ├── daemon ├── deployment # deployment controller │ ├── deployment_controller.go # NewDeploymentController、Run、syncDeployment │ ├── progress.go # syncRolloutStatus │ ├── recreate.go # rolloutRecreate │ ├── rollback.go # rollback │ ├── rolling.go # rolloutRolling │ ├── sync.go ├── disruption # disruption controller ├── endpoint ├── garbagecollector ├── history ├── job ├── lookup_cache.go ├── namespace # namespace controller ├── nodeipam ├── nodelifecycle ├── podautoscaler ├── podgc ├── replicaset # replicaset controller ├── replication # replication controller ├── resourcequota ├── route ├── service # service controller ├── serviceaccount ├── statefulset # statefulset controller └── volume # PersistentVolumeController、AttachDetachController、PVCProtectionController 1. startDeploymentController func startDeploymentController(ctx ControllerContext) (http.Handler, bool, error) { if !ctx.AvailableResources[schema.GroupVersionResource{Group: \"apps\", Version: \"v1\", Resource: \"deployments\"}] { return nil, false, nil } dc, err := deployment.NewDeploymentController( ctx.InformerFactory.Apps().V1().Deployments(), ctx.InformerFactory.Apps().V1().ReplicaSets(), ctx.InformerFactory.Core().V1().Pods(), ctx.ClientBuilder.ClientOrDie(\"deployment-controller\"), ) if err != nil { return nil, true, fmt.Errorf(\"error creating Deployment controller: %v\", err) } go dc.Run(int(ctx.ComponentConfig.DeploymentController.ConcurrentDeploymentSyncs), ctx.Stop) return nil, true, nil } startDeploymentController主要调用的函数为NewDeploymentController和对应的Run函数。该部分逻辑在kubernetes/pkg/controller中。 2. NewDeploymentController NewDeploymentController主要构建DeploymentController结构体。 该部分主要处理了以下逻辑： 构建并运行事件处理器eventBroadcaster。 初始化赋值rsControl、clientset、workqueue。 添加dInformer、rsInformer、podInformer的ResourceEventHandlerFuncs，其中主要为AddFunc、UpdateFunc、DeleteFunc三类方法。 构造deployment、rs、pod的Informer的Lister函数和HasSynced函数。 调用syncHandler，来实现syncDeployment。 2.1. eventBroadcaster 调用事件处理器来记录deployment相关的事件。 eventBroadcaster := record.NewBroadcaster() eventBroadcaster.StartLogging(glog.Infof) // TODO: remove the wrapper when every clients have moved to use the clientset. eventBroadcaster.StartRecordingToSink(&v1core.EventSinkImpl{Interface: v1core.New(client.CoreV1().RESTClient()).Events(\"\")}) 2.2. rsControl 构造DeploymentController，包括clientset、workqueue和rsControl。其中rsControl是具体实现rs逻辑的controller。 dc := &DeploymentController{ client: client, eventRecorder: eventBroadcaster.NewRecorder(scheme.Scheme, v1.EventSource{Component: \"deployment-controller\"}), queue: workqueue.NewNamedRateLimitingQueue(workqueue.DefaultControllerRateLimiter(), \"deployment\"), } dc.rsControl = controller.RealRSControl{ KubeClient: client, Recorder: dc.eventRecorder, } 2.3. Informer().AddEventHandler 添加dInformer、rsInformer、podInformer的ResourceEventHandlerFuncs，其中主要为AddFunc、UpdateFunc、DeleteFunc三类方法。 dInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ AddFunc: dc.addDeployment, UpdateFunc: dc.updateDeployment, // This will enter the sync loop and no-op, because the deployment has been deleted from the store. DeleteFunc: dc.deleteDeployment, }) rsInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ AddFunc: dc.addReplicaSet, UpdateFunc: dc.updateReplicaSet, DeleteFunc: dc.deleteReplicaSet, }) podInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ DeleteFunc: dc.deletePod, }) 2.4. Informer.Lister() 调用dInformer、rsInformer和podInformer的Lister()方法。 dc.dLister = dInformer.Lister() dc.rsLister = rsInformer.Lister() dc.podLister = podInformer.Lister() 2.5. Informer().HasSynced 调用Informer().HasSynced，判断是否缓存完成； dc.dListerSynced = dInformer.Informer().HasSynced dc.rsListerSynced = rsInformer.Informer().HasSynced dc.podListerSynced = podInformer.Informer().HasSynced 2.6. syncHandler syncHandler具体为syncDeployment，syncHandler负责deployment的同步实现。 dc.syncHandler = dc.syncDeployment dc.enqueueDeployment = dc.enqueue 完整代码如下： // NewDeploymentController creates a new DeploymentController. func NewDeploymentController(dInformer extensionsinformers.DeploymentInformer, rsInformer extensionsinformers.ReplicaSetInformer, podInformer coreinformers.PodInformer, client clientset.Interface) (*DeploymentController, error) { eventBroadcaster := record.NewBroadcaster() eventBroadcaster.StartLogging(glog.Infof) // TODO: remove the wrapper when every clients have moved to use the clientset. eventBroadcaster.StartRecordingToSink(&v1core.EventSinkImpl{Interface: v1core.New(client.CoreV1().RESTClient()).Events(\"\")}) if client != nil && client.CoreV1().RESTClient().GetRateLimiter() != nil { if err := metrics.RegisterMetricAndTrackRateLimiterUsage(\"deployment_controller\", client.CoreV1().RESTClient().GetRateLimiter()); err != nil { return nil, err } } dc := &DeploymentController{ client: client, eventRecorder: eventBroadcaster.NewRecorder(scheme.Scheme, v1.EventSource{Component: \"deployment-controller\"}), queue: workqueue.NewNamedRateLimitingQueue(workqueue.DefaultControllerRateLimiter(), \"deployment\"), } dc.rsControl = controller.RealRSControl{ KubeClient: client, Recorder: dc.eventRecorder, } dInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ AddFunc: dc.addDeployment, UpdateFunc: dc.updateDeployment, // This will enter the sync loop and no-op, because the deployment has been deleted from the store. DeleteFunc: dc.deleteDeployment, }) rsInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ AddFunc: dc.addReplicaSet, UpdateFunc: dc.updateReplicaSet, DeleteFunc: dc.deleteReplicaSet, }) podInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ DeleteFunc: dc.deletePod, }) dc.syncHandler = dc.syncDeployment dc.enqueueDeployment = dc.enqueue dc.dLister = dInformer.Lister() dc.rsLister = rsInformer.Lister() dc.podLister = podInformer.Lister() dc.dListerSynced = dInformer.Informer().HasSynced dc.rsListerSynced = rsInformer.Informer().HasSynced dc.podListerSynced = podInformer.Informer().HasSynced return dc, nil } 3. DeploymentController.Run Run执行watch和sync的操作。 // Run begins watching and syncing. func (dc *DeploymentController) Run(workers int, stopCh 3.1. WaitForCacheSync WaitForCacheSync主要是用来在List-Watch机制中可以保持当前cache的数据与etcd的数据一致。 // WaitForCacheSync is a wrapper around cache.WaitForCacheSync that generates log messages // indicating that the controller identified by controllerName is waiting for syncs, followed by // either a successful or failed sync. func WaitForCacheSync(controllerName string, stopCh 3.2. dc.worker worker调用了processNextWorkItem，processNextWorkItem最终调用了syncHandler，而syncHandler在NewDeploymentController中赋值的具体函数为syncDeployment。 // worker runs a worker thread that just dequeues items, processes them, and marks them done. // It enforces that the syncHandler is never invoked concurrently with the same key. func (dc *DeploymentController) worker() { for dc.processNextWorkItem() { } } func (dc *DeploymentController) processNextWorkItem() bool { key, quit := dc.queue.Get() if quit { return false } defer dc.queue.Done(key) err := dc.syncHandler(key.(string)) dc.handleErr(err, key) return true } NewDeploymentController中的syncHandler赋值： func NewDeploymentController(dInformer appsinformers.DeploymentInformer, rsInformer appsinformers.ReplicaSetInformer, podInformer coreinformers.PodInformer, client clientset.Interface) (*DeploymentController, error) { ... dc.syncHandler = dc.syncDeployment ... } 4. syncDeployment syncDeployment基于给定的key执行sync deployment的操作。 主要流程如下： 通过SplitMetaNamespaceKey获取namespace和deployment对象的name。 调用Lister的接口获取的deployment的对象。 getReplicaSetsForDeployment获取deployment管理的ReplicaSet对象。 getPodMapForDeployment获取deployment管理的pod，基于ReplicaSet来分组。 checkPausedConditions检查deployment是否是pause状态并添加合适的condition。 isScalingEvent检查deployment的更新是否来自于一个scale的事件，如果是则执行scale的操作。 根据DeploymentStrategyType类型执行rolloutRecreate或rolloutRolling。 完整代码如下： // syncDeployment will sync the deployment with the given key. // This function is not meant to be invoked concurrently with the same key. func (dc *DeploymentController) syncDeployment(key string) error { startTime := time.Now() glog.V(4).Infof(\"Started syncing deployment %q (%v)\", key, startTime) defer func() { glog.V(4).Infof(\"Finished syncing deployment %q (%v)\", key, time.Since(startTime)) }() namespace, name, err := cache.SplitMetaNamespaceKey(key) if err != nil { return err } deployment, err := dc.dLister.Deployments(namespace).Get(name) if errors.IsNotFound(err) { glog.V(2).Infof(\"Deployment %v has been deleted\", key) return nil } if err != nil { return err } // Deep-copy otherwise we are mutating our cache. // TODO: Deep-copy only when needed. d := deployment.DeepCopy() everything := metav1.LabelSelector{} if reflect.DeepEqual(d.Spec.Selector, &everything) { dc.eventRecorder.Eventf(d, v1.EventTypeWarning, \"SelectingAll\", \"This deployment is selecting all pods. A non-empty selector is required.\") if d.Status.ObservedGeneration 4.1. Get deployment // get namespace and deployment name namespace, name, err := cache.SplitMetaNamespaceKey(key) // get deployment by name deployment, err := dc.dLister.Deployments(namespace).Get(name) 4.2. getReplicaSetsForDeployment // List ReplicaSets owned by this Deployment, while reconciling ControllerRef // through adoption/orphaning. rsList, err := dc.getReplicaSetsForDeployment(d) getReplicaSetsForDeployment具体代码: // getReplicaSetsForDeployment uses ControllerRefManager to reconcile // ControllerRef by adopting and orphaning. // It returns the list of ReplicaSets that this Deployment should manage. func (dc *DeploymentController) getReplicaSetsForDeployment(d *apps.Deployment) ([]*apps.ReplicaSet, error) { // List all ReplicaSets to find those we own but that no longer match our // selector. They will be orphaned by ClaimReplicaSets(). rsList, err := dc.rsLister.ReplicaSets(d.Namespace).List(labels.Everything()) if err != nil { return nil, err } deploymentSelector, err := metav1.LabelSelectorAsSelector(d.Spec.Selector) if err != nil { return nil, fmt.Errorf(\"deployment %s/%s has invalid label selector: %v\", d.Namespace, d.Name, err) } // If any adoptions are attempted, we should first recheck for deletion with // an uncached quorum read sometime after listing ReplicaSets (see #42639). canAdoptFunc := controller.RecheckDeletionTimestamp(func() (metav1.Object, error) { fresh, err := dc.client.AppsV1().Deployments(d.Namespace).Get(d.Name, metav1.GetOptions{}) if err != nil { return nil, err } if fresh.UID != d.UID { return nil, fmt.Errorf(\"original Deployment %v/%v is gone: got uid %v, wanted %v\", d.Namespace, d.Name, fresh.UID, d.UID) } return fresh, nil }) cm := controller.NewReplicaSetControllerRefManager(dc.rsControl, d, deploymentSelector, controllerKind, canAdoptFunc) return cm.ClaimReplicaSets(rsList) } 4.3. getPodMapForDeployment // List all Pods owned by this Deployment, grouped by their ReplicaSet. // Current uses of the podMap are: // // * check if a Pod is labeled correctly with the pod-template-hash label. // * check that no old Pods are running in the middle of Recreate Deployments. podMap, err := dc.getPodMapForDeployment(d, rsList) getPodMapForDeployment具体代码： // getPodMapForDeployment returns the Pods managed by a Deployment. // // It returns a map from ReplicaSet UID to a list of Pods controlled by that RS, // according to the Pod's ControllerRef. func (dc *DeploymentController) getPodMapForDeployment(d *apps.Deployment, rsList []*apps.ReplicaSet) (map[types.UID]*v1.PodList, error) { // Get all Pods that potentially belong to this Deployment. selector, err := metav1.LabelSelectorAsSelector(d.Spec.Selector) if err != nil { return nil, err } pods, err := dc.podLister.Pods(d.Namespace).List(selector) if err != nil { return nil, err } // Group Pods by their controller (if it's in rsList). podMap := make(map[types.UID]*v1.PodList, len(rsList)) for _, rs := range rsList { podMap[rs.UID] = &v1.PodList{} } for _, pod := range pods { // Do not ignore inactive Pods because Recreate Deployments need to verify that no // Pods from older versions are running before spinning up new Pods. controllerRef := metav1.GetControllerOf(pod) if controllerRef == nil { continue } // Only append if we care about this UID. if podList, ok := podMap[controllerRef.UID]; ok { podList.Items = append(podList.Items, *pod) } } return podMap, nil } 4.4. checkPausedConditions // Update deployment conditions with an Unknown condition when pausing/resuming // a deployment. In this way, we can be sure that we won't timeout when a user // resumes a Deployment with a set progressDeadlineSeconds. if err = dc.checkPausedConditions(d); err != nil { return err } if d.Spec.Paused { return dc.sync(d, rsList) } checkPausedConditions具体代码: // checkPausedConditions checks if the given deployment is paused or not and adds an appropriate condition. // These conditions are needed so that we won't accidentally report lack of progress for resumed deployments // that were paused for longer than progressDeadlineSeconds. func (dc *DeploymentController) checkPausedConditions(d *apps.Deployment) error { if !deploymentutil.HasProgressDeadline(d) { return nil } cond := deploymentutil.GetDeploymentCondition(d.Status, apps.DeploymentProgressing) if cond != nil && cond.Reason == deploymentutil.TimedOutReason { // If we have reported lack of progress, do not overwrite it with a paused condition. return nil } pausedCondExists := cond != nil && cond.Reason == deploymentutil.PausedDeployReason needsUpdate := false if d.Spec.Paused && !pausedCondExists { condition := deploymentutil.NewDeploymentCondition(apps.DeploymentProgressing, v1.ConditionUnknown, deploymentutil.PausedDeployReason, \"Deployment is paused\") deploymentutil.SetDeploymentCondition(&d.Status, *condition) needsUpdate = true } else if !d.Spec.Paused && pausedCondExists { condition := deploymentutil.NewDeploymentCondition(apps.DeploymentProgressing, v1.ConditionUnknown, deploymentutil.ResumedDeployReason, \"Deployment is resumed\") deploymentutil.SetDeploymentCondition(&d.Status, *condition) needsUpdate = true } if !needsUpdate { return nil } var err error d, err = dc.client.AppsV1().Deployments(d.Namespace).UpdateStatus(d) return err } 4.5. isScalingEvent scalingEvent, err := dc.isScalingEvent(d, rsList) if err != nil { return err } if scalingEvent { return dc.sync(d, rsList) } isScalingEvent具体代码: // isScalingEvent checks whether the provided deployment has been updated with a scaling event // by looking at the desired-replicas annotation in the active replica sets of the deployment. // // rsList should come from getReplicaSetsForDeployment(d). // podMap should come from getPodMapForDeployment(d, rsList). func (dc *DeploymentController) isScalingEvent(d *apps.Deployment, rsList []*apps.ReplicaSet) (bool, error) { newRS, oldRSs, err := dc.getAllReplicaSetsAndSyncRevision(d, rsList, false) if err != nil { return false, err } allRSs := append(oldRSs, newRS) for _, rs := range controller.FilterActiveReplicaSets(allRSs) { desired, ok := deploymentutil.GetDesiredReplicasAnnotation(rs) if !ok { continue } if desired != *(d.Spec.Replicas) { return true, nil } } return false, nil } 4.6. rolloutRecreate switch d.Spec.Strategy.Type { case apps.RecreateDeploymentStrategyType: return dc.rolloutRecreate(d, rsList, podMap) rolloutRecreate具体代码: // rolloutRecreate implements the logic for recreating a replica set. func (dc *DeploymentController) rolloutRecreate(d *apps.Deployment, rsList []*apps.ReplicaSet, podMap map[types.UID]*v1.PodList) error { // Don't create a new RS if not already existed, so that we avoid scaling up before scaling down. newRS, oldRSs, err := dc.getAllReplicaSetsAndSyncRevision(d, rsList, false) if err != nil { return err } allRSs := append(oldRSs, newRS) activeOldRSs := controller.FilterActiveReplicaSets(oldRSs) // scale down old replica sets. scaledDown, err := dc.scaleDownOldReplicaSetsForRecreate(activeOldRSs, d) if err != nil { return err } if scaledDown { // Update DeploymentStatus. return dc.syncRolloutStatus(allRSs, newRS, d) } // Do not process a deployment when it has old pods running. if oldPodsRunning(newRS, oldRSs, podMap) { return dc.syncRolloutStatus(allRSs, newRS, d) } // If we need to create a new RS, create it now. if newRS == nil { newRS, oldRSs, err = dc.getAllReplicaSetsAndSyncRevision(d, rsList, true) if err != nil { return err } allRSs = append(oldRSs, newRS) } // scale up new replica set. if _, err := dc.scaleUpNewReplicaSetForRecreate(newRS, d); err != nil { return err } if util.DeploymentComplete(d, &d.Status) { if err := dc.cleanupDeployment(oldRSs, d); err != nil { return err } } // Sync deployment status. return dc.syncRolloutStatus(allRSs, newRS, d) } 4.7. rolloutRolling switch d.Spec.Strategy.Type { case apps.RecreateDeploymentStrategyType: return dc.rolloutRecreate(d, rsList, podMap) case apps.RollingUpdateDeploymentStrategyType: return dc.rolloutRolling(d, rsList) } rolloutRolling具体代码: // rolloutRolling implements the logic for rolling a new replica set. func (dc *DeploymentController) rolloutRolling(d *apps.Deployment, rsList []*apps.ReplicaSet) error { newRS, oldRSs, err := dc.getAllReplicaSetsAndSyncRevision(d, rsList, true) if err != nil { return err } allRSs := append(oldRSs, newRS) // Scale up, if we can. scaledUp, err := dc.reconcileNewReplicaSet(allRSs, newRS, d) if err != nil { return err } if scaledUp { // Update DeploymentStatus return dc.syncRolloutStatus(allRSs, newRS, d) } // Scale down, if we can. scaledDown, err := dc.reconcileOldReplicaSets(allRSs, controller.FilterActiveReplicaSets(oldRSs), newRS, d) if err != nil { return err } if scaledDown { // Update DeploymentStatus return dc.syncRolloutStatus(allRSs, newRS, d) } if deploymentutil.DeploymentComplete(d, &d.Status) { if err := dc.cleanupDeployment(oldRSs, d); err != nil { return err } } // Sync deployment status return dc.syncRolloutStatus(allRSs, newRS, d) } 5. 总结 startDeploymentController主要包括NewDeploymentController和DeploymentController.Run两部分。 NewDeploymentController主要构建DeploymentController结构体。 该部分主要处理了以下逻辑： 构建并运行事件处理器eventBroadcaster。 初始化赋值rsControl、clientset、workqueue。 添加dInformer、rsInformer、podInformer的ResourceEventHandlerFuncs，其中主要为AddFunc、UpdateFunc、DeleteFunc三类方法。 构造deployment、rs、pod的Informer的Lister函数和HasSynced函数。 赋值syncHandler，来实现syncDeployment。 DeploymentController.Run主要包含WaitForCacheSync和syncDeployment两部分。 syncDeployment基于给定的key执行sync deployment的操作。 主要流程如下： 通过SplitMetaNamespaceKey获取namespace和deployment对象的name。 调用Lister的接口获取的deployment的对象。 getReplicaSetsForDeployment获取deployment管理的ReplicaSet对象。 getPodMapForDeployment获取deployment管理的pod，基于ReplicaSet来分组。 checkPausedConditions检查deployment是否是pause状态并添加合适的condition。 isScalingEvent检查deployment的更新是否来自于一个scale的事件，如果是则执行scale的操作。 根据DeploymentStrategyType类型执行rolloutRecreate或rolloutRolling。 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/controller/deployment/deployment_controller.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/controller/deployment/rolling.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-controller-manager/app/apps.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-controller-manager/sharedIndexInformer.html":{"url":"code-analysis/kube-controller-manager/sharedIndexInformer.html","title":"Informer机制","keywords":"","body":"kube-controller-manager源码分析（三）之 Informer机制0. 原理示意图0.1. client-go组件0.2. 自定义controller组件1. sharedInformerFactory.Start2. sharedIndexInformer.Run2.1. NewDeltaFIFO2.2. Config2.3. controller2.4. cacheMutationDetector.Run2.5. processor.run2.6. controller.Run3. Reflector3.1. Reflector3.2. NewReflector3.3. Reflector.Run3.4. ListAndWatch3.4.1. List3.4.2. store.Resync3.4.3. Watch3.4.4. watchHandler4. DeltaFIFO4.1. NewDeltaFIFO4.2. DeltaFIFO4.3. Queue & Store5. store6.1. cache6.2. ThreadSafeStore6. processLoop5.1. DeltaFIFO.Pop5.2. HandleDeltas5.3. sharedProcessor.distribute7. processor7.1. sharedProcessor.Run7.1.1. listener.pop7.1.2. listener.run7.2. ResourceEventHandler7.2.1. addDeployment8. 总结8.1. Reflector8.2. ListAndWatch8.3. DeltaFIFO8.4. store8.5. processor8.6. 主要步骤kube-controller-manager源码分析（三）之 Informer机制 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析k8s中各个核心组件经常使用到的Informer机制(即List-Watch)。该部分的代码主要位于client-go这个第三方包中。 此部分的逻辑主要位于/vendor/k8s.io/client-go/tools/cache包中，代码目录结构如下： cache ├── controller.go # 包含：Config、Run、processLoop、NewInformer、NewIndexerInformer ├── delta_fifo.go # 包含：NewDeltaFIFO、DeltaFIFO、AddIfNotPresent ├── expiration_cache.go ├── expiration_cache_fakes.go ├── fake_custom_store.go ├── fifo.go # 包含：Queue、FIFO、NewFIFO ├── heap.go ├── index.go # 包含：Indexer、MetaNamespaceIndexFunc ├── listers.go ├── listwatch.go # 包含：ListerWatcher、ListWatch、List、Watch ├── mutation_cache.go ├── mutation_detector.go ├── reflector.go # 包含：Reflector、NewReflector、Run、ListAndWatch ├── reflector_metrics.go ├── shared_informer.go # 包含：NewSharedInformer、WaitForCacheSync、Run、HasSynced ├── store.go # 包含：Store、MetaNamespaceKeyFunc、SplitMetaNamespaceKey ├── testing │ ├── fake_controller_source.go ├── thread_safe_store.go # 包含：ThreadSafeStore、threadSafeMap ├── undelta_store.go 0. 原理示意图 示意图1： 示意图2： 0.1. client-go组件 Reflector：reflector用来watch特定的k8s API资源。具体的实现是通过ListAndWatch的方法，watch可以是k8s内建的资源或者是自定义的资源。当reflector通过watch API接收到有关新资源实例存在的通知时，它使用相应的列表API获取新创建的对象，并将其放入watchHandler函数内的Delta Fifo队列中。 Informer：informer从Delta Fifo队列中弹出对象。执行此操作的功能是processLoop。base controller的作用是保存对象以供以后检索，并调用我们的控制器将对象传递给它。 Indexer：索引器提供对象的索引功能。典型的索引用例是基于对象标签创建索引。 Indexer可以根据多个索引函数维护索引。Indexer使用线程安全的数据存储来存储对象及其键。 在Store中定义了一个名为MetaNamespaceKeyFunc的默认函数，该函数生成对象的键作为该对象的 / 组合。 0.2. 自定义controller组件 Informer reference：指的是Informer实例的引用，定义如何使用自定义资源对象。 自定义控制器代码需要创建对应的Informer。 Indexer reference: 自定义控制器对Indexer实例的引用。自定义控制器需要创建对应的Indexser。 client-go中提供NewIndexerInformer函数可以创建Informer 和 Indexer。 Resource Event Handlers：资源事件回调函数，当它想要将对象传递给控制器时，它将被调用。 编写这些函数的典型模式是获取调度对象的key，并将该key排入工作队列以进行进一步处理。 Work queue：任务队列。 编写资源事件处理程序函数以提取传递的对象的key并将其添加到任务队列。 Process Item：处理任务队列中对象的函数， 这些函数通常使用Indexer引用或Listing包装器来重试与该key对应的对象。 1. sharedInformerFactory.Start 在controller-manager的Run函数部分调用了InformerFactory.Start的方法。 此部分代码位于/cmd/kube-controller-manager/app/controllermanager.go // Run runs the KubeControllerManagerOptions. This should never exit. func Run(c *config.CompletedConfig, stopCh InformerFactory是一个SharedInformerFactory的接口，接口定义如下： 此部分代码位于vendor/k8s.io/client-go/informers/internalinterfaces/factory_interfaces.go // SharedInformerFactory a small interface to allow for adding an informer without an import cycle type SharedInformerFactory interface { Start(stopCh Start方法初始化各种类型的informer，并且每个类型起了个informer.Run的goroutine。 此部分代码位于vendor/k8s.io/client-go/informers/factory.go // Start initializes all requested informers. func (f *sharedInformerFactory) Start(stopCh 2. sharedIndexInformer.Run 此部分的代码位于/vendor/k8s.io/client-go/tools/cache/shared_informer.go func (s *sharedIndexInformer) Run(stopCh 2.1. NewDeltaFIFO DeltaFIFO是一个对象变化的存储队列，依据先进先出的原则，process的函数接收该队列的Pop方法的输出对象来处理相关功能。 fifo := NewDeltaFIFO(MetaNamespaceKeyFunc, nil, s.indexer) 2.2. Config 构造controller的配置文件，构造process，即HandleDeltas，该函数为后面使用到的process函数。 cfg := &Config{ Queue: fifo, ListerWatcher: s.listerWatcher, ObjectType: s.objectType, FullResyncPeriod: s.resyncCheckPeriod, RetryOnError: false, ShouldResync: s.processor.shouldResync, Process: s.HandleDeltas, } 2.3. controller 调用New(cfg)，构建sharedIndexInformer的controller。 func() { s.startedLock.Lock() defer s.startedLock.Unlock() s.controller = New(cfg) s.controller.(*controller).clock = s.clock s.started = true }() 2.4. cacheMutationDetector.Run 调用s.cacheMutationDetector.Run，检查缓存对象是否变化。 wg.StartWithChannel(processorStopCh, s.cacheMutationDetector.Run) defaultCacheMutationDetector.Run func (d *defaultCacheMutationDetector) Run(stopCh CompareObjects func (d *defaultCacheMutationDetector) CompareObjects() { d.lock.Lock() defer d.lock.Unlock() altered := false for i, obj := range d.cachedObjs { if !reflect.DeepEqual(obj.cached, obj.copied) { fmt.Printf(\"CACHE %s[%d] ALTERED!\\n%v\\n\", d.name, i, diff.ObjectDiff(obj.cached, obj.copied)) altered = true } } if altered { msg := fmt.Sprintf(\"cache %s modified\", d.name) if d.failureFunc != nil { d.failureFunc(msg) return } panic(msg) } } 2.5. processor.run 调用s.processor.run，将调用sharedProcessor.run，会调用Listener.run和Listener.pop,执行处理queue的函数。 wg.StartWithChannel(processorStopCh, s.processor.run) sharedProcessor.Run func (p *sharedProcessor) run(stopCh 该部分逻辑待后面分析。 2.6. controller.Run 调用s.controller.Run，构建Reflector，进行对etcd的缓存 defer func() { s.startedLock.Lock() defer s.startedLock.Unlock() s.stopped = true // Don't want any new listeners }() s.controller.Run(stopCh) controller.Run 此部分代码位于/vendor/k8s.io/client-go/tools/cache/controller.go // Run begins processing items, and will continue until a value is sent down stopCh. // It's an error to call Run more than once. // Run blocks; call via go. func (c *controller) Run(stopCh 核心代码： // 构建Reflector r := NewReflector( c.config.ListerWatcher, c.config.ObjectType, c.config.Queue, c.config.FullResyncPeriod, ) // 运行Reflector wg.StartWithChannel(stopCh, r.Run) // 执行processLoop wait.Until(c.processLoop, time.Second, stopCh) 3. Reflector 3.1. Reflector Reflector的主要作用是watch指定的k8s资源，并将变化同步到本地是store中。Reflector只会放置指定的expectedType类型的资源到store中，除非expectedType为nil。如果resyncPeriod不为零，那么Reflector为以resyncPeriod为周期定期执行list的操作，这样就可以使用Reflector来定期处理所有的对象，也可以逐步处理变化的对象。 常用属性说明： expectedType：期望放入缓存store的资源类型。 store：watch的资源对应的本地缓存。 listerWatcher：list和watch的接口。 period：watch的周期，默认为1秒。 resyncPeriod：resync的周期，当非零的时候，会按该周期执行list。 lastSyncResourceVersion：最新一次看到的资源的版本号，主要在watch时候使用。 // Reflector watches a specified resource and causes all changes to be reflected in the given store. type Reflector struct { // name identifies this reflector. By default it will be a file:line if possible. name string // metrics tracks basic metric information about the reflector metrics *reflectorMetrics // The type of object we expect to place in the store. expectedType reflect.Type // The destination to sync up with the watch source store Store // listerWatcher is used to perform lists and watches. listerWatcher ListerWatcher // period controls timing between one watch ending and // the beginning of the next one. period time.Duration resyncPeriod time.Duration ShouldResync func() bool // clock allows tests to manipulate time clock clock.Clock // lastSyncResourceVersion is the resource version token last // observed when doing a sync with the underlying store // it is thread safe, but not synchronized with the underlying store lastSyncResourceVersion string // lastSyncResourceVersionMutex guards read/write access to lastSyncResourceVersion lastSyncResourceVersionMutex sync.RWMutex } 3.2. NewReflector NewReflector主要用来构建Reflector的结构体。 此部分的代码位于/vendor/k8s.io/client-go/tools/cache/reflector.go // NewReflector creates a new Reflector object which will keep the given store up to // date with the server's contents for the given resource. Reflector promises to // only put things in the store that have the type of expectedType, unless expectedType // is nil. If resyncPeriod is non-zero, then lists will be executed after every // resyncPeriod, so that you can use reflectors to periodically process everything as // well as incrementally processing the things that change. func NewReflector(lw ListerWatcher, expectedType interface{}, store Store, resyncPeriod time.Duration) *Reflector { return NewNamedReflector(getDefaultReflectorName(internalPackages...), lw, expectedType, store, resyncPeriod) } // reflectorDisambiguator is used to disambiguate started reflectors. // initialized to an unstable value to ensure meaning isn't attributed to the suffix. var reflectorDisambiguator = int64(time.Now().UnixNano() % 12345) // NewNamedReflector same as NewReflector, but with a specified name for logging func NewNamedReflector(name string, lw ListerWatcher, expectedType interface{}, store Store, resyncPeriod time.Duration) *Reflector { reflectorSuffix := atomic.AddInt64(&reflectorDisambiguator, 1) r := &Reflector{ name: name, // we need this to be unique per process (some names are still the same)but obvious who it belongs to metrics: newReflectorMetrics(makeValidPromethusMetricLabel(fmt.Sprintf(\"reflector_\"+name+\"_%d\", reflectorSuffix))), listerWatcher: lw, store: store, expectedType: reflect.TypeOf(expectedType), period: time.Second, resyncPeriod: resyncPeriod, clock: &clock.RealClock{}, } return r } 3.3. Reflector.Run Reflector.Run主要执行了ListAndWatch的方法。 // Run starts a watch and handles watch events. Will restart the watch if it is closed. // Run will exit when stopCh is closed. func (r *Reflector) Run(stopCh 3.4. ListAndWatch ListAndWatch第一次会列出所有的对象，并获取资源对象的版本号，然后watch资源对象的版本号来查看是否有被变更。首先会将资源版本号设置为0，list()可能会导致本地的缓存相对于etcd里面的内容存在延迟，Reflector会通过watch的方法将延迟的部分补充上，使得本地的缓存数据与etcd的数据保持一致。 3.4.1. List // ListAndWatch first lists all items and get the resource version at the moment of call, // and then use the resource version to watch. // It returns error if ListAndWatch didn't even try to initialize watch. func (r *Reflector) ListAndWatch(stopCh 首先将资源的版本号设置为0，然后调用listerWatcher.List(options)，列出所有list的内容。 // 版本号设置为0 options := metav1.ListOptions{ResourceVersion: \"0\"} // list接口 list, err := r.listerWatcher.List(options) 获取资源版本号，并将list的内容提取成对象列表。 // 获取版本号 resourceVersion = listMetaInterface.GetResourceVersion() // 将list的内容提取成对象列表 items, err := meta.ExtractList(list) 将list中对象列表的内容和版本号存储到本地的缓存store中，并全量替换已有的store的内容。 err := r.syncWith(items, resourceVersion) syncWith调用了store的Replace的方法来替换原来store中的数据。 // syncWith replaces the store's items with the given list. func (r *Reflector) syncWith(items []runtime.Object, resourceVersion string) error { found := make([]interface{}, 0, len(items)) for _, item := range items { found = append(found, item) } return r.store.Replace(found, resourceVersion) } Store.Replace方法定义如下： type Store interface { ... // Replace will delete the contents of the store, using instead the // given list. Store takes ownership of the list, you should not reference // it after calling this function. Replace([]interface{}, string) error ... } 最后设置最新的资源版本号。 r.setLastSyncResourceVersion(resourceVersion) setLastSyncResourceVersion: func (r *Reflector) setLastSyncResourceVersion(v string) { r.lastSyncResourceVersionMutex.Lock() defer r.lastSyncResourceVersionMutex.Unlock() r.lastSyncResourceVersion = v rv, err := strconv.Atoi(v) if err == nil { r.metrics.lastResourceVersion.Set(float64(rv)) } } 3.4.2. store.Resync resyncerrc := make(chan error, 1) cancelCh := make(chan struct{}) defer close(cancelCh) go func() { resyncCh, cleanup := r.resyncChan() defer func() { cleanup() // Call the last one written into cleanup }() for { select { case 核心代码： err := r.store.Resync() store的具体对象为DeltaFIFO，即调用DeltaFIFO.Resync // Resync will send a sync event for each item func (f *DeltaFIFO) Resync() error { f.lock.Lock() defer f.lock.Unlock() if f.knownObjects == nil { return nil } keys := f.knownObjects.ListKeys() for _, k := range keys { if err := f.syncKeyLocked(k); err != nil { return err } } return nil } 3.4.3. Watch for { // give the stopCh a chance to stop the loop, even in case of continue statements further down on errors select { case 设置watch的超时时间，默认为5分钟。 timemoutseconds := int64(minWatchTimeout.Seconds() * (rand.Float64() + 1.0)) options = metav1.ListOptions{ ResourceVersion: resourceVersion, // We want to avoid situations of hanging watchers. Stop any wachers that do not // receive any events within the timeout window. TimeoutSeconds: &timemoutseconds, } 执行listerWatcher.Watch(options)。 w, err := r.listerWatcher.Watch(options) 执行watchHandler。 err := r.watchHandler(w, &resourceVersion, resyncerrc, stopCh) 3.4.4. watchHandler watchHandler主要是通过watch的方式保证当前的资源版本是最新的。 // watchHandler watches w and keeps *resourceVersion up to date. func (r *Reflector) watchHandler(w watch.Interface, resourceVersion *string, errc chan error, stopCh 获取watch接口中的事件的channel，来获取事件的内容。 for { select { ... case event, ok := 当获得添加、更新、删除的事件时，将对应的对象更新到本地缓存store中。 switch event.Type { case watch.Added: err := r.store.Add(event.Object) if err != nil { utilruntime.HandleError(fmt.Errorf(\"%s: unable to add watch event object (%#v) to store: %v\", r.name, event.Object, err)) } case watch.Modified: err := r.store.Update(event.Object) if err != nil { utilruntime.HandleError(fmt.Errorf(\"%s: unable to update watch event object (%#v) to store: %v\", r.name, event.Object, err)) } case watch.Deleted: // TODO: Will any consumers need access to the \"last known // state\", which is passed in event.Object? If so, may need // to change this. err := r.store.Delete(event.Object) if err != nil { utilruntime.HandleError(fmt.Errorf(\"%s: unable to delete watch event object (%#v) from store: %v\", r.name, event.Object, err)) } default: utilruntime.HandleError(fmt.Errorf(\"%s: unable to understand watch event %#v\", r.name, event)) } 更新当前的最新版本号。 newResourceVersion := meta.GetResourceVersion() *resourceVersion = newResourceVersion r.setLastSyncResourceVersion(newResourceVersion) 通过对Reflector模块的分析，可以看到多次使用到本地缓存store模块，而store的数据由DeltaFIFO赋值而来，以下针对DeltaFIFO和store做分析。 4. DeltaFIFO DeltaFIFO由NewDeltaFIFO初始化，并赋值给config.Queue。 func (s *sharedIndexInformer) Run(stopCh 4.1. NewDeltaFIFO // NewDeltaFIFO returns a Store which can be used process changes to items. // // keyFunc is used to figure out what key an object should have. (It's // exposed in the returned DeltaFIFO's KeyOf() method, with bonus features.) // // 'compressor' may compress as many or as few items as it wants // (including returning an empty slice), but it should do what it // does quickly since it is called while the queue is locked. // 'compressor' may be nil if you don't want any delta compression. // // 'keyLister' is expected to return a list of keys that the consumer of // this queue \"knows about\". It is used to decide which items are missing // when Replace() is called; 'Deleted' deltas are produced for these items. // It may be nil if you don't need to detect all deletions. // TODO: consider merging keyLister with this object, tracking a list of // \"known\" keys when Pop() is called. Have to think about how that // affects error retrying. // TODO(lavalamp): I believe there is a possible race only when using an // external known object source that the above TODO would // fix. // // Also see the comment on DeltaFIFO. func NewDeltaFIFO(keyFunc KeyFunc, compressor DeltaCompressor, knownObjects KeyListerGetter) *DeltaFIFO { f := &DeltaFIFO{ items: map[string]Deltas{}, queue: []string{}, keyFunc: keyFunc, deltaCompressor: compressor, knownObjects: knownObjects, } f.cond.L = &f.lock return f } controller.Run的部分调用了NewReflector。 func (c *controller) Run(stopCh NewReflector构造函数，将c.config.Queue赋值给Reflector.store的属性。 func NewReflector(lw ListerWatcher, expectedType interface{}, store Store, resyncPeriod time.Duration) *Reflector { return NewNamedReflector(getDefaultReflectorName(internalPackages...), lw, expectedType, store, resyncPeriod) } // NewNamedReflector same as NewReflector, but with a specified name for logging func NewNamedReflector(name string, lw ListerWatcher, expectedType interface{}, store Store, resyncPeriod time.Duration) *Reflector { reflectorSuffix := atomic.AddInt64(&reflectorDisambiguator, 1) r := &Reflector{ name: name, // we need this to be unique per process (some names are still the same)but obvious who it belongs to metrics: newReflectorMetrics(makeValidPromethusMetricLabel(fmt.Sprintf(\"reflector_\"+name+\"_%d\", reflectorSuffix))), listerWatcher: lw, store: store, expectedType: reflect.TypeOf(expectedType), period: time.Second, resyncPeriod: resyncPeriod, clock: &clock.RealClock{}, } return r } 4.2. DeltaFIFO DeltaFIFO是一个生产者与消费者的队列，其中Reflector是生产者，消费者调用Pop()的方法。 DeltaFIFO主要用在以下场景： 希望对象变更最多处理一次 处理对象时，希望查看自上次处理对象以来发生的所有事情 要处理对象的删除 希望定期重新处理对象 // DeltaFIFO is like FIFO, but allows you to process deletes. // // DeltaFIFO is a producer-consumer queue, where a Reflector is // intended to be the producer, and the consumer is whatever calls // the Pop() method. // // DeltaFIFO solves this use case: // * You want to process every object change (delta) at most once. // * When you process an object, you want to see everything // that's happened to it since you last processed it. // * You want to process the deletion of objects. // * You might want to periodically reprocess objects. // // DeltaFIFO's Pop(), Get(), and GetByKey() methods return // interface{} to satisfy the Store/Queue interfaces, but it // will always return an object of type Deltas. // // A note on threading: If you call Pop() in parallel from multiple // threads, you could end up with multiple threads processing slightly // different versions of the same object. // // A note on the KeyLister used by the DeltaFIFO: It's main purpose is // to list keys that are \"known\", for the purpose of figuring out which // items have been deleted when Replace() or Delete() are called. The deleted // object will be included in the DeleteFinalStateUnknown markers. These objects // could be stale. // // You may provide a function to compress deltas (e.g., represent a // series of Updates as a single Update). type DeltaFIFO struct { // lock/cond protects access to 'items' and 'queue'. lock sync.RWMutex cond sync.Cond // We depend on the property that items in the set are in // the queue and vice versa, and that all Deltas in this // map have at least one Delta. items map[string]Deltas queue []string // populated is true if the first batch of items inserted by Replace() has been populated // or Delete/Add/Update was called first. populated bool // initialPopulationCount is the number of items inserted by the first call of Replace() initialPopulationCount int // keyFunc is used to make the key used for queued item // insertion and retrieval, and should be deterministic. keyFunc KeyFunc // deltaCompressor tells us how to combine two or more // deltas. It may be nil. deltaCompressor DeltaCompressor // knownObjects list keys that are \"known\", for the // purpose of figuring out which items have been deleted // when Replace() or Delete() is called. knownObjects KeyListerGetter // Indication the queue is closed. // Used to indicate a queue is closed so a control loop can exit when a queue is empty. // Currently, not used to gate any of CRED operations. closed bool closedLock sync.Mutex } 4.3. Queue & Store DeltaFIFO的类型是Queue接口，Reflector.store是Store接口，Queue接口是一个存储队列，Process的方法执行Queue.Pop出来的数据对象， // Queue is exactly like a Store, but has a Pop() method too. type Queue interface { Store // Pop blocks until it has something to process. // It returns the object that was process and the result of processing. // The PopProcessFunc may return an ErrRequeue{...} to indicate the item // should be requeued before releasing the lock on the queue. Pop(PopProcessFunc) (interface{}, error) // AddIfNotPresent adds a value previously // returned by Pop back into the queue as long // as nothing else (presumably more recent) // has since been added. AddIfNotPresent(interface{}) error // Return true if the first batch of items has been popped HasSynced() bool // Close queue Close() } 5. store Store是一个通用的存储接口，Reflector通过watch server的方式更新数据到store中，store给Reflector提供本地的缓存，让Reflector可以像消息队列一样的工作。 Store实现的是一种可以准确的写入对象和获取对象的机制。 // Store is a generic object storage interface. Reflector knows how to watch a server // and update a store. A generic store is provided, which allows Reflector to be used // as a local caching system, and an LRU store, which allows Reflector to work like a // queue of items yet to be processed. // // Store makes no assumptions about stored object identity; it is the responsibility // of a Store implementation to provide a mechanism to correctly key objects and to // define the contract for obtaining objects by some arbitrary key type. type Store interface { Add(obj interface{}) error Update(obj interface{}) error Delete(obj interface{}) error List() []interface{} ListKeys() []string Get(obj interface{}) (item interface{}, exists bool, err error) GetByKey(key string) (item interface{}, exists bool, err error) // Replace will delete the contents of the store, using instead the // given list. Store takes ownership of the list, you should not reference // it after calling this function. Replace([]interface{}, string) error Resync() error } 其中Replace方法会删除原来store中的内容，并将新增的list的内容存入store中，即完全替换数据。 6.1. cache cache实现了store的接口，而cache的具体实现又是调用ThreadSafeStore接口来实现功能的。 cache的功能主要有以下两点： 通过keyFunc计算对象的key 调用ThreadSafeStorage接口的方法 // cache responsibilities are limited to: // 1. Computing keys for objects via keyFunc // 2. Invoking methods of a ThreadSafeStorage interface type cache struct { // cacheStorage bears the burden of thread safety for the cache cacheStorage ThreadSafeStore // keyFunc is used to make the key for objects stored in and retrieved from items, and // should be deterministic. keyFunc KeyFunc } 其中ListAndWatch主要用到以下的方法： cache.Replace // Replace will delete the contents of 'c', using instead the given list. // 'c' takes ownership of the list, you should not reference the list again // after calling this function. func (c *cache) Replace(list []interface{}, resourceVersion string) error { items := map[string]interface{}{} for _, item := range list { key, err := c.keyFunc(item) if err != nil { return KeyError{item, err} } items[key] = item } c.cacheStorage.Replace(items, resourceVersion) return nil } cache.Add // Add inserts an item into the cache. func (c *cache) Add(obj interface{}) error { key, err := c.keyFunc(obj) if err != nil { return KeyError{obj, err} } c.cacheStorage.Add(key, obj) return nil } cache.Update // Update sets an item in the cache to its updated state. func (c *cache) Update(obj interface{}) error { key, err := c.keyFunc(obj) if err != nil { return KeyError{obj, err} } c.cacheStorage.Update(key, obj) return nil } cache.Delete // Delete removes an item from the cache. func (c *cache) Delete(obj interface{}) error { key, err := c.keyFunc(obj) if err != nil { return KeyError{obj, err} } c.cacheStorage.Delete(key) return nil } 6.2. ThreadSafeStore cache的具体是调用ThreadSafeStore来实现的。 // ThreadSafeStore is an interface that allows concurrent access to a storage backend. // TL;DR caveats: you must not modify anything returned by Get or List as it will break // the indexing feature in addition to not being thread safe. // // The guarantees of thread safety provided by List/Get are only valid if the caller // treats returned items as read-only. For example, a pointer inserted in the store // through `Add` will be returned as is by `Get`. Multiple clients might invoke `Get` // on the same key and modify the pointer in a non-thread-safe way. Also note that // modifying objects stored by the indexers (if any) will *not* automatically lead // to a re-index. So it's not a good idea to directly modify the objects returned by // Get/List, in general. type ThreadSafeStore interface { Add(key string, obj interface{}) Update(key string, obj interface{}) Delete(key string) Get(key string) (item interface{}, exists bool) List() []interface{} ListKeys() []string Replace(map[string]interface{}, string) Index(indexName string, obj interface{}) ([]interface{}, error) IndexKeys(indexName, indexKey string) ([]string, error) ListIndexFuncValues(name string) []string ByIndex(indexName, indexKey string) ([]interface{}, error) GetIndexers() Indexers // AddIndexers adds more indexers to this store. If you call this after you already have data // in the store, the results are undefined. AddIndexers(newIndexers Indexers) error Resync() error } threadSafeMap // threadSafeMap implements ThreadSafeStore type threadSafeMap struct { lock sync.RWMutex items map[string]interface{} // indexers maps a name to an IndexFunc indexers Indexers // indices maps a name to an Index indices Indices } 6. processLoop func (c *controller) Run(stopCh 在controller.Run方法中会调用processLoop，以下分析processLoop的处理逻辑。 // processLoop drains the work queue. // TODO: Consider doing the processing in parallel. This will require a little thought // to make sure that we don't end up processing the same object multiple times // concurrently. // // TODO: Plumb through the stopCh here (and down to the queue) so that this can // actually exit when the controller is stopped. Or just give up on this stuff // ever being stoppable. Converting this whole package to use Context would // also be helpful. func (c *controller) processLoop() { for { obj, err := c.config.Queue.Pop(PopProcessFunc(c.config.Process)) if err != nil { if err == FIFOClosedError { return } if c.config.RetryOnError { // This is the safe way to re-enqueue. c.config.Queue.AddIfNotPresent(obj) } } } } processLoop主要处理任务队列中的任务，其中处理逻辑是调用具体的ProcessFunc函数来实现，核心代码为： obj, err := c.config.Queue.Pop(PopProcessFunc(c.config.Process)) 5.1. DeltaFIFO.Pop Pop会阻塞住直到队列里面添加了新的对象，如果有多个对象，按照先进先出的原则处理，如果某个对象没有处理成功会重新被加入该队列中。 Pop中会调用具体的process函数来处理对象。 // Pop blocks until an item is added to the queue, and then returns it. If // multiple items are ready, they are returned in the order in which they were // added/updated. The item is removed from the queue (and the store) before it // is returned, so if you don't successfully process it, you need to add it back // with AddIfNotPresent(). // process function is called under lock, so it is safe update data structures // in it that need to be in sync with the queue (e.g. knownKeys). The PopProcessFunc // may return an instance of ErrRequeue with a nested error to indicate the current // item should be requeued (equivalent to calling AddIfNotPresent under the lock). // // Pop returns a 'Deltas', which has a complete list of all the things // that happened to the object (deltas) while it was sitting in the queue. func (f *DeltaFIFO) Pop(process PopProcessFunc) (interface{}, error) { f.lock.Lock() defer f.lock.Unlock() for { for len(f.queue) == 0 { // When the queue is empty, invocation of Pop() is blocked until new item is enqueued. // When Close() is called, the f.closed is set and the condition is broadcasted. // Which causes this loop to continue and return from the Pop(). if f.IsClosed() { return nil, FIFOClosedError } f.cond.Wait() } id := f.queue[0] f.queue = f.queue[1:] item, ok := f.items[id] if f.initialPopulationCount > 0 { f.initialPopulationCount-- } if !ok { // Item may have been deleted subsequently. continue } delete(f.items, id) err := process(item) if e, ok := err.(ErrRequeue); ok { f.addIfNotPresent(id, item) err = e.Err } // Don't need to copyDeltas here, because we're transferring // ownership to the caller. return item, err } } 核心代码： for { ... item, ok := f.items[id] ... err := process(item) if e, ok := err.(ErrRequeue); ok { f.addIfNotPresent(id, item) err = e.Err } // Don't need to copyDeltas here, because we're transferring // ownership to the caller. return item, err } 5.2. HandleDeltas cfg := &Config{ Queue: fifo, ListerWatcher: s.listerWatcher, ObjectType: s.objectType, FullResyncPeriod: s.resyncCheckPeriod, RetryOnError: false, ShouldResync: s.processor.shouldResync, Process: s.HandleDeltas, } 其中process函数就是在sharedIndexInformer.Run方法中，给config.Process赋值的HandleDeltas函数。 func (s *sharedIndexInformer) HandleDeltas(obj interface{}) error { s.blockDeltas.Lock() defer s.blockDeltas.Unlock() // from oldest to newest for _, d := range obj.(Deltas) { switch d.Type { case Sync, Added, Updated: isSync := d.Type == Sync s.cacheMutationDetector.AddObject(d.Object) if old, exists, err := s.indexer.Get(d.Object); err == nil && exists { if err := s.indexer.Update(d.Object); err != nil { return err } s.processor.distribute(updateNotification{oldObj: old, newObj: d.Object}, isSync) } else { if err := s.indexer.Add(d.Object); err != nil { return err } s.processor.distribute(addNotification{newObj: d.Object}, isSync) } case Deleted: if err := s.indexer.Delete(d.Object); err != nil { return err } s.processor.distribute(deleteNotification{oldObj: d.Object}, false) } } return nil } 核心代码： switch d.Type { case Sync, Added, Updated: ... if old, exists, err := s.indexer.Get(d.Object); err == nil && exists { ... s.processor.distribute(updateNotification{oldObj: old, newObj: d.Object}, isSync) } else { ... s.processor.distribute(addNotification{newObj: d.Object}, isSync) } case Deleted: ... s.processor.distribute(deleteNotification{oldObj: d.Object}, false) } 根据不同的类型，调用processor.distribute方法，该方法将对象加入processorListener的channel中。 5.3. sharedProcessor.distribute func (p *sharedProcessor) distribute(obj interface{}, sync bool) { p.listenersLock.RLock() defer p.listenersLock.RUnlock() if sync { for _, listener := range p.syncingListeners { listener.add(obj) } } else { for _, listener := range p.listeners { listener.add(obj) } } } processorListener.add: func (p *processorListener) add(notification interface{}) { p.addCh 综合以上的分析，可以看出processLoop通过调用HandleDeltas，再调用distribute，processorListener.add最终将不同更新类型的对象加入processorListener的channel中，供processorListener.Run使用。以下分析processorListener.Run的部分。 7. processor processor的主要功能就是记录了所有的回调函数实例(即 ResourceEventHandler 实例)，并负责触发这些函数。在sharedIndexInformer.Run部分会调用processor.run。 流程： listenser的add函数负责将notify装进pendingNotifications。 pop函数取出pendingNotifications的第一个nofify,输出到nextCh channel。 run函数则负责取出notify，然后根据notify的类型(增加、删除、更新)触发相应的处理函数，这些函数是在不同的NewXxxcontroller实现中注册的。 func (s *sharedIndexInformer) Run(stopCh 7.1. sharedProcessor.Run func (p *sharedProcessor) run(stopCh 7.1.1. listener.pop pop函数取出pendingNotifications的第一个nofify,输出到nextCh channel。 func (p *processorListener) pop() { defer utilruntime.HandleCrash() defer close(p.nextCh) // Tell .run() to stop var nextCh chan 7.1.2. listener.run listener.run部分根据不同的更新类型调用不同的处理函数。 func (p *processorListener) run() { defer utilruntime.HandleCrash() for next := range p.nextCh { switch notification := next.(type) { case updateNotification: p.handler.OnUpdate(notification.oldObj, notification.newObj) case addNotification: p.handler.OnAdd(notification.newObj) case deleteNotification: p.handler.OnDelete(notification.oldObj) default: utilruntime.HandleError(fmt.Errorf(\"unrecognized notification: %#v\", next)) } } } 其中具体的实现函数handler是在NewDeploymentController（其他不同类型的controller类似）中赋值的，而该handler是一个接口，具体如下： // ResourceEventHandler can handle notifications for events that happen to a // resource. The events are informational only, so you can't return an // error. // * OnAdd is called when an object is added. // * OnUpdate is called when an object is modified. Note that oldObj is the // last known state of the object-- it is possible that several changes // were combined together, so you can't use this to see every single // change. OnUpdate is also called when a re-list happens, and it will // get called even if nothing changed. This is useful for periodically // evaluating or syncing something. // * OnDelete will get the final state of the item if it is known, otherwise // it will get an object of type DeletedFinalStateUnknown. This can // happen if the watch is closed and misses the delete event and we don't // notice the deletion until the subsequent re-list. type ResourceEventHandler interface { OnAdd(obj interface{}) OnUpdate(oldObj, newObj interface{}) OnDelete(obj interface{}) } 7.2. ResourceEventHandler 以下以DeploymentController的处理逻辑为例。 在NewDeploymentController部分会注册deployment的事件函数，以下注册了三种类型的事件函数，其中包括：dInformer、rsInformer和podInformer。 // NewDeploymentController creates a new DeploymentController. func NewDeploymentController(dInformer extensionsinformers.DeploymentInformer, rsInformer extensionsinformers.ReplicaSetInformer, podInformer coreinformers.PodInformer, client clientset.Interface) (*DeploymentController, error) { ... dInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ AddFunc: dc.addDeployment, UpdateFunc: dc.updateDeployment, // This will enter the sync loop and no-op, because the deployment has been deleted from the store. DeleteFunc: dc.deleteDeployment, }) rsInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ AddFunc: dc.addReplicaSet, UpdateFunc: dc.updateReplicaSet, DeleteFunc: dc.deleteReplicaSet, }) podInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ DeleteFunc: dc.deletePod, }) ... } 7.2.1. addDeployment 以下以addDeployment为例，addDeployment主要是将对象加入到enqueueDeployment的队列中。 func (dc *DeploymentController) addDeployment(obj interface{}) { d := obj.(*extensions.Deployment) glog.V(4).Infof(\"Adding deployment %s\", d.Name) dc.enqueueDeployment(d) } enqueueDeployment的定义 type DeploymentController struct { ... enqueueDeployment func(deployment *extensions.Deployment) ... } 将dc.enqueue赋值给dc.enqueueDeployment dc.enqueueDeployment = dc.enqueue dc.enqueue调用了dc.queue.Add(key) func (dc *DeploymentController) enqueue(deployment *extensions.Deployment) { key, err := controller.KeyFunc(deployment) if err != nil { utilruntime.HandleError(fmt.Errorf(\"Couldn't get key for object %#v: %v\", deployment, err)) return } dc.queue.Add(key) } dc.queue主要记录了需要被同步的deployment的对象，供syncDeployment使用。 dc := &DeploymentController{ ... queue: workqueue.NewNamedRateLimitingQueue(workqueue.DefaultControllerRateLimiter(), \"deployment\"), } NewNamedRateLimitingQueue func NewNamedRateLimitingQueue(rateLimiter RateLimiter, name string) RateLimitingInterface { return &rateLimitingType{ DelayingInterface: NewNamedDelayingQueue(name), rateLimiter: rateLimiter, } } 通过以上分析，可以看出processor记录了不同类似的事件函数，其中事件函数在NewXxxController构造函数部分注册，具体事件函数的处理，一般是将需要处理的对象加入对应的controller的任务队列中，然后由类似syncDeployment的同步函数来维持期望状态的同步逻辑。 8. 总结 本文分析的部分主要是k8s的informer机制，即List-Watch机制。 8.1. Reflector Reflector的主要作用是watch指定的k8s资源，并将变化同步到本地是store中。Reflector只会放置指定的expectedType类型的资源到store中，除非expectedType为nil。如果resyncPeriod不为零，那么Reflector为以resyncPeriod为周期定期执行list的操作，这样就可以使用Reflector来定期处理所有的对象，也可以逐步处理变化的对象。 8.2. ListAndWatch ListAndWatch第一次会列出所有的对象，并获取资源对象的版本号，然后watch资源对象的版本号来查看是否有被变更。首先会将资源版本号设置为0，list()可能会导致本地的缓存相对于etcd里面的内容存在延迟，Reflector会通过watch的方法将延迟的部分补充上，使得本地的缓存数据与etcd的数据保持一致。 8.3. DeltaFIFO DeltaFIFO是一个生产者与消费者的队列，其中Reflector是生产者，消费者调用Pop()的方法。 DeltaFIFO主要用在以下场景： 希望对象变更最多处理一次 处理对象时，希望查看自上次处理对象以来发生的所有事情 要处理对象的删除 希望定期重新处理对象 8.4. store Store是一个通用的存储接口，Reflector通过watch server的方式更新数据到store中，store给Reflector提供本地的缓存，让Reflector可以像消息队列一样的工作。 Store实现的是一种可以准确的写入对象和获取对象的机制。 8.5. processor processor的主要功能就是记录了所有的回调函数实例(即 ResourceEventHandler 实例)，并负责触发这些函数。在sharedIndexInformer.Run部分会调用processor.run。 流程： listenser的add函数负责将notify装进pendingNotifications。 pop函数取出pendingNotifications的第一个nofify,输出到nextCh channel。 run函数则负责取出notify，然后根据notify的类型(增加、删除、更新)触发相应的处理函数，这些函数是在不同的NewXxxcontroller实现中注册的。 processor记录了不同类似的事件函数，其中事件函数在NewXxxController构造函数部分注册，具体事件函数的处理，一般是将需要处理的对象加入对应的controller的任务队列中，然后由类似syncDeployment的同步函数来维持期望状态的同步逻辑。 8.6. 主要步骤 在controller-manager的Run函数部分调用了InformerFactory.Start的方法，Start方法初始化各种类型的informer，并且每个类型起了个informer.Run的goroutine。 informer.Run的部分先生成一个DeltaFIFO的队列来存储对象变化的数据。然后调用processor.Run和controller.Run函数。 controller.Run函数会生成一个Reflector，Reflector的主要作用是watch指定的k8s资源，并将变化同步到本地是store中。Reflector以resyncPeriod为周期定期执行list的操作，这样就可以使用Reflector来定期处理所有的对象，也可以逐步处理变化的对象。 Reflector接着执行ListAndWatch函数，ListAndWatch第一次会列出所有的对象，并获取资源对象的版本号，然后watch资源对象的版本号来查看是否有被变更。首先会将资源版本号设置为0，list()可能会导致本地的缓存相对于etcd里面的内容存在延迟，Reflector会通过watch的方法将延迟的部分补充上，使得本地的缓存数据与etcd的数据保持一致。 controller.Run函数还会调用processLoop函数，processLoop通过调用HandleDeltas，再调用distribute，processorListener.add最终将不同更新类型的对象加入processorListener的channel中，供processorListener.Run使用。 processor的主要功能就是记录了所有的回调函数实例(即 ResourceEventHandler 实例)，并负责触发这些函数。processor记录了不同类型的事件函数，其中事件函数在NewXxxController构造函数部分注册，具体事件函数的处理，一般是将需要处理的对象加入对应的controller的任务队列中，然后由类似syncDeployment的同步函数来维持期望状态的同步逻辑。 参考文章： https://github.com/kubernetes/client-go/tree/master/tools/cache https://github.com/kubernetes/sample-controller/blob/master/docs/controller-client-go.md https://github.com/kubernetes/client-go/blob/master/examples/workqueue/main.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-scheduler/_index.html":{"url":"code-analysis/kube-scheduler/_index.html","title":"kube-scheduler","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-scheduler/NewSchedulerCommand.html":{"url":"code-analysis/kube-scheduler/NewSchedulerCommand.html","title":"NewSchedulerCommand","keywords":"","body":"kube-scheduler源码分析（一）之 NewSchedulerCommand1. Main函数2. NewSchedulerCommand2.1. NewOptions2.2. Options.Config2.3. AddFlags3. Run3.1. NewSchedulerConfig3.2. InformerFactory.Start3.3. WaitForCacheSync3.3.1. InformerFactory.WaitForCacheSync3.3.2. controller.WaitForCacheSync3.4. LeaderElection3.5. Scheduler.Runkube-scheduler源码分析（一）之 NewSchedulerCommand 以下代码分析基于 kubernetes v1.12.0 版本。 scheduler的cmd代码目录结构如下： kube-scheduler ├── BUILD ├── OWNERS ├── app # app的目录下主要为运行scheduler相关的对象 │ ├── BUILD │ ├── config │ │ ├── BUILD │ │ └── config.go # Scheduler的配置对象config │ ├── options # options主要记录 Scheduler 使用到的参数 │ │ ├── BUILD │ │ ├── configfile.go │ │ ├── deprecated.go │ │ ├── deprecated_test.go │ │ ├── insecure_serving.go │ │ ├── insecure_serving_test.go │ │ ├── options.go # 主要包括Options、NewOptions、AddFlags、Config等函数 │ │ └── options_test.go │ └── server.go # 主要包括 NewSchedulerCommand、NewSchedulerConfig、Run等函数 └── scheduler.go # main入口函数 1. Main函数 此部分的代码为/cmd/kube-scheduler/scheduler.go kube-scheduler的入口函数Main函数，仍然是采用统一的代码风格，使用Cobra命令行框架。 func main() { rand.Seed(time.Now().UTC().UnixNano()) command := app.NewSchedulerCommand() // TODO: once we switch everything over to Cobra commands, we can go back to calling // utilflag.InitFlags() (by removing its pflag.Parse() call). For now, we have to set the // normalize func and add the go flag set by hand. pflag.CommandLine.SetNormalizeFunc(utilflag.WordSepNormalizeFunc) pflag.CommandLine.AddGoFlagSet(goflag.CommandLine) // utilflag.InitFlags() logs.InitLogs() defer logs.FlushLogs() if err := command.Execute(); err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } } 核心代码： // 初始化scheduler命令结构体 command := app.NewSchedulerCommand() // 执行Execute err := command.Execute() 2. NewSchedulerCommand 此部分的代码为/cmd/kube-scheduler/app/server.go NewSchedulerCommand主要用来构造和初始化SchedulerCommand结构体， // NewSchedulerCommand creates a *cobra.Command object with default parameters func NewSchedulerCommand() *cobra.Command { opts, err := options.NewOptions() if err != nil { glog.Fatalf(\"unable to initialize command options: %v\", err) } cmd := &cobra.Command{ Use: \"kube-scheduler\", Long: `The Kubernetes scheduler is a policy-rich, topology-aware, workload-specific function that significantly impacts availability, performance, and capacity. The scheduler needs to take into account individual and collective resource requirements, quality of service requirements, hardware/software/policy constraints, affinity and anti-affinity specifications, data locality, inter-workload interference, deadlines, and so on. Workload-specific requirements will be exposed through the API as necessary.`, Run: func(cmd *cobra.Command, args []string) { verflag.PrintAndExitIfRequested() utilflag.PrintFlags(cmd.Flags()) if len(args) != 0 { fmt.Fprint(os.Stderr, \"arguments are not supported\\n\") } if errs := opts.Validate(); len(errs) > 0 { fmt.Fprintf(os.Stderr, \"%v\\n\", utilerrors.NewAggregate(errs)) os.Exit(1) } if len(opts.WriteConfigTo) > 0 { if err := options.WriteConfigFile(opts.WriteConfigTo, &opts.ComponentConfig); err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } glog.Infof(\"Wrote configuration to: %s\\n\", opts.WriteConfigTo) return } c, err := opts.Config() if err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } stopCh := make(chan struct{}) if err := Run(c.Complete(), stopCh); err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } }, } opts.AddFlags(cmd.Flags()) cmd.MarkFlagFilename(\"config\", \"yaml\", \"yml\", \"json\") return cmd } 核心代码： // 构造option opts, err := options.NewOptions() // 初始化config对象 c, err := opts.Config() // 执行run函数 err := Run(c.Complete(), stopCh) // 添加参数 opts.AddFlags(cmd.Flags()) 2.1. NewOptions NewOptions主要用来构造SchedulerServer使用的参数和上下文，其中核心参数是KubeSchedulerConfiguration。 opts, err := options.NewOptions() NewOptions: // NewOptions returns default scheduler app options. func NewOptions() (*Options, error) { cfg, err := newDefaultComponentConfig() if err != nil { return nil, err } hhost, hport, err := splitHostIntPort(cfg.HealthzBindAddress) if err != nil { return nil, err } o := &Options{ ComponentConfig: *cfg, SecureServing: nil, // TODO: enable with apiserveroptions.NewSecureServingOptions() CombinedInsecureServing: &CombinedInsecureServingOptions{ Healthz: &apiserveroptions.DeprecatedInsecureServingOptions{ BindNetwork: \"tcp\", }, Metrics: &apiserveroptions.DeprecatedInsecureServingOptions{ BindNetwork: \"tcp\", }, BindPort: hport, BindAddress: hhost, }, Authentication: nil, // TODO: enable with apiserveroptions.NewDelegatingAuthenticationOptions() Authorization: nil, // TODO: enable with apiserveroptions.NewDelegatingAuthorizationOptions() Deprecated: &DeprecatedOptions{ UseLegacyPolicyConfig: false, PolicyConfigMapNamespace: metav1.NamespaceSystem, }, } return o, nil } 2.2. Options.Config Config初始化调度器的配置对象。 c, err := opts.Config() Config函数主要执行以下操作： 构建scheduler client、leaderElectionClient、eventClient。 创建event recorder 设置leader选举 创建informer对象，主要函数有NewSharedInformerFactory和NewPodInformer。 Config具体代码如下： // Config return a scheduler config object func (o *Options) Config() (*schedulerappconfig.Config, error) { c := &schedulerappconfig.Config{} if err := o.ApplyTo(c); err != nil { return nil, err } // prepare kube clients. client, leaderElectionClient, eventClient, err := createClients(c.ComponentConfig.ClientConnection, o.Master, c.ComponentConfig.LeaderElection.RenewDeadline.Duration) if err != nil { return nil, err } // Prepare event clients. eventBroadcaster := record.NewBroadcaster() recorder := eventBroadcaster.NewRecorder(legacyscheme.Scheme, corev1.EventSource{Component: c.ComponentConfig.SchedulerName}) // Set up leader election if enabled. var leaderElectionConfig *leaderelection.LeaderElectionConfig if c.ComponentConfig.LeaderElection.LeaderElect { leaderElectionConfig, err = makeLeaderElectionConfig(c.ComponentConfig.LeaderElection, leaderElectionClient, recorder) if err != nil { return nil, err } } c.Client = client c.InformerFactory = informers.NewSharedInformerFactory(client, 0) c.PodInformer = factory.NewPodInformer(client, 0) c.EventClient = eventClient c.Recorder = recorder c.Broadcaster = eventBroadcaster c.LeaderElection = leaderElectionConfig return c, nil } 2.3. AddFlags AddFlags为SchedulerServer添加指定的参数。 opts.AddFlags(cmd.Flags()) AddFlags函数的具体代码如下： // AddFlags adds flags for the scheduler options. func (o *Options) AddFlags(fs *pflag.FlagSet) { fs.StringVar(&o.ConfigFile, \"config\", o.ConfigFile, \"The path to the configuration file. Flags override values in this file.\") fs.StringVar(&o.WriteConfigTo, \"write-config-to\", o.WriteConfigTo, \"If set, write the configuration values to this file and exit.\") fs.StringVar(&o.Master, \"master\", o.Master, \"The address of the Kubernetes API server (overrides any value in kubeconfig)\") o.SecureServing.AddFlags(fs) o.CombinedInsecureServing.AddFlags(fs) o.Authentication.AddFlags(fs) o.Authorization.AddFlags(fs) o.Deprecated.AddFlags(fs, &o.ComponentConfig) leaderelectionconfig.BindFlags(&o.ComponentConfig.LeaderElection.LeaderElectionConfiguration, fs) utilfeature.DefaultFeatureGate.AddFlag(fs) } 3. Run 此部分的代码为/cmd/kube-scheduler/app/server.go err := Run(c.Complete(), stopCh) Run运行一个不退出的常驻进程，来执行scheduler的相关操作。 Run函数的主要内容如下： 通过scheduler config来创建scheduler的结构体。 运行event broadcaster、healthz server、metrics server。 运行所有的informer并在调度前等待cache的同步（重点）。 执行sched.Run()来运行scheduler的调度逻辑。 如果多个scheduler并开启了LeaderElect，则执行leader选举。 以下对重点代码分开分析： 3.1. NewSchedulerConfig NewSchedulerConfig初始化SchedulerConfig（此部分具体逻辑待后续专门分析），最后初始化生成scheduler结构体。 // Build a scheduler config from the provided algorithm source. schedulerConfig, err := NewSchedulerConfig(c) if err != nil { return err } // Create the scheduler. sched := scheduler.NewFromConfig(schedulerConfig) 3.2. InformerFactory.Start 运行PodInformer，并运行InformerFactory。此部分的逻辑为client-go的informer机制，在Informer机制中有详细分析。 // Start all informers. go c.PodInformer.Informer().Run(stopCh) c.InformerFactory.Start(stopCh) 3.3. WaitForCacheSync 在调度前等待cache同步。 // Wait for all caches to sync before scheduling. c.InformerFactory.WaitForCacheSync(stopCh) controller.WaitForCacheSync(\"scheduler\", stopCh, c.PodInformer.Informer().HasSynced) 3.3.1. InformerFactory.WaitForCacheSync InformerFactory.WaitForCacheSync等待所有启动的informer的cache进行同步，保持本地的store信息与etcd的信息是最新一致的。 // WaitForCacheSync waits for all started informers' cache were synced. func (f *sharedInformerFactory) WaitForCacheSync(stopCh 接着调用cache.WaitForCacheSync。 // WaitForCacheSync waits for caches to populate. It returns true if it was successful, false // if the controller should shutdown func WaitForCacheSync(stopCh 3.3.2. controller.WaitForCacheSync controller.WaitForCacheSync是对cache.WaitForCacheSync的一层封装，通过不同的controller的名字来记录不同controller等待cache同步。 controller.WaitForCacheSync(\"scheduler\", stop, s.PodInformer.Informer().HasSynced) controller.WaitForCacheSync具体代码如下： // WaitForCacheSync is a wrapper around cache.WaitForCacheSync that generates log messages // indicating that the controller identified by controllerName is waiting for syncs, followed by // either a successful or failed sync. func WaitForCacheSync(controllerName string, stopCh 3.4. LeaderElection 如果有多个scheduler，并开启leader选举，则运行LeaderElector直到选举结束或退出。 // If leader election is enabled, run via LeaderElector until done and exit. if c.LeaderElection != nil { c.LeaderElection.Callbacks = leaderelection.LeaderCallbacks{ OnStartedLeading: run, OnStoppedLeading: func() { utilruntime.HandleError(fmt.Errorf(\"lost master\")) }, } leaderElector, err := leaderelection.NewLeaderElector(*c.LeaderElection) if err != nil { return fmt.Errorf(\"couldn't create leader elector: %v\", err) } leaderElector.Run(ctx) return fmt.Errorf(\"lost lease\") } 3.5. Scheduler.Run // Prepare a reusable run function. run := func(ctx context.Context) { sched.Run() Scheduler.Run先等待cache同步，然后开启调度逻辑的goroutine。 Scheduler.Run的具体代码如下： // Run begins watching and scheduling. It waits for cache to be synced, then starts a goroutine and returns immediately. func (sched *Scheduler) Run() { if !sched.config.WaitForCacheSync() { return } go wait.Until(sched.scheduleOne, 0, sched.config.StopEverything) } 以上是对/cmd/kube-scheduler/scheduler.go部分代码的分析，Scheduler.Run后续的具体代码位于pkg/scheduler/scheduler.go待后续文章分析。 参考： https://github.com/kubernetes/kubernetes/tree/v1.12.0/cmd/kube-scheduler https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-scheduler/scheduler.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-scheduler/app/server.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-scheduler/registerAlgorithmProvider.html":{"url":"code-analysis/kube-scheduler/registerAlgorithmProvider.html","title":"registerAlgorithmProvider","keywords":"","body":"kube-scheduler源码分析（二）之 registerAlgorithmProvider1. ApplyFeatureGates2. init2.1. registerAlgorithmProvider2.2. RegisterFitPredicate2.3. RegisterPriorityFunction23. defaultPredicates4. defaultPrioritieskube-scheduler源码分析（二）之 registerAlgorithmProvider 以下代码分析基于 kubernetes v1.12.0 版本。 此部分主要介绍调度中使用的各种调度算法，包括调度算法的注册部分。注册部分的代码主要在/pkg/scheduler/algorithmprovider中，具体的预选策略和优选策略的算法实现在/pkg/scheduler/algorithm中。 1. ApplyFeatureGates 注册调度算法的调用入口在SchedulerCommand的Run函数中。 此部分代码位于/cmd/kube-scheduler/app/server.go // Run runs the Scheduler. func Run(c schedulerserverconfig.CompletedConfig, stopCh ApplyFeatureGates的具体实现在pkg/scheduler/algorithmprovider的包中。 此部分代码位于/pkg/scheduler/algorithmprovider/plugins.go // ApplyFeatureGates applies algorithm by feature gates. func ApplyFeatureGates() { defaults.ApplyFeatureGates() } ApplyFeatureGates具体实现如下： 此部分代码位于/pkg/scheduler/algorithmprovider/defaults/defaults.go 根据feature移除部分调度策略。 // ApplyFeatureGates applies algorithm by feature gates. func ApplyFeatureGates() { if utilfeature.DefaultFeatureGate.Enabled(features.TaintNodesByCondition) { // Remove \"CheckNodeCondition\", \"CheckNodeMemoryPressure\", \"CheckNodePIDPressurePred\" // and \"CheckNodeDiskPressure\" predicates factory.RemoveFitPredicate(predicates.CheckNodeConditionPred) factory.RemoveFitPredicate(predicates.CheckNodeMemoryPressurePred) factory.RemoveFitPredicate(predicates.CheckNodeDiskPressurePred) factory.RemoveFitPredicate(predicates.CheckNodePIDPressurePred) // Remove key \"CheckNodeCondition\", \"CheckNodeMemoryPressure\" and \"CheckNodeDiskPressure\" // from ALL algorithm provider // The key will be removed from all providers which in algorithmProviderMap[] // if you just want remove specific provider, call func RemovePredicateKeyFromAlgoProvider() factory.RemovePredicateKeyFromAlgorithmProviderMap(predicates.CheckNodeConditionPred) factory.RemovePredicateKeyFromAlgorithmProviderMap(predicates.CheckNodeMemoryPressurePred) factory.RemovePredicateKeyFromAlgorithmProviderMap(predicates.CheckNodeDiskPressurePred) factory.RemovePredicateKeyFromAlgorithmProviderMap(predicates.CheckNodePIDPressurePred) // Fit is determined based on whether a pod can tolerate all of the node's taints factory.RegisterMandatoryFitPredicate(predicates.PodToleratesNodeTaintsPred, predicates.PodToleratesNodeTaints) // Fit is determined based on whether a pod can tolerate unschedulable of node factory.RegisterMandatoryFitPredicate(predicates.CheckNodeUnschedulablePred, predicates.CheckNodeUnschedulablePredicate) // Insert Key \"PodToleratesNodeTaints\" and \"CheckNodeUnschedulable\" To All Algorithm Provider // The key will insert to all providers which in algorithmProviderMap[] // if you just want insert to specific provider, call func InsertPredicateKeyToAlgoProvider() factory.InsertPredicateKeyToAlgorithmProviderMap(predicates.PodToleratesNodeTaintsPred) factory.InsertPredicateKeyToAlgorithmProviderMap(predicates.CheckNodeUnschedulablePred) glog.Warningf(\"TaintNodesByCondition is enabled, PodToleratesNodeTaints predicate is mandatory\") } // Prioritizes nodes that satisfy pod's resource limits if utilfeature.DefaultFeatureGate.Enabled(features.ResourceLimitsPriorityFunction) { factory.RegisterPriorityFunction2(\"ResourceLimitsPriority\", priorities.ResourceLimitsPriorityMap, nil, 1) } } 2. init 当函数逻辑调用到algorithmprovider包时，就会自动调用init的初始化函数，此部分主要包括对预选算法和优选算法的注册。 此部分代码位于/pkg/scheduler/algorithmprovider/defaults/defaults.go func init() { // Register functions that extract metadata used by predicates and priorities computations. factory.RegisterPredicateMetadataProducerFactory( func(args factory.PluginFactoryArgs) algorithm.PredicateMetadataProducer { return predicates.NewPredicateMetadataFactory(args.PodLister) }) factory.RegisterPriorityMetadataProducerFactory( func(args factory.PluginFactoryArgs) algorithm.PriorityMetadataProducer { return priorities.NewPriorityMetadataFactory(args.ServiceLister, args.ControllerLister, args.ReplicaSetLister, args.StatefulSetLister) }) registerAlgorithmProvider(defaultPredicates(), defaultPriorities()) // IMPORTANT NOTES for predicate developers: // We are using cached predicate result for pods belonging to the same equivalence class. // So when implementing a new predicate, you are expected to check whether the result // of your predicate function can be affected by related API object change (ADD/DELETE/UPDATE). // If yes, you are expected to invalidate the cached predicate result for related API object change. // For example: // https://github.com/kubernetes/kubernetes/blob/36a218e/plugin/pkg/scheduler/factory/factory.go#L422 // Registers predicates and priorities that are not enabled by default, but user can pick when creating their // own set of priorities/predicates. // PodFitsPorts has been replaced by PodFitsHostPorts for better user understanding. // For backwards compatibility with 1.0, PodFitsPorts is registered as well. factory.RegisterFitPredicate(\"PodFitsPorts\", predicates.PodFitsHostPorts) // Fit is defined based on the absence of port conflicts. // This predicate is actually a default predicate, because it is invoked from // predicates.GeneralPredicates() factory.RegisterFitPredicate(predicates.PodFitsHostPortsPred, predicates.PodFitsHostPorts) // Fit is determined by resource availability. // This predicate is actually a default predicate, because it is invoked from // predicates.GeneralPredicates() factory.RegisterFitPredicate(predicates.PodFitsResourcesPred, predicates.PodFitsResources) // Fit is determined by the presence of the Host parameter and a string match // This predicate is actually a default predicate, because it is invoked from // predicates.GeneralPredicates() factory.RegisterFitPredicate(predicates.HostNamePred, predicates.PodFitsHost) // Fit is determined by node selector query. factory.RegisterFitPredicate(predicates.MatchNodeSelectorPred, predicates.PodMatchNodeSelector) // ServiceSpreadingPriority is a priority config factory that spreads pods by minimizing // the number of pods (belonging to the same service) on the same node. // Register the factory so that it's available, but do not include it as part of the default priorities // Largely replaced by \"SelectorSpreadPriority\", but registered for backward compatibility with 1.0 factory.RegisterPriorityConfigFactory( \"ServiceSpreadingPriority\", factory.PriorityConfigFactory{ MapReduceFunction: func(args factory.PluginFactoryArgs) (algorithm.PriorityMapFunction, algorithm.PriorityReduceFunction) { return priorities.NewSelectorSpreadPriority(args.ServiceLister, algorithm.EmptyControllerLister{}, algorithm.EmptyReplicaSetLister{}, algorithm.EmptyStatefulSetLister{}) }, Weight: 1, }, ) // EqualPriority is a prioritizer function that gives an equal weight of one to all nodes // Register the priority function so that its available // but do not include it as part of the default priorities factory.RegisterPriorityFunction2(\"EqualPriority\", core.EqualPriorityMap, nil, 1) // Optional, cluster-autoscaler friendly priority function - give used nodes higher priority. factory.RegisterPriorityFunction2(\"MostRequestedPriority\", priorities.MostRequestedPriorityMap, nil, 1) factory.RegisterPriorityFunction2( \"RequestedToCapacityRatioPriority\", priorities.RequestedToCapacityRatioResourceAllocationPriorityDefault().PriorityMap, nil, 1) } 以下对init中的注册进行拆分介绍。 2.1. registerAlgorithmProvider 此部分主要注册默认的预选和优选策略。 // Register functions that extract metadata used by predicates and priorities computations. factory.RegisterPredicateMetadataProducerFactory( func(args factory.PluginFactoryArgs) algorithm.PredicateMetadataProducer { return predicates.NewPredicateMetadataFactory(args.PodLister) }) factory.RegisterPriorityMetadataProducerFactory( func(args factory.PluginFactoryArgs) algorithm.PriorityMetadataProducer { return priorities.NewPriorityMetadataFactory(args.ServiceLister, args.ControllerLister, args.ReplicaSetLister, args.StatefulSetLister) }) registerAlgorithmProvider(defaultPredicates(), defaultPriorities()) registerAlgorithmProvider 注册AlgorithmProvider，其中包括DefaultProvider和ClusterAutoscalerProvider。 func registerAlgorithmProvider(predSet, priSet sets.String) { // Registers algorithm providers. By default we use 'DefaultProvider', but user can specify one to be used // by specifying flag. factory.RegisterAlgorithmProvider(factory.DefaultProvider, predSet, priSet) // Cluster autoscaler friendly scheduling algorithm. factory.RegisterAlgorithmProvider(ClusterAutoscalerProvider, predSet, copyAndReplace(priSet, \"LeastRequestedPriority\", \"MostRequestedPriority\")) } 2.2. RegisterFitPredicate 在init部分注册预选策略函数。 预选策略如下： 调度策略 函数 描述 PodFitsPorts PodFitsHostPorts PodFitsPorts已经被PodFitsHostPorts代替，此处主要是为了兼容性。 PodFitsHostPortsPred PodFitsHostPorts 判断是否与宿主机的端口冲突。 PodFitsResourcesPred PodFitsResources 判断node资源是否充足。 HostNamePred PodFitsHost 判断pod所指定调度的节点是否是当前的节点。 MatchNodeSelectorPred PodMatchNodeSelector 判断pod指定的node selector是否匹配当前的node。 具体代码如下： // PodFitsPorts has been replaced by PodFitsHostPorts for better user understanding. // For backwards compatibility with 1.0, PodFitsPorts is registered as well. factory.RegisterFitPredicate(\"PodFitsPorts\", predicates.PodFitsHostPorts) // Fit is defined based on the absence of port conflicts. // This predicate is actually a default predicate, because it is invoked from // predicates.GeneralPredicates() factory.RegisterFitPredicate(predicates.PodFitsHostPortsPred, predicates.PodFitsHostPorts) // Fit is determined by resource availability. // This predicate is actually a default predicate, because it is invoked from // predicates.GeneralPredicates() factory.RegisterFitPredicate(predicates.PodFitsResourcesPred, predicates.PodFitsResources) // Fit is determined by the presence of the Host parameter and a string match // This predicate is actually a default predicate, because it is invoked from // predicates.GeneralPredicates() factory.RegisterFitPredicate(predicates.HostNamePred, predicates.PodFitsHost) // Fit is determined by node selector query. factory.RegisterFitPredicate(predicates.MatchNodeSelectorPred, predicates.PodMatchNodeSelector) 2.3. RegisterPriorityFunction2 在init部分注册优选策略函数。 // EqualPriority is a prioritizer function that gives an equal weight of one to all nodes // Register the priority function so that its available // but do not include it as part of the default priorities factory.RegisterPriorityFunction2(\"EqualPriority\", core.EqualPriorityMap, nil, 1) // Optional, cluster-autoscaler friendly priority function - give used nodes higher priority. factory.RegisterPriorityFunction2(\"MostRequestedPriority\", priorities.MostRequestedPriorityMap, nil, 1) factory.RegisterPriorityFunction2( \"RequestedToCapacityRatioPriority\", priorities.RequestedToCapacityRatioResourceAllocationPriorityDefault().PriorityMap, nil, 1) 3. defaultPredicates 此部分为默认预选策略的注册函数。 默认的预选策略如下： 预选策略 函数 描述 NoVolumeZoneConflictPred NewVolumeZonePredicate 判断pod使用到的volume是否有节点的要求。目前只支持pvc。 MaxEBSVolumeCountPred NewMaxPDVolumeCountPredicate 判断pod使用EBSVolume在该节点上是否已经达到上限了。 MaxGCEPDVolumeCountPred NewMaxPDVolumeCountPredicate 判断pod使用GCEPDVolume在该节点上是否已经达到上限了。 MaxAzureDiskVolumeCountPred NewMaxPDVolumeCountPredicate 判断pod使用AzureDiskVolume在该节点上是否已经达到上限了。 MaxCSIVolumeCountPred NewCSIMaxVolumeLimitPredicate 判断CSIVolume是否达到上限了。 MatchInterPodAffinityPred NewPodAffinityPredicate 匹配pod的亲缘性。 NoDiskConflictPred NoDiskConflict 判断是否有disk volumes的冲突。 GeneralPred GeneralPredicates 通用的预选策略 CheckNodeMemoryPressurePred CheckNodeMemoryPressurePredicate 判断节点内存是否充足。 CheckNodeDiskPressurePred CheckNodeDiskPressurePredicate 判断节点是否有磁盘压力。 CheckNodePIDPressurePred CheckNodePIDPressurePredicate 判断节点上的PID CheckNodeConditionPred CheckNodeConditionPredicate 判断node是否ready。 PodToleratesNodeTaintsPred PodToleratesNodeTaints 判断pod是否可以容忍节点的taints。 CheckVolumeBindingPred NewVolumeBindingPredicate 判断是否有volume拓扑的要求。 具体代码如下： func defaultPredicates() sets.String { return sets.NewString( // Fit is determined by volume zone requirements. factory.RegisterFitPredicateFactory( predicates.NoVolumeZoneConflictPred, func(args factory.PluginFactoryArgs) algorithm.FitPredicate { return predicates.NewVolumeZonePredicate(args.PVInfo, args.PVCInfo, args.StorageClassInfo) }, ), // Fit is determined by whether or not there would be too many AWS EBS volumes attached to the node factory.RegisterFitPredicateFactory( predicates.MaxEBSVolumeCountPred, func(args factory.PluginFactoryArgs) algorithm.FitPredicate { return predicates.NewMaxPDVolumeCountPredicate(predicates.EBSVolumeFilterType, args.PVInfo, args.PVCInfo) }, ), // Fit is determined by whether or not there would be too many GCE PD volumes attached to the node factory.RegisterFitPredicateFactory( predicates.MaxGCEPDVolumeCountPred, func(args factory.PluginFactoryArgs) algorithm.FitPredicate { return predicates.NewMaxPDVolumeCountPredicate(predicates.GCEPDVolumeFilterType, args.PVInfo, args.PVCInfo) }, ), // Fit is determined by whether or not there would be too many Azure Disk volumes attached to the node factory.RegisterFitPredicateFactory( predicates.MaxAzureDiskVolumeCountPred, func(args factory.PluginFactoryArgs) algorithm.FitPredicate { return predicates.NewMaxPDVolumeCountPredicate(predicates.AzureDiskVolumeFilterType, args.PVInfo, args.PVCInfo) }, ), factory.RegisterFitPredicateFactory( predicates.MaxCSIVolumeCountPred, func(args factory.PluginFactoryArgs) algorithm.FitPredicate { return predicates.NewCSIMaxVolumeLimitPredicate(args.PVInfo, args.PVCInfo) }, ), // Fit is determined by inter-pod affinity. factory.RegisterFitPredicateFactory( predicates.MatchInterPodAffinityPred, func(args factory.PluginFactoryArgs) algorithm.FitPredicate { return predicates.NewPodAffinityPredicate(args.NodeInfo, args.PodLister) }, ), // Fit is determined by non-conflicting disk volumes. factory.RegisterFitPredicate(predicates.NoDiskConflictPred, predicates.NoDiskConflict), // GeneralPredicates are the predicates that are enforced by all Kubernetes components // (e.g. kubelet and all schedulers) factory.RegisterFitPredicate(predicates.GeneralPred, predicates.GeneralPredicates), // Fit is determined by node memory pressure condition. factory.RegisterFitPredicate(predicates.CheckNodeMemoryPressurePred, predicates.CheckNodeMemoryPressurePredicate), // Fit is determined by node disk pressure condition. factory.RegisterFitPredicate(predicates.CheckNodeDiskPressurePred, predicates.CheckNodeDiskPressurePredicate), // Fit is determined by node pid pressure condition. factory.RegisterFitPredicate(predicates.CheckNodePIDPressurePred, predicates.CheckNodePIDPressurePredicate), // Fit is determined by node conditions: not ready, network unavailable or out of disk. factory.RegisterMandatoryFitPredicate(predicates.CheckNodeConditionPred, predicates.CheckNodeConditionPredicate), // Fit is determined based on whether a pod can tolerate all of the node's taints factory.RegisterFitPredicate(predicates.PodToleratesNodeTaintsPred, predicates.PodToleratesNodeTaints), // Fit is determined by volume topology requirements. factory.RegisterFitPredicateFactory( predicates.CheckVolumeBindingPred, func(args factory.PluginFactoryArgs) algorithm.FitPredicate { return predicates.NewVolumeBindingPredicate(args.VolumeBinder) }, ), ) } 4. defaultPriorities 此部分主要为默认优选策略的注册函数。 默认优选策略如下： 优选策略 函数 描述 SelectorSpreadPriority NewSelectorSpreadPriority 属于相同service和rs下的pod尽量分布在不同的node上。 InterPodAffinityPriority NewInterPodAffinityPriority 根据pod的亲缘性，将相同拓扑域中的pod放在同一个节点 LeastRequestedPriority LeastRequestedPriorityMap 按最少请求的利用率对节点进行优先级排序。 BalancedResourceAllocation BalancedResourceAllocationMap 实现资源的平衡使用。 NodePreferAvoidPodsPriority CalculateNodePreferAvoidPodsPriorityMap 将此权重设置为足以覆盖所有其他优先级函数。 NodeAffinityPriority CalculateNodeAffinityPriorityMap pod指定label节点调度，来匹配node亲缘性。 TaintTolerationPriority ComputeTaintTolerationPriorityMap pod有设置tolerate属性来容忍node的taint。 ImageLocalityPriority ImageLocalityPriorityMap 根据节点上是否有该pod使用到的镜像打分。 具体代码实现如下： func defaultPriorities() sets.String { return sets.NewString( // spreads pods by minimizing the number of pods (belonging to the same service or replication controller) on the same node. factory.RegisterPriorityConfigFactory( \"SelectorSpreadPriority\", factory.PriorityConfigFactory{ MapReduceFunction: func(args factory.PluginFactoryArgs) (algorithm.PriorityMapFunction, algorithm.PriorityReduceFunction) { return priorities.NewSelectorSpreadPriority(args.ServiceLister, args.ControllerLister, args.ReplicaSetLister, args.StatefulSetLister) }, Weight: 1, }, ), // pods should be placed in the same topological domain (e.g. same node, same rack, same zone, same power domain, etc.) // as some other pods, or, conversely, should not be placed in the same topological domain as some other pods. factory.RegisterPriorityConfigFactory( \"InterPodAffinityPriority\", factory.PriorityConfigFactory{ Function: func(args factory.PluginFactoryArgs) algorithm.PriorityFunction { return priorities.NewInterPodAffinityPriority(args.NodeInfo, args.NodeLister, args.PodLister, args.HardPodAffinitySymmetricWeight) }, Weight: 1, }, ), // Prioritize nodes by least requested utilization. factory.RegisterPriorityFunction2(\"LeastRequestedPriority\", priorities.LeastRequestedPriorityMap, nil, 1), // Prioritizes nodes to help achieve balanced resource usage factory.RegisterPriorityFunction2(\"BalancedResourceAllocation\", priorities.BalancedResourceAllocationMap, nil, 1), // Set this weight large enough to override all other priority functions. // TODO: Figure out a better way to do this, maybe at same time as fixing #24720. factory.RegisterPriorityFunction2(\"NodePreferAvoidPodsPriority\", priorities.CalculateNodePreferAvoidPodsPriorityMap, nil, 10000), // Prioritizes nodes that have labels matching NodeAffinity factory.RegisterPriorityFunction2(\"NodeAffinityPriority\", priorities.CalculateNodeAffinityPriorityMap, priorities.CalculateNodeAffinityPriorityReduce, 1), // Prioritizes nodes that marked with taint which pod can tolerate. factory.RegisterPriorityFunction2(\"TaintTolerationPriority\", priorities.ComputeTaintTolerationPriorityMap, priorities.ComputeTaintTolerationPriorityReduce, 1), // ImageLocalityPriority prioritizes nodes that have images requested by the pod present. factory.RegisterPriorityFunction2(\"ImageLocalityPriority\", priorities.ImageLocalityPriorityMap, nil, 1), ) } 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/algorithmprovider/defaults/defaults.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-scheduler/scheduleOne.html":{"url":"code-analysis/kube-scheduler/scheduleOne.html","title":"scheduleOne","keywords":"","body":"kube-scheduler源码分析（三）之 scheduleOne1. Scheduler.Run2. Scheduler.scheduleOne3. config.NextPod3.1. getNextPod4. Scheduler.schedule4.1. ScheduleAlgorithm5. genericScheduler.Schedule5.1. podPassesBasicChecks5.2. findNodesThatFit5.3. PrioritizeNodes5.4. selectHost5.4.1. findMaxScores6. Scheduler.preempt7. Scheduler.assume8. Scheduler.bind9. 总结kube-scheduler源码分析（三）之 scheduleOne 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析/pkg/scheduler/中调度的基本流程。具体的预选调度逻辑、优选调度逻辑、节点抢占逻辑待后续再独立分析。 scheduler的pkg代码目录结构如下： scheduler ├── algorithm # 主要包含调度的算法 │ ├── predicates # 预选的策略 │ ├── priorities # 优选的策略 │ ├── scheduler_interface.go # ScheduleAlgorithm、SchedulerExtender接口定义 │ ├── types.go # 使用到的type的定义 ├── algorithmprovider │ ├── defaults │ │ ├── defaults.go # 默认算法的初始化操作，包括预选和优选策略 ├── cache # scheduler调度使用到的cache │ ├── cache.go # schedulerCache │ ├── interface.go │ ├── node_info.go │ ├── node_tree.go ├── core # 调度逻辑的核心代码 │ ├── equivalence │ │ ├── eqivalence.go # 存储相同pod的调度结果缓存，主要给预选策略使用 │ ├── extender.go │ ├── generic_scheduler.go # genericScheduler,主要包含默认调度器的调度逻辑 │ ├── scheduling_queue.go # 调度使用到的队列，主要用来存储需要被调度的pod ├── factory │ ├── factory.go # 主要包括NewConfigFactory、NewPodInformer，监听pod事件来更新调度队列 ├── metrics │ └── metrics.go # 主要给prometheus使用 ├── scheduler.go # pkg部分的Run入口(核心代码)，主要包含Run、scheduleOne、schedule、preempt等函数 └── volumebinder └── volume_binder.go # volume bind 1. Scheduler.Run 此部分代码位于pkg/scheduler/scheduler.go 此处为具体调度逻辑的入口。 // Run begins watching and scheduling. It waits for cache to be synced, then starts a goroutine and returns immediately. func (sched *Scheduler) Run() { if !sched.config.WaitForCacheSync() { return } go wait.Until(sched.scheduleOne, 0, sched.config.StopEverything) } 2. Scheduler.scheduleOne 此部分代码位于pkg/scheduler/scheduler.go scheduleOne主要为单个pod选择一个适合的节点，为调度逻辑的核心函数。 对单个pod进行调度的基本流程如下： 通过podQueue的待调度队列中弹出需要调度的pod。 通过具体的调度算法为该pod选出合适的节点，其中调度算法就包括预选和优选两步策略。 如果上述调度失败，则会尝试抢占机制，将优先级低的pod剔除，让优先级高的pod调度成功。 将该pod和选定的节点进行假性绑定，存入scheduler cache中，方便具体绑定操作可以异步进行。 实际执行绑定操作，将node的名字添加到pod的节点相关属性中。 完整代码如下： // scheduleOne does the entire scheduling workflow for a single pod. It is serialized on the scheduling algorithm's host fitting. func (sched *Scheduler) scheduleOne() { pod := sched.config.NextPod() if pod.DeletionTimestamp != nil { sched.config.Recorder.Eventf(pod, v1.EventTypeWarning, \"FailedScheduling\", \"skip schedule deleting pod: %v/%v\", pod.Namespace, pod.Name) glog.V(3).Infof(\"Skip schedule deleting pod: %v/%v\", pod.Namespace, pod.Name) return } glog.V(3).Infof(\"Attempting to schedule pod: %v/%v\", pod.Namespace, pod.Name) // Synchronously attempt to find a fit for the pod. start := time.Now() suggestedHost, err := sched.schedule(pod) if err != nil { // schedule() may have failed because the pod would not fit on any host, so we try to // preempt, with the expectation that the next time the pod is tried for scheduling it // will fit due to the preemption. It is also possible that a different pod will schedule // into the resources that were preempted, but this is harmless. if fitError, ok := err.(*core.FitError); ok { preemptionStartTime := time.Now() sched.preempt(pod, fitError) metrics.PreemptionAttempts.Inc() metrics.SchedulingAlgorithmPremptionEvaluationDuration.Observe(metrics.SinceInMicroseconds(preemptionStartTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PreemptionEvaluation).Observe(metrics.SinceInSeconds(preemptionStartTime)) } return } metrics.SchedulingAlgorithmLatency.Observe(metrics.SinceInMicroseconds(start)) // Tell the cache to assume that a pod now is running on a given node, even though it hasn't been bound yet. // This allows us to keep scheduling without waiting on binding to occur. assumedPod := pod.DeepCopy() // Assume volumes first before assuming the pod. // // If all volumes are completely bound, then allBound is true and binding will be skipped. // // Otherwise, binding of volumes is started after the pod is assumed, but before pod binding. // // This function modifies 'assumedPod' if volume binding is required. allBound, err := sched.assumeVolumes(assumedPod, suggestedHost) if err != nil { return } // assume modifies `assumedPod` by setting NodeName=suggestedHost err = sched.assume(assumedPod, suggestedHost) if err != nil { return } // bind the pod to its host asynchronously (we can do this b/c of the assumption step above). go func() { // Bind volumes first before Pod if !allBound { err = sched.bindVolumes(assumedPod) if err != nil { return } } err := sched.bind(assumedPod, &v1.Binding{ ObjectMeta: metav1.ObjectMeta{Namespace: assumedPod.Namespace, Name: assumedPod.Name, UID: assumedPod.UID}, Target: v1.ObjectReference{ Kind: \"Node\", Name: suggestedHost, }, }) metrics.E2eSchedulingLatency.Observe(metrics.SinceInMicroseconds(start)) if err != nil { glog.Errorf(\"Internal error binding pod: (%v)\", err) } }() } 以下对重要代码分别进行分析。 3. config.NextPod 通过podQueue的方式存储待调度的pod队列，NextPod拿出下一个需要被调度的pod。 pod := sched.config.NextPod() if pod.DeletionTimestamp != nil { sched.config.Recorder.Eventf(pod, v1.EventTypeWarning, \"FailedScheduling\", \"skip schedule deleting pod: %v/%v\", pod.Namespace, pod.Name) glog.V(3).Infof(\"Skip schedule deleting pod: %v/%v\", pod.Namespace, pod.Name) return } glog.V(3).Infof(\"Attempting to schedule pod: %v/%v\", pod.Namespace, pod.Name) NextPod的具体函数在factory.go的CreateFromKey函数中定义，如下： func (c *configFactory) CreateFromKeys(predicateKeys, priorityKeys sets.String, extenders []algorithm.SchedulerExtender) (*scheduler.Config, error) { ... return &scheduler.Config{ ... NextPod: func() *v1.Pod { return c.getNextPod() } ... } 3.1. getNextPod 通过一个podQueue来存储需要调度的pod的队列，通过队列Pop的方式弹出需要被调度的pod。 func (c *configFactory) getNextPod() *v1.Pod { pod, err := c.podQueue.Pop() if err == nil { glog.V(4).Infof(\"About to try and schedule pod %v/%v\", pod.Namespace, pod.Name) return pod } glog.Errorf(\"Error while retrieving next pod from scheduling queue: %v\", err) return nil } 4. Scheduler.schedule 此部分代码位于pkg/scheduler/scheduler.go 此部分为调度逻辑的核心，通过不同的算法为具体的pod选择一个最合适的节点。 // Synchronously attempt to find a fit for the pod. start := time.Now() suggestedHost, err := sched.schedule(pod) if err != nil { // schedule() may have failed because the pod would not fit on any host, so we try to // preempt, with the expectation that the next time the pod is tried for scheduling it // will fit due to the preemption. It is also possible that a different pod will schedule // into the resources that were preempted, but this is harmless. if fitError, ok := err.(*core.FitError); ok { preemptionStartTime := time.Now() sched.preempt(pod, fitError) metrics.PreemptionAttempts.Inc() metrics.SchedulingAlgorithmPremptionEvaluationDuration.Observe(metrics.SinceInMicroseconds(preemptionStartTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PreemptionEvaluation).Observe(metrics.SinceInSeconds(preemptionStartTime)) } return } schedule通过调度算法返回一个最优的节点。 // schedule implements the scheduling algorithm and returns the suggested host. func (sched *Scheduler) schedule(pod *v1.Pod) (string, error) { host, err := sched.config.Algorithm.Schedule(pod, sched.config.NodeLister) if err != nil { pod = pod.DeepCopy() sched.config.Error(pod, err) sched.config.Recorder.Eventf(pod, v1.EventTypeWarning, \"FailedScheduling\", \"%v\", err) sched.config.PodConditionUpdater.Update(pod, &v1.PodCondition{ Type: v1.PodScheduled, Status: v1.ConditionFalse, Reason: v1.PodReasonUnschedulable, Message: err.Error(), }) return \"\", err } return host, err } 4.1. ScheduleAlgorithm ScheduleAlgorithm是一个调度算法的接口，主要的实现体是genericScheduler，后续分析genericScheduler.Schedule。 ScheduleAlgorithm接口定义如下： // ScheduleAlgorithm is an interface implemented by things that know how to schedule pods // onto machines. type ScheduleAlgorithm interface { Schedule(*v1.Pod, NodeLister) (selectedMachine string, err error) // Preempt receives scheduling errors for a pod and tries to create room for // the pod by preempting lower priority pods if possible. // It returns the node where preemption happened, a list of preempted pods, a // list of pods whose nominated node name should be removed, and error if any. Preempt(*v1.Pod, NodeLister, error) (selectedNode *v1.Node, preemptedPods []*v1.Pod, cleanupNominatedPods []*v1.Pod, err error) // Predicates() returns a pointer to a map of predicate functions. This is // exposed for testing. Predicates() map[string]FitPredicate // Prioritizers returns a slice of priority config. This is exposed for // testing. Prioritizers() []PriorityConfig } 5. genericScheduler.Schedule 此部分代码位于/pkg/scheduler/core/generic_scheduler.go genericScheduler.Schedule实现了基本的调度逻辑，基于给定需要调度的pod和node列表，如果执行成功返回调度的节点的名字，如果执行失败，则返回错误和原因。主要通过预选和优选两步操作完成调度的逻辑。 基本流程如下： 对pod做基本性检查，目前主要是对pvc的检查。 通过findNodesThatFit预选策略选出满足调度条件的node列表。 通过PrioritizeNodes优选策略给预选的node列表中的node进行打分。 在打分的node列表中选择一个分数最高的node作为调度的节点。 完整代码如下： // Schedule tries to schedule the given pod to one of the nodes in the node list. // If it succeeds, it will return the name of the node. // If it fails, it will return a FitError error with reasons. func (g *genericScheduler) Schedule(pod *v1.Pod, nodeLister algorithm.NodeLister) (string, error) { trace := utiltrace.New(fmt.Sprintf(\"Scheduling %s/%s\", pod.Namespace, pod.Name)) defer trace.LogIfLong(100 * time.Millisecond) if err := podPassesBasicChecks(pod, g.pvcLister); err != nil { return \"\", err } nodes, err := nodeLister.List() if err != nil { return \"\", err } if len(nodes) == 0 { return \"\", ErrNoNodesAvailable } // Used for all fit and priority funcs. err = g.cache.UpdateNodeNameToInfoMap(g.cachedNodeInfoMap) if err != nil { return \"\", err } trace.Step(\"Computing predicates\") startPredicateEvalTime := time.Now() filteredNodes, failedPredicateMap, err := g.findNodesThatFit(pod, nodes) if err != nil { return \"\", err } if len(filteredNodes) == 0 { return \"\", &FitError{ Pod: pod, NumAllNodes: len(nodes), FailedPredicates: failedPredicateMap, } } metrics.SchedulingAlgorithmPredicateEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPredicateEvalTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PredicateEvaluation).Observe(metrics.SinceInSeconds(startPredicateEvalTime)) trace.Step(\"Prioritizing\") startPriorityEvalTime := time.Now() // When only one node after predicate, just use it. if len(filteredNodes) == 1 { metrics.SchedulingAlgorithmPriorityEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPriorityEvalTime)) return filteredNodes[0].Name, nil } metaPrioritiesInterface := g.priorityMetaProducer(pod, g.cachedNodeInfoMap) priorityList, err := PrioritizeNodes(pod, g.cachedNodeInfoMap, metaPrioritiesInterface, g.prioritizers, filteredNodes, g.extenders) if err != nil { return \"\", err } metrics.SchedulingAlgorithmPriorityEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPriorityEvalTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PriorityEvaluation).Observe(metrics.SinceInSeconds(startPriorityEvalTime)) trace.Step(\"Selecting host\") return g.selectHost(priorityList) } 5.1. podPassesBasicChecks podPassesBasicChecks主要做一下基本性检查，目前主要是对pvc的检查。 if err := podPassesBasicChecks(pod, g.pvcLister); err != nil { return \"\", err } podPassesBasicChecks具体实现如下： // podPassesBasicChecks makes sanity checks on the pod if it can be scheduled. func podPassesBasicChecks(pod *v1.Pod, pvcLister corelisters.PersistentVolumeClaimLister) error { // Check PVCs used by the pod namespace := pod.Namespace manifest := &(pod.Spec) for i := range manifest.Volumes { volume := &manifest.Volumes[i] if volume.PersistentVolumeClaim == nil { // Volume is not a PVC, ignore continue } pvcName := volume.PersistentVolumeClaim.ClaimName pvc, err := pvcLister.PersistentVolumeClaims(namespace).Get(pvcName) if err != nil { // The error has already enough context (\"persistentvolumeclaim \"myclaim\" not found\") return err } if pvc.DeletionTimestamp != nil { return fmt.Errorf(\"persistentvolumeclaim %q is being deleted\", pvc.Name) } } return nil } 5.2. findNodesThatFit 预选，通过预选函数来判断每个节点是否适合被该Pod调度。 具体的findNodesThatFit代码实现细节待后续文章独立分析。 genericScheduler.Schedule中对findNodesThatFit的调用过程如下： trace.Step(\"Computing predicates\") startPredicateEvalTime := time.Now() filteredNodes, failedPredicateMap, err := g.findNodesThatFit(pod, nodes) if err != nil { return \"\", err } if len(filteredNodes) == 0 { return \"\", &FitError{ Pod: pod, NumAllNodes: len(nodes), FailedPredicates: failedPredicateMap, } } metrics.SchedulingAlgorithmPredicateEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPredicateEvalTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PredicateEvaluation).Observe(metrics.SinceInSeconds(startPredicateEvalTime)) 5.3. PrioritizeNodes 优选，从满足的节点中选择出最优的节点。 具体操作如下： PrioritizeNodes通过并行运行各个优先级函数来对节点进行优先级排序。 每个优先级函数会给节点打分，打分范围为0-10分。 0 表示优先级最低的节点，10表示优先级最高的节点。 每个优先级函数也有各自的权重。 优先级函数返回的节点分数乘以权重以获得加权分数。 最后组合（添加）所有分数以获得所有节点的总加权分数。 具体PrioritizeNodes的实现逻辑待后续文章独立分析。 genericScheduler.Schedule中对PrioritizeNodes的调用过程如下： trace.Step(\"Prioritizing\") startPriorityEvalTime := time.Now() // When only one node after predicate, just use it. if len(filteredNodes) == 1 { metrics.SchedulingAlgorithmPriorityEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPriorityEvalTime)) return filteredNodes[0].Name, nil } metaPrioritiesInterface := g.priorityMetaProducer(pod, g.cachedNodeInfoMap) priorityList, err := PrioritizeNodes(pod, g.cachedNodeInfoMap, metaPrioritiesInterface, g.prioritizers, filteredNodes, g.extenders) if err != nil { return \"\", err } metrics.SchedulingAlgorithmPriorityEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPriorityEvalTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PriorityEvaluation).Observe(metrics.SinceInSeconds(startPriorityEvalTime)) 5.4. selectHost scheduler在最后会从priorityList中选择分数最高的一个节点。 trace.Step(\"Selecting host\") return g.selectHost(priorityList) selectHost获取优先级的节点列表，然后从分数最高的节点以循环方式选择一个节点。 具体代码如下： // selectHost takes a prioritized list of nodes and then picks one // in a round-robin manner from the nodes that had the highest score. func (g *genericScheduler) selectHost(priorityList schedulerapi.HostPriorityList) (string, error) { if len(priorityList) == 0 { return \"\", fmt.Errorf(\"empty priorityList\") } maxScores := findMaxScores(priorityList) ix := int(g.lastNodeIndex % uint64(len(maxScores))) g.lastNodeIndex++ return priorityList[maxScores[ix]].Host, nil } 5.4.1. findMaxScores findMaxScores返回priorityList中具有最高Score的节点的索引。 // findMaxScores returns the indexes of nodes in the \"priorityList\" that has the highest \"Score\". func findMaxScores(priorityList schedulerapi.HostPriorityList) []int { maxScoreIndexes := make([]int, 0, len(priorityList)/2) maxScore := priorityList[0].Score for i, hp := range priorityList { if hp.Score > maxScore { maxScore = hp.Score maxScoreIndexes = maxScoreIndexes[:0] maxScoreIndexes = append(maxScoreIndexes, i) } else if hp.Score == maxScore { maxScoreIndexes = append(maxScoreIndexes, i) } } return maxScoreIndexes } 6. Scheduler.preempt 如果pod在预选和优选调度中失败，则执行抢占操作。抢占主要是将低优先级的pod的资源空间腾出给待调度的高优先级的pod。 具体Scheduler.preempt的实现逻辑待后续文章独立分析。 suggestedHost, err := sched.schedule(pod) if err != nil { // schedule() may have failed because the pod would not fit on any host, so we try to // preempt, with the expectation that the next time the pod is tried for scheduling it // will fit due to the preemption. It is also possible that a different pod will schedule // into the resources that were preempted, but this is harmless. if fitError, ok := err.(*core.FitError); ok { preemptionStartTime := time.Now() sched.preempt(pod, fitError) metrics.PreemptionAttempts.Inc() metrics.SchedulingAlgorithmPremptionEvaluationDuration.Observe(metrics.SinceInMicroseconds(preemptionStartTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PreemptionEvaluation).Observe(metrics.SinceInSeconds(preemptionStartTime)) } return } 7. Scheduler.assume 将该pod和选定的节点进行假性绑定，存入scheduler cache中，方便可以继续执行调度逻辑，而不需要等待绑定操作的发生，具体绑定操作可以异步进行。 // Tell the cache to assume that a pod now is running on a given node, even though it hasn't been bound yet. // This allows us to keep scheduling without waiting on binding to occur. assumedPod := pod.DeepCopy() // Assume volumes first before assuming the pod. // // If all volumes are completely bound, then allBound is true and binding will be skipped. // // Otherwise, binding of volumes is started after the pod is assumed, but before pod binding. // // This function modifies 'assumedPod' if volume binding is required. allBound, err := sched.assumeVolumes(assumedPod, suggestedHost) if err != nil { return } // assume modifies `assumedPod` by setting NodeName=suggestedHost err = sched.assume(assumedPod, suggestedHost) if err != nil { return } 如果假性绑定成功则发送请求给apiserver，如果失败则scheduler会立即释放已分配给假性绑定的pod的资源。 assume方法的具体实现： // assume signals to the cache that a pod is already in the cache, so that binding can be asynchronous. // assume modifies `assumed`. func (sched *Scheduler) assume(assumed *v1.Pod, host string) error { // Optimistically assume that the binding will succeed and send it to apiserver // in the background. // If the binding fails, scheduler will release resources allocated to assumed pod // immediately. assumed.Spec.NodeName = host // NOTE: Because the scheduler uses snapshots of SchedulerCache and the live // version of Ecache, updates must be written to SchedulerCache before // invalidating Ecache. if err := sched.config.SchedulerCache.AssumePod(assumed); err != nil { glog.Errorf(\"scheduler cache AssumePod failed: %v\", err) // This is most probably result of a BUG in retrying logic. // We report an error here so that pod scheduling can be retried. // This relies on the fact that Error will check if the pod has been bound // to a node and if so will not add it back to the unscheduled pods queue // (otherwise this would cause an infinite loop). sched.config.Error(assumed, err) sched.config.Recorder.Eventf(assumed, v1.EventTypeWarning, \"FailedScheduling\", \"AssumePod failed: %v\", err) sched.config.PodConditionUpdater.Update(assumed, &v1.PodCondition{ Type: v1.PodScheduled, Status: v1.ConditionFalse, Reason: \"SchedulerError\", Message: err.Error(), }) return err } // Optimistically assume that the binding will succeed, so we need to invalidate affected // predicates in equivalence cache. // If the binding fails, these invalidated item will not break anything. if sched.config.Ecache != nil { sched.config.Ecache.InvalidateCachedPredicateItemForPodAdd(assumed, host) } return nil } 8. Scheduler.bind 异步的方式给pod绑定到具体的调度节点上。 // bind the pod to its host asynchronously (we can do this b/c of the assumption step above). go func() { // Bind volumes first before Pod if !allBound { err = sched.bindVolumes(assumedPod) if err != nil { return } } err := sched.bind(assumedPod, &v1.Binding{ ObjectMeta: metav1.ObjectMeta{Namespace: assumedPod.Namespace, Name: assumedPod.Name, UID: assumedPod.UID}, Target: v1.ObjectReference{ Kind: \"Node\", Name: suggestedHost, }, }) metrics.E2eSchedulingLatency.Observe(metrics.SinceInMicroseconds(start)) if err != nil { glog.Errorf(\"Internal error binding pod: (%v)\", err) } }() bind具体实现如下： // bind binds a pod to a given node defined in a binding object. We expect this to run asynchronously, so we // handle binding metrics internally. func (sched *Scheduler) bind(assumed *v1.Pod, b *v1.Binding) error { bindingStart := time.Now() // If binding succeeded then PodScheduled condition will be updated in apiserver so that // it's atomic with setting host. err := sched.config.GetBinder(assumed).Bind(b) if err := sched.config.SchedulerCache.FinishBinding(assumed); err != nil { glog.Errorf(\"scheduler cache FinishBinding failed: %v\", err) } if err != nil { glog.V(1).Infof(\"Failed to bind pod: %v/%v\", assumed.Namespace, assumed.Name) if err := sched.config.SchedulerCache.ForgetPod(assumed); err != nil { glog.Errorf(\"scheduler cache ForgetPod failed: %v\", err) } sched.config.Error(assumed, err) sched.config.Recorder.Eventf(assumed, v1.EventTypeWarning, \"FailedScheduling\", \"Binding rejected: %v\", err) sched.config.PodConditionUpdater.Update(assumed, &v1.PodCondition{ Type: v1.PodScheduled, Status: v1.ConditionFalse, Reason: \"BindingRejected\", }) return err } metrics.BindingLatency.Observe(metrics.SinceInMicroseconds(bindingStart)) metrics.SchedulingLatency.WithLabelValues(metrics.Binding).Observe(metrics.SinceInSeconds(bindingStart)) sched.config.Recorder.Eventf(assumed, v1.EventTypeNormal, \"Scheduled\", \"Successfully assigned %v/%v to %v\", assumed.Namespace, assumed.Name, b.Target.Name) return nil } 9. 总结 本文主要分析了单个pod的调度过程。具体流程如下： 通过podQueue的待调度队列中弹出需要调度的pod。 通过具体的调度算法为该pod选出合适的节点，其中调度算法就包括预选和优选两步策略。 如果上述调度失败，则会尝试抢占机制，将优先级低的pod剔除，让优先级高的pod调度成功。 将该pod和选定的节点进行假性绑定，存入scheduler cache中，方便具体绑定操作可以异步进行。 实际执行绑定操作，将node的名字添加到pod的节点相关属性中。 其中核心的部分为通过具体的调度算法选出调度节点的过程，即genericScheduler.Schedule的实现部分。该部分包括预选和优选两个部分。 genericScheduler.Schedule调度的基本流程如下： 对pod做基本性检查，目前主要是对pvc的检查。 通过findNodesThatFit预选策略选出满足调度条件的node列表。 通过PrioritizeNodes优选策略给预选的node列表中的node进行打分。 在打分的node列表中选择一个分数最高的node作为调度的节点。 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/scheduler.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/core/generic_scheduler.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-scheduler/findNodesThatFit.html":{"url":"code-analysis/kube-scheduler/findNodesThatFit.html","title":"findNodesThatFit","keywords":"","body":"kube-scheduler源码分析（四）之 findNodesThatFit1. 调用入口2. findNodesThatFit3. numFeasibleNodesToFind4. checkNode5. podFitsOnNode5.1. predicateFuncs6. PodFitsResources6.1. NodeInfo6.2. Resource6.3. allowedPodNumber6.4. podRequest6.5. AllocatableResource6.6. ScalarResources7. 总结7.1. checkNode7.2. podFitsOnNode7.3. PodFitsResourceskube-scheduler源码分析（四）之 findNodesThatFit 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析调度逻辑中的预选策略，即第一步筛选出符合pod调度条件的节点。 1. 调用入口 预选，通过预选函数来判断每个节点是否适合被该Pod调度。 genericScheduler.Schedule中对findNodesThatFit的调用过程如下： 此部分代码位于pkg/scheduler/core/generic_scheduler.go func (g *genericScheduler) Schedule(pod *v1.Pod, nodeLister algorithm.NodeLister) (string, error) { ... // 列出所有的节点 nodes, err := nodeLister.List() if err != nil { return \"\", err } if len(nodes) == 0 { return \"\", ErrNoNodesAvailable } // Used for all fit and priority funcs. err = g.cache.UpdateNodeNameToInfoMap(g.cachedNodeInfoMap) if err != nil { return \"\", err } trace.Step(\"Computing predicates\") startPredicateEvalTime := time.Now() // 调用findNodesThatFit过滤出预选节点 filteredNodes, failedPredicateMap, err := g.findNodesThatFit(pod, nodes) if err != nil { return \"\", err } if len(filteredNodes) == 0 { return \"\", &FitError{ Pod: pod, NumAllNodes: len(nodes), FailedPredicates: failedPredicateMap, } } // metrics metrics.SchedulingAlgorithmPredicateEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPredicateEvalTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PredicateEvaluation).Observe(metrics.SinceInSeconds(startPredicateEvalTime)) ... } 核心代码： // 调用findNodesThatFit过滤出预选节点 filteredNodes, failedPredicateMap, err := g.findNodesThatFit(pod, nodes) 2. findNodesThatFit findNodesThatFit基于给定的预选函数过滤node，每个node传入到预选函数中来确实该节点是否符合要求。 findNodesThatFit的入参是被调度的pod和当前的节点列表，返回预选节点列表和错误。 findNodesThatFit基本流程如下： 设置可行节点的总数，作为预选节点数组的容量，避免总节点过多需要筛选的节点过多。 通过NodeTree不断获取下一个节点来判断该节点是否满足pod的调度条件。 通过之前注册的各种预选函数来判断当前节点是否符合pod的调度条件。 最后返回满足调度条件的node列表，供下一步的优选操作。 findNodesThatFit完整代码如下： 此部分代码位于pkg/scheduler/core/generic_scheduler.go // Filters the nodes to find the ones that fit based on the given predicate functions // Each node is passed through the predicate functions to determine if it is a fit func (g *genericScheduler) findNodesThatFit(pod *v1.Pod, nodes []*v1.Node) ([]*v1.Node, FailedPredicateMap, error) { var filtered []*v1.Node failedPredicateMap := FailedPredicateMap{} if len(g.predicates) == 0 { filtered = nodes } else { allNodes := int32(g.cache.NodeTree().NumNodes) numNodesToFind := g.numFeasibleNodesToFind(allNodes) // Create filtered list with enough space to avoid growing it // and allow assigning. filtered = make([]*v1.Node, numNodesToFind) errs := errors.MessageCountMap{} var ( predicateResultLock sync.Mutex filteredLen int32 equivClass *equivalence.Class ) ctx, cancel := context.WithCancel(context.Background()) // We can use the same metadata producer for all nodes. meta := g.predicateMetaProducer(pod, g.cachedNodeInfoMap) if g.equivalenceCache != nil { // getEquivalenceClassInfo will return immediately if no equivalence pod found equivClass = equivalence.NewClass(pod) } checkNode := func(i int) { var nodeCache *equivalence.NodeCache nodeName := g.cache.NodeTree().Next() if g.equivalenceCache != nil { nodeCache, _ = g.equivalenceCache.GetNodeCache(nodeName) } fits, failedPredicates, err := podFitsOnNode( pod, meta, g.cachedNodeInfoMap[nodeName], g.predicates, g.cache, nodeCache, g.schedulingQueue, g.alwaysCheckAllPredicates, equivClass, ) if err != nil { predicateResultLock.Lock() errs[err.Error()]++ predicateResultLock.Unlock() return } if fits { length := atomic.AddInt32(&filteredLen, 1) if length > numNodesToFind { cancel() atomic.AddInt32(&filteredLen, -1) } else { filtered[length-1] = g.cachedNodeInfoMap[nodeName].Node() } } else { predicateResultLock.Lock() failedPredicateMap[nodeName] = failedPredicates predicateResultLock.Unlock() } } // Stops searching for more nodes once the configured number of feasible nodes // are found. workqueue.ParallelizeUntil(ctx, 16, int(allNodes), checkNode) filtered = filtered[:filteredLen] if len(errs) > 0 { return []*v1.Node{}, FailedPredicateMap{}, errors.CreateAggregateFromMessageCountMap(errs) } } if len(filtered) > 0 && len(g.extenders) != 0 { for _, extender := range g.extenders { if !extender.IsInterested(pod) { continue } filteredList, failedMap, err := extender.Filter(pod, filtered, g.cachedNodeInfoMap) if err != nil { if extender.IsIgnorable() { glog.Warningf(\"Skipping extender %v as it returned error %v and has ignorable flag set\", extender, err) continue } else { return []*v1.Node{}, FailedPredicateMap{}, err } } for failedNodeName, failedMsg := range failedMap { if _, found := failedPredicateMap[failedNodeName]; !found { failedPredicateMap[failedNodeName] = []algorithm.PredicateFailureReason{} } failedPredicateMap[failedNodeName] = append(failedPredicateMap[failedNodeName], predicates.NewFailureReason(failedMsg)) } filtered = filteredList if len(filtered) == 0 { break } } } return filtered, failedPredicateMap, nil } 以下对findNodesThatFit分段分析。 3. numFeasibleNodesToFind findNodesThatFit先基于所有的节点找出可行的节点是总数。numFeasibleNodesToFind的作用主要是避免当节点过多（超过100）影响调度的效率。 allNodes := int32(g.cache.NodeTree().NumNodes) numNodesToFind := g.numFeasibleNodesToFind(allNodes) // Create filtered list with enough space to avoid growing it // and allow assigning. filtered = make([]*v1.Node, numNodesToFind) numFeasibleNodesToFind基本流程如下： 如果所有的node节点小于minFeasibleNodesToFind(当前默认为100)则返回节点数。 如果节点数超100，则取指定计分的百分比的节点数，当该百分比后的数目仍小于minFeasibleNodesToFind，则返回minFeasibleNodesToFind。 如果百分比后的数目大于minFeasibleNodesToFind，则返回该百分比。 // numFeasibleNodesToFind returns the number of feasible nodes that once found, the scheduler stops // its search for more feasible nodes. func (g *genericScheduler) numFeasibleNodesToFind(numAllNodes int32) int32 { if numAllNodes = 100 { return numAllNodes } numNodes := numAllNodes * g.percentageOfNodesToScore / 100 if numNodes 4. checkNode checkNode是一个校验node是否符合要求的函数，其中实际调用到的核心函数是podFitsOnNode。再通过workqueue并发执行checkNode操作。 checkNode主要流程如下： 通过cache中的nodeTree不断获取下一个node。 将当前node和pod传入podFitsOnNode判断当前node是否符合要求。 如果当前node符合要求就将当前node加入预选节点的数组中filtered。 如果当前node不满足要求，则加入到失败的数组中，并记录原因。 通过workqueue.ParallelizeUntil并发执行checkNode函数，一旦找到配置的可行节点数，就停止搜索更多节点。 checkNode := func(i int) { var nodeCache *equivalence.NodeCache nodeName := g.cache.NodeTree().Next() if g.equivalenceCache != nil { nodeCache, _ = g.equivalenceCache.GetNodeCache(nodeName) } fits, failedPredicates, err := podFitsOnNode( pod, meta, g.cachedNodeInfoMap[nodeName], g.predicates, g.cache, nodeCache, g.schedulingQueue, g.alwaysCheckAllPredicates, equivClass, ) if err != nil { predicateResultLock.Lock() errs[err.Error()]++ predicateResultLock.Unlock() return } if fits { length := atomic.AddInt32(&filteredLen, 1) if length > numNodesToFind { cancel() atomic.AddInt32(&filteredLen, -1) } else { filtered[length-1] = g.cachedNodeInfoMap[nodeName].Node() } } else { predicateResultLock.Lock() failedPredicateMap[nodeName] = failedPredicates predicateResultLock.Unlock() } } workqueue的并发操作： // Stops searching for more nodes once the configured number of feasible nodes // are found. workqueue.ParallelizeUntil(ctx, 16, int(allNodes), checkNode) ParallelizeUntil具体代码如下： // ParallelizeUntil is a framework that allows for parallelizing N // independent pieces of work until done or the context is canceled. func ParallelizeUntil(ctx context.Context, workers, pieces int, doWorkPiece DoWorkPieceFunc) { var stop 5. podFitsOnNode podFitsOnNode主要内容如下： podFitsOnNode会检查给定的某个Node是否满足预选的函数。 对于给定的pod，podFitsOnNode会检查是否有相同的pod存在，尽量复用缓存过的预选结果。 podFitsOnNode主要在Schedule（调度）和Preempt（抢占）的时候被调用。 当在Schedule中被调用的时候，主要判断是否可以被调度到当前节点，依据为当前节点上所有已存在的pod及被提名要运行到该节点的具有相等或更高优先级的pod。 当在Preempt中被调用的时候，即发生抢占的时候，通过SelectVictimsOnNode函数选出需要被移除的pod，移除后然后将预调度的pod调度到该节点上。 podFitsOnNode基本流程如下： 遍历之前注册好的预选策略predicates.Ordering，并获取预选策略的执行函数。 遍历执行每个预选函数，并返回是否合适，预选失败的原因和错误。 如果预选函数执行的结果不合适，则加入预选失败的数组中。 最后返回预选失败的个数是否为0，和预选失败的原因。 入参： pod PredicateMetadata NodeInfo predicateFuncs schedulercache.Cache nodeCache SchedulingQueue alwaysCheckAllPredicates equivClass 出参： fit PredicateFailureReason 完整代码如下： 此部分代码位于pkg/scheduler/core/generic_scheduler.go // podFitsOnNode checks whether a node given by NodeInfo satisfies the given predicate functions. // For given pod, podFitsOnNode will check if any equivalent pod exists and try to reuse its cached // predicate results as possible. // This function is called from two different places: Schedule and Preempt. // When it is called from Schedule, we want to test whether the pod is schedulable // on the node with all the existing pods on the node plus higher and equal priority // pods nominated to run on the node. // When it is called from Preempt, we should remove the victims of preemption and // add the nominated pods. Removal of the victims is done by SelectVictimsOnNode(). // It removes victims from meta and NodeInfo before calling this function. func podFitsOnNode( pod *v1.Pod, meta algorithm.PredicateMetadata, info *schedulercache.NodeInfo, predicateFuncs map[string]algorithm.FitPredicate, cache schedulercache.Cache, nodeCache *equivalence.NodeCache, queue SchedulingQueue, alwaysCheckAllPredicates bool, equivClass *equivalence.Class, ) (bool, []algorithm.PredicateFailureReason, error) { var ( eCacheAvailable bool failedPredicates []algorithm.PredicateFailureReason ) podsAdded := false // We run predicates twice in some cases. If the node has greater or equal priority // nominated pods, we run them when those pods are added to meta and nodeInfo. // If all predicates succeed in this pass, we run them again when these // nominated pods are not added. This second pass is necessary because some // predicates such as inter-pod affinity may not pass without the nominated pods. // If there are no nominated pods for the node or if the first run of the // predicates fail, we don't run the second pass. // We consider only equal or higher priority pods in the first pass, because // those are the current \"pod\" must yield to them and not take a space opened // for running them. It is ok if the current \"pod\" take resources freed for // lower priority pods. // Requiring that the new pod is schedulable in both circumstances ensures that // we are making a conservative decision: predicates like resources and inter-pod // anti-affinity are more likely to fail when the nominated pods are treated // as running, while predicates like pod affinity are more likely to fail when // the nominated pods are treated as not running. We can't just assume the // nominated pods are running because they are not running right now and in fact, // they may end up getting scheduled to a different node. for i := 0; i 5.1. predicateFuncs 根据之前初注册好的预选策略函数来执行预选，判断节点是否符合调度。 for _, predicateKey := range predicates.Ordering() { if predicate, exist := predicateFuncs[predicateKey]; exist { if eCacheAvailable { fit, reasons, err = nodeCache.RunPredicate(predicate, predicateKey, pod, metaToUse, nodeInfoToUse, equivClass, cache) } else { fit, reasons, err = predicate(pod, metaToUse, nodeInfoToUse) } 预选策略如下： var ( predicatesOrdering = []string{CheckNodeConditionPred, CheckNodeUnschedulablePred, GeneralPred, HostNamePred, PodFitsHostPortsPred, MatchNodeSelectorPred, PodFitsResourcesPred, NoDiskConflictPred, PodToleratesNodeTaintsPred, PodToleratesNodeNoExecuteTaintsPred, CheckNodeLabelPresencePred, CheckServiceAffinityPred, MaxEBSVolumeCountPred, MaxGCEPDVolumeCountPred, MaxCSIVolumeCountPred, MaxAzureDiskVolumeCountPred, CheckVolumeBindingPred, NoVolumeZoneConflictPred, CheckNodeMemoryPressurePred, CheckNodePIDPressurePred, CheckNodeDiskPressurePred, MatchInterPodAffinityPred} ) 6. PodFitsResources 以下以PodFitsResources这个预选函数为例做分析，其他重要的预选函数待后续单独分析。 PodFitsResources用来检查一个节点是否有足够的资源来运行当前的pod，包括CPU、内存、GPU等。 PodFitsResources基本流程如下： 判断当前节点上pod总数加上预调度pod个数是否大于node的可分配pod总数，若是则不允许调度。 判断pod的request值是否都为0，若是则允许调度。 判断pod的request值加上当前node上所有pod的request值总和是否大于node的可分配资源，若是则不允许调度。 判断pod的拓展资源request值加上当前node上所有pod对应的request值总和是否大于node对应的可分配资源，若是则不允许调度。 PodFitsResources的注册代码如下： factory.RegisterFitPredicate(predicates.PodFitsResourcesPred, predicates.PodFitsResources) PodFitsResources入参： pod nodeInfo PredicateMetadata PodFitsResources出参： fit PredicateFailureReason PodFitsResources完整代码： 此部分的代码位于pkg/scheduler/algorithm/predicates/predicates.go // PodFitsResources checks if a node has sufficient resources, such as cpu, memory, gpu, opaque int resources etc to run a pod. // First return value indicates whether a node has sufficient resources to run a pod while the second return value indicates the // predicate failure reasons if the node has insufficient resources to run the pod. func PodFitsResources(pod *v1.Pod, meta algorithm.PredicateMetadata, nodeInfo *schedulercache.NodeInfo) (bool, []algorithm.PredicateFailureReason, error) { node := nodeInfo.Node() if node == nil { return false, nil, fmt.Errorf(\"node not found\") } var predicateFails []algorithm.PredicateFailureReason allowedPodNumber := nodeInfo.AllowedPodNumber() if len(nodeInfo.Pods())+1 > allowedPodNumber { predicateFails = append(predicateFails, NewInsufficientResourceError(v1.ResourcePods, 1, int64(len(nodeInfo.Pods())), int64(allowedPodNumber))) } // No extended resources should be ignored by default. ignoredExtendedResources := sets.NewString() var podRequest *schedulercache.Resource if predicateMeta, ok := meta.(*predicateMetadata); ok { podRequest = predicateMeta.podRequest if predicateMeta.ignoredExtendedResources != nil { ignoredExtendedResources = predicateMeta.ignoredExtendedResources } } else { // We couldn't parse metadata - fallback to computing it. podRequest = GetResourceRequest(pod) } if podRequest.MilliCPU == 0 && podRequest.Memory == 0 && podRequest.EphemeralStorage == 0 && len(podRequest.ScalarResources) == 0 { return len(predicateFails) == 0, predicateFails, nil } allocatable := nodeInfo.AllocatableResource() if allocatable.MilliCPU 6.1. NodeInfo NodeInfo是node的聚合信息，主要包括： node：k8s node的结构体 pods：当前node上pod的数量 requestedResource：当前node上所有pod的request总和 allocatableResource：node的实际所有的可分配资源(对应于Node.Status.Allocatable.*)，可理解为node的资源总量。 此部分代码位于pkg/scheduler/cache/node_info.go // NodeInfo is node level aggregated information. type NodeInfo struct { // Overall node information. node *v1.Node pods []*v1.Pod podsWithAffinity []*v1.Pod usedPorts util.HostPortInfo // Total requested resource of all pods on this node. // It includes assumed pods which scheduler sends binding to apiserver but // didn't get it as scheduled yet. requestedResource *Resource nonzeroRequest *Resource // We store allocatedResources (which is Node.Status.Allocatable.*) explicitly // as int64, to avoid conversions and accessing map. allocatableResource *Resource // Cached taints of the node for faster lookup. taints []v1.Taint taintsErr error // imageStates holds the entry of an image if and only if this image is on the node. The entry can be used for // checking an image's existence and advanced usage (e.g., image locality scheduling policy) based on the image // state information. imageStates map[string]*ImageStateSummary // TransientInfo holds the information pertaining to a scheduling cycle. This will be destructed at the end of // scheduling cycle. // TODO: @ravig. Remove this once we have a clear approach for message passing across predicates and priorities. TransientInfo *transientSchedulerInfo // Cached conditions of node for faster lookup. memoryPressureCondition v1.ConditionStatus diskPressureCondition v1.ConditionStatus pidPressureCondition v1.ConditionStatus // Whenever NodeInfo changes, generation is bumped. // This is used to avoid cloning it if the object didn't change. generation int64 } 6.2. Resource Resource是可计算资源的集合体。主要包括： MilliCPU Memory EphemeralStorage AllowedPodNumber：允许的pod总数(对应于Node.Status.Allocatable.Pods().Value())，一般为110。 ScalarResources // Resource is a collection of compute resource. type Resource struct { MilliCPU int64 Memory int64 EphemeralStorage int64 // We store allowedPodNumber (which is Node.Status.Allocatable.Pods().Value()) // explicitly as int, to avoid conversions and improve performance. AllowedPodNumber int // ScalarResources ScalarResources map[v1.ResourceName]int64 } 以下分析podFitsOnNode的具体流程。 6.3. allowedPodNumber 首先获取节点的信息，先判断如果该节点当前所有的pod的个数加上当前预调度的pod是否会大于该节点允许的pod的总数，一般为110个。如果超过，则predicateFails数组增加1，即当前节点不适合该pod。 node := nodeInfo.Node() if node == nil { return false, nil, fmt.Errorf(\"node not found\") } var predicateFails []algorithm.PredicateFailureReason allowedPodNumber := nodeInfo.AllowedPodNumber() if len(nodeInfo.Pods())+1 > allowedPodNumber { predicateFails = append(predicateFails, NewInsufficientResourceError(v1.ResourcePods, 1, int64(len(nodeInfo.Pods())), int64(allowedPodNumber))) } 6.4. podRequest 如果podRequest都为0，则允许调度到该节点，直接返回结果。 if podRequest.MilliCPU == 0 && podRequest.Memory == 0 && podRequest.EphemeralStorage == 0 && len(podRequest.ScalarResources) == 0 { return len(predicateFails) == 0, predicateFails, nil } 6.5. AllocatableResource 如果当前预调度的pod的request资源加上当前node上所有pod的request总和大于该node的可分配资源总量，则不允许调度到该节点，直接返回结果。其中request资源包括CPU、内存、storage。 allocatable := nodeInfo.AllocatableResource() if allocatable.MilliCPU 6.6. ScalarResources 判断其他拓展的标量资源，是否该pod的request值加上当前node上所有pod的对应资源的request总和大于该node上对应资源的可分配总量，如果是，则不允许调度到该节点。 for rName, rQuant := range podRequest.ScalarResources { if v1helper.IsExtendedResourceName(rName) { // If this resource is one of the extended resources that should be // ignored, we will skip checking it. if ignoredExtendedResources.Has(string(rName)) { continue } } if allocatable.ScalarResources[rName] 7. 总结 findNodesThatFit基于给定的预选函数过滤node，每个node传入到预选函数中来确实该节点是否符合要求。 findNodesThatFit的入参是被调度的pod和当前的节点列表，返回预选节点列表和错误。 findNodesThatFit基本流程如下： 设置可行节点的总数，作为预选节点数组的容量，避免总节点过多导致需要筛选的节点过多，效率低。 通过NodeTree不断获取下一个节点来判断该节点是否满足pod的调度条件。 通过之前注册的各种预选函数来判断当前节点是否符合pod的调度条件。 最后返回满足调度条件的node列表，供下一步的优选操作。 7.1. checkNode checkNode是一个校验node是否符合要求的函数，其中实际调用到的核心函数是podFitsOnNode。再通过workqueue并发执行checkNode操作。 checkNode主要流程如下： 通过cache中的nodeTree不断获取下一个node。 将当前node和pod传入podFitsOnNode判断当前node是否符合要求。 如果当前node符合要求就将当前node加入预选节点的数组中filtered。 如果当前node不满足要求，则加入到失败的数组中，并记录原因。 通过workqueue.ParallelizeUntil并发执行checkNode函数，一旦找到配置的可行节点数，就停止搜索更多节点。 7.2. podFitsOnNode 其中会调用到核心函数podFitsOnNode。 podFitsOnNode主要内容如下： podFitsOnNode会检查给定的某个Node是否满足预选的函数。 对于给定的pod，podFitsOnNode会检查是否有相同的pod存在，尽量复用缓存过的预选结果。 podFitsOnNode主要在Schedule（调度）和Preempt（抢占）的时候被调用。 当在Schedule中被调用的时候，主要判断是否可以被调度到当前节点，依据为当前节点上所有已存在的pod及被提名要运行到该节点的具有相等或更高优先级的pod。 当在Preempt中被调用的时候，即发生抢占的时候，通过SelectVictimsOnNode函数选出需要被移除的pod，移除后然后将预调度的pod调度到该节点上。 podFitsOnNode基本流程如下： 遍历之前注册好的预选策略predicates.Ordering，并获取预选策略的执行函数。 遍历执行每个预选函数，并返回是否合适，预选失败的原因和错误。 如果预选函数执行的结果不合适，则加入预选失败的数组中。 最后返回预选失败的个数是否为0，和预选失败的原因。 7.3. PodFitsResources 本文只示例分析了其中一个重要的预选函数：PodFitsResources PodFitsResources用来检查一个节点是否有足够的资源来运行当前的pod，包括CPU、内存、GPU等。 PodFitsResources基本流程如下： 判断当前节点上pod总数加上预调度pod个数是否大于node的可分配pod总数，若是则不允许调度。 判断pod的request值是否都为0，若是则允许调度。 判断pod的request值加上当前node上所有pod的request值总和是否大于node的可分配资源，若是则不允许调度。 判断pod的拓展资源request值加上当前node上所有pod对应的request值总和是否大于node对应的可分配资源，若是则不允许调度。 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/core/generic_scheduler.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/algorithm/predicates/predicates.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-scheduler/PrioritizeNodes.html":{"url":"code-analysis/kube-scheduler/PrioritizeNodes.html","title":"PrioritizeNodes","keywords":"","body":"kube-scheduler源码分析（五）之 PrioritizeNodes1. 调用入口2. PrioritizeNodes3. EqualPriorityMap4. processNode5. PriorityMapFunction6. PriorityReduceFunction7. Summarize all scores8. NewSelectorSpreadPriority8.1. CalculateSpreadPriorityMap8.2. CalculateSpreadPriorityReduce9. 总结9.1. PrioritizeNodes9.2. NewSelectorSpreadPriority9.2.1. CalculateSpreadPriorityMap9.2.2. CalculateSpreadPriorityReducekube-scheduler源码分析（五）之 PrioritizeNodes 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析优选策略逻辑，即从预选的节点中选择出最优的节点。优选策略的具体实现函数为PrioritizeNodes。PrioritizeNodes最终返回是一个记录了各个节点分数的列表。 1. 调用入口 genericScheduler.Schedule中对PrioritizeNodes的调用过程如下： 此部分代码位于pkg/scheduler/core/generic_scheduler.go func (g *genericScheduler) Schedule(pod *v1.Pod, nodeLister algorithm.NodeLister) (string, error) { ... trace.Step(\"Prioritizing\") startPriorityEvalTime := time.Now() // When only one node after predicate, just use it. if len(filteredNodes) == 1 { metrics.SchedulingAlgorithmPriorityEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPriorityEvalTime)) return filteredNodes[0].Name, nil } metaPrioritiesInterface := g.priorityMetaProducer(pod, g.cachedNodeInfoMap) // 执行优选逻辑的操作，返回记录各个节点分数的列表 priorityList, err := PrioritizeNodes(pod, g.cachedNodeInfoMap, metaPrioritiesInterface, g.prioritizers, filteredNodes, g.extenders) if err != nil { return \"\", err } metrics.SchedulingAlgorithmPriorityEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPriorityEvalTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PriorityEvaluation).Observe(metrics.SinceInSeconds(startPriorityEvalTime)) ... } 核心代码： // 基于预选节点filteredNodes进一步筛选优选的节点，返回记录各个节点分数的列表 priorityList, err := PrioritizeNodes(pod, g.cachedNodeInfoMap, metaPrioritiesInterface, g.prioritizers, filteredNodes, g.extenders) 2. PrioritizeNodes 优选，从满足的节点中选择出最优的节点。PrioritizeNodes最终返回是一个记录了各个节点分数的列表。 具体操作如下： PrioritizeNodes通过并行运行各个优先级函数来对节点进行优先级排序。 每个优先级函数会给节点打分，打分范围为0-10分。 0 表示优先级最低的节点，10表示优先级最高的节点。 每个优先级函数也有各自的权重。 优先级函数返回的节点分数乘以权重以获得加权分数。 最后组合（添加）所有分数以获得所有节点的总加权分数。 PrioritizeNodes主要流程如下： 如果没有设置优选函数和拓展函数，则全部节点设置相同的分数，直接返回。 依次给node执行map函数进行打分。 再对上述map函数的执行结果执行reduce函数计算最终得分。 最后根据不同优先级函数的权重对得分取加权平均数。 入参： pod nodeNameToInfo meta interface{}, priorityConfigs nodes extenders 出参： HostPriorityList：记录节点分数的列表。 HostPriority定义如下: // HostPriority represents the priority of scheduling to a particular host, higher priority is better. type HostPriority struct { // Name of the host Host string // Score associated with the host Score int } PrioritizeNodes完整代码如下： 此部分代码位于pkg/scheduler/core/generic_scheduler.go // PrioritizeNodes prioritizes the nodes by running the individual priority functions in parallel. // Each priority function is expected to set a score of 0-10 // 0 is the lowest priority score (least preferred node) and 10 is the highest // Each priority function can also have its own weight // The node scores returned by the priority function are multiplied by the weights to get weighted scores // All scores are finally combined (added) to get the total weighted scores of all nodes func PrioritizeNodes( pod *v1.Pod, nodeNameToInfo map[string]*schedulercache.NodeInfo, meta interface{}, priorityConfigs []algorithm.PriorityConfig, nodes []*v1.Node, extenders []algorithm.SchedulerExtender, ) (schedulerapi.HostPriorityList, error) { // If no priority configs are provided, then the EqualPriority function is applied // This is required to generate the priority list in the required format if len(priorityConfigs) == 0 && len(extenders) == 0 { result := make(schedulerapi.HostPriorityList, 0, len(nodes)) for i := range nodes { hostPriority, err := EqualPriorityMap(pod, meta, nodeNameToInfo[nodes[i].Name]) if err != nil { return nil, err } result = append(result, hostPriority) } return result, nil } var ( mu = sync.Mutex{} wg = sync.WaitGroup{} errs []error ) appendError := func(err error) { mu.Lock() defer mu.Unlock() errs = append(errs, err) } results := make([]schedulerapi.HostPriorityList, len(priorityConfigs), len(priorityConfigs)) for i, priorityConfig := range priorityConfigs { if priorityConfig.Function != nil { // DEPRECATED wg.Add(1) go func(index int, config algorithm.PriorityConfig) { defer wg.Done() var err error results[index], err = config.Function(pod, nodeNameToInfo, nodes) if err != nil { appendError(err) } }(i, priorityConfig) } else { results[i] = make(schedulerapi.HostPriorityList, len(nodes)) } } processNode := func(index int) { nodeInfo := nodeNameToInfo[nodes[index].Name] var err error for i := range priorityConfigs { if priorityConfigs[i].Function != nil { continue } results[i][index], err = priorityConfigs[i].Map(pod, meta, nodeInfo) if err != nil { appendError(err) return } } } workqueue.Parallelize(16, len(nodes), processNode) for i, priorityConfig := range priorityConfigs { if priorityConfig.Reduce == nil { continue } wg.Add(1) go func(index int, config algorithm.PriorityConfig) { defer wg.Done() if err := config.Reduce(pod, meta, nodeNameToInfo, results[index]); err != nil { appendError(err) } if glog.V(10) { for _, hostPriority := range results[index] { glog.Infof(\"%v -> %v: %v, Score: (%d)\", pod.Name, hostPriority.Host, config.Name, hostPriority.Score) } } }(i, priorityConfig) } // Wait for all computations to be finished. wg.Wait() if len(errs) != 0 { return schedulerapi.HostPriorityList{}, errors.NewAggregate(errs) } // Summarize all scores. result := make(schedulerapi.HostPriorityList, 0, len(nodes)) for i := range nodes { result = append(result, schedulerapi.HostPriority{Host: nodes[i].Name, Score: 0}) for j := range priorityConfigs { result[i].Score += results[j][i].Score * priorityConfigs[j].Weight } } if len(extenders) != 0 && nodes != nil { combinedScores := make(map[string]int, len(nodeNameToInfo)) for _, extender := range extenders { if !extender.IsInterested(pod) { continue } wg.Add(1) go func(ext algorithm.SchedulerExtender) { defer wg.Done() prioritizedList, weight, err := ext.Prioritize(pod, nodes) if err != nil { // Prioritization errors from extender can be ignored, let k8s/other extenders determine the priorities return } mu.Lock() for i := range *prioritizedList { host, score := (*prioritizedList)[i].Host, (*prioritizedList)[i].Score combinedScores[host] += score * weight } mu.Unlock() }(extender) } // wait for all go routines to finish wg.Wait() for i := range result { result[i].Score += combinedScores[result[i].Host] } } if glog.V(10) { for i := range result { glog.V(10).Infof(\"Host %s => Score %d\", result[i].Host, result[i].Score) } } return result, nil } 以下对PrioritizeNodes分段进行分析。 3. EqualPriorityMap 如果没有提供优选函数和拓展函数，则将所有的节点设置为相同的优先级，即节点的score都为1，然后直接返回结果。(但一般情况下优选函数列表都不为空) // If no priority configs are provided, then the EqualPriority function is applied // This is required to generate the priority list in the required format if len(priorityConfigs) == 0 && len(extenders) == 0 { result := make(schedulerapi.HostPriorityList, 0, len(nodes)) for i := range nodes { hostPriority, err := EqualPriorityMap(pod, meta, nodeNameToInfo[nodes[i].Name]) if err != nil { return nil, err } result = append(result, hostPriority) } return result, nil } EqualPriorityMap具体实现如下： // EqualPriorityMap is a prioritizer function that gives an equal weight of one to all nodes func EqualPriorityMap(_ *v1.Pod, _ interface{}, nodeInfo *schedulercache.NodeInfo) (schedulerapi.HostPriority, error) { node := nodeInfo.Node() if node == nil { return schedulerapi.HostPriority{}, fmt.Errorf(\"node not found\") } return schedulerapi.HostPriority{ Host: node.Name, Score: 1, }, nil } 4. processNode processNode就是基于index拿出node的信息，调用之前注册的各种优选函数（此处是mapFunction），通过优选函数对node和pod进行处理，最后返回一个记录node分数的列表result。processNode同样也使用workqueue.Parallelize来进行并行处理。(processNode类似于预选逻辑findNodesThatFit中使用到的checkNode的作用) 其中优选函数是通过priorityConfigs来记录，每类优选函数包括PriorityMapFunction和PriorityReduceFunction两种函数。优选函数的注册部分可参考registerAlgorithmProvider。 processNode := func(index int) { nodeInfo := nodeNameToInfo[nodes[index].Name] var err error for i := range priorityConfigs { if priorityConfigs[i].Function != nil { continue } results[i][index], err = priorityConfigs[i].Map(pod, meta, nodeInfo) if err != nil { appendError(err) return } } } // 并行执行processNode workqueue.Parallelize(16, len(nodes), processNode) priorityConfigs定义如下： 核心属性： Map ：PriorityMapFunction Reduce：PriorityReduceFunction // PriorityConfig is a config used for a priority function. type PriorityConfig struct { Name string Map PriorityMapFunction Reduce PriorityReduceFunction // TODO: Remove it after migrating all functions to // Map-Reduce pattern. Function PriorityFunction Weight int } 具体的优选函数处理逻辑待下文分析，本文会以NewSelectorSpreadPriority函数为例。 5. PriorityMapFunction PriorityMapFunction是一个计算给定节点的每个节点结果的函数。 PriorityMapFunction定义如下： // PriorityMapFunction is a function that computes per-node results for a given node. // TODO: Figure out the exact API of this method. // TODO: Change interface{} to a specific type. type PriorityMapFunction func(pod *v1.Pod, meta interface{}, nodeInfo *schedulercache.NodeInfo) (schedulerapi.HostPriority, error) PriorityMapFunction是在processNode中调用的，代码如下： results[i][index], err = priorityConfigs[i].Map(pod, meta, nodeInfo) 下文会分析NewSelectorSpreadPriority在的map函数CalculateSpreadPriorityMap。 6. PriorityReduceFunction PriorityReduceFunction是一个聚合每个节点结果并计算所有节点的最终得分的函数。 PriorityReduceFunction定义如下： // PriorityReduceFunction is a function that aggregated per-node results and computes // final scores for all nodes. // TODO: Figure out the exact API of this method. // TODO: Change interface{} to a specific type. type PriorityReduceFunction func(pod *v1.Pod, meta interface{}, nodeNameToInfo map[string]*schedulercache.NodeInfo, result schedulerapi.HostPriorityList) error PrioritizeNodes中对reduce函数调用部分如下： for i, priorityConfig := range priorityConfigs { if priorityConfig.Reduce == nil { continue } wg.Add(1) go func(index int, config algorithm.PriorityConfig) { defer wg.Done() if err := config.Reduce(pod, meta, nodeNameToInfo, results[index]); err != nil { appendError(err) } if glog.V(10) { for _, hostPriority := range results[index] { glog.Infof(\"%v -> %v: %v, Score: (%d)\", pod.Name, hostPriority.Host, config.Name, hostPriority.Score) } } }(i, priorityConfig) } 下文会分析NewSelectorSpreadPriority在的reduce函数CalculateSpreadPriorityReduce。 7. Summarize all scores 先等待计算完成再计算加权平均数。 // Wait for all computations to be finished. wg.Wait() if len(errs) != 0 { return schedulerapi.HostPriorityList{}, errors.NewAggregate(errs) } 计算所有节点的加权平均数。 // Summarize all scores. result := make(schedulerapi.HostPriorityList, 0, len(nodes)) for i := range nodes { result = append(result, schedulerapi.HostPriority{Host: nodes[i].Name, Score: 0}) for j := range priorityConfigs { result[i].Score += results[j][i].Score * priorityConfigs[j].Weight } } 当设置了拓展的计算方式，则增加拓展计算方式的加权平均数。 if len(extenders) != 0 && nodes != nil { combinedScores := make(map[string]int, len(nodeNameToInfo)) for _, extender := range extenders { if !extender.IsInterested(pod) { continue } wg.Add(1) go func(ext algorithm.SchedulerExtender) { defer wg.Done() prioritizedList, weight, err := ext.Prioritize(pod, nodes) if err != nil { // Prioritization errors from extender can be ignored, let k8s/other extenders determine the priorities return } mu.Lock() for i := range *prioritizedList { host, score := (*prioritizedList)[i].Host, (*prioritizedList)[i].Score combinedScores[host] += score * weight } mu.Unlock() }(extender) } // wait for all go routines to finish wg.Wait() for i := range result { result[i].Score += combinedScores[result[i].Host] } } 8. NewSelectorSpreadPriority 以下以NewSelectorSpreadPriority这个优选函数来做分析，其他重要的优选函数待后续专门分析。 NewSelectorSpreadPriority主要的功能是将属于相同service和rs下的pod尽量分布在不同的node上。 该函数的注册代码如下： 此部分代码位于pkg/scheduler/algorithmprovider/defaults/defaults.go // ServiceSpreadingPriority is a priority config factory that spreads pods by minimizing // the number of pods (belonging to the same service) on the same node. // Register the factory so that it's available, but do not include it as part of the default priorities // Largely replaced by \"SelectorSpreadPriority\", but registered for backward compatibility with 1.0 factory.RegisterPriorityConfigFactory( \"ServiceSpreadingPriority\", factory.PriorityConfigFactory{ MapReduceFunction: func(args factory.PluginFactoryArgs) (algorithm.PriorityMapFunction, algorithm.PriorityReduceFunction) { return priorities.NewSelectorSpreadPriority(args.ServiceLister, algorithm.EmptyControllerLister{}, algorithm.EmptyReplicaSetLister{}, algorithm.EmptyStatefulSetLister{}) }, Weight: 1, }, ) NewSelectorSpreadPriority的具体实现如下： 此部分代码位于pkg/scheduler/algorithm/priorities/selector_spreading.go // NewSelectorSpreadPriority creates a SelectorSpread. func NewSelectorSpreadPriority( serviceLister algorithm.ServiceLister, controllerLister algorithm.ControllerLister, replicaSetLister algorithm.ReplicaSetLister, statefulSetLister algorithm.StatefulSetLister) (algorithm.PriorityMapFunction, algorithm.PriorityReduceFunction) { selectorSpread := &SelectorSpread{ serviceLister: serviceLister, controllerLister: controllerLister, replicaSetLister: replicaSetLister, statefulSetLister: statefulSetLister, } return selectorSpread.CalculateSpreadPriorityMap, selectorSpread.CalculateSpreadPriorityReduce } NewSelectorSpreadPriority主要包括map和reduce两种函数，分别对应CalculateSpreadPriorityMap，CalculateSpreadPriorityReduce。 8.1. CalculateSpreadPriorityMap CalculateSpreadPriorityMap的主要作用是将相同service、RC、RS或statefulset的pod分布在不同的节点上。当调度一个pod的时候，先寻找与该pod匹配的service、RS、RC或statefulset，然后寻找与其selector匹配的已存在的pod，寻找存在这类pod最少的节点。 基本流程如下： 寻找与该pod对应的service、RS、RC、statefulset匹配的selector。 遍历当前节点的所有pod，将该节点上已存在的selector匹配到的pod的个数作为该节点的分数（此时，分数大的表示匹配到的pod越多，越不符合被调度的条件，该分数在reduce阶段会被按10分制处理成分数大的越符合被调度的条件）。 此部分代码位于pkg/scheduler/algorithm/priorities/selector_spreading.go // CalculateSpreadPriorityMap spreads pods across hosts, considering pods // belonging to the same service,RC,RS or StatefulSet. // When a pod is scheduled, it looks for services, RCs,RSs and StatefulSets that match the pod, // then finds existing pods that match those selectors. // It favors nodes that have fewer existing matching pods. // i.e. it pushes the scheduler towards a node where there's the smallest number of // pods which match the same service, RC,RSs or StatefulSets selectors as the pod being scheduled. func (s *SelectorSpread) CalculateSpreadPriorityMap(pod *v1.Pod, meta interface{}, nodeInfo *schedulercache.NodeInfo) (schedulerapi.HostPriority, error) { var selectors []labels.Selector node := nodeInfo.Node() if node == nil { return schedulerapi.HostPriority{}, fmt.Errorf(\"node not found\") } priorityMeta, ok := meta.(*priorityMetadata) if ok { selectors = priorityMeta.podSelectors } else { selectors = getSelectors(pod, s.serviceLister, s.controllerLister, s.replicaSetLister, s.statefulSetLister) } if len(selectors) == 0 { return schedulerapi.HostPriority{ Host: node.Name, Score: int(0), }, nil } count := int(0) for _, nodePod := range nodeInfo.Pods() { if pod.Namespace != nodePod.Namespace { continue } // When we are replacing a failed pod, we often see the previous // deleted version while scheduling the replacement. // Ignore the previous deleted version for spreading purposes // (it can still be considered for resource restrictions etc.) if nodePod.DeletionTimestamp != nil { glog.V(4).Infof(\"skipping pending-deleted pod: %s/%s\", nodePod.Namespace, nodePod.Name) continue } for _, selector := range selectors { if selector.Matches(labels.Set(nodePod.ObjectMeta.Labels)) { count++ break } } } return schedulerapi.HostPriority{ Host: node.Name, Score: int(count), }, nil } 以下分段分析： 先获得selector。 selectors = getSelectors(pod, s.serviceLister, s.controllerLister, s.replicaSetLister, s.statefulSetLister) 计算节点上匹配selector的pod的个数，作为该节点分数，该分数并不是最终节点的分数，只是中间过渡的记录状态。 count := int(0) for _, nodePod := range nodeInfo.Pods() { ... for _, selector := range selectors { if selector.Matches(labels.Set(nodePod.ObjectMeta.Labels)) { count++ break } } } 8.2. CalculateSpreadPriorityReduce CalculateSpreadPriorityReduce根据节点上现有匹配pod的数量计算每个节点的十分制的分数，具有较少现有匹配pod的节点的分数越高，表示节点越可能被调度到。 基本流程如下： 记录所有节点中匹配到pod个数最多的节点的分数（即匹配到的pod最多的个数）。 遍历所有的节点，按比例取十分制的得分，计算方式为：(节点中最多匹配pod的个数-当前节点pod的个数)/节点中最多匹配pod的个数。此时，分数越高表示该节点上匹配到的pod的个数越少，越可能被调度到，即满足把相同selector的pod分散到不同节点的需求。 此部分代码位于pkg/scheduler/algorithm/priorities/selector_spreading.go // CalculateSpreadPriorityReduce calculates the source of each node // based on the number of existing matching pods on the node // where zone information is included on the nodes, it favors nodes // in zones with fewer existing matching pods. func (s *SelectorSpread) CalculateSpreadPriorityReduce(pod *v1.Pod, meta interface{}, nodeNameToInfo map[string]*schedulercache.NodeInfo, result schedulerapi.HostPriorityList) error { countsByZone := make(map[string]int, 10) maxCountByZone := int(0) maxCountByNodeName := int(0) for i := range result { if result[i].Score > maxCountByNodeName { maxCountByNodeName = result[i].Score } zoneID := utilnode.GetZoneKey(nodeNameToInfo[result[i].Host].Node()) if zoneID == \"\" { continue } countsByZone[zoneID] += result[i].Score } for zoneID := range countsByZone { if countsByZone[zoneID] > maxCountByZone { maxCountByZone = countsByZone[zoneID] } } haveZones := len(countsByZone) != 0 maxCountByNodeNameFloat64 := float64(maxCountByNodeName) maxCountByZoneFloat64 := float64(maxCountByZone) MaxPriorityFloat64 := float64(schedulerapi.MaxPriority) for i := range result { // initializing to the default/max node score of maxPriority fScore := MaxPriorityFloat64 if maxCountByNodeName > 0 { fScore = MaxPriorityFloat64 * (float64(maxCountByNodeName-result[i].Score) / maxCountByNodeNameFloat64) } // If there is zone information present, incorporate it if haveZones { zoneID := utilnode.GetZoneKey(nodeNameToInfo[result[i].Host].Node()) if zoneID != \"\" { zoneScore := MaxPriorityFloat64 if maxCountByZone > 0 { zoneScore = MaxPriorityFloat64 * (float64(maxCountByZone-countsByZone[zoneID]) / maxCountByZoneFloat64) } fScore = (fScore * (1.0 - zoneWeighting)) + (zoneWeighting * zoneScore) } } result[i].Score = int(fScore) if glog.V(10) { glog.Infof( \"%v -> %v: SelectorSpreadPriority, Score: (%d)\", pod.Name, result[i].Host, int(fScore), ) } } return nil } 以下分段分析： 先获取所有节点中匹配到的pod最多的个数。 for i := range result { if result[i].Score > maxCountByNodeName { maxCountByNodeName = result[i].Score } zoneID := utilnode.GetZoneKey(nodeNameToInfo[result[i].Host].Node()) if zoneID == \"\" { continue } countsByZone[zoneID] += result[i].Score } 遍历所有的节点，按比例取十分制的得分。 for i := range result { // initializing to the default/max node score of maxPriority fScore := MaxPriorityFloat64 if maxCountByNodeName > 0 { fScore = MaxPriorityFloat64 * (float64(maxCountByNodeName-result[i].Score) / maxCountByNodeNameFloat64) } ... } 9. 总结 优选，从满足的节点中选择出最优的节点。PrioritizeNodes最终返回是一个记录了各个节点分数的列表。 9.1. PrioritizeNodes 主要流程如下： 如果没有设置优选函数和拓展函数，则全部节点设置相同的分数，直接返回。 依次给node执行map函数进行打分。 再对上述map函数的执行结果执行reduce函数计算最终得分。 最后根据不同优先级函数的权重对得分取加权平均数。 其中每类优选函数会包含map函数和reduce函数两种。 9.2. NewSelectorSpreadPriority 其中以NewSelectorSpreadPriority这个优选函数为例作分析，该函数的功能是将相同service、RS、RC或statefulset下pod尽量分散到不同的节点上。包括map函数和reduce函数两部分，具体如下。 9.2.1. CalculateSpreadPriorityMap 基本流程如下： 寻找与该pod对应的service、RS、RC、statefulset匹配的selector。 遍历当前节点的所有pod，将该节点上已存在的selector匹配到的pod的个数作为该节点的分数（此时，分数大的表示匹配到的pod越多，越不符合被调度的条件，该分数在reduce阶段会被按10分制处理成分数大的越符合被调度的条件）。 9.2.2. CalculateSpreadPriorityReduce 基本流程如下： 记录所有节点中匹配到pod个数最多的节点的分数（即匹配到的pod最多的个数）。 遍历所有的节点，按比例取十分制的得分，计算方式为：(节点中最多匹配pod的个数-当前节点pod的个数)/节点中最多匹配pod的个数。此时，分数越高表示该节点上匹配到的pod的个数越少，越可能被调度到，即满足把相同selector的pod分散到不同节点的需求。 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/core/generic_scheduler.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/algorithm/priorities/selector_spreading.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-scheduler/preempt.html":{"url":"code-analysis/kube-scheduler/preempt.html","title":"preempt","keywords":"","body":"kube-scheduler源码分析（六）之 preempt1. 调用入口2. Scheduler.preempt2.1. preempt2.2. NominatedNodeName3. genericScheduler.Preempt3.1. selectNodesForPreemption3.1.1. selectVictimsOnNode3.2. processPreemptionWithExtenders3.3. pickOneNodeForPreemption3.4. getLowerPriorityNominatedPods4. 总结4.1. Scheduler.preempt4.2. genericScheduler.Preemptkube-scheduler源码分析（六）之 preempt 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析调度中的抢占逻辑，当pod不适合任何节点的时候，可能pod会调度失败，这时候可能会发生抢占。抢占逻辑的具体实现函数为Scheduler.preempt。 1. 调用入口 当pod不适合任何节点的时候，可能pod会调度失败。这时候可能会发生抢占。 scheduleOne函数中关于抢占调用的逻辑如下： 此部分的代码位于/pkg/scheduler/scheduler.go // scheduleOne does the entire scheduling workflow for a single pod. It is serialized on the scheduling algorithm's host fitting. func (sched *Scheduler) scheduleOne() { ... suggestedHost, err := sched.schedule(pod) if err != nil { // schedule() may have failed because the pod would not fit on any host, so we try to // preempt, with the expectation that the next time the pod is tried for scheduling it // will fit due to the preemption. It is also possible that a different pod will schedule // into the resources that were preempted, but this is harmless. if fitError, ok := err.(*core.FitError); ok { preemptionStartTime := time.Now() // 执行抢占逻辑 sched.preempt(pod, fitError) metrics.PreemptionAttempts.Inc() metrics.SchedulingAlgorithmPremptionEvaluationDuration.Observe(metrics.SinceInMicroseconds(preemptionStartTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PreemptionEvaluation).Observe(metrics.SinceInSeconds(preemptionStartTime)) } return } ... } 其中核心代码为： // 基于sched.schedule(pod)返回的err和当前待调度的pod执行抢占策略 sched.preempt(pod, fitError) 2. Scheduler.preempt 当pod调度失败的时候，会抢占低优先级pod的空间来给高优先级的pod。其中入参为调度失败的pod对象和调度失败的err。 抢占的基本流程如下： 判断是否有关闭抢占机制，如果关闭抢占机制则直接返回。 获取调度失败pod的最新对象数据。 执行抢占算法Algorithm.Preempt，返回预调度节点和需要被剔除的pod列表。 将抢占算法返回的node添加到pod的Status.NominatedNodeName中，并删除需要被剔除的pod。 当抢占算法返回的node是nil的时候，清除pod的Status.NominatedNodeName信息。 整个抢占流程的最终结果实际上是更新Pod.Status.NominatedNodeName属性的信息。如果抢占算法返回的节点不为空，则将该node更新到Pod.Status.NominatedNodeName中，否则就将Pod.Status.NominatedNodeName设置为空。 2.1. preempt preempt的具体实现函数： 此部分的代码位于/pkg/scheduler/scheduler.go // preempt tries to create room for a pod that has failed to schedule, by preempting lower priority pods if possible. // If it succeeds, it adds the name of the node where preemption has happened to the pod annotations. // It returns the node name and an error if any. func (sched *Scheduler) preempt(preemptor *v1.Pod, scheduleErr error) (string, error) { if !util.PodPriorityEnabled() || sched.config.DisablePreemption { glog.V(3).Infof(\"Pod priority feature is not enabled or preemption is disabled by scheduler configuration.\" + \" No preemption is performed.\") return \"\", nil } preemptor, err := sched.config.PodPreemptor.GetUpdatedPod(preemptor) if err != nil { glog.Errorf(\"Error getting the updated preemptor pod object: %v\", err) return \"\", err } node, victims, nominatedPodsToClear, err := sched.config.Algorithm.Preempt(preemptor, sched.config.NodeLister, scheduleErr) metrics.PreemptionVictims.Set(float64(len(victims))) if err != nil { glog.Errorf(\"Error preempting victims to make room for %v/%v.\", preemptor.Namespace, preemptor.Name) return \"\", err } var nodeName = \"\" if node != nil { nodeName = node.Name err = sched.config.PodPreemptor.SetNominatedNodeName(preemptor, nodeName) if err != nil { glog.Errorf(\"Error in preemption process. Cannot update pod %v/%v annotations: %v\", preemptor.Namespace, preemptor.Name, err) return \"\", err } for _, victim := range victims { if err := sched.config.PodPreemptor.DeletePod(victim); err != nil { glog.Errorf(\"Error preempting pod %v/%v: %v\", victim.Namespace, victim.Name, err) return \"\", err } sched.config.Recorder.Eventf(victim, v1.EventTypeNormal, \"Preempted\", \"by %v/%v on node %v\", preemptor.Namespace, preemptor.Name, nodeName) } } // Clearing nominated pods should happen outside of \"if node != nil\". Node could // be nil when a pod with nominated node name is eligible to preempt again, // but preemption logic does not find any node for it. In that case Preempt() // function of generic_scheduler.go returns the pod itself for removal of the annotation. for _, p := range nominatedPodsToClear { rErr := sched.config.PodPreemptor.RemoveNominatedNodeName(p) if rErr != nil { glog.Errorf(\"Cannot remove nominated node annotation of pod: %v\", rErr) // We do not return as this error is not critical. } } return nodeName, err } 以下对preempt的实现分段分析。 如果设置关闭抢占机制，则直接返回。 if !util.PodPriorityEnabled() || sched.config.DisablePreemption { glog.V(3).Infof(\"Pod priority feature is not enabled or preemption is disabled by scheduler configuration.\" + \" No preemption is performed.\") return \"\", nil } 获取当前pod的最新状态。 preemptor, err := sched.config.PodPreemptor.GetUpdatedPod(preemptor) if err != nil { glog.Errorf(\"Error getting the updated preemptor pod object: %v\", err) return \"\", err } GetUpdatedPod的实现就是去拿pod的对象。 func (p *podPreemptor) GetUpdatedPod(pod *v1.Pod) (*v1.Pod, error) { return p.Client.CoreV1().Pods(pod.Namespace).Get(pod.Name, metav1.GetOptions{}) } 接着执行抢占的算法。抢占的算法返回预调度节点的信息和因抢占被剔除的pod的信息。具体的抢占算法逻辑下文分析。 node, victims, nominatedPodsToClear, err := sched.config.Algorithm.Preempt(preemptor, sched.config.NodeLister, scheduleErr) 将预调度节点的信息更新到pod的Status.NominatedNodeName属性中。 err = sched.config.PodPreemptor.SetNominatedNodeName(preemptor, nodeName) SetNominatedNodeName的具体实现为： func (p *podPreemptor) SetNominatedNodeName(pod *v1.Pod, nominatedNodeName string) error { podCopy := pod.DeepCopy() podCopy.Status.NominatedNodeName = nominatedNodeName _, err := p.Client.CoreV1().Pods(pod.Namespace).UpdateStatus(podCopy) return err } 接着删除因抢占而需要被剔除的pod。 err := sched.config.PodPreemptor.DeletePod(victim) PodPreemptor.DeletePod的具体实现就是删除具体的pod。 func (p *podPreemptor) DeletePod(pod *v1.Pod) error { return p.Client.CoreV1().Pods(pod.Namespace).Delete(pod.Name, &metav1.DeleteOptions{}) } 如果抢占算法得出的node对象为nil，则将pod的Status.NominatedNodeName属性设置为空。 // Clearing nominated pods should happen outside of \"if node != nil\". Node could // be nil when a pod with nominated node name is eligible to preempt again, // but preemption logic does not find any node for it. In that case Preempt() // function of generic_scheduler.go returns the pod itself for removal of the annotation. for _, p := range nominatedPodsToClear { rErr := sched.config.PodPreemptor.RemoveNominatedNodeName(p) if rErr != nil { glog.Errorf(\"Cannot remove nominated node annotation of pod: %v\", rErr) // We do not return as this error is not critical. } } RemoveNominatedNodeName的具体实现如下： func (p *podPreemptor) RemoveNominatedNodeName(pod *v1.Pod) error { if len(pod.Status.NominatedNodeName) == 0 { return nil } return p.SetNominatedNodeName(pod, \"\") } 2.2. NominatedNodeName Pod.Status.NominatedNodeName的说明： nominatedNodeName是调度失败的pod抢占别的pod的时候，被抢占pod的运行节点。但在剔除被抢占pod之前该调度失败的pod不会被调度。同时也不保证最终该pod一定会调度到nominatedNodeName的机器上，也可能因为之后资源充足等原因调度到其他节点上。最终该pod会被加到调度的队列中。 其中加入到调度队列的具体过程如下： func NewConfigFactory(args *ConfigFactoryArgs) scheduler.Configurator { ... // unscheduled pod queue args.PodInformer.Informer().AddEventHandler( ... Handler: cache.ResourceEventHandlerFuncs{ AddFunc: c.addPodToSchedulingQueue, UpdateFunc: c.updatePodInSchedulingQueue, DeleteFunc: c.deletePodFromSchedulingQueue, }, }, ) ... } addPodToSchedulingQueue: func (c *configFactory) addPodToSchedulingQueue(obj interface{}) { if err := c.podQueue.Add(obj.(*v1.Pod)); err != nil { runtime.HandleError(fmt.Errorf(\"unable to queue %T: %v\", obj, err)) } } PriorityQueue.Add: // Add adds a pod to the active queue. It should be called only when a new pod // is added so there is no chance the pod is already in either queue. func (p *PriorityQueue) Add(pod *v1.Pod) error { p.lock.Lock() defer p.lock.Unlock() err := p.activeQ.Add(pod) if err != nil { glog.Errorf(\"Error adding pod %v/%v to the scheduling queue: %v\", pod.Namespace, pod.Name, err) } else { if p.unschedulableQ.get(pod) != nil { glog.Errorf(\"Error: pod %v/%v is already in the unschedulable queue.\", pod.Namespace, pod.Name) p.deleteNominatedPodIfExists(pod) p.unschedulableQ.delete(pod) } p.addNominatedPodIfNeeded(pod) p.cond.Broadcast() } return err } addNominatedPodIfNeeded: // addNominatedPodIfNeeded adds a pod to nominatedPods if it has a NominatedNodeName and it does not // already exist in the map. Adding an existing pod is not going to update the pod. func (p *PriorityQueue) addNominatedPodIfNeeded(pod *v1.Pod) { nnn := NominatedNodeName(pod) if len(nnn) > 0 { for _, np := range p.nominatedPods[nnn] { if np.UID == pod.UID { glog.Errorf(\"Pod %v/%v already exists in the nominated map!\", pod.Namespace, pod.Name) return } } p.nominatedPods[nnn] = append(p.nominatedPods[nnn], pod) } } NominatedNodeName: // NominatedNodeName returns nominated node name of a Pod. func NominatedNodeName(pod *v1.Pod) string { return pod.Status.NominatedNodeName } 3. genericScheduler.Preempt 抢占算法依然是在ScheduleAlgorithm接口中定义。 // ScheduleAlgorithm is an interface implemented by things that know how to schedule pods // onto machines. type ScheduleAlgorithm interface { Schedule(*v1.Pod, NodeLister) (selectedMachine string, err error) // Preempt receives scheduling errors for a pod and tries to create room for // the pod by preempting lower priority pods if possible. // It returns the node where preemption happened, a list of preempted pods, a // list of pods whose nominated node name should be removed, and error if any. Preempt(*v1.Pod, NodeLister, error) (selectedNode *v1.Node, preemptedPods []*v1.Pod, cleanupNominatedPods []*v1.Pod, err error) // Predicates() returns a pointer to a map of predicate functions. This is // exposed for testing. Predicates() map[string]FitPredicate // Prioritizers returns a slice of priority config. This is exposed for // testing. Prioritizers() []PriorityConfig } Preempt的具体实现为genericScheduler结构体。 Preempt的主要实现是找到可以调度的节点和上面因抢占而需要被剔除的pod。 基本流程如下： 根据调度失败的原因对所有节点先进行一批筛选，筛选出潜在的被调度节点列表。 通过selectNodesForPreemption筛选出需要牺牲的pod和其节点。 基于拓展抢占逻辑再次对上述筛选出来的牺牲者做过滤。 基于上述的过滤结果，选择一个最终可能因抢占被调度的节点。 基于上述的候选节点，找出该节点上优先级低于当前被调度pod的牺牲者pod列表。 完整代码如下： 此部分代码位于pkg/scheduler/core/generic_scheduler.go // preempt finds nodes with pods that can be preempted to make room for \"pod\" to // schedule. It chooses one of the nodes and preempts the pods on the node and // returns 1) the node, 2) the list of preempted pods if such a node is found, // 3) A list of pods whose nominated node name should be cleared, and 4) any // possible error. func (g *genericScheduler) Preempt(pod *v1.Pod, nodeLister algorithm.NodeLister, scheduleErr error) (*v1.Node, []*v1.Pod, []*v1.Pod, error) { // Scheduler may return various types of errors. Consider preemption only if // the error is of type FitError. fitError, ok := scheduleErr.(*FitError) if !ok || fitError == nil { return nil, nil, nil, nil } err := g.cache.UpdateNodeNameToInfoMap(g.cachedNodeInfoMap) if err != nil { return nil, nil, nil, err } if !podEligibleToPreemptOthers(pod, g.cachedNodeInfoMap) { glog.V(5).Infof(\"Pod %v/%v is not eligible for more preemption.\", pod.Namespace, pod.Name) return nil, nil, nil, nil } allNodes, err := nodeLister.List() if err != nil { return nil, nil, nil, err } if len(allNodes) == 0 { return nil, nil, nil, ErrNoNodesAvailable } potentialNodes := nodesWherePreemptionMightHelp(allNodes, fitError.FailedPredicates) if len(potentialNodes) == 0 { glog.V(3).Infof(\"Preemption will not help schedule pod %v/%v on any node.\", pod.Namespace, pod.Name) // In this case, we should clean-up any existing nominated node name of the pod. return nil, nil, []*v1.Pod{pod}, nil } pdbs, err := g.cache.ListPDBs(labels.Everything()) if err != nil { return nil, nil, nil, err } // 找出可能被抢占的节点 nodeToVictims, err := selectNodesForPreemption(pod, g.cachedNodeInfoMap, potentialNodes, g.predicates, g.predicateMetaProducer, g.schedulingQueue, pdbs) if err != nil { return nil, nil, nil, err } // We will only check nodeToVictims with extenders that support preemption. // Extenders which do not support preemption may later prevent preemptor from being scheduled on the nominated // node. In that case, scheduler will find a different host for the preemptor in subsequent scheduling cycles. nodeToVictims, err = g.processPreemptionWithExtenders(pod, nodeToVictims) if err != nil { return nil, nil, nil, err } // 选出最终被抢占的节点 candidateNode := pickOneNodeForPreemption(nodeToVictims) if candidateNode == nil { return nil, nil, nil, err } // Lower priority pods nominated to run on this node, may no longer fit on // this node. So, we should remove their nomination. Removing their // nomination updates these pods and moves them to the active queue. It // lets scheduler find another place for them. // 找出被强占节点上牺牲者pod列表 nominatedPods := g.getLowerPriorityNominatedPods(pod, candidateNode.Name) if nodeInfo, ok := g.cachedNodeInfoMap[candidateNode.Name]; ok { return nodeInfo.Node(), nodeToVictims[candidateNode].Pods, nominatedPods, err } return nil, nil, nil, fmt.Errorf( \"preemption failed: the target node %s has been deleted from scheduler cache\", candidateNode.Name) } 以下对genericScheduler.Preempt分段进行分析。 3.1. selectNodesForPreemption selectNodesForPreemption并行地所有节点中找可能被抢占的节点。 nodeToVictims, err := selectNodesForPreemption(pod, g.cachedNodeInfoMap, potentialNodes, g.predicates,g.predicateMetaProducer, g.schedulingQueue, pdbs) selectNodesForPreemption主要基于selectVictimsOnNode构造一个checkNode的函数，然后并发执行该函数。 selectNodesForPreemption具体实现如下： // selectNodesForPreemption finds all the nodes with possible victims for // preemption in parallel. func selectNodesForPreemption(pod *v1.Pod, nodeNameToInfo map[string]*schedulercache.NodeInfo, potentialNodes []*v1.Node, predicates map[string]algorithm.FitPredicate, metadataProducer algorithm.PredicateMetadataProducer, queue SchedulingQueue, pdbs []*policy.PodDisruptionBudget, ) (map[*v1.Node]*schedulerapi.Victims, error) { nodeToVictims := map[*v1.Node]*schedulerapi.Victims{} var resultLock sync.Mutex // We can use the same metadata producer for all nodes. meta := metadataProducer(pod, nodeNameToInfo) checkNode := func(i int) { nodeName := potentialNodes[i].Name var metaCopy algorithm.PredicateMetadata if meta != nil { metaCopy = meta.ShallowCopy() } pods, numPDBViolations, fits := selectVictimsOnNode(pod, metaCopy, nodeNameToInfo[nodeName], predicates, queue, pdbs) if fits { resultLock.Lock() victims := schedulerapi.Victims{ Pods: pods, NumPDBViolations: numPDBViolations, } nodeToVictims[potentialNodes[i]] = &victims resultLock.Unlock() } } workqueue.Parallelize(16, len(potentialNodes), checkNode) return nodeToVictims, nil } 3.1.1. selectVictimsOnNode selectVictimsOnNode找到应该被抢占的给定节点上的最小pod集合，以便给调度失败的pod安排足够的空间。该函数最终返回的是一个pod的数组。当有更低优先级的pod可能被选择的时候，较高优先级的pod不会被选入该待剔除的pod集合。 基本流程如下： 先检查当该节点上所有低于预被调度pod优先级的pod移除后，该pod能否被调度到当前节点上。 如果上述检查可以，则将该节点的所有低优先级pod按照优先级来排序。 // selectVictimsOnNode finds minimum set of pods on the given node that should // be preempted in order to make enough room for \"pod\" to be scheduled. The // minimum set selected is subject to the constraint that a higher-priority pod // is never preempted when a lower-priority pod could be (higher/lower relative // to one another, not relative to the preemptor \"pod\"). // The algorithm first checks if the pod can be scheduled on the node when all the // lower priority pods are gone. If so, it sorts all the lower priority pods by // their priority and then puts them into two groups of those whose PodDisruptionBudget // will be violated if preempted and other non-violating pods. Both groups are // sorted by priority. It first tries to reprieve as many PDB violating pods as // possible and then does them same for non-PDB-violating pods while checking // that the \"pod\" can still fit on the node. // NOTE: This function assumes that it is never called if \"pod\" cannot be scheduled // due to pod affinity, node affinity, or node anti-affinity reasons. None of // these predicates can be satisfied by removing more pods from the node. func selectVictimsOnNode( pod *v1.Pod, meta algorithm.PredicateMetadata, nodeInfo *schedulercache.NodeInfo, fitPredicates map[string]algorithm.FitPredicate, queue SchedulingQueue, pdbs []*policy.PodDisruptionBudget, ) ([]*v1.Pod, int, bool) { potentialVictims := util.SortableList{CompFunc: util.HigherPriorityPod} nodeInfoCopy := nodeInfo.Clone() removePod := func(rp *v1.Pod) { nodeInfoCopy.RemovePod(rp) if meta != nil { meta.RemovePod(rp) } } addPod := func(ap *v1.Pod) { nodeInfoCopy.AddPod(ap) if meta != nil { meta.AddPod(ap, nodeInfoCopy) } } // As the first step, remove all the lower priority pods from the node and // check if the given pod can be scheduled. podPriority := util.GetPodPriority(pod) for _, p := range nodeInfoCopy.Pods() { if util.GetPodPriority(p) 3.2. processPreemptionWithExtenders processPreemptionWithExtenders基于selectNodesForPreemption选出的牺牲者进行扩展的抢占逻辑继续筛选牺牲者。 // We will only check nodeToVictims with extenders that support preemption. // Extenders which do not support preemption may later prevent preemptor from being scheduled on the nominated // node. In that case, scheduler will find a different host for the preemptor in subsequent scheduling cycles. nodeToVictims, err = g.processPreemptionWithExtenders(pod, nodeToVictims) if err != nil { return nil, nil, nil, err } processPreemptionWithExtenders完整代码如下： // processPreemptionWithExtenders processes preemption with extenders func (g *genericScheduler) processPreemptionWithExtenders( pod *v1.Pod, nodeToVictims map[*v1.Node]*schedulerapi.Victims, ) (map[*v1.Node]*schedulerapi.Victims, error) { if len(nodeToVictims) > 0 { for _, extender := range g.extenders { if extender.SupportsPreemption() && extender.IsInterested(pod) { newNodeToVictims, err := extender.ProcessPreemption( pod, nodeToVictims, g.cachedNodeInfoMap, ) if err != nil { if extender.IsIgnorable() { glog.Warningf(\"Skipping extender %v as it returned error %v and has ignorable flag set\", extender, err) continue } return nil, err } // Replace nodeToVictims with new result after preemption. So the // rest of extenders can continue use it as parameter. nodeToVictims = newNodeToVictims // If node list becomes empty, no preemption can happen regardless of other extenders. if len(nodeToVictims) == 0 { break } } } } return nodeToVictims, nil } 3.3. pickOneNodeForPreemption pickOneNodeForPreemption从筛选出的node中再挑选一个节点作为最终调度节点。 candidateNode := pickOneNodeForPreemption(nodeToVictims) if candidateNode == nil { return nil, nil, nil, err } pickOneNodeForPreemption完整代码如下： // pickOneNodeForPreemption chooses one node among the given nodes. It assumes // pods in each map entry are ordered by decreasing priority. // It picks a node based on the following criteria: // 1. A node with minimum number of PDB violations. // 2. A node with minimum highest priority victim is picked. // 3. Ties are broken by sum of priorities of all victims. // 4. If there are still ties, node with the minimum number of victims is picked. // 5. If there are still ties, the first such node is picked (sort of randomly). // The 'minNodes1' and 'minNodes2' are being reused here to save the memory // allocation and garbage collection time. func pickOneNodeForPreemption(nodesToVictims map[*v1.Node]*schedulerapi.Victims) *v1.Node { if len(nodesToVictims) == 0 { return nil } minNumPDBViolatingPods := math.MaxInt32 var minNodes1 []*v1.Node lenNodes1 := 0 for node, victims := range nodesToVictims { if len(victims.Pods) == 0 { // We found a node that doesn't need any preemption. Return it! // This should happen rarely when one or more pods are terminated between // the time that scheduler tries to schedule the pod and the time that // preemption logic tries to find nodes for preemption. return node } numPDBViolatingPods := victims.NumPDBViolations if numPDBViolatingPods = 0. This is // needed so that a node with a few pods with negative priority is not // picked over a node with a smaller number of pods with the same negative // priority (and similar scenarios). sumPriorities += int64(util.GetPodPriority(pod)) + int64(math.MaxInt32+1) } if sumPriorities 0 { return minNodes2[0] } glog.Errorf(\"Error in logic of node scoring for preemption. We should never reach here!\") return nil } 3.4. getLowerPriorityNominatedPods getLowerPriorityNominatedPods的基本流程如下： 获取候选节点上的pod列表。 获取待调度pod的优先级值。 遍历该节点的pod列表，如果低于待调度pod的优先级则放入低优先级pod列表中。 genericScheduler.Preempt中相关代码如下： // Lower priority pods nominated to run on this node, may no longer fit on // this node. So, we should remove their nomination. Removing their // nomination updates these pods and moves them to the active queue. It // lets scheduler find another place for them. nominatedPods := g.getLowerPriorityNominatedPods(pod, candidateNode.Name) if nodeInfo, ok := g.cachedNodeInfoMap[candidateNode.Name]; ok { return nodeInfo.Node(), nodeToVictims[candidateNode].Pods, nominatedPods, err } getLowerPriorityNominatedPods代码如下： 此部分代码位于pkg/scheduler/core/generic_scheduler.go // getLowerPriorityNominatedPods returns pods whose priority is smaller than the // priority of the given \"pod\" and are nominated to run on the given node. // Note: We could possibly check if the nominated lower priority pods still fit // and return those that no longer fit, but that would require lots of // manipulation of NodeInfo and PredicateMeta per nominated pod. It may not be // worth the complexity, especially because we generally expect to have a very // small number of nominated pods per node. func (g *genericScheduler) getLowerPriorityNominatedPods(pod *v1.Pod, nodeName string) []*v1.Pod { pods := g.schedulingQueue.WaitingPodsForNode(nodeName) if len(pods) == 0 { return nil } var lowerPriorityPods []*v1.Pod podPriority := util.GetPodPriority(pod) for _, p := range pods { if util.GetPodPriority(p) 4. 总结 4.1. Scheduler.preempt 当pod调度失败的时候，会抢占低优先级pod的空间来给高优先级的pod。其中入参为调度失败的pod对象和调度失败的err。 抢占的基本流程如下： 判断是否有关闭抢占机制，如果关闭抢占机制则直接返回。 获取调度失败pod的最新对象数据。 执行抢占算法Algorithm.Preempt，返回预调度节点和需要被剔除的pod列表。 将抢占算法返回的node添加到pod的Status.NominatedNodeName中，并删除需要被剔除的pod。 当抢占算法返回的node是nil的时候，清除pod的Status.NominatedNodeName信息。 整个抢占流程的最终结果实际上是更新Pod.Status.NominatedNodeName属性的信息。如果抢占算法返回的节点不为空，则将该node更新到Pod.Status.NominatedNodeName中，否则就将Pod.Status.NominatedNodeName设置为空。 4.2. genericScheduler.Preempt Preempt的主要实现是找到可以调度的节点和上面因抢占而需要被剔除的pod。 基本流程如下： 根据调度失败的原因对所有节点先进行一批筛选，筛选出潜在的被调度节点列表。 通过selectNodesForPreemption筛选出需要牺牲的pod和其节点。 基于拓展抢占逻辑再次对上述筛选出来的牺牲者做过滤。 基于上述的过滤结果，选择一个最终可能因抢占被调度的节点。 基于上述的候选节点，找出该节点上优先级低于当前被调度pod的牺牲者pod列表。 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/scheduler.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/core/generic_scheduler.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-apiserver/_index.html":{"url":"code-analysis/kube-apiserver/_index.html","title":"kube-apiserver","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-apiserver/NewAPIServerCommand.html":{"url":"code-analysis/kube-apiserver/NewAPIServerCommand.html","title":"NewAPIServerCommand","keywords":"","body":"kube-apiserver源码分析（一）之 NewAPIServerCommand1. Main2. NewAPIServerCommand3. NewServerRunOptions3.1. ServerRunOptions3.2. NewServerRunOptions3.3. Complete3. AddFlagSet3.1. Flags4. Run4.1. CreateServerChain4.2. PrepareRun4.3. preparedGenericAPIServer.Run5. 总结kube-apiserver源码分析（一）之 NewAPIServerCommand 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析kube-apiserver中cmd部分的代码，即NewAPIServerCommand相关的代码。更多具体的逻辑待后续文章分析。 kube-apiserver的cmd部分目录代码结构如下： kube-apiserver ├── apiserver.go # kube-apiserver的main入口 └── app ├── aggregator.go ├── apiextensions.go ├── options # 初始化kube-apiserver使用到的option │ ├── options.go # 包括：NewServerRunOptions、Flags等 │ ├── options_test.go │ └── validation.go ├── server.go # 包括：NewAPIServerCommand、Run、CreateServerChain、Complete等 1. Main 此部分代码位于cmd/kube-apiserver/apiserver.go func main() { rand.Seed(time.Now().UTC().UnixNano()) command := app.NewAPIServerCommand(server.SetupSignalHandler()) // TODO: once we switch everything over to Cobra commands, we can go back to calling // utilflag.InitFlags() (by removing its pflag.Parse() call). For now, we have to set the // normalize func and add the go flag set by hand. pflag.CommandLine.SetNormalizeFunc(utilflag.WordSepNormalizeFunc) pflag.CommandLine.AddGoFlagSet(goflag.CommandLine) // utilflag.InitFlags() logs.InitLogs() defer logs.FlushLogs() if err := command.Execute(); err != nil { fmt.Fprintf(os.Stderr, \"error: %v\\n\", err) os.Exit(1) } } 核心代码： // 初始化APIServerCommand command := app.NewAPIServerCommand(server.SetupSignalHandler()) // 执行Execute err := command.Execute() 2. NewAPIServerCommand 此部分的代码位于/cmd/kube-apiserver/app/server.go NewAPIServerCommand即Cobra命令行框架的构造函数，主要包括三部分： 构造option 添加Flags 执行Run函数 完整代码如下： 此部分代码位于cmd/kube-apiserver/app/server.go // NewAPIServerCommand creates a *cobra.Command object with default parameters func NewAPIServerCommand(stopCh 核心代码： // 构造option s := options.NewServerRunOptions() // 添加flags fs := cmd.Flags() namedFlagSets := s.Flags() for _, f := range namedFlagSets.FlagSets { fs.AddFlagSet(f) } // set default options completedOptions, err := Complete(s) // Run Run(completedOptions, stopCh) 3. NewServerRunOptions NewServerRunOptions基于默认的参数构造ServerRunOptions结构体。ServerRunOptions是apiserver运行的配置信息。具体结构体定义如下。 3.1. ServerRunOptions 其中主要的配置如下： GenericServerRunOptions Etcd SecureServing KubeletConfig ... // ServerRunOptions runs a kubernetes api server. type ServerRunOptions struct { GenericServerRunOptions *genericoptions.ServerRunOptions Etcd *genericoptions.EtcdOptions SecureServing *genericoptions.SecureServingOptionsWithLoopback InsecureServing *genericoptions.DeprecatedInsecureServingOptionsWithLoopback Audit *genericoptions.AuditOptions Features *genericoptions.FeatureOptions Admission *kubeoptions.AdmissionOptions Authentication *kubeoptions.BuiltInAuthenticationOptions Authorization *kubeoptions.BuiltInAuthorizationOptions CloudProvider *kubeoptions.CloudProviderOptions StorageSerialization *kubeoptions.StorageSerializationOptions APIEnablement *genericoptions.APIEnablementOptions AllowPrivileged bool EnableLogsHandler bool EventTTL time.Duration KubeletConfig kubeletclient.KubeletClientConfig KubernetesServiceNodePort int MaxConnectionBytesPerSec int64 ServiceClusterIPRange net.IPNet // TODO: make this a list ServiceNodePortRange utilnet.PortRange SSHKeyfile string SSHUser string ProxyClientCertFile string ProxyClientKeyFile string EnableAggregatorRouting bool MasterCount int EndpointReconcilerType string ServiceAccountSigningKeyFile string } 3.2. NewServerRunOptions NewServerRunOptions初始化配置结构体。 // NewServerRunOptions creates a new ServerRunOptions object with default parameters func NewServerRunOptions() *ServerRunOptions { s := ServerRunOptions{ GenericServerRunOptions: genericoptions.NewServerRunOptions(), Etcd: genericoptions.NewEtcdOptions(storagebackend.NewDefaultConfig(kubeoptions.DefaultEtcdPathPrefix, nil)), SecureServing: kubeoptions.NewSecureServingOptions(), InsecureServing: kubeoptions.NewInsecureServingOptions(), Audit: genericoptions.NewAuditOptions(), Features: genericoptions.NewFeatureOptions(), Admission: kubeoptions.NewAdmissionOptions(), Authentication: kubeoptions.NewBuiltInAuthenticationOptions().WithAll(), Authorization: kubeoptions.NewBuiltInAuthorizationOptions(), CloudProvider: kubeoptions.NewCloudProviderOptions(), StorageSerialization: kubeoptions.NewStorageSerializationOptions(), APIEnablement: genericoptions.NewAPIEnablementOptions(), EnableLogsHandler: true, EventTTL: 1 * time.Hour, MasterCount: 1, EndpointReconcilerType: string(reconcilers.LeaseEndpointReconcilerType), KubeletConfig: kubeletclient.KubeletClientConfig{ Port: ports.KubeletPort, ReadOnlyPort: ports.KubeletReadOnlyPort, PreferredAddressTypes: []string{ // --override-hostname string(api.NodeHostName), // internal, preferring DNS if reported string(api.NodeInternalDNS), string(api.NodeInternalIP), // external, preferring DNS if reported string(api.NodeExternalDNS), string(api.NodeExternalIP), }, EnableHttps: true, HTTPTimeout: time.Duration(5) * time.Second, }, ServiceNodePortRange: kubeoptions.DefaultServiceNodePortRange, } s.ServiceClusterIPRange = kubeoptions.DefaultServiceIPCIDR // Overwrite the default for storage data format. s.Etcd.DefaultStorageMediaType = \"application/vnd.kubernetes.protobuf\" return &s } 3.3. Complete 当kube-apiserver的flags被解析后，调用Complete完成默认配置。 此部分代码位于cmd/kube-apiserver/app/server.go // Should be called after kube-apiserver flags parsed. func Complete(s *options.ServerRunOptions) (completedServerRunOptions, error) { var options completedServerRunOptions // set defaults if err := s.GenericServerRunOptions.DefaultAdvertiseAddress(s.SecureServing.SecureServingOptions); err != nil { return options, err } if err := kubeoptions.DefaultAdvertiseAddress(s.GenericServerRunOptions, s.InsecureServing.DeprecatedInsecureServingOptions); err != nil { return options, err } serviceIPRange, apiServerServiceIP, err := master.DefaultServiceIPRange(s.ServiceClusterIPRange) if err != nil { return options, fmt.Errorf(\"error determining service IP ranges: %v\", err) } s.ServiceClusterIPRange = serviceIPRange if err := s.SecureServing.MaybeDefaultWithSelfSignedCerts(s.GenericServerRunOptions.AdvertiseAddress.String(), []string{\"kubernetes.default.svc\", \"kubernetes.default\", \"kubernetes\"}, []net.IP{apiServerServiceIP}); err != nil { return options, fmt.Errorf(\"error creating self-signed certificates: %v\", err) } if len(s.GenericServerRunOptions.ExternalHost) == 0 { if len(s.GenericServerRunOptions.AdvertiseAddress) > 0 { s.GenericServerRunOptions.ExternalHost = s.GenericServerRunOptions.AdvertiseAddress.String() } else { if hostname, err := os.Hostname(); err == nil { s.GenericServerRunOptions.ExternalHost = hostname } else { return options, fmt.Errorf(\"error finding host name: %v\", err) } } glog.Infof(\"external host was not specified, using %v\", s.GenericServerRunOptions.ExternalHost) } s.Authentication.ApplyAuthorization(s.Authorization) // Use (ServiceAccountSigningKeyFile != \"\") as a proxy to the user enabling // TokenRequest functionality. This defaulting was convenient, but messed up // a lot of people when they rotated their serving cert with no idea it was // connected to their service account keys. We are taking this oppurtunity to // remove this problematic defaulting. if s.ServiceAccountSigningKeyFile == \"\" { // Default to the private server key for service account token signing if len(s.Authentication.ServiceAccounts.KeyFiles) == 0 && s.SecureServing.ServerCert.CertKey.KeyFile != \"\" { if kubeauthenticator.IsValidServiceAccountKeyFile(s.SecureServing.ServerCert.CertKey.KeyFile) { s.Authentication.ServiceAccounts.KeyFiles = []string{s.SecureServing.ServerCert.CertKey.KeyFile} } else { glog.Warning(\"No TLS key provided, service account token authentication disabled\") } } } if s.Etcd.StorageConfig.DeserializationCacheSize == 0 { // When size of cache is not explicitly set, estimate its size based on // target memory usage. glog.V(2).Infof(\"Initializing deserialization cache size based on %dMB limit\", s.GenericServerRunOptions.TargetRAMMB) // This is the heuristics that from memory capacity is trying to infer // the maximum number of nodes in the cluster and set cache sizes based // on that value. // From our documentation, we officially recommend 120GB machines for // 2000 nodes, and we scale from that point. Thus we assume ~60MB of // capacity per node. // TODO: We may consider deciding that some percentage of memory will // be used for the deserialization cache and divide it by the max object // size to compute its size. We may even go further and measure // collective sizes of the objects in the cache. clusterSize := s.GenericServerRunOptions.TargetRAMMB / 60 s.Etcd.StorageConfig.DeserializationCacheSize = 25 * clusterSize if s.Etcd.StorageConfig.DeserializationCacheSize 3. AddFlagSet AddFlagSet主要的作用是通过外部传入的flag的具体值，解析的时候传递给option的结构体，最终给apiserver使用。 其中NewAPIServerCommand关于AddFlagSet的相关代码如下： fs := cmd.Flags() namedFlagSets := s.Flags() for _, f := range namedFlagSets.FlagSets { fs.AddFlagSet(f) } 3.1. Flags Flags完整代码如下： 此部分代码位于cmd/kube-apiserver/app/options/options.go // Flags returns flags for a specific APIServer by section name func (s *ServerRunOptions) Flags() (fss apiserverflag.NamedFlagSets) { // Add the generic flags. s.GenericServerRunOptions.AddUniversalFlags(fss.FlagSet(\"generic\")) s.Etcd.AddFlags(fss.FlagSet(\"etcd\")) s.SecureServing.AddFlags(fss.FlagSet(\"secure serving\")) s.InsecureServing.AddFlags(fss.FlagSet(\"insecure serving\")) s.InsecureServing.AddUnqualifiedFlags(fss.FlagSet(\"insecure serving\")) // TODO: remove it until kops stops using `--address` s.Audit.AddFlags(fss.FlagSet(\"auditing\")) s.Features.AddFlags(fss.FlagSet(\"features\")) s.Authentication.AddFlags(fss.FlagSet(\"authentication\")) s.Authorization.AddFlags(fss.FlagSet(\"authorization\")) s.CloudProvider.AddFlags(fss.FlagSet(\"cloud provider\")) s.StorageSerialization.AddFlags(fss.FlagSet(\"storage\")) s.APIEnablement.AddFlags(fss.FlagSet(\"api enablement\")) s.Admission.AddFlags(fss.FlagSet(\"admission\")) // Note: the weird \"\"+ in below lines seems to be the only way to get gofmt to // arrange these text blocks sensibly. Grrr. fs := fss.FlagSet(\"misc\") fs.DurationVar(&s.EventTTL, \"event-ttl\", s.EventTTL, \"Amount of time to retain events.\") fs.BoolVar(&s.AllowPrivileged, \"allow-privileged\", s.AllowPrivileged, \"If true, allow privileged containers. [default=false]\") fs.BoolVar(&s.EnableLogsHandler, \"enable-logs-handler\", s.EnableLogsHandler, \"If true, install a /logs handler for the apiserver logs.\") // Deprecated in release 1.9 fs.StringVar(&s.SSHUser, \"ssh-user\", s.SSHUser, \"If non-empty, use secure SSH proxy to the nodes, using this user name\") fs.MarkDeprecated(\"ssh-user\", \"This flag will be removed in a future version.\") // Deprecated in release 1.9 fs.StringVar(&s.SSHKeyfile, \"ssh-keyfile\", s.SSHKeyfile, \"If non-empty, use secure SSH proxy to the nodes, using this user keyfile\") fs.MarkDeprecated(\"ssh-keyfile\", \"This flag will be removed in a future version.\") fs.Int64Var(&s.MaxConnectionBytesPerSec, \"max-connection-bytes-per-sec\", s.MaxConnectionBytesPerSec, \"\"+ \"If non-zero, throttle each user connection to this number of bytes/sec. \"+ \"Currently only applies to long-running requests.\") fs.IntVar(&s.MasterCount, \"apiserver-count\", s.MasterCount, \"The number of apiservers running in the cluster, must be a positive number. (In use when --endpoint-reconciler-type=master-count is enabled.)\") fs.StringVar(&s.EndpointReconcilerType, \"endpoint-reconciler-type\", string(s.EndpointReconcilerType), \"Use an endpoint reconciler (\"+strings.Join(reconcilers.AllTypes.Names(), \", \")+\")\") // See #14282 for details on how to test/try this option out. // TODO: remove this comment once this option is tested in CI. fs.IntVar(&s.KubernetesServiceNodePort, \"kubernetes-service-node-port\", s.KubernetesServiceNodePort, \"\"+ \"If non-zero, the Kubernetes master service (which apiserver creates/maintains) will be \"+ \"of type NodePort, using this as the value of the port. If zero, the Kubernetes master \"+ \"service will be of type ClusterIP.\") fs.IPNetVar(&s.ServiceClusterIPRange, \"service-cluster-ip-range\", s.ServiceClusterIPRange, \"\"+ \"A CIDR notation IP range from which to assign service cluster IPs. This must not \"+ \"overlap with any IP ranges assigned to nodes for pods.\") fs.Var(&s.ServiceNodePortRange, \"service-node-port-range\", \"\"+ \"A port range to reserve for services with NodePort visibility. \"+ \"Example: '30000-32767'. Inclusive at both ends of the range.\") // Kubelet related flags: fs.BoolVar(&s.KubeletConfig.EnableHttps, \"kubelet-https\", s.KubeletConfig.EnableHttps, \"Use https for kubelet connections.\") fs.StringSliceVar(&s.KubeletConfig.PreferredAddressTypes, \"kubelet-preferred-address-types\", s.KubeletConfig.PreferredAddressTypes, \"List of the preferred NodeAddressTypes to use for kubelet connections.\") fs.UintVar(&s.KubeletConfig.Port, \"kubelet-port\", s.KubeletConfig.Port, \"DEPRECATED: kubelet port.\") fs.MarkDeprecated(\"kubelet-port\", \"kubelet-port is deprecated and will be removed.\") fs.UintVar(&s.KubeletConfig.ReadOnlyPort, \"kubelet-read-only-port\", s.KubeletConfig.ReadOnlyPort, \"DEPRECATED: kubelet port.\") fs.DurationVar(&s.KubeletConfig.HTTPTimeout, \"kubelet-timeout\", s.KubeletConfig.HTTPTimeout, \"Timeout for kubelet operations.\") fs.StringVar(&s.KubeletConfig.CertFile, \"kubelet-client-certificate\", s.KubeletConfig.CertFile, \"Path to a client cert file for TLS.\") fs.StringVar(&s.KubeletConfig.KeyFile, \"kubelet-client-key\", s.KubeletConfig.KeyFile, \"Path to a client key file for TLS.\") fs.StringVar(&s.KubeletConfig.CAFile, \"kubelet-certificate-authority\", s.KubeletConfig.CAFile, \"Path to a cert file for the certificate authority.\") // TODO: delete this flag in 1.13 repair := false fs.BoolVar(&repair, \"repair-malformed-updates\", false, \"deprecated\") fs.MarkDeprecated(\"repair-malformed-updates\", \"This flag will be removed in a future version\") fs.StringVar(&s.ProxyClientCertFile, \"proxy-client-cert-file\", s.ProxyClientCertFile, \"\"+ \"Client certificate used to prove the identity of the aggregator or kube-apiserver \"+ \"when it must call out during a request. This includes proxying requests to a user \"+ \"api-server and calling out to webhook admission plugins. It is expected that this \"+ \"cert includes a signature from the CA in the --requestheader-client-ca-file flag. \"+ \"That CA is published in the 'extension-apiserver-authentication' configmap in \"+ \"the kube-system namespace. Components receiving calls from kube-aggregator should \"+ \"use that CA to perform their half of the mutual TLS verification.\") fs.StringVar(&s.ProxyClientKeyFile, \"proxy-client-key-file\", s.ProxyClientKeyFile, \"\"+ \"Private key for the client certificate used to prove the identity of the aggregator or kube-apiserver \"+ \"when it must call out during a request. This includes proxying requests to a user \"+ \"api-server and calling out to webhook admission plugins.\") fs.BoolVar(&s.EnableAggregatorRouting, \"enable-aggregator-routing\", s.EnableAggregatorRouting, \"Turns on aggregator routing requests to endpoints IP rather than cluster IP.\") fs.StringVar(&s.ServiceAccountSigningKeyFile, \"service-account-signing-key-file\", s.ServiceAccountSigningKeyFile, \"\"+ \"Path to the file that contains the current private key of the service account token issuer. The issuer will sign issued ID tokens with this private key. (Requires the 'TokenRequest' feature gate.)\") return fss } 4. Run Run以常驻的方式运行apiserver。 主要内容如下： 构造一个聚合的server结构体。 执行PrepareRun。 最终执行Run。 此部分代码位于cmd/kube-apiserver/app/server.go // Run runs the specified APIServer. This should never exit. func Run(completeOptions completedServerRunOptions, stopCh 4.1. CreateServerChain 构造聚合的Server。 基本流程如下： 首先生成config对象，包括kubeAPIServerConfig、apiExtensionsConfig。 再通过config生成server对象，包括apiExtensionsServer、kubeAPIServer。 执行apiExtensionsServer、kubeAPIServer的PrepareRun部分。 生成聚合的config对象aggregatorConfig。 基于aggregatorConfig、kubeAPIServer、apiExtensionsServer生成聚合的serveraggregatorServer。 此部分代码位于cmd/kube-apiserver/app/server.go // CreateServerChain creates the apiservers connected via delegation. func CreateServerChain(completedOptions completedServerRunOptions, stopCh 4.2. PrepareRun PrepareRun主要执行一些API安装操作。 此部分的代码位于vendor/k8s.io/apiserver/pkg/server/genericapiserver.go // PrepareRun does post API installation setup steps. func (s *GenericAPIServer) PrepareRun() preparedGenericAPIServer { if s.swaggerConfig != nil { routes.Swagger{Config: s.swaggerConfig}.Install(s.Handler.GoRestfulContainer) } if s.openAPIConfig != nil { routes.OpenAPI{ Config: s.openAPIConfig, }.Install(s.Handler.GoRestfulContainer, s.Handler.NonGoRestfulMux) } s.installHealthz() // Register audit backend preShutdownHook. if s.AuditBackend != nil { s.AddPreShutdownHook(\"audit-backend\", func() error { s.AuditBackend.Shutdown() return nil }) } return preparedGenericAPIServer{s} } 4.3. preparedGenericAPIServer.Run preparedGenericAPIServer.Run运行一个安全的http server。具体的实现逻辑待后续文章分析。 此部分代码位于vendor/k8s.io/apiserver/pkg/server/genericapiserver.go // Run spawns the secure http server. It only returns if stopCh is closed // or the secure port cannot be listened on initially. func (s preparedGenericAPIServer) Run(stopCh 核心函数： err := s.NonBlockingRun(stopCh) preparedGenericAPIServer.Run主要是调用NonBlockingRun函数，最终运行一个http server。该部分逻辑待后续文章分析。 5. 总结 NewAPIServerCommand采用了Cobra命令行框架，该框架使用主要包含以下部分： 构造option参数，提供给执行主体(例如 本文的server)作为配置参数使用。 添加Flags，主要用来通过传入的flags参数最终解析成option中使用的结构体属性。 执行Run函数，执行主体的运行逻辑部分（核心部分）。 其中Run函数的主要内容如下： 构造一个聚合的server结构体。 执行PrepareRun。 最终执行preparedGenericAPIServer.Run。 preparedGenericAPIServer.Run主要是调用NonBlockingRun函数，最终运行一个http server。NonBlockingRun的具体逻辑待后续文章再单独分析。 参考： https://github.com/kubernetes/kubernetes/tree/v1.12.0/cmd/kube-apiserver https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-apiserver/app/server.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-apiserver/app/aggregator.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-apiserver/app/options/options.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"runtime/_index.html":{"url":"runtime/_index.html","title":"Runtime","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"runtime/runtime.html":{"url":"runtime/runtime.html","title":"Runc和Containerd概述","keywords":"","body":"1. 概述2. OCI（Open Container Initiative）3. RunC4. Containerd4.1. containerd的架构图4.2. docker与containerd、runc的关系图5. CRI（Container Runtime Interface ）5.1. docker与k8s调用containerd的关系图5.2. cri-api5.2.1. runtime service5.2.2. image service5.3. cri-containerd5.3.1. CRI Plugin调用流程5.3.2. k8s对runtime调用的演进5.4. Dockershim5.5. CRI-O 本文主要分析OCI，CRI，runc，containerd，cri-containerd，dockershim等组件说明及调用关系。 1. 概述 各个组件调用关系图如下： 图片 - 关系图 图片来源：https://www.jianshu.com/p/62e71584d1cb 2. OCI（Open Container Initiative） OCI（Open Container Initiative）即开放的容器运行时规范，目的在于定义一个容器运行时及镜像的相关标准和规范，其中包括 runtime-spec：容器的生命周期管理，具体参考runtime-spec。 image-spec：镜像的生命周期管理，具体参考image-spec。 实现OCI标准的容器运行时有runc，kata等。 3. RunC runc(run container)是一个基于OCI标准实现的一个轻量级容器运行工具，用来创建和运行容器。而Containerd是用来维持通过runc创建的容器的运行状态。即runc用来创建和运行容器，containerd作为常驻进程用来管理容器。 runc包含libcontainer，包括对namespace和cgroup的调用操作。 命令参数： To start a new instance of a container: # runc run [ -b bundle ] USAGE: runc [global options] command [command options] [arguments...] COMMANDS: checkpoint checkpoint a running container create create a container delete delete any resources held by the container often used with detached container events display container events such as OOM notifications, cpu, memory, and IO usage statistics exec execute new process inside the container init initialize the namespaces and launch the process (do not call it outside of runc) kill kill sends the specified signal (default: SIGTERM) to the container's init process list lists containers started by runc with the given root pause pause suspends all processes inside the container ps ps displays the processes running inside a container restore restore a container from a previous checkpoint resume resumes all processes that have been previously paused run create and run a container spec create a new specification file start executes the user defined process in a created container state output the state of a container update update container resource constraints help, h Shows a list of commands or help for one command 4. Containerd containerd（container daemon）是一个daemon进程用来管理和运行容器，可以用来拉取/推送镜像和管理容器的存储和网络。其中可以调用runc来创建和运行容器。 4.1. containerd的架构图 4.2. docker与containerd、runc的关系图 更具体的调用逻辑： 5. CRI（Container Runtime Interface ） CRI即容器运行时接口，主要用来定义k8s与容器运行时的API调用，kubelet通过CRI来调用容器运行时，只要实现了CRI接口的容器运行时就可以对接到k8s的kubelet组件。 5.1. docker与k8s调用containerd的关系图 5.2. cri-api 5.2.1. runtime service // Runtime service defines the public APIs for remote container runtimes service RuntimeService { // Version returns the runtime name, runtime version, and runtime API version. rpc Version(VersionRequest) returns (VersionResponse) {} // RunPodSandbox creates and starts a pod-level sandbox. Runtimes must ensure // the sandbox is in the ready state on success. rpc RunPodSandbox(RunPodSandboxRequest) returns (RunPodSandboxResponse) {} // StopPodSandbox stops any running process that is part of the sandbox and // reclaims network resources (e.g., IP addresses) allocated to the sandbox. // If there are any running containers in the sandbox, they must be forcibly // terminated. // This call is idempotent, and must not return an error if all relevant // resources have already been reclaimed. kubelet will call StopPodSandbox // at least once before calling RemovePodSandbox. It will also attempt to // reclaim resources eagerly, as soon as a sandbox is not needed. Hence, // multiple StopPodSandbox calls are expected. rpc StopPodSandbox(StopPodSandboxRequest) returns (StopPodSandboxResponse) {} // RemovePodSandbox removes the sandbox. If there are any running containers // in the sandbox, they must be forcibly terminated and removed. // This call is idempotent, and must not return an error if the sandbox has // already been removed. rpc RemovePodSandbox(RemovePodSandboxRequest) returns (RemovePodSandboxResponse) {} // PodSandboxStatus returns the status of the PodSandbox. If the PodSandbox is not // present, returns an error. rpc PodSandboxStatus(PodSandboxStatusRequest) returns (PodSandboxStatusResponse) {} // ListPodSandbox returns a list of PodSandboxes. rpc ListPodSandbox(ListPodSandboxRequest) returns (ListPodSandboxResponse) {} // CreateContainer creates a new container in specified PodSandbox rpc CreateContainer(CreateContainerRequest) returns (CreateContainerResponse) {} // StartContainer starts the container. rpc StartContainer(StartContainerRequest) returns (StartContainerResponse) {} // StopContainer stops a running container with a grace period (i.e., timeout). // This call is idempotent, and must not return an error if the container has // already been stopped. // The runtime must forcibly kill the container after the grace period is // reached. rpc StopContainer(StopContainerRequest) returns (StopContainerResponse) {} // RemoveContainer removes the container. If the container is running, the // container must be forcibly removed. // This call is idempotent, and must not return an error if the container has // already been removed. rpc RemoveContainer(RemoveContainerRequest) returns (RemoveContainerResponse) {} // ListContainers lists all containers by filters. rpc ListContainers(ListContainersRequest) returns (ListContainersResponse) {} // ContainerStatus returns status of the container. If the container is not // present, returns an error. rpc ContainerStatus(ContainerStatusRequest) returns (ContainerStatusResponse) {} // UpdateContainerResources updates ContainerConfig of the container. rpc UpdateContainerResources(UpdateContainerResourcesRequest) returns (UpdateContainerResourcesResponse) {} // ReopenContainerLog asks runtime to reopen the stdout/stderr log file // for the container. This is often called after the log file has been // rotated. If the container is not running, container runtime can choose // to either create a new log file and return nil, or return an error. // Once it returns error, new container log file MUST NOT be created. rpc ReopenContainerLog(ReopenContainerLogRequest) returns (ReopenContainerLogResponse) {} // ExecSync runs a command in a container synchronously. rpc ExecSync(ExecSyncRequest) returns (ExecSyncResponse) {} // Exec prepares a streaming endpoint to execute a command in the container. rpc Exec(ExecRequest) returns (ExecResponse) {} // Attach prepares a streaming endpoint to attach to a running container. rpc Attach(AttachRequest) returns (AttachResponse) {} // PortForward prepares a streaming endpoint to forward ports from a PodSandbox. rpc PortForward(PortForwardRequest) returns (PortForwardResponse) {} // ContainerStats returns stats of the container. If the container does not // exist, the call returns an error. rpc ContainerStats(ContainerStatsRequest) returns (ContainerStatsResponse) {} // ListContainerStats returns stats of all running containers. rpc ListContainerStats(ListContainerStatsRequest) returns (ListContainerStatsResponse) {} // UpdateRuntimeConfig updates the runtime configuration based on the given request. rpc UpdateRuntimeConfig(UpdateRuntimeConfigRequest) returns (UpdateRuntimeConfigResponse) {} // Status returns the status of the runtime. rpc Status(StatusRequest) returns (StatusResponse) {} } 5.2.2. image service // ImageService defines the public APIs for managing images. service ImageService { // ListImages lists existing images. rpc ListImages(ListImagesRequest) returns (ListImagesResponse) {} // ImageStatus returns the status of the image. If the image is not // present, returns a response with ImageStatusResponse.Image set to // nil. rpc ImageStatus(ImageStatusRequest) returns (ImageStatusResponse) {} // PullImage pulls an image with authentication config. rpc PullImage(PullImageRequest) returns (PullImageResponse) {} // RemoveImage removes the image. // This call is idempotent, and must not return an error if the image has // already been removed. rpc RemoveImage(RemoveImageRequest) returns (RemoveImageResponse) {} // ImageFSInfo returns information of the filesystem that is used to store images. rpc ImageFsInfo(ImageFsInfoRequest) returns (ImageFsInfoResponse) {} } 5.3. cri-containerd 5.3.1. CRI Plugin调用流程 kubelet调用CRI插件，通过CRI Runtime Service接口创建pod cri通过CNI接口创建和配置pod的network namespace cri调用containerd创建sandbox container（pause container ）并将容器放入pod的cgroup和namespace中 kubelet调用CRI插件，通过image service接口拉取镜像，接着通过containerd来拉取镜像 kubelet调用CRI插件，通过runtime service接口运行拉取下来的镜像服务，最后通过containerd来运行业务容器，并将容器放入pod的cgroup和namespace中。 具体参考：https://github.com/containerd/cri/blob/release/1.4/docs/architecture.md 5.3.2. k8s对runtime调用的演进 由原来通过dockershim调用docker再调用containerd，直接变成通过cri-containerd调用containerd，从而减少了一层docker调用逻辑。 具体参考：https://github.com/containerd/cri/blob/release/1.4/docs/proposal.md 5.4. Dockershim 在旧版本的k8s中，由于docker没有实现CRI接口，因此增加一个Dockershim来实现k8s对docker的调用。（shim：垫片，一般用来表示对第三方组件API调用的适配插件，例如k8s使用Dockershim来实现对docker接口的适配调用） 5.5. CRI-O cri-o与containerd类似，用来实现容器的管理，可替换containerd的使用。 参考： https://opencontainers.org/about/overview/ https://github.com/opencontainers/runtime-spec https://github.com/kubernetes/kubernetes/blob/242a97307b34076d5d8f5bbeb154fa4d97c9ef1d/docs/devel/container-runtime-interface.md https://github.com/containerd/containerd/blob/main/docs/cri/architecture.md https://www.tutorialworks.com/difference-docker-containerd-runc-crio-oci/ https://kubernetes.io/zh/docs/setup/production-environment/container-runtimes/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"runtime/containerd/_index.html":{"url":"runtime/containerd/_index.html","title":"Containerd","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"runtime/containerd/install-containerd.html":{"url":"runtime/containerd/install-containerd.html","title":"安装Containerd","keywords":"","body":"1. Ubuntu安装containerd1. 卸载旧版本2. 准备包环境3. 安装containerd4. 修改配置2. 离线二进制安装containerd3. Containerd配置代理1. Ubuntu安装containerd 以下以Ubuntu为例 说明：安装containerd与安装docker流程基本一致，差别在于不需要安装docker-ce containerd: apt-get install -y containerd.io docker: apt-get install docker-ce docker-ce-cli containerd.io 1. 卸载旧版本 sudo apt-get remove docker docker-engine docker.io containerd runc 如果需要删除镜像及容器数据则执行以下命令 sudo rm -rf /var/lib/docker sudo rm -rf /var/lib/containerd 2. 准备包环境 1、更新apt，允许使用https。 sudo apt-get update sudo apt-get install \\ ca-certificates \\ curl \\ gnupg \\ lsb-release 2、添加docker官方GPG key。 sudo mkdir -p /etc/apt/keyrings curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg 3、设置软件仓库源 echo \\ \"deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \\ $(lsb_release -cs) stable\" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null 3. 安装containerd # 安装containerd sudo apt-get update sudo apt-get install -y containerd.io # 如果是安装docker则执行： sudo apt-get install docker-ce docker-ce-cli containerd.io # 查看运行状态 systemctl enable containerd systemctl status containerd 安装指定版本 # 查看版本 apt-cache madison containerd # sudo apt-get install containerd= 4. 修改配置 在 Linux 上，containerd 的默认 CRI 套接字是 /run/containerd/containerd.sock。 1、生成默认配置 containerd config default > /etc/containerd/config.toml 2、修改CgroupDriver为systemd k8s官方推荐使用systemd类型的CgroupDriver。 [plugins.\"io.containerd.grpc.v1.cri\".containerd.runtimes.runc] ... [plugins.\"io.containerd.grpc.v1.cri\".containerd.runtimes.runc.options] SystemdCgroup = true 3、重启containerd systemctl restart containerd 2. 离线二进制安装containerd 把containerd、runc、cni-plugins、nerdctl二进制下载到本地，再上传到对应服务器，解压文件到对应目录，修改containerd配置文件，启动containerd。 #!/bin/bash set -e ContainerdVersion=$1 ContainerdVersion=${ContainerdVersion:-1.6.6} RuncVersion=$2 RuncVersion=${RuncVersion:-1.1.3} CniVersion=$3 CniVersion=${CniVersion:-1.1.1} NerdctlVersion=$4 NerdctlVersion=${NerdctlVersion:-0.21.0} CrictlVersion=$5 CrictlVersion=${CrictlVersion:-1.24.2} echo \"--------------install containerd--------------\" wget https://github.com/containerd/containerd/releases/download/v${ContainerdVersion}/containerd-${ContainerdVersion}-linux-amd64.tar.gz tar Cxzvf /usr/local containerd-${ContainerdVersion}-linux-amd64.tar.gz echo \"--------------install containerd service--------------\" wget https://raw.githubusercontent.com/containerd/containerd/681aaf68b7dcbe08a51c3372cbb8f813fb4466e0/containerd.service mv containerd.service /lib/systemd/system/ mkdir -p /etc/containerd/ containerd config default > /etc/containerd/config.toml sed -i 's/SystemdCgroup = false/SystemdCgroup = true/' /etc/containerd/config.toml echo \"--------------install runc--------------\" wget https://github.com/opencontainers/runc/releases/download/v${RuncVersion}/runc.amd64 chmod +x runc.amd64 mv runc.amd64 /usr/local/bin/runc echo \"--------------install cni plugins--------------\" wget https://github.com/containernetworking/plugins/releases/download/v${CniVersion}/cni-plugins-linux-amd64-v${CniVersion}.tgz rm -fr /opt/cni/bin mkdir -p /opt/cni/bin tar Cxzvf /opt/cni/bin cni-plugins-linux-amd64-v${CniVersion}.tgz echo \"--------------install nerdctl--------------\" wget https://github.com/containerd/nerdctl/releases/download/v${NerdctlVersion}/nerdctl-${NerdctlVersion}-linux-amd64.tar.gz tar Cxzvf /usr/local/bin nerdctl-${NerdctlVersion}-linux-amd64.tar.gz echo \"--------------install crictl--------------\" wget https://github.com/kubernetes-sigs/cri-tools/releases/download/v${CrictlVersion}/crictl-v${CrictlVersion}-linux-amd64.tar.gz tar Cxzvf /usr/local/bin crictl-v${CrictlVersion}-linux-amd64.tar.gz cat > /etc/crictl.yaml 3. Containerd配置代理 由于节点到k8s官方仓库网络不通，或者设备处于内网，可以通过配置http_proxy代理的方式来拉取镜像。 vi /lib/systemd/system/containerd.service # 添加代理环境变量 [Unit] Description=containerd container runtime Documentation=https://containerd.io After=network.target local-fs.target [Service] Environment=\"HTTP_PROXY=http://squid:3128/\" # 添加环境变量代理 Environment=\"HTTPS_PROXY=http://squid:3128/\" # 添加环境变量代理 ExecStartPre=-/sbin/modprobe overlay ExecStart=/usr/local/bin/containerd Type=notify Delegate=yes KillMode=process Restart=always RestartSec=5 # Having non-zero Limit*s causes performance problems due to accounting overhead # in the kernel. We recommend using cgroups to do container-local accounting. LimitNPROC=infinity LimitCORE=infinity LimitNOFILE=infinity # Comment TasksMax if your systemd version does not supports it. # Only systemd 226 and above support this version. TasksMax=infinity OOMScoreAdjust=-999 [Install] WantedBy=multi-user.target # 重启服务 systemctl daemon-reload systemctl restart containerd 参考： https://github.com/containerd/containerd https://github.com/containerd/containerd/blob/main/docs/getting-started.md https://docs.docker.com/engine/install/ubuntu/ https://kubernetes.io/docs/setup/production-environment/container-runtimes/#containerd containerd/containerd.service at main · containerd/containerd · GitHub GitHub - containerd/nerdctl: containerd ctl GitHub - kubernetes-sigs/cri-tools: CLI and validation tools for Kubelet Container Runtime Interface (CRI) . Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"runtime/docker/_index.html":{"url":"runtime/docker/_index.html","title":"Docker","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"runtime/docker/docker-notes.html":{"url":"runtime/docker/docker-notes.html","title":"Docker学习笔记","keywords":"","body":"Docker学习笔记Docker学习笔记 详见：Docker学习笔记 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"runtime/kata/_index.html":{"url":"runtime/kata/_index.html","title":"Kata Container","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"runtime/kata/kata-container.html":{"url":"runtime/kata/kata-container.html","title":"kata容器简介","keywords":"","body":"Kata-container简介1. kata-container架构2. kata-runtime3. shim4. kata-agent5. kata-proxy6. Hypervisor7. QEMU/KVMKata-container简介 kata-container通过轻量型虚拟机技术构建一个安全的容器运行时，表现像容器一样，但通硬件虚拟化技术提供强隔离，作为第二层的安全防护。 特点： 安全：独立的内核，提供网络、I/O、内存的隔离。 兼容性：支持OCI容器标准，k8s的CRI接口。 性能：兼容虚拟机的安全和容器的轻量特点。 简单：使用标准的接口。 1. kata-container架构 kata-container与传统container的比较 2. kata-runtime Kata Containers runtime (kata-runtime)通过QEMU*/KVM技术创建了一种轻量型的虚拟机，兼容 OCI runtime specification 标准，支持Kubernetes* Container Runtime Interface (CRI)接口，可替换CRI shim runtime (runc) 通过k8s来创建pod或容器。 3. shim shim类似Docker的 containerd-shim 或CRI-O的 conmon，主要用来监控和回收容器的进程，kata-shim需要处理所有的容器的IO流(stdout, stdin and stderr)和转发相关信号。 containerd-shim-kata-v2实现了Containerd Runtime V2 (Shim API)，k8s可以通过containerd-shim-kata-v2（替代2N+1个shims[由一个containerd-shim和kata-shim组成]）来创建pod。 4. kata-agent 在虚拟机内kata-agent作为一个daemon进程运行，并拉起容器的进程。kata-agent使用VIRTIO或VSOCK接口（QEMU在主机上暴露的socket文件）在guest虚拟机中运行gRPC服务器。kata-runtime通过grpc协议与kata-agent通信，向kata-agent发送管理容器的命令。该协议还用于容器和管理引擎（例如Docker Engine）之间传送I / O流（stdout，stderr，stdin）。 容器内所有的执行命令和相关的IO流都需要通过QEMU在宿主机暴露的virtio-serial或vsock接口，当使用VIRTIO的情况下，每个虚拟机会创建一个Kata Containers proxy (kata-proxy) 来处理命令和IO流。 kata-agent使用libcontainer 来管理容器的生命周期，复用了runc的部分代码。 5. kata-proxy kata-proxy提供了 kata-shim 和 kata-runtime 与VM中的kata-agent通信的方式，其中通信方式是使用virtio-serial或vsock，默认是使用virtio-serial。 6. Hypervisor kata-container通过QEMU/KVM来创建虚拟机给容器运行，可以支持多种hypervisors。 7. QEMU/KVM 待补充 参考文档： https://katacontainers.io/ https://github.com/kata-containers/documentation/blob/master/design/architecture.md Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"runtime/kata/kata-container-conf.html":{"url":"runtime/kata/kata-container-conf.html","title":"kata配置","keywords":"","body":"1. 配置文件路径2. kata-env3. configuration.toml1. 配置文件路径 默认的配置文件位于/usr/share/defaults/kata-containers/configuration.toml，如果/etc/kata-containers/configuration.toml的配置文件存在，则会替代默认的配置文件。 查看配置文件的路径命令如下： # kata-runtime --kata-show-default-config-paths /etc/kata-containers/configuration.toml /usr/share/defaults/kata-containers/configuration.toml 指定自定义配置文件运行 kata-runtime --kata-config=/some/where/configuration.toml ... 2. kata-env 查看runtime使用到的环境参数， kata-runtime kata-env 输出内容如下： [Meta] Version = \"1.0.23\" [Runtime] Debug = false Trace = false DisableGuestSeccomp = true DisableNewNetNs = false Path = \"/usr/bin/kata-runtime\" [Runtime.Version] Semver = \"1.7.2\" Commit = \"9b9282693cfbcf70d442916bea56771cc6fc3afe\" OCI = \"1.0.1-dev\" [Runtime.Config] Path = \"/usr/share/defaults/kata-containers/configuration.toml\" [Hypervisor] MachineType = \"pc\" Version = \"QEMU emulator version 2.11.0\\nCopyright (c) 2003-2017 Fabrice Bellard and the QEMU Project developers\" Path = \"/usr/bin/qemu-lite-system-x86_64\" BlockDeviceDriver = \"virtio-scsi\" EntropySource = \"/dev/urandom\" Msize9p = 8192 MemorySlots = 10 Debug = false UseVSock = false SharedFS = \"virtio-9p\" [Image] Path = \"/usr/share/kata-containers/kata-containers-image_centos_1.7.2_agent_20190702.img\" [Kernel] Path = \"/usr/share/kata-containers/vmlinuz-4.19.28.42-6.1.container\" Parameters = \"init=/usr/lib/systemd/systemd systemd.unit=kata-containers.target systemd.mask=systemd-networkd.service systemd.mask=systemd-networkd.socket systemd.mask=systemd-journald.service systemd.mask=systemd-journald.socket systemd.mask=systemd-journal-flush.service systemd.mask=systemd-journald-dev-log.socket systemd.mask=systemd-udevd.service systemd.mask=systemd-udevd.socket systemd.mask=systemd-udev-trigger.service systemd.mask=systemd-udevd-kernel.socket systemd.mask=systemd-udevd-control.socket systemd.mask=systemd-timesyncd.service systemd.mask=systemd-update-utmp.service systemd.mask=systemd-tmpfiles-setup.service systemd.mask=systemd-tmpfiles-cleanup.service systemd.mask=systemd-tmpfiles-cleanup.timer systemd.mask=tmp.mount systemd.mask=systemd-random-seed.service systemd.mask=systemd-coredump@.service\" [Initrd] Path = \"\" [Proxy] Type = \"kataProxy\" Version = \"kata-proxy version 1.7.2-a56df7c\" Path = \"/usr/libexec/kata-containers/kata-proxy\" Debug = false [Shim] Type = \"kataShim\" Version = \"kata-shim version 1.7.2-2ea178c\" Path = \"/usr/libexec/kata-containers/kata-shim\" Debug = false [Agent] Type = \"kata\" Debug = false Trace = false TraceMode = \"\" TraceType = \"\" [Host] Kernel = \"4.14.105-1-tlinux3-0008\" Architecture = \"amd64\" VMContainerCapable = true SupportVSocks = true [Host.Distro] Name = \"Tencent tlinux\" Version = \"2.2\" [Host.CPU] Vendor = \"GenuineIntel\" Model = \"Intel(R) Xeon(R) CPU X3440 @ 2.53GHz\" [Netmon] Version = \"kata-netmon version 1.7.2\" Path = \"/usr/libexec/kata-containers/kata-netmon\" Debug = false Enable = false 3. configuration.toml # Copyright (c) 2017-2019 Intel Corporation # # SPDX-License-Identifier: Apache-2.0 # # XXX: WARNING: this file is auto-generated. # XXX: # XXX: Source file: \"cli/config/configuration-qemu.toml.in\" # XXX: Project: # XXX: Name: Kata Containers # XXX: Type: kata [hypervisor.qemu] path = \"/usr/bin/qemu-lite-system-x86_64\" kernel = \"/usr/share/kata-containers/vmlinuz.container\" image = \"/usr/share/kata-containers/kata-containers.img\" machine_type = \"pc\" # Optional space-separated list of options to pass to the guest kernel. # For example, use `kernel_params = \"vsyscall=emulate\"` if you are having # trouble running pre-2.15 glibc. # # WARNING: - any parameter specified here will take priority over the default # parameter value of the same name used to start the virtual machine. # Do not set values here unless you understand the impact of doing so as you # may stop the virtual machine from booting. # To see the list of default parameters, enable hypervisor debug, create a # container and look for 'default-kernel-parameters' log entries. kernel_params = \"\" # Path to the firmware. # If you want that qemu uses the default firmware leave this option empty firmware = \"\" # Machine accelerators # comma-separated list of machine accelerators to pass to the hypervisor. # For example, `machine_accelerators = \"nosmm,nosmbus,nosata,nopit,static-prt,nofw\"` machine_accelerators=\"\" # Default number of vCPUs per SB/VM: # unspecified or 0 --> will be set to 1 # will be set to the actual number of physical cores # > 0 will be set to the specified number # > number of physical cores --> will be set to the actual number of physical cores default_vcpus = 1 # Default maximum number of vCPUs per SB/VM: # unspecified or == 0 --> will be set to the actual number of physical cores or to the maximum number # of vCPUs supported by KVM if that number is exceeded # > 0 will be set to the specified number # > number of physical cores --> will be set to the actual number of physical cores or to the maximum number # of vCPUs supported by KVM if that number is exceeded # WARNING: Depending of the architecture, the maximum number of vCPUs supported by KVM is used when # the actual number of physical cores is greater than it. # WARNING: Be aware that this value impacts the virtual machine's memory footprint and CPU # the hotplug functionality. For example, `default_maxvcpus = 240` specifies that until 240 vCPUs # can be added to a SB/VM, but the memory footprint will be big. Another example, with # `default_maxvcpus = 8` the memory footprint will be small, but 8 will be the maximum number of # vCPUs supported by the SB/VM. In general, we recommend that you do not edit this variable, # unless you know what are you doing. default_maxvcpus = 0 # Bridges can be used to hot plug devices. # Limitations: # * Currently only pci bridges are supported # * Until 30 devices per bridge can be hot plugged. # * Until 5 PCI bridges can be cold plugged per VM. # This limitation could be a bug in qemu or in the kernel # Default number of bridges per SB/VM: # unspecified or 0 --> will be set to 1 # > 1 will be set to the specified number # > 5 --> will be set to 5 default_bridges = 1 # Default memory size in MiB for SB/VM. # If unspecified then it will be set 2048 MiB. default_memory = 2048 # # Default memory slots per SB/VM. # If unspecified then it will be set 10. # This is will determine the times that memory will be hotadded to sandbox/VM. #memory_slots = 10 # The size in MiB will be plused to max memory of hypervisor. # It is the memory address space for the NVDIMM devie. # If set block storage driver (block_device_driver) to \"nvdimm\", # should set memory_offset to the size of block device. # Default 0 #memory_offset = 0 # Disable block device from being used for a container's rootfs. # In case of a storage driver like devicemapper where a container's # root file system is backed by a block device, the block device is passed # directly to the hypervisor for performance reasons. # This flag prevents the block device from being passed to the hypervisor, # 9pfs is used instead to pass the rootfs. disable_block_device_use = false # Shared file system type: # - virtio-9p (default) # - virtio-fs shared_fs = \"virtio-9p\" # Path to vhost-user-fs daemon. virtio_fs_daemon = \"/usr/bin/virtiofsd-x86_64\" # Default size of DAX cache in MiB virtio_fs_cache_size = 1024 # Cache mode: # # - none # Metadata, data, and pathname lookup are not cached in guest. They are # always fetched from host and any changes are immediately pushed to host. # # - auto # Metadata and pathname lookup cache expires after a configured amount of # time (default is 1 second). Data is cached while the file is open (close # to open consistency). # # - always # Metadata, data, and pathname lookup are cached in guest and never expire. virtio_fs_cache = \"always\" # Block storage driver to be used for the hypervisor in case the container # rootfs is backed by a block device. This is virtio-scsi, virtio-blk # or nvdimm. block_device_driver = \"virtio-scsi\" # Specifies cache-related options will be set to block devices or not. # Default false #block_device_cache_set = true # Specifies cache-related options for block devices. # Denotes whether use of O_DIRECT (bypass the host page cache) is enabled. # Default false #block_device_cache_direct = true # Specifies cache-related options for block devices. # Denotes whether flush requests for the device are ignored. # Default false #block_device_cache_noflush = true # Enable iothreads (data-plane) to be used. This causes IO to be # handled in a separate IO thread. This is currently only implemented # for SCSI. # enable_iothreads = false # Enable pre allocation of VM RAM, default false # Enabling this will result in lower container density # as all of the memory will be allocated and locked # This is useful when you want to reserve all the memory # upfront or in the cases where you want memory latencies # to be very predictable # Default false #enable_mem_prealloc = true # Enable huge pages for VM RAM, default false # Enabling this will result in the VM memory # being allocated using huge pages. # This is useful when you want to use vhost-user network # stacks within the container. This will automatically # result in memory pre allocation #enable_hugepages = true # Enable swap of vm memory. Default false. # The behaviour is undefined if mem_prealloc is also set to true #enable_swap = true # This option changes the default hypervisor and kernel parameters # to enable debug output where available. This extra output is added # to the proxy logs, but only when proxy debug is also enabled. # # Default false #enable_debug = true # Disable the customizations done in the runtime when it detects # that it is running on top a VMM. This will result in the runtime # behaving as it would when running on bare metal. # #disable_nesting_checks = true # This is the msize used for 9p shares. It is the number of bytes # used for 9p packet payload. #msize_9p = 8192 # If true and vsocks are supported, use vsocks to communicate directly # with the agent and no proxy is started, otherwise use unix # sockets and start a proxy to communicate with the agent. # Default false #use_vsock = true # VFIO devices are hotplugged on a bridge by default. # Enable hotplugging on root bus. This may be required for devices with # a large PCI bar, as this is a current limitation with hotplugging on # a bridge. This value is valid for \"pc\" machine type. # Default false #hotplug_vfio_on_root_bus = true # If host doesn't support vhost_net, set to true. Thus we won't create vhost fds for nics. # Default false #disable_vhost_net = true # # Default entropy source. # The path to a host source of entropy (including a real hardware RNG) # /dev/urandom and /dev/random are two main options. # Be aware that /dev/random is a blocking source of entropy. If the host # runs out of entropy, the VMs boot time will increase leading to get startup # timeouts. # The source of entropy /dev/urandom is non-blocking and provides a # generally acceptable source of entropy. It should work well for pretty much # all practical purposes. #entropy_source= \"/dev/urandom\" # Path to OCI hook binaries in the *guest rootfs*. # This does not affect host-side hooks which must instead be added to # the OCI spec passed to the runtime. # # You can create a rootfs with hooks by customizing the osbuilder scripts: # https://github.com/kata-containers/osbuilder # # Hooks must be stored in a subdirectory of guest_hook_path according to their # hook type, i.e. \"guest_hook_path/{prestart,postart,poststop}\". # The agent will scan these directories for executable files and add them, in # lexicographical order, to the lifecycle of the guest container. # Hooks are executed in the runtime namespace of the guest. See the official documentation: # https://github.com/opencontainers/runtime-spec/blob/v1.0.1/config.md#posix-platform-hooks # Warnings will be logged if any error is encountered will scanning for hooks, # but it will not abort container execution. #guest_hook_path = \"/usr/share/oci/hooks\" [factory] # VM templating support. Once enabled, new VMs are created from template # using vm cloning. They will share the same initial kernel, initramfs and # agent memory by mapping it readonly. It helps speeding up new container # creation and saves a lot of memory if there are many kata containers running # on the same host. # # When disabled, new VMs are created from scratch. # # Note: Requires \"initrd=\" to be set (\"image=\" is not supported). # # Default false #enable_template = true # Specifies the path of template. # # Default \"/run/vc/vm/template\" #template_path = \"/run/vc/vm/template\" # The number of caches of VMCache: # unspecified or == 0 --> VMCache is disabled # > 0 --> will be set to the specified number # # VMCache is a function that creates VMs as caches before using it. # It helps speed up new container creation. # The function consists of a server and some clients communicating # through Unix socket. The protocol is gRPC in protocols/cache/cache.proto. # The VMCache server will create some VMs and cache them by factory cache. # It will convert the VM to gRPC format and transport it when gets # requestion from clients. # Factory grpccache is the VMCache client. It will request gRPC format # VM and convert it back to a VM. If VMCache function is enabled, # kata-runtime will request VM from factory grpccache when it creates # a new sandbox. # # Default 0 #vm_cache_number = 0 # Specify the address of the Unix socket that is used by VMCache. # # Default /var/run/kata-containers/cache.sock #vm_cache_endpoint = \"/var/run/kata-containers/cache.sock\" [proxy.kata] path = \"/usr/libexec/kata-containers/kata-proxy\" # If enabled, proxy messages will be sent to the system log # (default: disabled) #enable_debug = true [shim.kata] path = \"/usr/libexec/kata-containers/kata-shim\" # If enabled, shim messages will be sent to the system log # (default: disabled) #enable_debug = true # If enabled, the shim will create opentracing.io traces and spans. # (See https://www.jaegertracing.io/docs/getting-started). # # Note: By default, the shim runs in a separate network namespace. Therefore, # to allow it to send trace details to the Jaeger agent running on the host, # it is necessary to set 'disable_new_netns=true' so that it runs in the host # network namespace. # # (default: disabled) #enable_tracing = true [agent.kata] # If enabled, make the agent display debug-level messages. # (default: disabled) #enable_debug = true # Enable agent tracing. # # If enabled, the default trace mode is \"dynamic\" and the # default trace type is \"isolated\". The trace mode and type are set # explicity with the `trace_type=` and `trace_mode=` options. # # Notes: # # - Tracing is ONLY enabled when `enable_tracing` is set: explicitly # setting `trace_mode=` and/or `trace_type=` without setting `enable_tracing` # will NOT activate agent tracing. # # - See https://github.com/kata-containers/agent/blob/master/TRACING.md for # full details. # # (default: disabled) #enable_tracing = true # #trace_mode = \"dynamic\" #trace_type = \"isolated\" [netmon] # If enabled, the network monitoring process gets started when the # sandbox is created. This allows for the detection of some additional # network being added to the existing network namespace, after the # sandbox has been created. # (default: disabled) #enable_netmon = true # Specify the path to the netmon binary. path = \"/usr/libexec/kata-containers/kata-netmon\" # If enabled, netmon messages will be sent to the system log # (default: disabled) #enable_debug = true [runtime] # If enabled, the runtime will log additional debug messages to the # system log # (default: disabled) #enable_debug = true # # Internetworking model # Determines how the VM should be connected to the # the container network interface # Options: # # - bridged # Uses a linux bridge to interconnect the container interface to # the VM. Works for most cases except macvlan and ipvlan. # # - macvtap # Used when the Container network interface can be bridged using # macvtap. # # - none # Used when customize network. Only creates a tap device. No veth pair. # # - tcfilter # Uses tc filter rules to redirect traffic from the network interface # provided by plugin to a tap interface connected to the VM. # internetworking_model=\"tcfilter\" # disable guest seccomp # Determines whether container seccomp profiles are passed to the virtual # machine and applied by the kata agent. If set to true, seccomp is not applied # within the guest # (default: true) disable_guest_seccomp=true # If enabled, the runtime will create opentracing.io traces and spans. # (See https://www.jaegertracing.io/docs/getting-started). # (default: disabled) #enable_tracing = true # If enabled, the runtime will not create a network namespace for shim and hypervisor processes. # This option may have some potential impacts to your host. It should only be used when you know what you're doing. # `disable_new_netns` conflicts with `enable_netmon` # `disable_new_netns` conflicts with `internetworking_model=bridged` and `internetworking_model=macvtap`. It works only # with `internetworking_model=none`. The tap device will be in the host network namespace and can connect to a bridge # (like OVS) directly. # If you are using docker, `disable_new_netns` only works with `docker run --net=none` # (default: false) #disable_new_netns = true # Enabled experimental feature list, format: [\"a\", \"b\"]. # Experimental features are features not stable enough for production, # They may break compatibility, and are prepared for a big version bump. # Supported experimental features: # 1. \"newstore\": new persist storage driver which breaks backward compatibility, # expected to move out of experimental in 2.0.0. # (default: []) experimental=[] 参考： https://github.com/kata-containers/runtime#configuration Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"runtime/gpu/_index.html":{"url":"runtime/gpu/_index.html","title":"GPU","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"runtime/gpu/nvidia-device-plugin.html":{"url":"runtime/gpu/nvidia-device-plugin.html","title":"nvidia-device-plugin介绍","keywords":"","body":"1. 简介2. 要求3. 使用3.1. 安装NVIDIA drivers和nvidia-docker3.2. 配置docker runtime3.3. 部署nvidia-device-plugin3.4. 运行GPU任务4. 构建和运行nvidia-device-plugin4.1. docker方式4.1.1. 编译4.1.2. 运行4.2. 非docker方式4.2.1. 编译4.2.2. 本地运行1. 简介 NVIDIA device plugin 通过k8s daemonset的方式部署到每个k8s的node节点上，实现了Kubernetes device plugin的接口。 提供以下功能： 暴露每个节点的GPU数量给集群 跟踪GPU的健康情况 使在k8s的节点可以运行GPU容器 2. 要求 NVIDIA drivers ~= 384.81 nvidia-docker version > 2.0 (see how to install and it's prerequisites#prerequisites)) docker configured with nvidia as the default runtime. Kubernetes version >= 1.10 3. 使用 3.1. 安装NVIDIA drivers和nvidia-docker 提供GPU节点的机器，准备工作如下 安装NVIDIA drivers ~= 384.81 安装nvidia-docker version > 2.0 3.2. 配置docker runtime 配置nvidia runtime作为GPU节点的默认runtime。 修改文件/etc/docker/daemon.json，增加以下runtime内容。 { \"default-runtime\": \"nvidia\", \"runtimes\": { \"nvidia\": { \"path\": \"/usr/bin/nvidia-container-runtime\", \"runtimeArgs\": [] } } } 3.3. 部署nvidia-device-plugin $ kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/1.0.0-beta4/nvidia-device-plugin.yml nvidia-device-plugin的daemonset yaml文件如下： # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. apiVersion: apps/v1 kind: DaemonSet metadata: name: nvidia-device-plugin-daemonset namespace: kube-system spec: selector: matchLabels: name: nvidia-device-plugin-ds updateStrategy: type: RollingUpdate template: metadata: # This annotation is deprecated. Kept here for backward compatibility # See https://kubernetes.io/docs/tasks/administer-cluster/guaranteed-scheduling-critical-addon-pods/ annotations: scheduler.alpha.kubernetes.io/critical-pod: \"\" labels: name: nvidia-device-plugin-ds spec: tolerations: # This toleration is deprecated. Kept here for backward compatibility # See https://kubernetes.io/docs/tasks/administer-cluster/guaranteed-scheduling-critical-addon-pods/ - key: CriticalAddonsOnly operator: Exists - key: nvidia.com/gpu operator: Exists effect: NoSchedule # Mark this pod as a critical add-on; when enabled, the critical add-on # scheduler reserves resources for critical add-on pods so that they can # be rescheduled after a failure. # See https://kubernetes.io/docs/tasks/administer-cluster/guaranteed-scheduling-critical-addon-pods/ priorityClassName: \"system-node-critical\" containers: - image: nvidia/k8s-device-plugin:1.0.0-beta4 name: nvidia-device-plugin-ctr securityContext: allowPrivilegeEscalation: false capabilities: drop: [\"ALL\"] volumeMounts: - name: device-plugin mountPath: /var/lib/kubelet/device-plugins volumes: - name: device-plugin hostPath: path: /var/lib/kubelet/device-plugins 3.4. 运行GPU任务 创建一个GPU的pod，pod的资源类型指定为nvidia.com/gpu。 apiVersion: v1 kind: Pod metadata: name: gpu-pod spec: containers: - name: cuda-container image: nvidia/cuda:9.0-devel resources: limits: nvidia.com/gpu: 2 # requesting 2 GPUs - name: digits-container image: nvidia/digits:6.0 resources: limits: nvidia.com/gpu: 2 # requesting 2 GPUs 4. 构建和运行nvidia-device-plugin 4.1. docker方式 4.1.1. 编译 直接拉取dockerhub的镜像 $ docker pull nvidia/k8s-device-plugin:1.0.0-beta4 拉取代码构建镜像 $ docker build -t nvidia/k8s-device-plugin:1.0.0-beta4 https://github.com/NVIDIA/k8s-device-plugin.git#1.0.0-beta4 修改nvidia-device-plugin后构建镜像 $ git clone https://github.com/NVIDIA/k8s-device-plugin.git && cd k8s-device-plugin $ git checkout 1.0.0-beta4 $ docker build -t nvidia/k8s-device-plugin:1.0.0-beta4 . 4.1.2. 运行 docker本地运行 $ docker run --security-opt=no-new-privileges --cap-drop=ALL --network=none -it -v /var/lib/kubelet/device-plugins:/var/lib/kubelet/device-plugins nvidia/k8s-device-plugin:1.0.0-beta4 daemonset运行 $ kubectl create -f nvidia-device-plugin.yml 4.2. 非docker方式 4.2.1. 编译 $ C_INCLUDE_PATH=/usr/local/cuda/include LIBRARY_PATH=/usr/local/cuda/lib64 go build 4.2.2. 本地运行 $ ./k8s-device-plugin 参考： https://github.com/NVIDIA/k8s-device-plugin k8s-device-plugin gpu-support Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"etcd/etcd-introduction.html":{"url":"etcd/etcd-introduction.html","title":"Etcd介绍","keywords":"","body":"1. Etcd是什么（what）2. 为什么使用Etcd（why）2.1. Etcd的优势3. 如何实现Etcd架构（how）3.1. Etcd的相关名词解释3.2. Etcd的架构图1、HTTP Server:2、Raft:3、WAL:4、Store:1. Etcd是什么（what） etcd is a distributed, consistent key-value store for shared configuration and service discovery, with a focus on being: Secure: automatic TLS with optional client cert authentication[可选的SSL客户端证书认证：支持https访问 ] Fast: benchmarked 10,000 writes/sec[单实例每秒 1000 次写操作] Reliable: properly distributed using Raft[使用Raft保证一致性] etcd是一个分布式、一致性的键值存储系统，主要用于配置共享和服务发现。[以上内容来自etcd官网] 2. 为什么使用Etcd（why） 2.1. Etcd的优势 简单。使用Go语言编写部署简单；使用HTTP作为接口使用简单；使用Raft算法保证强一致性让用户易于理解。 数据持久化。etcd默认数据一更新就进行持久化。 安全。etcd支持SSL客户端安全认证。 3. 如何实现Etcd架构（how） 3.1. Etcd的相关名词解释 Raft：etcd所采用的保证分布式系统强一致性的算法。 Node：一个Raft状态机实例。 Member： 一个etcd实例。它管理着一个Node，并且可以为客户端请求提供服务。 Cluster：由多个Member构成可以协同工作的etcd集群。 Peer：对同一个etcd集群中另外一个Member的称呼。 Client： 向etcd集群发送HTTP请求的客户端。 WAL：预写式日志，etcd用于持久化存储的日志格式。 snapshot：etcd防止WAL文件过多而设置的快照，存储etcd数据状态。 Proxy：etcd的一种模式，为etcd集群提供反向代理服务。 Leader：Raft算法中通过竞选而产生的处理所有数据提交的节点。 Follower：竞选失败的节点作为Raft中的从属节点，为算法提供强一致性保证。 Candidate：当Follower超过一定时间接收不到Leader的心跳时转变为Candidate开始竞选。【候选人】 Term：某个节点成为Leader到下一次竞选时间，称为一个Term。【任期】 Index：数据项编号。Raft中通过Term和Index来定位数据。 3.2. Etcd的架构图 图片 - etcd的架构图 一个用户的请求发送过来，会经由HTTP Server转发给Store进行具体的事务处理，如果涉及到节点的修改，则交给Raft模块进行状态的变更、日志的记录，然后再同步给别的etcd节点以确认数据提交，最后进行数据的提交，再次同步。 1、HTTP Server: 用于处理用户发送的API请求以及其它etcd节点的同步与心跳信息请求。 2、Raft: Raft强一致性算法的具体实现，是etcd的核心。 3、WAL: Write Ahead Log（预写式日志），是etcd的数据存储方式，用于系统提供原子性和持久性的一系列技术。除了在内存中存有所有数据的状态以及节点的索引以外，etcd就通过WAL进行持久化存储。WAL中，所有的数据提交前都会事先记录日志。 Entry[日志内容]: 负责存储具体日志的内容。 Snapshot[快照内容]: Snapshot是为了防止数据过多而进行的状态快照，日志内容发生变化时保存Raft的状态。 4、Store: 用于处理etcd支持的各类功能的事务，包括数据索引、节点状态变更、监控与反馈、事件处理与执行等等，是etcd对用户提供的大多数API功能的具体实现。 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"etcd/raft.html":{"url":"etcd/raft.html","title":"Raft算法","keywords":"","body":"1. Raft协议[分布式一致性算法]2. 过程2.1. 选举2.2. 同步2.3. 网络分区1. Raft协议[分布式一致性算法] 图片 - raft raft算法中涉及三种角色，分别是： follower: 跟随者 candidate: 候选者，选举过程中的中间状态角色 leader: 领导者 2. 过程 2.1. 选举 有两个timeout来控制选举，第一个是election timeout，该时间是节点从follower到成为candidate的时间，该时间是150到300毫秒之间的随机值。另一个是heartbeat timeout。 当某个节点经历完election timeout成为candidate后，开启新的一个选举周期，他向其他节点发起投票请求（Request Vote），如果接收到消息的节点在该周期内还没投过票则给这个candidate投票，然后节点重置他的election timeout。 当该candidate获得大部分的选票，则可以当选为leader。 leader就开始发送append entries给其他follower节点，这个消息会在内部指定的heartbeat timeout时间内发出，follower收到该信息则响应给leader。 这个选举周期会继续，直到某个follower没有收到心跳，并成为candidate。 如果某个选举周期内，有两个candidate同时获得相同多的选票，则会等待一个新的周期重新选举。 2.2. 同步 当选举过程结束，选出了leader，则leader需要把所有的变更同步的系统中的其他节点，该同步也是通过发送Append Entries的消息的方式。 首先一个客户端发送一个更新给leader，这个更新会添加到leader的日志中。 然后leader会在给follower的下次心跳探测中发送该更新。 一旦大多数follower收到这个更新并返回给leader，leader提交这个更新，然后返回给客户端。 2.3. 网络分区 当发生网络分区的时候，在不同分区的节点接收不到leader的心跳，则会开启一轮选举，形成不同leader的多个分区集群。 当客户端给不同leader的发送更新消息时，不同分区集群中的节点个数小于原先集群的一半时，更新不会被提交，而节点个数大于集群数一半时，更新会被提交。 当网络分区恢复后，被提交的更新会同步到其他的节点上，其他节点未提交的日志会被回滚并匹配新leader的日志，保证全局的数据是一致的。 参考： http://thesecretlivesofdata.com/raft/ https://raft.github.io/raft.pdf https://raft.github.io/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"etcd/etcd-setup-flags.html":{"url":"etcd/etcd-setup-flags.html","title":"Etcd启动配置参数","keywords":"","body":"1. Etcd配置参数1.1. member flags1.2. clustering flags1.3. proxy flags1.4. security flags1.5. logging flags1.6. unsafe flags1.7. profiling flags1.8. auth flags1.9. experimental flags1. Etcd配置参数 / # etcd --help usage: etcd [flags] start an etcd server etcd --version show the version of etcd etcd -h | --help show the help information about etcd etcd --config-file path to the server configuration file etcd gateway run the stateless pass-through etcd TCP connection forwarding proxy etcd grpc-proxy run the stateless etcd v3 gRPC L7 reverse proxy 1.1. member flags member flags: --name 'default' human-readable name for this member. --data-dir '${name}.etcd' path to the data directory. --wal-dir '' path to the dedicated wal directory. --snapshot-count '100000' number of committed transactions to trigger a snapshot to disk. --heartbeat-interval '100' time (in milliseconds) of a heartbeat interval. --election-timeout '1000' time (in milliseconds) for an election to timeout. See tuning documentation for details. --initial-election-tick-advance 'true' whether to fast-forward initial election ticks on boot for faster election. --listen-peer-urls 'http://localhost:2380' list of URLs to listen on for peer traffic. --listen-client-urls 'http://localhost:2379' list of URLs to listen on for client traffic. --max-snapshots '5' maximum number of snapshot files to retain (0 is unlimited). --max-wals '5' maximum number of wal files to retain (0 is unlimited). --cors '' comma-separated whitelist of origins for CORS (cross-origin resource sharing). --quota-backend-bytes '0' raise alarms when backend size exceeds the given quota (0 defaults to low space quota). --max-txn-ops '128' maximum number of operations permitted in a transaction. --max-request-bytes '1572864' maximum client request size in bytes the server will accept. --grpc-keepalive-min-time '5s' minimum duration interval that a client should wait before pinging server. --grpc-keepalive-interval '2h' frequency duration of server-to-client ping to check if a connection is alive (0 to disable). --grpc-keepalive-timeout '20s' additional duration of wait before closing a non-responsive connection (0 to disable). 1.2. clustering flags clustering flags: --initial-advertise-peer-urls 'http://localhost:2380' list of this member's peer URLs to advertise to the rest of the cluster. --initial-cluster 'default=http://localhost:2380' initial cluster configuration for bootstrapping. --initial-cluster-state 'new' initial cluster state ('new' or 'existing'). --initial-cluster-token 'etcd-cluster' initial cluster token for the etcd cluster during bootstrap. Specifying this can protect you from unintended cross-cluster interaction when running multiple clusters. --advertise-client-urls 'http://localhost:2379' list of this member's client URLs to advertise to the public. The client URLs advertised should be accessible to machines that talk to etcd cluster. etcd client libraries parse these URLs to connect to the cluster. --discovery '' discovery URL used to bootstrap the cluster. --discovery-fallback 'proxy' expected behavior ('exit' or 'proxy') when discovery services fails. \"proxy\" supports v2 API only. --discovery-proxy '' HTTP proxy to use for traffic to discovery service. --discovery-srv '' dns srv domain used to bootstrap the cluster. --strict-reconfig-check 'true' reject reconfiguration requests that would cause quorum loss. --auto-compaction-retention '0' auto compaction retention length. 0 means disable auto compaction. --auto-compaction-mode 'periodic' interpret 'auto-compaction-retention' one of: periodic|revision. 'periodic' for duration based retention, defaulting to hours if no time unit is provided (e.g. '5m'). 'revision' for revision number based retention. --enable-v2 'true' Accept etcd V2 client requests. 1.3. proxy flags proxy flags: \"proxy\" supports v2 API only. --proxy 'off' proxy mode setting ('off', 'readonly' or 'on'). --proxy-failure-wait 5000 time (in milliseconds) an endpoint will be held in a failed state. --proxy-refresh-interval 30000 time (in milliseconds) of the endpoints refresh interval. --proxy-dial-timeout 1000 time (in milliseconds) for a dial to timeout. --proxy-write-timeout 5000 time (in milliseconds) for a write to timeout. --proxy-read-timeout 0 time (in milliseconds) for a read to timeout. 1.4. security flags security flags: --ca-file '' [DEPRECATED] path to the client server TLS CA file. '-ca-file ca.crt' could be replaced by '-trusted-ca-file ca.crt -client-cert-auth' and etcd will perform the same. --cert-file '' path to the client server TLS cert file. --key-file '' path to the client server TLS key file. --client-cert-auth 'false' enable client cert authentication. --client-crl-file '' path to the client certificate revocation list file. --trusted-ca-file '' path to the client server TLS trusted CA cert file. --auto-tls 'false' client TLS using generated certificates. --peer-ca-file '' [DEPRECATED] path to the peer server TLS CA file. '-peer-ca-file ca.crt' could be replaced by '-peer-trusted-ca-file ca.crt -peer-client-cert-auth' and etcd will perform the same. --peer-cert-file '' path to the peer server TLS cert file. --peer-key-file '' path to the peer server TLS key file. --peer-client-cert-auth 'false' enable peer client cert authentication. --peer-trusted-ca-file '' path to the peer server TLS trusted CA file. --peer-auto-tls 'false' peer TLS using self-generated certificates if --peer-key-file and --peer-cert-file are not provided. --peer-crl-file '' path to the peer certificate revocation list file. 1.5. logging flags logging flags --debug 'false' enable debug-level logging for etcd. --log-package-levels '' specify a particular log level for each etcd package (eg: 'etcdmain=CRITICAL,etcdserver=DEBUG'). --log-output 'default' specify 'stdout' or 'stderr' to skip journald logging even when running under systemd. 1.6. unsafe flags unsafe flags: Please be CAUTIOUS when using unsafe flags because it will break the guarantees given by the consensus protocol. --force-new-cluster 'false' force to create a new one-member cluster. 1.7. profiling flags profiling flags: --enable-pprof 'false' Enable runtime profiling data via HTTP server. Address is at client URL + \"/debug/pprof/\" --metrics 'basic' Set level of detail for exported metrics, specify 'extensive' to include histogram metrics. --listen-metrics-urls '' List of URLs to listen on for metrics. 1.8. auth flags auth flags: --auth-token 'simple' Specify a v3 authentication token type and its options ('simple' or 'jwt'). 1.9. experimental flags experimental flags: --experimental-initial-corrupt-check 'false' enable to check data corruption before serving any client/peer traffic. --experimental-corrupt-check-time '0s' duration of time between cluster corruption check passes. --experimental-enable-v2v3 '' serve v2 requests through the v3 backend under a given prefix. Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"etcd/etcd-auth-and-security.html":{"url":"etcd/etcd-auth-and-security.html","title":"Etcd访问控制","keywords":"","body":"1. ETCD资源类型2. 权限资源3. ETCD访问控制3.1. 访问控制相关命令3.2. user相关命令3.2.1. 添加root用户并设置密码3.2.2. 添加非root用户并设置密码3.2.3. 查看当前所有用户3.2.4. 将用户添加到对应角色3.2.5. 查看用户拥有哪些角色3.3. role相关命令3.3.1. 添加角色3.3.2. 查看所有角色3.3.3. 给角色分配权限3.3.4. 查看角色所拥有的权限3.4. auth相关操作3.4.1. 开启认证4. 访问控制设置步骤5. 访问认证的API调用1. ETCD资源类型 There are three types of resources in etcd permission resources: users and roles in the user store key-value resources: key-value pairs in the key-value store settings resources: security settings, auth settings, and dynamic etcd cluster settings (election/heartbeat) 2. 权限资源 Users：user用来设置身份认证（user：passwd），一个用户可以拥有多个角色，每个角色被分配一定的权限（只读、只写、可读写），用户分为root用户和非root用户。 Roles：角色用来关联权限，角色主要三类：root角色。默认创建root用户时即创建了root角色，该角色拥有所有权限；guest角色，默认自动创建，主要用于非认证使用。普通角色，由root用户创建角色，并分配指定权限。 注意：如果没有指定任何验证方式，即没显示指定以什么用户进行访问，那么默认会设定为 guest 角色。默认情况下 guest 也是具有全局访问权限的。如果不希望未授权就获取或修改etcd的数据，则可收回guest角色的权限或删除该角色，etcdctl role revoke 。 Permissions:权限分为只读、只写、可读写三种权限，权限即对指定目录或key的读写权限。 3. ETCD访问控制 3.1. 访问控制相关命令 NAME: etcdctl - A simple command line client for etcd. USAGE: etcdctl [global options] command [command options] [arguments...] VERSION: 2.2.0 COMMANDS: user user add, grant and revoke subcommands role role add, grant and revoke subcommands auth overall auth controls GLOBAL OPTIONS: --peers, -C a comma-delimited list of machine addresses in the cluster (default: \"http://127.0.0.1:4001,http://127.0.0.1:2379\") --endpoint a comma-delimited list of machine addresses in the cluster (default: \"http://127.0.0.1:4001,http://127.0.0.1:2379\") --cert-file identify HTTPS client using this SSL certificate file --key-file identify HTTPS client using this SSL key file --ca-file verify certificates of HTTPS-enabled servers using this CA bundle --username, -u provide username[:password] and prompt if password is not supplied. --timeout '1s' connection timeout per request 3.2. user相关命令 [root@localhost etcd]# etcdctl user --help NAME: etcdctl user - user add, grant and revoke subcommands USAGE: etcdctl user command [command options] [arguments...] COMMANDS: add add a new user for the etcd cluster get get details for a user list list all current users remove remove a user for the etcd cluster grant grant roles to an etcd user revoke revoke roles for an etcd user passwd change password for a user help, h Shows a list of commands or help for one command OPTIONS: --help, -h show help 3.2.1. 添加root用户并设置密码 etcdctl --endpoints http://172.16.22.36:2379 user add root 3.2.2. 添加非root用户并设置密码 etcdctl --endpoints http://172.16.22.36:2379 --username root:123 user add huwh 3.2.3. 查看当前所有用户 etcdctl --endpoints http://172.16.22.36:2379 --username root:123 user list 3.2.4. 将用户添加到对应角色 etcdctl --endpoints http://172.16.22.36:2379 --username root:123 user grant --roles test1 phpor 3.2.5. 查看用户拥有哪些角色 etcdctl --endpoints http://172.16.22.36:2379 --username root:123 user get phpor 3.3. role相关命令 [root@localhost etcd]# etcdctl role --help NAME: etcdctl role - role add, grant and revoke subcommands USAGE: etcdctl role command [command options] [arguments...] COMMANDS: add add a new role for the etcd cluster get get details for a role list list all roles remove remove a role from the etcd cluster grant grant path matches to an etcd role revoke revoke path matches for an etcd role help, h Shows a list of commands or help for one command OPTIONS: --help, -h show help 3.3.1. 添加角色 etcdctl --endpoints http://172.16.22.36:2379 --username root:2379 role add test1 3.3.2. 查看所有角色 etcdctl --endpoints http://172.16.22.36:2379 --username root:123 role list 3.3.3. 给角色分配权限 [root@localhost etcd]# etcdctl role grant --help NAME: grant - grant path matches to an etcd role USAGE: command grant [command options] [arguments...] OPTIONS: --path Path granted for the role to access --read Grant read-only access --write Grant write-only access --readwrite Grant read-write access 1、只包含目录 etcdctl --endpoints http://172.16.22.36:2379 --username root:123 role grant --readwrite --path /test1 test1 2、包括目录和子目录或文件 etcdctl --endpoints http://172.16.22.36:2379 --username root:123 role grant --readwrite --path /test1/* test1 3.3.4. 查看角色所拥有的权限 etcdctl --endpoints http://172.16.22.36:2379 --username root:2379 role get test1 3.4. auth相关操作 [root@localhost etcd]# etcdctl auth --help NAME: etcdctl auth - overall auth controls USAGE: etcdctl auth command [command options] [arguments...] COMMANDS: enable enable auth access controls disable disable auth access controls help, h Shows a list of commands or help for one command OPTIONS: --help, -h show help 3.4.1. 开启认证 etcdctl --endpoints http://172.16.22.36:2379 auth enable 4. 访问控制设置步骤 顺序 步骤 命令 1 添加root用户 etcdctl --endpoints http://: user add root 2 开启认证 etcdctl --endpoints http://: auth enable 3 添加非root用户 etcdctl --endpoints http://: –username root: user add 4 添加角色 etcdctl --endpoints http://: –username root: role add 5 给角色授权（只读、只写、可读写） etcdctl --endpoints http://: –username root: role grant --readwrite --path 6 给用户分配角色（即分配了角色对应的权限） etcdctl --endpoints http://: –username root: user grant --roles 5. 访问认证的API调用 更多参考 https://coreos.com/etcd/docs/latest/v2/auth_api.html https://coreos.com/etcd/docs/latest/v2/authentication.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"etcd/etcdctl/_index.html":{"url":"etcd/etcdctl/_index.html","title":"etcdctl命令工具","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"etcd/etcdctl/etcdctl-v3.html":{"url":"etcd/etcdctl/etcdctl-v3.html","title":"etcdctl命令工具-V3","keywords":"","body":"1. etcdctl的安装2. etcdctl V33. etcdctl 常用命令3.1. 指定etcd集群3.2. 增删改查3.3. 集群状态3.4. 集群成员4. etcdctl get etcdctl的v3版本与v2版本使用命令有所不同，本文介绍etcdctl v3版本的命令工具的使用方式。 1. etcdctl的安装 etcdctl的二进制文件可以在 github.com/coreos/etcd/releases 选择对应的版本下载，例如可以执行以下install_etcdctl.sh的脚本，修改其中的版本信息。 #!/bin/bash ETCD_VER=v3.3.4 ETCD_DIR=etcd-download DOWNLOAD_URL=https://github.com/coreos/etcd/releases/download # Download mkdir ${ETCD_DIR} cd ${ETCD_DIR} wget ${DOWNLOAD_URL}/${ETCD_VER}/etcd-${ETCD_VER}-linux-amd64.tar.gz tar -xzvf etcd-${ETCD_VER}-linux-amd64.tar.gz # install cd etcd-${ETCD_VER}-linux-amd64 cp etcdctl /usr/local/bin/ 2. etcdctl V3 使用etcdctlv3的版本时，需设置环境变量ETCDCTL_API=3。 export ETCDCTL_API=3 # 或者在`/etc/profile`文件中添加环境变量 vi /etc/profile ... export ETCDCTL_API=3 ... source /etc/profile # 或者在命令执行前加 ETCDCTL_API=3 ETCDCTL_API=3 etcdctl --endpoints=$ENDPOINTS member list 查看当前etcdctl的版本信息etcdctl version。 [root@k8s-dbg-master-1 etcd]# etcdctl version etcdctl version: 3.3.4 API version: 3.3 更多命令帮助可以查询etcdctl —help。 [root@k8s-dbg-master-1 etcd]# etcdctl --help NAME: etcdctl - A simple command line client for etcd3. USAGE: etcdctl VERSION: 3.3.4 API VERSION: 3.3 COMMANDS: get Gets the key or a range of keys put Puts the given key into the store del Removes the specified key or range of keys [key, range_end) txn Txn processes all the requests in one transaction compaction Compacts the event history in etcd alarm disarm Disarms all alarms alarm list Lists all alarms defrag Defragments the storage of the etcd members with given endpoints endpoint health Checks the healthiness of endpoints specified in `--endpoints` flag endpoint status Prints out the status of endpoints specified in `--endpoints` flag endpoint hashkv Prints the KV history hash for each endpoint in --endpoints move-leader Transfers leadership to another etcd cluster member. watch Watches events stream on keys or prefixes version Prints the version of etcdctl lease grant Creates leases lease revoke Revokes leases lease timetolive Get lease information lease list List all active leases lease keep-alive Keeps leases alive (renew) member add Adds a member into the cluster member remove Removes a member from the cluster member update Updates a member in the cluster member list Lists all members in the cluster snapshot save Stores an etcd node backend snapshot to a given file snapshot restore Restores an etcd member snapshot to an etcd directory snapshot status Gets backend snapshot status of a given file make-mirror Makes a mirror at the destination etcd cluster migrate Migrates keys in a v2 store to a mvcc store lock Acquires a named lock elect Observes and participates in leader election auth enable Enables authentication auth disable Disables authentication user add Adds a new user user delete Deletes a user user get Gets detailed information of a user user list Lists all users user passwd Changes password of user user grant-role Grants a role to a user user revoke-role Revokes a role from a user role add Adds a new role role delete Deletes a role role get Gets detailed information of a role role list Lists all roles role grant-permission Grants a key to a role role revoke-permission Revokes a key from a role check perf Check the performance of the etcd cluster help Help about any command OPTIONS: --cacert=\"\" verify certificates of TLS-enabled secure servers using this CA bundle --cert=\"\" identify secure client using this TLS certificate file --command-timeout=5s timeout for short running command (excluding dial timeout) --debug[=false] enable client-side debug logging --dial-timeout=2s dial timeout for client connections -d, --discovery-srv=\"\" domain name to query for SRV records describing cluster endpoints --endpoints=[127.0.0.1:2379] gRPC endpoints --hex[=false] print byte strings as hex encoded strings --insecure-discovery[=true] accept insecure SRV records describing cluster endpoints --insecure-skip-tls-verify[=false] skip server certificate verification --insecure-transport[=true] disable transport security for client connections --keepalive-time=2s keepalive time for client connections --keepalive-timeout=6s keepalive timeout for client connections --key=\"\" identify secure client using this TLS key file --user=\"\" username[:password] for authentication (prompt if password is not supplied) -w, --write-out=\"simple\" set the output format (fields, json, protobuf, simple, table) 3. etcdctl 常用命令 3.1. 指定etcd集群 HOST_1=10.240.0.17 HOST_2=10.240.0.18 HOST_3=10.240.0.19 ENDPOINTS=$HOST_1:2379,$HOST_2:2379,$HOST_3:2379 etcdctl --endpoints=$ENDPOINTS member list 如果etcd设置了证书访问，则需要添加证书相关参数： ETCDCTL_API=3 etcdctl --endpoints=$ENDPOINTS --cacert= --cert= --key= 参数说明如下： --cacert=\"\" verify certificates of TLS-enabled secure servers using this CA bundle --cert=\"\" identify secure client using this TLS certificate file --key=\"\" identify secure client using this TLS key file --endpoints=[127.0.0.1:2379] gRPC endpoints 可以自定义alias命令 # alias 命令，避免每次需要输入证书参数 alias ectl='ETCDCTL_API=3 etcdctl --endpoints=$ENDPOINTS --cacert= --cert= --key=' # 直接使用别名执行命令 ectl 3.2. 增删改查 1、增 etcdctl --endpoints=$ENDPOINTS put foo \"Hello World!\" 2、查 etcdctl --endpoints=$ENDPOINTS get foo etcdctl --endpoints=$ENDPOINTS --write-out=\"json\" get foo 基于相同前缀查找 etcdctl --endpoints=$ENDPOINTS put web1 value1 etcdctl --endpoints=$ENDPOINTS put web2 value2 etcdctl --endpoints=$ENDPOINTS put web3 value3 etcdctl --endpoints=$ENDPOINTS get web --prefix 列出所有的key etcdctl --endpoints=$ENDPOINTS get / --prefix --keys-only 3、删 etcdctl --endpoints=$ENDPOINTS put key myvalue etcdctl --endpoints=$ENDPOINTS del key etcdctl --endpoints=$ENDPOINTS put k1 value1 etcdctl --endpoints=$ENDPOINTS put k2 value2 etcdctl --endpoints=$ENDPOINTS del k --prefix 3.3. 集群状态 集群状态主要是etcdctl endpoint status 和etcdctl endpoint health两条命令。 etcdctl --write-out=table --endpoints=$ENDPOINTS endpoint status +------------------+------------------+---------+---------+-----------+-----------+------------+ | ENDPOINT | ID | VERSION | DB SIZE | IS LEADER | RAFT TERM | RAFT INDEX | +------------------+------------------+---------+---------+-----------+-----------+------------+ | 10.240.0.17:2379 | 4917a7ab173fabe7 | 3.0.0 | 45 kB | true | 4 | 16726 | | 10.240.0.18:2379 | 59796ba9cd1bcd72 | 3.0.0 | 45 kB | false | 4 | 16726 | | 10.240.0.19:2379 | 94df724b66343e6c | 3.0.0 | 45 kB | false | 4 | 16726 | +------------------+------------------+---------+---------+-----------+-----------+------------+ etcdctl --endpoints=$ENDPOINTS endpoint health 10.240.0.17:2379 is healthy: successfully committed proposal: took = 3.345431ms 10.240.0.19:2379 is healthy: successfully committed proposal: took = 3.767967ms 10.240.0.18:2379 is healthy: successfully committed proposal: took = 4.025451ms 3.4. 集群成员 跟集群成员相关的命令如下： member add Adds a member into the cluster member remove Removes a member from the cluster member update Updates a member in the cluster member list Lists all members in the cluster 例如 etcdctl member list列出集群成员的命令。 etcdctl --endpoints=http://172.16.5.4:12379 member list -w table +-----------------+---------+-------+------------------------+-----------------------------------------------+ | ID | STATUS | NAME | PEER ADDRS | CLIENT ADDRS | +-----------------+---------+-------+------------------------+-----------------------------------------------+ | c856d92a82ba66a | started | etcd0 | http://172.16.5.4:2380 | http://172.16.5.4:2379,http://172.16.5.4:4001 | +-----------------+---------+-------+------------------------+-----------------------------------------------+ 4. etcdctl get 使用etcdctl {command} --help可以查看具体命令的帮助信息。 # etcdctl get --help NAME: get - Gets the key or a range of keys USAGE: etcdctl get [options] [range_end] OPTIONS: --consistency=\"l\" Linearizable(l) or Serializable(s) --from-key[=false] Get keys that are greater than or equal to the given key using byte compare --keys-only[=false] Get only the keys --limit=0 Maximum number of results --order=\"\" Order of results; ASCEND or DESCEND (ASCEND by default) --prefix[=false] Get keys with matching prefix --print-value-only[=false] Only write values when using the \"simple\" output format --rev=0 Specify the kv revision --sort-by=\"\" Sort target; CREATE, KEY, MODIFY, VALUE, or VERSION GLOBAL OPTIONS: --cacert=\"\" verify certificates of TLS-enabled secure servers using this CA bundle --cert=\"\" identify secure client using this TLS certificate file --command-timeout=5s timeout for short running command (excluding dial timeout) --debug[=false] enable client-side debug logging --dial-timeout=2s dial timeout for client connections --endpoints=[127.0.0.1:2379] gRPC endpoints --hex[=false] print byte strings as hex encoded strings --insecure-skip-tls-verify[=false] skip server certificate verification --insecure-transport[=true] disable transport security for client connections --key=\"\" identify secure client using this TLS key file --user=\"\" username[:password] for authentication (prompt if password is not supplied) -w, --write-out=\"simple\" set the output format (fields, json, protobuf, simple, table) 文章参考： https://coreos.com/etcd/docs/latest/demo.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"etcd/etcdctl/etcdctl-v2.html":{"url":"etcd/etcdctl/etcdctl-v2.html","title":"etcdctl命令工具-V2","keywords":"","body":"1. etcdctl介绍2. Etcd常用命令2.1. 数据库操作命令2.1.1. 对象为键值set[增:无论是否存在]:etcdctl set key valuemk[增:必须不存在]:etcdctl mk key valuerm[删]:etcdctl rm keyupdate[改]:etcdctl update key valueget[查]:etcdctl get key2.1.2. 对象为目录setdir[增:无论是否存在]:etcdctl setdir dirmkdir[增:必须不存在]: etcdctl mkdir dirrmdir[删]:etcdctl rmdir dirupdatedir[改]:etcdctl updatedir dirls[查]:etcdclt ls2.2. 非数据库操作命令backup[备份 etcd 的数据]watch[监测一个键值的变化，一旦键值发生更新，就会输出最新的值并退出]exec-watch[监测一个键值的变化，一旦键值发生更新，就执行给定命令]member[通过 list、add、remove、update 命令列出、添加、删除 、更新etcd 实例到 etcd 集群中]etcdctl cluster-health[检查集群健康状态]2.3. 常用配置参数1. etcdctl介绍 etcdctl是一个命令行的客户端，它提供了一下简洁的命令，可理解为命令工具集，可以方便我们在对服务进行测试或者手动修改数据库内容。etcdctl与其他xxxctl的命令原理及操作类似（例如kubectl，systemctl）。 用法：etcdctl [global options] command [command options][args...] 2. Etcd常用命令 2.1. 数据库操作命令 etcd 在键的组织上采用了层次化的空间结构（类似于文件系统中目录的概念），数据库操作围绕对键值和目录的 CRUD [增删改查]（符合 REST 风格的一套操作：Create, Read, Update, Delete）完整生命周期的管理。 具体的命令选项参数可以通过 etcdctl command --help来获取相关帮助。 2.1.1. 对象为键值 set[增:无论是否存在]:etcdctl set key value mk[增:必须不存在]:etcdctl mk key value rm[删]:etcdctl rm key update[改]:etcdctl update key value get[查]:etcdctl get key 2.1.2. 对象为目录 setdir[增:无论是否存在]:etcdctl setdir dir mkdir[增:必须不存在]: etcdctl mkdir dir rmdir[删]:etcdctl rmdir dir updatedir[改]:etcdctl updatedir dir ls[查]:etcdclt ls 2.2. 非数据库操作命令 backup[备份 etcd 的数据] etcdctl backup watch[监测一个键值的变化，一旦键值发生更新，就会输出最新的值并退出] etcdctl watch key exec-watch[监测一个键值的变化，一旦键值发生更新，就执行给定命令] etcdctl exec-watch key --sh -c \"ls\" member[通过 list、add、remove、update 命令列出、添加、删除 、更新etcd 实例到 etcd 集群中] etcdctl member list；etcdctl member add 实例；etcdctl member remove 实例；etcdctl member update 实例。 etcdctl cluster-health[检查集群健康状态] 2.3. 常用配置参数 设置配置文件，默认为/etc/etcd/etcd.conf。 配置参数 参数说明 配置参数 参数说明 -name 节点名称 -data-dir 保存日志和快照的目录，默认为当前工作目录，指定节点的数据存储目录 -addr 公布的ip地址和端口。 默认为127.0.0.1:2379 -bind-addr 用于客户端连接的监听地址，默认为-addr配置 -peers 集群成员逗号分隔的列表，例如 127.0.0.1:2380,127.0.0.1:2381 -peer-addr 集群服务通讯的公布的IP地址，默认为 127.0.0.1:2380. -peer-bind-addr 集群服务通讯的监听地址，默认为-peer-addr配置 -wal-dir 指定节点的was文件的存储目录，若指定了该参数，wal文件会和其他数据文件分开存储 -listen-client-urls -listen-peer-urls 监听URL，用于与其他节点通讯 -initial-advertise-peer-urls 告知集群其他节点url. -advertise-client-urls 告知客户端url, 也就是服务的url -initial-cluster-token 集群的ID -initial-cluster 集群中所有节点 -initial-cluster-state -initial-cluster-state=new 表示从无到有搭建etcd集群 -discovery-srv 用于DNS动态服务发现，指定DNS SRV域名 -discovery 用于etcd动态发现，指定etcd发现服务的URL [https://discovery.etcd.io/],用环境变量表示 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"etcd/k8s-etcd-data.html":{"url":"etcd/k8s-etcd-data.html","title":"Etcd中的k8s数据","keywords":"","body":"1. 读取数据key2. 集群数据2.1. node3. k8s对象数据3.1. namespace3.2. namespace级别对象4. 读取数据value5. 注意事项6. 快捷命令6.1. etcdhelper的使用7. RBAC1. 读取数据key 使用以下命令列出所有的key。 ETCDCTL_API=3 etcdctl --endpoints=:2379,:2379,:2379 --cacert=/etc/kubernetes/pki/etcd/ca.crt --key=/etc/kubernetes/pki/apiserver-etcd-client.key --cert=/etc/kubernetes/pki/apiserver-etcd-client.crt get / --prefix --keys-only 参数说明： --cacert=\"\" verify certificates of TLS-enabled secure servers using this CA bundle --cert=\"\" identify secure client using this TLS certificate file --key=\"\" identify secure client using this TLS key file --endpoints=[127.0.0.1:2379] gRPC endpoints 可以使用alias来重命名etcdctl一串的命令 alias ectl='ETCDCTL_API=3 etcdctl --endpoints=:2379,:2379,:2379 --cacert=/etc/kubernetes/pki/etcd/ca.crt --key=/etc/kubernetes/pki/apiserver-etcd-client.key --cert=/etc/kubernetes/pki/apiserver-etcd-client.crt' 2. 集群数据 2.1. node /registry/minions/ /registry/minions/ /registry/minions/ 其他信息： /registry/leases/kube-node-lease/ /registry/leases/kube-node-lease/ /registry/leases/kube-node-lease/ /registry/masterleases/ /registry/masterleases/ 3. k8s对象数据 k8s对象数据的格式 3.1. namespace /registry/namespaces/default /registry/namespaces/game /registry/namespaces/kube-node-lease /registry/namespaces/kube-public /registry/namespaces/kube-system 3.2. namespace级别对象 /registry/{resource}/{namespace}/{resource_name} 以下以常见k8s对象为例： # deployment /registry/deployments/default/game-2048 /registry/deployments/kube-system/prometheus-operator # replicasets /registry/replicasets/default/game-2048-c7d589ccf # pod /registry/pods/default/game-2048-c7d589ccf-8lsbw # statefulsets /registry/statefulsets/kube-system/prometheus-k8s # daemonsets /registry/daemonsets/kube-system/kube-proxy # secrets /registry/secrets/default/default-token-tbfmb # serviceaccounts /registry/serviceaccounts/default/default service # service /registry/services/specs/default/game-2048 # endpoints /registry/services/endpoints/default/game-2048 4. 读取数据value 由于k8s默认etcd中的数据是通过protobuf格式存储，因此看到的key和value的值是一串字符串。 alias ectl='ETCDCTL_API=3 etcdctl --endpoints=:2379,:2379,:2379 --cacert=/etc/kubernetes/pki/etcd/ca.crt --key=/etc/kubernetes/pki/apiserver-etcd-client.key --cert=/etc/kubernetes/pki/apiserver-etcd-client.crt' # ectl get /registry/namespaces/test -w json |jq { \"header\": { \"cluster_id\": 12113422651334595000, \"member_id\": 8381627376898157000, \"revision\": 12321629, \"raft_term\": 20 }, \"kvs\": [ { \"key\": \"L3JlZ2lzdHJ5L25hbWVzcGFjZXMvdGVzdA==\", \"create_revision\": 11670741, \"mod_revision\": 11670741, \"version\": 1, \"value\": \"azhzAAoPCgJ2MRIJTmFtZXNwYWNlElwKQgoEdGVzdBIAGgAiACokYWM1YmJjOTQtNTkxZi0xMWVhLWJiOTQtNmM5MmJmM2I3NmI1MgA4AEIICJuf3fIFEAB6ABIMCgprdWJlcm5ldGVzGggKBkFjdGl2ZRoAIgA=\" } ], \"count\": 1 } 其中key可以通过base64解码出来 echo \"L3JlZ2lzdHJ5L25hbWVzcGFjZXMvdGVzdA==\" | base64 --decode # output /registry/namespaces/test value是值可以通过安装etcdhelper工具解析出来。 alias ehelper='etcdhelper -key /etc/kubernetes/pki/apiserver-etcd-client.key -cert /etc/kubernetes/pki/apiserver-etcd-client.crt -cacert /etc/kubernetes/pki/etcd/ca.crt' # ehelper get /registry/namespaces/test /v1, Kind=Namespace { \"kind\": \"Namespace\", \"apiVersion\": \"v1\", \"metadata\": { \"name\": \"test\", \"uid\": \"ac5bbc94-591f-11ea-bb94-6c92bf3b76b5\", \"creationTimestamp\": \"2020-02-27T05:11:55Z\" }, \"spec\": { \"finalizers\": [ \"kubernetes\" ] }, \"status\": { \"phase\": \"Active\" } } 5. 注意事项 由于k8s的etcd数据为了性能考虑，默认通过protobuf格式存储，不要通过手动的方式去修改或添加k8s数据。 不推荐使用json格式存储etcd数据，如果需要json格式，可以使用--storage-media-type=application/json参数存储，参考：https://github.com/kubernetes/kubernetes/issues/44670 6. 快捷命令 由于etcdctl的命令需要添加很多认证参数和endpoints的参数，因此可以使用别名的方式来简化命令。 # etcdctl alias ectl='ETCDCTL_API=3 etcdctl --endpoints=:2379,:2379,:2379 --cacert=/etc/kubernetes/pki/etcd/ca.crt --key=/etc/kubernetes/pki/apiserver-etcd-client.key --cert=/etc/kubernetes/pki/apiserver-etcd-client.crt' # etcdhelper alias ehelper='etcdhelper -key /etc/kubernetes/pki/apiserver-etcd-client.key -cert /etc/kubernetes/pki/apiserver-etcd-client.crt -cacert /etc/kubernetes/pki/etcd/ca.crt' 6.1. etcdhelper的使用 etcdhelper文档参考：https://github.com/openshift/origin/tree/master/tools/etcdhelper # 必要的认证参数 -key - points to master.etcd-client.key -cert - points to master.etcd-client.crt -cacert - points to ca.crt # 命令操作参数 ls - list all keys starting with prefix get - get the specific value of a key dump - dump the entire contents of the etcd 示例 $ ehelper ls /registry/leases/ /registry/leases/kube-node-lease/ /registry/leases/kube-node-lease/ /registry/leases/kube-node-lease/ $ ehelper get 7. RBAC 附RBAC相关的key。 clusterrolebindings /registry/clusterrolebindings/cluster-admin /registry/clusterrolebindings/flannel /registry/clusterrolebindings/galaxy /registry/clusterrolebindings/helm /registry/clusterrolebindings/kube-state-metrics /registry/clusterrolebindings/kubeadm:kubelet-bootstrap /registry/clusterrolebindings/kubeadm:node-autoapprove-bootstrap /registry/clusterrolebindings/kubeadm:node-autoapprove-certificate-rotation /registry/clusterrolebindings/kubeadm:node-proxier /registry/clusterrolebindings/lbcf-controller /registry/clusterrolebindings/prometheus-k8s /registry/clusterrolebindings/prometheus-operator /registry/clusterrolebindings/system:aws-cloud-provider /registry/clusterrolebindings/system:basic-user /registry/clusterrolebindings/system:controller:attachdetach-controller /registry/clusterrolebindings/system:controller:certificate-controller /registry/clusterrolebindings/system:controller:clusterrole-aggregation-controller /registry/clusterrolebindings/system:controller:cronjob-controller /registry/clusterrolebindings/system:controller:daemon-set-controller /registry/clusterrolebindings/system:controller:deployment-controller /registry/clusterrolebindings/system:controller:disruption-controller /registry/clusterrolebindings/system:controller:endpoint-controller /registry/clusterrolebindings/system:controller:expand-controller /registry/clusterrolebindings/system:controller:generic-garbage-collector /registry/clusterrolebindings/system:controller:horizontal-pod-autoscaler /registry/clusterrolebindings/system:controller:job-controller /registry/clusterrolebindings/system:controller:namespace-controller /registry/clusterrolebindings/system:controller:node-controller /registry/clusterrolebindings/system:controller:persistent-volume-binder /registry/clusterrolebindings/system:controller:pod-garbage-collector /registry/clusterrolebindings/system:controller:pv-protection-controller /registry/clusterrolebindings/system:controller:pvc-protection-controller /registry/clusterrolebindings/system:controller:replicaset-controller /registry/clusterrolebindings/system:controller:replication-controller /registry/clusterrolebindings/system:controller:resourcequota-controller /registry/clusterrolebindings/system:controller:route-controller /registry/clusterrolebindings/system:controller:service-account-controller /registry/clusterrolebindings/system:controller:service-controller /registry/clusterrolebindings/system:controller:statefulset-controller /registry/clusterrolebindings/system:controller:ttl-controller /registry/clusterrolebindings/system:coredns /registry/clusterrolebindings/system:discovery /registry/clusterrolebindings/system:kube-controller-manager /registry/clusterrolebindings/system:kube-dns /registry/clusterrolebindings/system:kube-scheduler /registry/clusterrolebindings/system:node /registry/clusterrolebindings/system:node-proxier /registry/clusterrolebindings/system:public-info-viewer /registry/clusterrolebindings/system:volume-scheduler clusterroles /registry/clusterroles/admin /registry/clusterroles/cluster-admin /registry/clusterroles/edit /registry/clusterroles/flannel /registry/clusterroles/kube-state-metrics /registry/clusterroles/lbcf-controller /registry/clusterroles/prometheus-k8s /registry/clusterroles/prometheus-operator /registry/clusterroles/system:aggregate-to-admin /registry/clusterroles/system:aggregate-to-edit /registry/clusterroles/system:aggregate-to-view /registry/clusterroles/system:auth-delegator /registry/clusterroles/system:aws-cloud-provider /registry/clusterroles/system:basic-user /registry/clusterroles/system:certificates.k8s.io:certificatesigningrequests:nodeclient /registry/clusterroles/system:certificates.k8s.io:certificatesigningrequests:selfnodeclient /registry/clusterroles/system:controller:attachdetach-controller /registry/clusterroles/system:controller:certificate-controller /registry/clusterroles/system:controller:clusterrole-aggregation-controller /registry/clusterroles/system:controller:cronjob-controller /registry/clusterroles/system:controller:daemon-set-controller /registry/clusterroles/system:controller:deployment-controller /registry/clusterroles/system:controller:disruption-controller /registry/clusterroles/system:controller:endpoint-controller /registry/clusterroles/system:controller:expand-controller /registry/clusterroles/system:controller:generic-garbage-collector /registry/clusterroles/system:controller:horizontal-pod-autoscaler /registry/clusterroles/system:controller:job-controller /registry/clusterroles/system:controller:namespace-controller /registry/clusterroles/system:controller:node-controller /registry/clusterroles/system:controller:persistent-volume-binder /registry/clusterroles/system:controller:pod-garbage-collector /registry/clusterroles/system:controller:pv-protection-controller /registry/clusterroles/system:controller:pvc-protection-controller /registry/clusterroles/system:controller:replicaset-controller /registry/clusterroles/system:controller:replication-controller /registry/clusterroles/system:controller:resourcequota-controller /registry/clusterroles/system:controller:route-controller /registry/clusterroles/system:controller:service-account-controller /registry/clusterroles/system:controller:service-controller /registry/clusterroles/system:controller:statefulset-controller /registry/clusterroles/system:controller:ttl-controller /registry/clusterroles/system:coredns /registry/clusterroles/system:csi-external-attacher /registry/clusterroles/system:csi-external-provisioner /registry/clusterroles/system:discovery /registry/clusterroles/system:heapster /registry/clusterroles/system:kube-aggregator /registry/clusterroles/system:kube-controller-manager /registry/clusterroles/system:kube-dns /registry/clusterroles/system:kube-scheduler /registry/clusterroles/system:kubelet-api-admin /registry/clusterroles/system:node /registry/clusterroles/system:node-bootstrapper /registry/clusterroles/system:node-problem-detector /registry/clusterroles/system:node-proxier /registry/clusterroles/system:persistent-volume-provisioner /registry/clusterroles/system:public-info-viewer /registry/clusterroles/system:volume-scheduler /registry/clusterroles/view rolebindings /registry/rolebindings/kube-public/kubeadm:bootstrap-signer-clusterinfo /registry/rolebindings/kube-public/system:controller:bootstrap-signer /registry/rolebindings/kube-system/kube-proxy /registry/rolebindings/kube-system/kube-state-metrics /registry/rolebindings/kube-system/kubeadm:kubeadm-certs /registry/rolebindings/kube-system/kubeadm:kubelet-config-1.14 /registry/rolebindings/kube-system/kubeadm:nodes-kubeadm-config /registry/rolebindings/kube-system/system::extension-apiserver-authentication-reader /registry/rolebindings/kube-system/system::leader-locking-kube-controller-manager /registry/rolebindings/kube-system/system::leader-locking-kube-scheduler /registry/rolebindings/kube-system/system:controller:bootstrap-signer /registry/rolebindings/kube-system/system:controller:cloud-provider /registry/rolebindings/kube-system/system:controller:token-cleaner roles /registry/roles/kube-public/kubeadm:bootstrap-signer-clusterinfo /registry/roles/kube-public/system:controller:bootstrap-signer /registry/roles/kube-system/extension-apiserver-authentication-reader /registry/roles/kube-system/kube-proxy /registry/roles/kube-system/kube-state-metrics-resizer /registry/roles/kube-system/kubeadm:kubeadm-certs /registry/roles/kube-system/kubeadm:kubelet-config-1.14 /registry/roles/kube-system/kubeadm:nodes-kubeadm-config /registry/roles/kube-system/system::leader-locking-kube-controller-manager /registry/roles/kube-system/system::leader-locking-kube-scheduler /registry/roles/kube-system/system:controller:bootstrap-signer /registry/roles/kube-system/system:controller:cloud-provider /registry/roles/kube-system/system:controller:token-cleaner 参考： https://github.com/etcd-io/etcd/tree/master/etcdctl https://github.com/openshift/origin/tree/master/tools/etcdhelper using-etcdctl-to-access-kubernetes-data 如何读取Kubernetes存储在etcd上的数据 https://github.com/kubernetes/kubernetes/issues/44670 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"etcd/etcd-operator-usage.html":{"url":"etcd/etcd-operator-usage.html","title":"Etcd-Operator的使用","keywords":"","body":"1. 部署RBAC1.1. create_role.sh 脚本1.2. cluster-role-binding-template.yaml1.3. cluster-role-template.yaml2. 部署etcd-operator3. 部署etcd集群4. 访问etcd集群5. 销毁etcd-operator 本文主要介绍etcd-operator的部署及使用 1. 部署RBAC 下载create_role.sh、cluster-role-binding-template.yaml、cluster-role-template.yaml 例如： |-- cluster-role-binding-template.yaml |-- cluster-role-template.yaml |-- create_role.sh # 部署rbac kubectl create ns operator bash create_role.sh --namespace=operator # namespace与etcd-operator的ns一致 示例： bash create_role.sh --namespace=operator + ROLE_NAME=etcd-operator + ROLE_BINDING_NAME=etcd-operator + NAMESPACE=default + for i in '\"$@\"' + case $i in + NAMESPACE=operator + echo 'Creating role with ROLE_NAME=etcd-operator, NAMESPACE=operator' Creating role with ROLE_NAME=etcd-operator, NAMESPACE=operator + sed -e 's//etcd-operator/g' -e 's//operator/g' cluster-role-template.yaml + kubectl create -f - clusterrole.rbac.authorization.k8s.io/etcd-operator created + echo 'Creating role binding with ROLE_NAME=etcd-operator, ROLE_BINDING_NAME=etcd-operator, NAMESPACE=operator' Creating role binding with ROLE_NAME=etcd-operator, ROLE_BINDING_NAME=etcd-operator, NAMESPACE=operator + sed -e 's//etcd-operator/g' -e 's//etcd-operator/g' -e 's//operator/g' cluster-role-binding-template.yaml + kubectl create -f - clusterrolebinding.rbac.authorization.k8s.io/etcd-operator created 1.1. create_role.sh 脚本 create_role.sh有三个入参，可以指定--namespace参数，该参数与etcd-operator部署的namespace应一致。默认为default。 #!/usr/bin/env bash set -o errexit set -o nounset set -o pipefail ETCD_OPERATOR_ROOT=$(dirname \"${BASH_SOURCE}\")/../.. print_usage() { echo \"$(basename \"$0\") - Create Kubernetes RBAC role and role bindings for etcd-operator Usage: $(basename \"$0\") [options...] Options: --role-name=STRING Name of ClusterRole to create (default=\\\"etcd-operator\\\", environment variable: ROLE_NAME) --role-binding-name=STRING Name of ClusterRoleBinding to create (default=\\\"etcd-operator\\\", environment variable: ROLE_BINDING_NAME) --namespace=STRING namespace to create role and role binding in. Must already exist. (default=\\\"default\\\", environment variable: NAMESPACE) \" >&2 } ROLE_NAME=\"${ROLE_NAME:-etcd-operator}\" ROLE_BINDING_NAME=\"${ROLE_BINDING_NAME:-etcd-operator}\" NAMESPACE=\"${NAMESPACE:-default}\" for i in \"$@\" do case $i in --role-name=*) ROLE_NAME=\"${i#*=}\" ;; --role-binding-name=*) ROLE_BINDING_NAME=\"${i#*=}\" ;; --namespace=*) NAMESPACE=\"${i#*=}\" ;; -h|--help) print_usage exit 0 ;; *) print_usage exit 1 ;; esac done echo \"Creating role with ROLE_NAME=${ROLE_NAME}, NAMESPACE=${NAMESPACE}\" sed -e \"s//${ROLE_NAME}/g\" \\ -e \"s//${NAMESPACE}/g\" \\ \"cluster-role-template.yaml\" | \\ kubectl create -f - echo \"Creating role binding with ROLE_NAME=${ROLE_NAME}, ROLE_BINDING_NAME=${ROLE_BINDING_NAME}, NAMESPACE=${NAMESPACE}\" sed -e \"s//${ROLE_NAME}/g\" \\ -e \"s//${ROLE_BINDING_NAME}/g\" \\ -e \"s//${NAMESPACE}/g\" \\ \"cluster-role-binding-template.yaml\" | \\ kubectl create -f - 1.2. cluster-role-binding-template.yaml apiVersion: rbac.authorization.k8s.io/v1beta1 kind: ClusterRoleBinding metadata: name: roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: subjects: - kind: ServiceAccount name: default namespace: 1.3. cluster-role-template.yaml apiVersion: rbac.authorization.k8s.io/v1beta1 kind: ClusterRole metadata: name: rules: - apiGroups: - etcd.database.coreos.com resources: - etcdclusters - etcdbackups - etcdrestores verbs: - \"*\" - apiGroups: - apiextensions.k8s.io resources: - customresourcedefinitions verbs: - \"*\" - apiGroups: - \"\" resources: - pods - services - endpoints - persistentvolumeclaims - events verbs: - \"*\" - apiGroups: - apps resources: - deployments verbs: - \"*\" # The following permissions can be removed if not using S3 backup and TLS - apiGroups: - \"\" resources: - secrets verbs: - get 2. 部署etcd-operator kubectl create -f etcd-operator.yaml etcd-operator.yaml如下： apiVersion: apps/v1 kind: Deployment metadata: name: etcd-operator namespace: operator # 与rbac指定的ns一致 labels: app: etcd-operator spec: replicas: 1 selector: matchLabels: app: etcd-operator template: metadata: labels: app: etcd-operator spec: containers: - name: etcd-operator image: registry.cn-shenzhen.aliyuncs.com/huweihuang/etcd-operator:v0.9.4 command: - etcd-operator # Uncomment to act for resources in all namespaces. More information in doc/user/clusterwide.md - -cluster-wide env: - name: MY_POD_NAMESPACE valueFrom: fieldRef: fieldPath: metadata.namespace - name: MY_POD_NAME valueFrom: fieldRef: fieldPath: metadata.name 查看CRD #kubectl get customresourcedefinitions NAME CREATED AT etcdclusters.etcd.database.coreos.com 2020-08-01T13:02:18Z 查看etcd-operator的日志是否OK。 k logs -f etcd-operator-545df8d445-qpf6n -n operator time=\"2020-08-01T13:02:18Z\" level=info msg=\"etcd-operator Version: 0.9.4\" time=\"2020-08-01T13:02:18Z\" level=info msg=\"Git SHA: c8a1c64\" time=\"2020-08-01T13:02:18Z\" level=info msg=\"Go Version: go1.11.5\" time=\"2020-08-01T13:02:18Z\" level=info msg=\"Go OS/Arch: linux/amd64\" time=\"2020-08-01T13:02:18Z\" level=info msg=\"Event(v1.ObjectReference{Kind:\\\"Endpoints\\\", Namespace:\\\"operator\\\", Name:\\\"etcd-operator\\\", UID:\\\"7de38cff-1b7b-4bf2-9837-473fa66c9366\\\", APIVersion:\\\"v1\\\", ResourceVersion:\\\"41195930\\\", FieldPath:\\\"\\\"}): type: 'Normal' reason: 'LeaderElection' etcd-operator-545df8d445-qpf6n became leader\" 以上内容表示etcd-operator运行正常。 3. 部署etcd集群 kubectl create -f etcd-cluster.yaml 当开启clusterwide则etcd集群与etcd-operator的ns可不同。 etcd-cluster.yaml apiVersion: \"etcd.database.coreos.com/v1beta2\" kind: \"EtcdCluster\" metadata: name: \"default-etcd-cluster\" ## Adding this annotation make this cluster managed by clusterwide operators ## namespaced operators ignore it annotations: etcd.database.coreos.com/scope: clusterwide namespace: etcd # 此处的ns表示etcd集群部署在哪个ns下 spec: size: 3 version: \"v3.3.18\" repository: registry.cn-shenzhen.aliyuncs.com/huweihuang/etcd pod: busyboxImage: registry.cn-shenzhen.aliyuncs.com/huweihuang/busybox:1.28.0-glibc 查看集群部署结果 $ kgpo -n etcd NAME READY STATUS RESTARTS AGE default-etcd-cluster-b6phnpf8z8 1/1 Running 0 3m3s default-etcd-cluster-hhgq4sbtgr 1/1 Running 0 109s default-etcd-cluster-ttfh5fj92b 1/1 Running 0 2m29s 4. 访问etcd集群 查看service $ kgsvc -n etcd NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE default-etcd-cluster ClusterIP None 2379/TCP,2380/TCP 5m37s default-etcd-cluster-client ClusterIP 192.168.255.244 2379/TCP 5m37s 使用service地址访问 # 查看集群健康状态 $ ETCDCTL_API=3 etcdctl --endpoints 192.168.255.244:2379 endpoint health 192.168.255.244:2379 is healthy: successfully committed proposal: took = 1.96126ms # 写数据 $ ETCDCTL_API=3 etcdctl --endpoints 192.168.255.244:2379 put foo bar OK # 读数据 $ ETCDCTL_API=3 etcdctl --endpoints 192.168.255.244:2379 get foo foo bar 5. 销毁etcd-operator kubectl delete -f example/deployment.yaml kubectl delete endpoints etcd-operator kubectl delete crd etcdclusters.etcd.database.coreos.com kubectl delete clusterrole etcd-operator kubectl delete clusterrolebinding etcd-operator 参考： https://github.com/coreos/etcd-operator https://github.com/coreos/etcd-operator/blob/master/doc/user/install_guide.md https://github.com/coreos/etcd-operator/blob/master/doc/user/client_service.md https://github.com/coreos/etcd-operator/blob/master/doc/user/spec_examples.md https://github.com/coreos/etcd-operator/blob/master/doc/user/cluster_tls.md Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"multi-cluster/k8s-multi-cluster-thinking.html":{"url":"multi-cluster/k8s-multi-cluster-thinking.html","title":"k8s多集群管理的思考","keywords":"","body":"k8s多集群的思考1. 为什么需要多集群2. 多集群部署需要解决哪些问题2.1. 多集群工作负载的分发2.2. 跨集群自动迁移与调度3. 目前的多集群方案3.1. Kubefed[Federation v2]3.2. virtual kubelet3.3. Karmadak8s多集群的思考 1. 为什么需要多集群 1、k8s单集群的承载能力有限。 Kubernetes v1.21 支持的最大节点数为 5000。 更具体地说，Kubernetes旨在适应满足以下所有标准的配置： 每个节点的 Pod 数量不超过 100 节点数不超过 5000 Pod 总数不超过 150000 容器总数不超过 300000 参考：https://kubernetes.io/zh/docs/setup/best-practices/cluster-large/ 且当节点数量较大时，会出现调度延迟，etcd读写延迟，apiserver负载高等问题，影响服务的正常创建。 2、分散集群服务风险。 全部服务都放在一个k8s集群中，当该集群出现异常，短期无法恢复的情况下，则影响全部服务和影响部署。为了避免机房等故障导致单集群异常，建议将k8s的master在分散在延迟较低的不同可用区部署，且在不同region部署多个k8s集群来进行集群级别的容灾。 3、当前混合云的使用方式和架构 当前部分公司会存在自建机房+不同云厂商的公有云从而来实现混部云的运营模式，那么自然会引入多集群管理的问题。 2. 多集群部署需要解决哪些问题 目标：让用户像使用单集群一样来使用多集群。 扩展集群的边界，服务的边界从单台物理机多个进程，发展到通过k8s集群来管理多台的物理机，再发展到管理多个的k8s集群。服务的边界从物理机发展到集群。 而多集群管理需要解决以下问题： 多集群服务的分发部署（deployment、daemonset等） 跨集群自动迁移与调度（当某个集群异常，服务可以在其他集群自动部署） 多集群服务发现，网络通信及负载均衡（service，ingress等） 而多集群服务的网络通信可以由Service mesh等来解决，本文不做重点讨论。 以上几个问题，可以先从k8s管理节点的思维进行分析 物理机视角 单集群视角 多集群视角 进程的边界 物理机 k8s集群 多集群 调度单元 进程或线程 容器或pod 工作负载（deployment） 服务的集合 工作负载（deployment） 不同集群工作负载的集合体（workloadGroup） 服务发现 service 不同集群service的集合体 服务迁移 工作负载（deployment）控制器 不同集群工作负载的集合体控制器 服务调度 nodename或者node selector clustername或cluster selector pod的反亲和（相同deployment下的pod不调度在相同节点） workload反亲和（相同workloadGroup分散在不同集群） 2.1. 多集群工作负载的分发 单集群中k8s的调度单元是pod，即一个pod只能跑在一个节点上，一个节点可以运行多个pod，而不同节点上的一组pod是通过一个workload来控制和分发。类似这个逻辑，那么在多集群的视角下，多集群的调度单元是一个集群的workload，一个workload只能跑在一个集群中，一个集群可以运行多个workload。 那么就需要有一个控制器来管理不同k8s集群的相同workload。例如 workloadGroup。而该workloadGroup在不侵入k8s原生API的情况下，主要包含两个部分。 workloadGroup: 资源模板（Resource Template）：服务的描述（workload） 分发策略（Propagaion Policy）：服务分发的集群（即多个workload应该被分发到哪些集群运行） workload描述的是什么服务运行在什么节点，workloadGroup描述的是什么服务运行在什么集群。 实现workloadGroup有两种方式： 一种是自定义API将workloadGroup中的Resource Template和Propagaion Policy合成在一个自定义的对象中，由用户直接指定该workloadGroup信息，从而将不同的workload分发到不同的集群中。 另一种方式是通过一个k8s载体来记录一个具体的workload对象，再由用户指定Propagaion Policy关联该workload对象，从而让控制器自动根据用户指定的Propagaion Policy将workload分发到不同的集群中。 2.2. 跨集群自动迁移与调度 单集群中k8s中通过workload中的nodeselector或者nodename以及亲和性来控制pod运行在哪个节点上。而多集群的视角下，则需要有一个控制器来实现集群级别的调度逻辑，例如clustername，cluster selector，cluster AntiAffinity，从而来自动控制workloadGroup下的workload分散在什么集群上。 3. 目前的多集群方案 3.1. Kubefed[Federation v2] 简介 基本思想 3.2. virtual kubelet 简介 基本思想 3.3. Karmada 简介 基本思想 参考： https://kubernetes.io/zh/docs/setup/best-practices/cluster-large/ CoreOS 是如何将 Kubernetes 的性能提高 10 倍的? 当 K8s 集群达到万级规模，阿里巴巴如何解决系统各组件性能问题？ https://github.com/kubernetes-sigs/kubefed https://jimmysong.io/kubernetes-handbook/practice/federation.html https://kubernetes.io/blog/2018/12/12/kubernetes-federation-evolution/ https://zhuanlan.zhihu.com/p/355193315 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"multi-cluster/virtual-kubelet/_index.html":{"url":"multi-cluster/virtual-kubelet/_index.html","title":"Virtual Kubelet","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"multi-cluster/virtual-kubelet/virtual-kubelet.html":{"url":"multi-cluster/virtual-kubelet/virtual-kubelet.html","title":"Virtual Kubelet介绍","keywords":"","body":"1. 简介2. 架构图3. 功能4. Providers5. 自定义provider5.1. PodLifecylceHandler5.2. PodNotifier(optional)5.3. NodeProvider5.4. 测试5.5. 示例代码1. 简介 Virtual Kubelet是 Kubernetes kubelet 的一种实现，作为一种虚拟的kubelet用来连接k8s集群和其他平台的API。这允许k8s的节点由其他提供者（provider）提供支持，这些提供者例如serverless平台（ACI, AWS Fargate）、IoT Edge等。 一句话概括：Kubernetes API on top, programmable back。 2. 架构图 3. 功能 virtual kubelet提供一个可以自定义k8s node的依赖库。 目前支持的功能如下： 创建、删除、更新 pod 容器的日志、exec命令、metrics 获取pod、pod列表、pod status node的地址、容量、daemon 操作系统 自定义virtual network 4. Providers virtual kubelet提供一个插件式的provider接口，让开发者可以自定义实现传统kubelet的功能。自定义的provider可以用自己的配置文件和环境参数。 自定义的provider必须提供以下功能： 提供pod、容器、资源的生命周期管理的功能 符合virtual kubelet提供的API 不直接访问k8s apiserver，定义获取数据的回调机制，例如configmap、secrets 开源的provider Alibaba Cloud ECI Provider Azure Container Instances Provider AWS Fargate Provider 5. 自定义provider 创建自定义provider的目录。 git clone https://github.com/virtual-kubelet/virtual-kubelet cd virtual-kubelet mkdir providers/my-provider 5.1. PodLifecylceHandler 当pod被k8s创建、更新、删除时，会调用以下方法。 type PodLifecycleHandler interface { // CreatePod takes a Kubernetes Pod and deploys it within the provider. CreatePod(ctx context.Context, pod *corev1.Pod) error // UpdatePod takes a Kubernetes Pod and updates it within the provider. UpdatePod(ctx context.Context, pod *corev1.Pod) error // DeletePod takes a Kubernetes Pod and deletes it from the provider. DeletePod(ctx context.Context, pod *corev1.Pod) error // GetPod retrieves a pod by name from the provider (can be cached). GetPod(ctx context.Context, namespace, name string) (*corev1.Pod, error) // GetPodStatus retrieves the status of a pod by name from the provider. GetPodStatus(ctx context.Context, namespace, name string) (*corev1.PodStatus, error) // GetPods retrieves a list of all pods running on the provider (can be cached). GetPods(context.Context) ([]*corev1.Pod, error) } PodLifecycleHandler是被PodController来调用，来管理被分配到node上的pod。 pc, _ := node.NewPodController(podControllerConfig) // 5.2. PodNotifier(optional) PodNotifier是可选实现，该接口主要用来通知virtual kubelet的pod状态变化。如果没有实现该接口，virtual-kubelet会定期检查所有pod的状态。 type PodNotifier interface { // NotifyPods instructs the notifier to call the passed in function when // the pod status changes. // // NotifyPods should not block callers. NotifyPods(context.Context, func(*corev1.Pod)) } 5.3. NodeProvider NodeProvider用来通知virtual-kubelet关于node状态的变化，virtual-kubelet会定期检查node是状态并相应地更新k8s。 type NodeProvider interface { // Ping checks if the node is still active. // This is intended to be lightweight as it will be called periodically as a // heartbeat to keep the node marked as ready in Kubernetes. Ping(context.Context) error // NotifyNodeStatus is used to asynchronously monitor the node. // The passed in callback should be called any time there is a change to the // node's status. // This will generally trigger a call to the Kubernetes API server to update // the status. // // NotifyNodeStatus should not block callers. NotifyNodeStatus(ctx context.Context, cb func(*corev1.Node)) } NodeProvider是被NodeController调用，来管理k8s中的node对象。 nc, _ := node.NewNodeController(nodeProvider, nodeSpec) // 5.4. 测试 进入到项目根目录 make test 5.5. 示例代码 Azure Container Instances Provider https://github.com/virtual-kubelet/azure-aci/blob/master/aci.go#L541 Alibaba Cloud ECI Provider https://github.com/virtual-kubelet/alibabacloud-eci/blob/master/eci.go#L177 AWS Fargate Provider https://github.com/virtual-kubelet/aws-fargate/blob/master/provider.go#L110 参考： https://github.com/virtual-kubelet/virtual-kubelet https://virtual-kubelet.io/docs/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"multi-cluster/virtual-kubelet/virtual-kubelet-cmd.html":{"url":"multi-cluster/virtual-kubelet/virtual-kubelet-cmd.html","title":"Virtual Kubelet 命令","keywords":"","body":"virtual-kubelet --helpvirtual-kubelet --help #./virtual-kubelet --help virtual-kubelet implements the Kubelet interface with a pluggable backend implementation allowing users to create kubernetes nodes without running the kubelet. This allows users to schedule kubernetes workloads on nodes that aren't running Kubernetes. Usage: virtual-kubelet [flags] virtual-kubelet [command] Available Commands: help Help about any command providers Show the list of supported providers version Show the version of the program Flags: --cluster-domain string kubernetes cluster-domain (default is 'cluster.local') (default \"cluster.local\") --disable-taint disable the virtual-kubelet node taint --enable-node-lease use node leases (1.13) for node heartbeats --full-resync-period duration how often to perform a full resync of pods between kubernetes and the provider (default 1m0s) -h, --help help for virtual-kubelet --klog.alsologtostderr log to standard error as well as files --klog.log_backtrace_at traceLocation when logging hits line file:N, emit a stack trace (default :0) --klog.log_dir string If non-empty, write log files in this directory --klog.log_file string If non-empty, use this log file --klog.log_file_max_size uint Defines the maximum size a log file can grow to. Unit is megabytes. If the value is 0, the maximum file size is unlimited. (default 1800) --klog.logtostderr log to standard error instead of files (default true) --klog.skip_headers If true, avoid header prefixes in the log messages --klog.skip_log_headers If true, avoid headers when opening log files --klog.stderrthreshold severity logs at or above this threshold go to stderr (default 2) --klog.v Level number for the log level verbosity --klog.vmodule moduleSpec comma-separated list of pattern=N settings for file-filtered logging --kubeconfig string kube config file to use for connecting to the Kubernetes API server (default \"/root/.kube/config\") --log-level string set the log level, e.g. \"debug\", \"info\", \"warn\", \"error\" (default \"info\") --metrics-addr string address to listen for metrics/stats requests (default \":10255\") --namespace string kubernetes namespace (default is 'all') --nodename string kubernetes node name (default \"virtual-kubelet\") --os string Operating System (Linux/Windows) (default \"Linux\") --pod-sync-workers int set the number of pod synchronization workers (default 10) --provider string cloud provider --provider-config string cloud provider configuration file --startup-timeout duration How long to wait for the virtual-kubelet to start --trace-exporter strings sets the tracing exporter to use, available exporters: [jaeger ocagent] --trace-sample-rate string set probability of tracing samples --trace-service-name string sets the name of the service used to register with the trace exporter (default \"virtual-kubelet\") --trace-tag map add tags to include with traces in key=value form Use \"virtual-kubelet [command] --help\" for more information about a command. Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"multi-cluster/karmada/_index.html":{"url":"multi-cluster/karmada/_index.html","title":"Karmada","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"multi-cluster/karmada/karmada-introduction.html":{"url":"multi-cluster/karmada/karmada-introduction.html","title":"Karmada介绍","keywords":"","body":"简介特性框架结构资源分发流程 本文由网络资源整理以作记录 简介 Karmada（Kubernetes Armada）是基于Kubernetes原生API的多集群管理系统。在多云和混合云场景下，Karmada提供可插拔，全自动化管理多集群应用，实现多云集中管理、高可用性、故障恢复和流量调度。 特性 基于K8s原生API的跨集群应用管理，用户可以方便快捷地将应用从单集群迁移到多集群。 中心式操作和管理Kubernetes集群。 跨集群应用可在多集群上自动扩展，故障转移和负载均衡。 高级的调度策略：区域，可用区，云提供商，集群亲和性/反亲和性。 支持创建分发用户自定义（CustomResourceDefinitions）资源。 框架结构 图片 - img ETCD：存储Karmada API对象。 Karmada Scheduler：提供高级的多集群调度策略。 Karmada Controller Manager: 包含多个Controller，Controller监听karmada对象并且与成员集群API server进行通信并创建成员集群的k8s对象。 Cluster Controller：成员集群的生命周期管理与对象管理。 Policy Controller：监听PropagationPolicy对象，创建ResourceBinding，配置资源分发策略。 Binding Controller：监听ResourceBinding对象，并创建work对象响应资源清单。 Execution Controller：监听work对象，并将资源分发到成员集群中。 资源分发流程 基本概念 资源模板（Resource Template）：Karmada使用K8s原生API定义作为资源模板，便于快速对接K8s生态工具链。 分发策略（Propagaion Policy）：Karmada提供独立的策略API，用来配置资源分发策略。 差异化策略（Override Policy）：Karmada提供独立的差异化API，用来配置与集群相关的差异化配置。比如配置不同集群使用不同的镜像。 Karmada资源分发流程图： 图片 - img 参考： https://github.com/karmada-io/karmada https://support.huaweicloud.com/productdesc-mcp/mcp_productdesc_0001.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"edge/kubeedge/kubeedge-arch.html":{"url":"edge/kubeedge/kubeedge-arch.html","title":"KubeEdge介绍","keywords":"","body":"1. KubeEdge简介2. 功能3. 组件3.1. 云端3.2. 边缘端4. 架构图1. KubeEdge简介 KubeEdge是基于kubernetes之上将容器化应用的编排能力拓展到边缘主机或边缘设备，在云端和边缘端提供网络通信，应用部署、元数据同步等功能。同时支持MQTT协议，允许开发者在边缘端自定义接入边缘设备。 2. 功能 边缘计算：提供边缘节点自治能力，边缘节点数据处理能力。 便捷部署：开发者可以开发http或mqtt协议的应用，运行在云端和边缘端。 k8s原生支持：可以通过k8s管理和监控边缘设备和边缘节点。 丰富的应用类型：可以在边缘端部署机器学习、图片识别、事件处理等应用。 3. 组件 3.1. 云端 CloudHub：一个web socket服务器，负责监听云端的更新、缓存及向EdgeHub发送消息。 EdgeController：一个扩展的k8s控制器，负责管理边缘节点和pod元数据，同步边缘节点的数据，是k8s-apiserver 与EdgeCore的通信桥梁。 DeviceController：一个扩展的k8s控制器，负责管理节点设备，同步云端和边缘端的设备元数据和状态。 3.2. 边缘端 EdgeHub：一个web socket客户端，负责云端与边缘端的信息交互，其中包括将云端的资源变更同步到边缘端及边缘端的状态变化同步到云端。 Edged：运行在边缘节点，管理容器化应用的agent，负责pod生命周期的管理，类似kubelet。 EventBus：一个MQTT客户端，与MQTT服务端交互，提供发布/订阅的能力。 ServiceBus：一个HTTP客户端，与HTTP服务端交互。为云组件提供HTTP客户端功能，以访问在边缘运行的HTTP服务器。 DeviceTwin：负责存储设备状态并同步设备状态到云端，同时提供应用的接口查询。 MetaManager：edged和edgehub之间的消息处理器，负责向轻量数据库（SQLite）存储或查询元数据。 4. 架构图 图片 - kubeedge-arch 参考： https://github.com/kubeedge/kubeedge https://kubeedge.readthedocs.io/en/latest/modules/kubeedge.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"edge/kubeedge/code-analysis/_index.html":{"url":"edge/kubeedge/code-analysis/_index.html","title":"KubeEdge源码分析","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"edge/kubeedge/code-analysis/cloudcore.html":{"url":"edge/kubeedge/code-analysis/cloudcore.html","title":"cloudcore","keywords":"","body":"kubeedge源码分析之cloudcore1. main函数2. NewCloudCoreCommand3. registerModules4. core.Run5. StartModules6. GracefulShutdownkubeedge源码分析之cloudcore 本文源码分析基于kubeedge v1.1.0 本文主要分析cloudcore中CloudCoreCommand的基本流程，具体的cloudhub、edgecontroller、devicecontroller模块的实现逻辑待后续单独文章分析。 目录结构： cloud/cmd/cloudcore cloudcore ├── app │ ├── options │ │ └── options.go │ └── server.go # NewCloudCoreCommand、registerModules └── cloudcore.go # main函数 cloudcore部分包含以下模块： cloudhub edgecontroller devicecontroller 1. main函数 kubeedge的代码采用cobra命令框架，代码风格与k8s源码风格类似。cmd目录主要为cobra command的基本内容及参数解析，pkg目录包含具体的实现逻辑。 cloud/cmd/cloudcore/cloudcore.go func main() { command := app.NewCloudCoreCommand() logs.InitLogs() defer logs.FlushLogs() if err := command.Execute(); err != nil { os.Exit(1) } } 2. NewCloudCoreCommand NewCloudCoreCommand为cobra command的构造函数，该类函数一般包含以下部分： 构造option 添加Flags 运行Run函数（核心） cloud/cmd/cloudcore/app/server.go func NewCloudCoreCommand() *cobra.Command { opts := options.NewCloudCoreOptions() cmd := &cobra.Command{ Use: \"cloudcore\", Long: `CloudCore is the core cloud part of KubeEdge, which contains three modules: cloudhub, edgecontroller, and devicecontroller. Cloudhub is a web server responsible for watching changes at the cloud side, caching and sending messages to EdgeHub. EdgeController is an extended kubernetes controller which manages edge nodes and pods metadata so that the data can be targeted to a specific edge node. DeviceController is an extended kubernetes controller which manages devices so that the device metadata/status date can be synced between edge and cloud.`, Run: func(cmd *cobra.Command, args []string) { verflag.PrintAndExitIfRequested() flag.PrintFlags(cmd.Flags()) // To help debugging, immediately log version klog.Infof(\"Version: %+v\", version.Get()) registerModules() // start all modules core.Run() }, } fs := cmd.Flags() namedFs := opts.Flags() verflag.AddFlags(namedFs.FlagSet(\"global\")) globalflag.AddGlobalFlags(namedFs.FlagSet(\"global\"), cmd.Name()) for _, f := range namedFs.FlagSets { fs.AddFlagSet(f) } usageFmt := \"Usage:\\n %s\\n\" cols, _, _ := term.TerminalSize(cmd.OutOrStdout()) cmd.SetUsageFunc(func(cmd *cobra.Command) error { fmt.Fprintf(cmd.OutOrStderr(), usageFmt, cmd.UseLine()) cliflag.PrintSections(cmd.OutOrStderr(), namedFs, cols) return nil }) cmd.SetHelpFunc(func(cmd *cobra.Command, args []string) { fmt.Fprintf(cmd.OutOrStdout(), \"%s\\n\\n\"+usageFmt, cmd.Long, cmd.UseLine()) cliflag.PrintSections(cmd.OutOrStdout(), namedFs, cols) }) return cmd } 核心代码： // 构造option opts := options.NewCloudCoreOptions() // 执行run函数 registerModules() core.Run() // 添加flags fs.AddFlagSet(f) 3. registerModules 由于kubeedge的代码的大部分模块都采用了基于go-channel的消息通信框架Beehive（待后续单独文章分析），因此在各模块启动之前，需要将该模块注册到beehive的框架中。 其中cloudcore部分涉及的模块有： cloudhub edgecontroller devicecontroller cloud/cmd/cloudcore/app/server.go // registerModules register all the modules started in cloudcore func registerModules() { cloudhub.Register() edgecontroller.Register() devicecontroller.Register() } 以下以cloudhub为例说明注册的过程。 cloudhub结构体主要包含： context：上下文，用来传递消息上下文 stopChan：go channel通信 beehive框架中的模块需要实现Module接口，因此cloudhub也实现了该接口，其中核心方法为Start，用来启动相应模块的运行。 vendor/github.com/kubeedge/beehive/pkg/core/module.go // Module interface type Module interface { Name() string Group() string Start(c *context.Context) Cleanup() } 以下为cloudHub结构体及注册函数。 cloud/pkg/cloudhub/cloudhub.go type cloudHub struct { context *context.Context stopChan chan bool } func Register() { core.Register(&cloudHub{}) } 具体的注册实现函数为core.Register，注册过程实际上就是将具体的模块结构体放入一个以模块名为key的map映射中，待后续调用。 vendor/github.com/kubeedge/beehive/pkg/core/module.go // Register register module func Register(m Module) { if isModuleEnabled(m.Name()) { modules[m.Name()] = m //将具体的模块结构体放入一个以模块名为key的map映射中 log.LOGGER.Info(\"module \" + m.Name() + \" registered\") } else { disabledModules[m.Name()] = m log.LOGGER.Info(\"module \" + m.Name() + \" is not register, please check modules.yaml\") } } 4. core.Run CloudCoreCommand命令的Run函数实际上是运行beehive框架中注册的所有模块。 其中包括两部分逻辑： 启动运行所有注册模块 监听信号并做优雅清理 vendor/github.com/kubeedge/beehive/pkg/core/core.go //Run starts the modules and in the end does module cleanup func Run() { //Address the module registration and start the core StartModules() // monitor system signal and shutdown gracefully GracefulShutdown() } 5. StartModules StartModules获取context上下文，并以goroutine的方式运行所有已注册的模块。其中Start函数即每个模块的具体实现Module接口中的Start方法。不同模块各自定义自己的具体Start方法实现。 coreContext := context.GetContext(context.MsgCtxTypeChannel) go module.Start(coreContext) 具体实现如下： vendor/github.com/kubeedge/beehive/pkg/core/core.go // StartModules starts modules that are registered func StartModules() { coreContext := context.GetContext(context.MsgCtxTypeChannel) modules := GetModules() for name, module := range modules { //Init the module coreContext.AddModule(name) //Assemble typeChannels for send2Group coreContext.AddModuleGroup(name, module.Group()) go module.Start(coreContext) log.LOGGER.Info(\"starting module \" + name) } } 6. GracefulShutdown 当收到相关信号，则执行各个模块实现的Cleanup方法。 vendor/github.com/kubeedge/beehive/pkg/core/core.go // GracefulShutdown is if it gets the special signals it does modules cleanup func GracefulShutdown() { c := make(chan os.Signal) signal.Notify(c, syscall.SIGINT, syscall.SIGHUP, syscall.SIGTERM, syscall.SIGQUIT, syscall.SIGILL, syscall.SIGTRAP, syscall.SIGABRT) select { case s := 参考： https://github.com/kubeedge/kubeedge/tree/release-1.1/cloud/cmd/cloudcore https://github.com/kubeedge/kubeedge/tree/release-1.1/vendor/github.com/kubeedge/beehive/pkg/core Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"edge/kubeedge/code-analysis/edgecore.html":{"url":"edge/kubeedge/code-analysis/edgecore.html","title":"edgecore","keywords":"","body":"kubeedge源码分析之edgecore1. main函数2. NewEdgeCoreCommand3. registerModules4. core.Runkubeedge源码分析之edgecore 本文源码分析基于kubeedge v1.1.0 本文主要分析edgecore中EdgeCoreCommand的基本流程，具体的edged、edgehub、metamanager等模块的实现逻辑待后续单独文章分析。 目录结构： edgecore ├── app │ ├── options │ │ └── options.go │ └── server.go # NewEdgeCoreCommand 、registerModules └── edgecore.go # main edgecore模块包含： edged edgehub metamanager eventbus servicebus devicetwin edgemesh 1. main函数 main入口函数，仍然是cobra命令框架格式。 edge/cmd/edgecore/edgecore.go func main() { command := app.NewEdgeCoreCommand() logs.InitLogs() defer logs.FlushLogs() if err := command.Execute(); err != nil { os.Exit(1) } } 2. NewEdgeCoreCommand NewEdgeCoreCommand与NewCloudCoreCommand一样构造对应的cobra command结构体。 edge/cmd/edgecore/app/server.go // NewEdgeCoreCommand create edgecore cmd func NewEdgeCoreCommand() *cobra.Command { opts := options.NewEdgeCoreOptions() cmd := &cobra.Command{ Use: \"edgecore\", Long: `Edgecore is the core edge part of KubeEdge, which contains six modules: devicetwin, edged, edgehub, eventbus, metamanager, and servicebus. DeviceTwin is responsible for storing device status and syncing device status to the cloud. It also provides query interfaces for applications. Edged is an agent that runs on edge nodes and manages containerized applications and devices. Edgehub is a web socket client responsible for interacting with Cloud Service for the edge computing (like Edge Controller as in the KubeEdge Architecture). This includes syncing cloud-side resource updates to the edge, and reporting edge-side host and device status changes to the cloud. EventBus is a MQTT client to interact with MQTT servers (mosquito), offering publish and subscribe capabilities to other components. MetaManager is the message processor between edged and edgehub. It is also responsible for storing/retrieving metadata to/from a lightweight database (SQLite).ServiceBus is a HTTP client to interact with HTTP servers (REST), offering HTTP client capabilities to components of cloud to reach HTTP servers running at edge. `, Run: func(cmd *cobra.Command, args []string) { verflag.PrintAndExitIfRequested() flag.PrintFlags(cmd.Flags()) // To help debugging, immediately log version klog.Infof(\"Version: %+v\", version.Get()) registerModules() // start all modules core.Run() }, } fs := cmd.Flags() namedFs := opts.Flags() verflag.AddFlags(namedFs.FlagSet(\"global\")) globalflag.AddGlobalFlags(namedFs.FlagSet(\"global\"), cmd.Name()) for _, f := range namedFs.FlagSets { fs.AddFlagSet(f) } usageFmt := \"Usage:\\n %s\\n\" cols, _, _ := term.TerminalSize(cmd.OutOrStdout()) cmd.SetUsageFunc(func(cmd *cobra.Command) error { fmt.Fprintf(cmd.OutOrStderr(), usageFmt, cmd.UseLine()) cliflag.PrintSections(cmd.OutOrStderr(), namedFs, cols) return nil }) cmd.SetHelpFunc(func(cmd *cobra.Command, args []string) { fmt.Fprintf(cmd.OutOrStdout(), \"%s\\n\\n\"+usageFmt, cmd.Long, cmd.UseLine()) cliflag.PrintSections(cmd.OutOrStdout(), namedFs, cols) }) return cmd } 核心代码： opts := options.NewEdgeCoreOptions() registerModules() core.Run() 3. registerModules edgecore仍然采用Beehive通信框架，模块调用前先注册对应的模块。具体参考cloudcore.registerModules处的分析，此处不再展开分析注册流程。此处注册的是edgecore中涉及的组件。 edge/cmd/edgecore/app/server.go // registerModules register all the modules started in edgecore func registerModules() { devicetwin.Register() edged.Register() edgehub.Register() eventbus.Register() edgemesh.Register() metamanager.Register() servicebus.Register() test.Register() dbm.InitDBManager() } 4. core.Run core.Run与cloudcore.run处逻辑一致不再展开分析。 vendor/github.com/kubeedge/beehive/pkg/core/core.go //Run starts the modules and in the end does module cleanup func Run() { //Address the module registration and start the core StartModules() // monitor system signal and shutdown gracefully GracefulShutdown() } 参考： https://github.com/kubeedge/kubeedge/tree/release-1.1/edge/cmd/edgecore Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"edge/openyurt/_index.html":{"url":"edge/openyurt/_index.html","title":"OpenYurt","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"edge/openyurt/install-openyurt.html":{"url":"edge/openyurt/install-openyurt.html","title":"OpenYurt部署","keywords":"","body":"1. 给云端节点和边缘节点打标签1.1. openyurt.io/is-edge-worker节点标签1.2. 给边缘节点开启自治模式2. 安装准备2.1. 调整k8s组件的配置2.2. 部署tunnel-dns3. 部署openyurt控制面3.1. yurt-app-manager3.2. openyurt4. 部署 Yurthub(edge)5. 重置 Kubelet6. yurthub部署脚本 本文主要介绍部署openyurt组件到k8s集群中。 1. 给云端节点和边缘节点打标签 openyurt将k8s节点分为云端节点和边缘节点，云端节点主要运行一些云端的业务，边缘节点运行边缘业务。当与 apiserver 断开连接时，只有运行在边缘自治的节点上的Pod才不会被驱逐。通过打 openyurt.io/is-edge-worker 的标签的方式来区分，false表示云端节点，true表示边缘节点。 云端组件： yurt-controller-manager yurt-tunnel-server 边缘组件： yurt-hub yurt-tunnel-agent 1.1. openyurt.io/is-edge-worker节点标签 # 云端节点，值为false kubectl label node us-west-1.192.168.0.87 openyurt.io/is-edge-worker=false # 边缘节点，值为true kubectl label node us-west-1.192.168.0.88 openyurt.io/is-edge-worker=true 1.2. 给边缘节点开启自治模式 kubectl annotate node us-west-1.192.168.0.88 node.beta.openyurt.io/autonomy=true 2. 安装准备 2.1. 调整k8s组件的配置 参考调整k8s组件的配置 2.2. 部署tunnel-dns wget https://raw.githubusercontent.com/openyurtio/openyurt/master/config/setup/yurt-tunnel-dns.yaml kubectl apply -f yurt-tunnel-dns.yaml 获取clusterIP，作为kube-apiserver的专用nameserver地址。 kubectl -n kube-system get svc yurt-tunnel-dns -o=jsonpath='{.spec.clusterIP}' 3. 部署openyurt控制面 通过helm来部署控制面，所有helm charts都可以在openyurt-helm 仓库中找到。 快捷安装可参考脚本：helm-install-openyurt.sh helm repo add openyurt https://openyurtio.github.io/openyurt-helm 3.1. yurt-app-manager helm upgrade --install yurt-app-manager -n kube-system openyurt/yurt-app-manager 3.2. openyurt 在openyurt/openyurt中的组件包括： yurt-controller-manager: 防止apiserver在断开连接时驱逐运行在边缘节点上的pod yurt-tunnel-server: 在云端构建云边隧道 yurt-tunnel-agent: 在边缘侧构建云边隧道 由于yurt-tunnel-server默认使用host模式，因此可能存在边缘端的agent无法访问云端的tunnel-server，需要为tunnel-server配置一个可访问的地址。 # 下载并解压 helm pull openyurt/openyurt --untar # 修改tunnel相关配置 cd openyurt vi values.yaml # 示例： yurtTunnelServer: replicaCount: 1 tolerations: [] parameters: certDnsNames: \"\" tunnelAgentConnectPort: certIps: \"\" yurtTunnelAgent: replicaCount: 1 tolerations: [] parameters: tunnelserverAddr: \"\" # install helm install openyurt ./openyurt 4. 部署 Yurthub(edge) 在 yurt-controller-manager 启动并正常运行后，以静态 pod 的方式部署 Yurthub。 为 yurthub 创建全局配置(即RBAC, configmap) wget https://raw.githubusercontent.com/openyurtio/openyurt/master/config/setup/yurthub-cfg.yaml kubectl apply -f yurthub-cfg.yaml 在边缘节点以static pod方式创建yurthub mkdir -p /etc/kubernetes/manifests/ cd /etc/kubernetes/manifests/ wget https://raw.githubusercontent.com/openyurtio/openyurt/master/config/setup/yurthub.yaml # 获取bootstrap token kubeadm token create # 假设 apiserver 的地址是 1.2.3.4:6443，bootstrap token 是 07401b.f395accd246ae52d sed -i 's|__kubernetes_master_address__|1.2.3.4:6443|; s|__bootstrap_token__|07401b.f395accd246ae52d|' /etc/kubernetes/manifests/yurthub.yaml 5. 重置 Kubelet 重置 kubelet 服务，让它通过 yurthub 访问apiserver。为 kubelet 服务创建一个新的 kubeconfig 文件来访问apiserver。 mkdir -p /var/lib/openyurt cat /var/lib/openyurt/kubelet.conf apiVersion: v1 clusters: - cluster: server: http://127.0.0.1:10261 name: default-cluster contexts: - context: cluster: default-cluster namespace: default user: default-auth name: default-context current-context: default-context kind: Config preferences: {} EOF 修改/etc/systemd/system/kubelet.service.d/10-kubeadm.conf sed -i \"s|KUBELET_KUBECONFIG_ARGS=--bootstrap-kubeconfig=\\/etc\\/kubernetes\\/bootstrap-kubelet.conf\\ --kubeconfig=\\/etc\\/kubernetes\\/kubelet.conf|KUBELET_KUBECONFIG_ARGS=--kubeconfig=\\/var\\/lib\\/openyurt\\/kubelet.conf|g\" \\ /etc/systemd/system/kubelet.service.d/10-kubeadm.conf 重启kubelet服务 systemctl daemon-reload && systemctl restart kubelet 6. yurthub部署脚本 根据以上部署步骤，整理部署脚本。需要修改脚本内容的master-addr和token字段。 #!/bin/bash set -e set -x ### install yurthub ### mkdir -p /etc/kubernetes/manifests/ cd /etc/kubernetes/manifests/ wget https://raw.githubusercontent.com/openyurtio/openyurt/master/config/setup/yurthub.yaml ### 修改master和token字段 sed -i 's|__kubernetes_master_address__|:6443|; s|__bootstrap_token__||' /etc/kubernetes/manifests/yurthub.yaml mkdir -p /var/lib/openyurt cat /var/lib/openyurt/kubelet.conf apiVersion: v1 clusters: - cluster: server: http://127.0.0.1:10261 name: default-cluster contexts: - context: cluster: default-cluster namespace: default user: default-auth name: default-context current-context: default-context kind: Config preferences: {} EOF cp /etc/systemd/system/kubelet.service.d/10-kubeadm.conf /etc/systemd/system/kubelet.service.d/10-kubeadm.conf.bak sed -i \"s|KUBELET_KUBECONFIG_ARGS=--bootstrap-kubeconfig=\\/etc\\/kubernetes\\/bootstrap-kubelet.conf\\ --kubeconfig=\\/etc\\/kubernetes\\/kubelet.conf|KUBELET_KUBECONFIG_ARGS=--kubeconfig=\\/var\\/lib\\/openyurt\\/kubelet.conf|g\" \\ /etc/systemd/system/kubelet.service.d/10-kubeadm.conf systemctl daemon-reload && systemctl restart kubelet 参考： https://openyurt.io/zh/docs/installation/manually-setup https://openyurt.io/zh/docs/installation/openyurt-prepare 在存量的K8s节点上安装OpenYurt Node组件 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"edge/openyurt/update-k8s-for-openyurt.html":{"url":"edge/openyurt/update-k8s-for-openyurt.html","title":"OpenYurt部署之调整k8s配置","keywords":"","body":"1. kube-apiserver2. kube-controller-manager3. CoreDNS4. kube-proxy安装openyurt，为了适配边缘场景，需要对k8s组件进行调整。其中包括： kube-apiserver kube-controller-manager kube-proxy CoreDNS 1. kube-apiserver 为了实现云边通信，即用户可以正常使用kubectl exec/logs的功能来登录或查看边缘容器的信息。需要将kube-apiserver访问kubelet的地址调整为hostname优先。 $ vi /etc/kubernetes/manifests/kube-apiserver.yaml apiVersion: v1 kind: Pod ... spec: dnsPolicy: \"None\" # 1. dnsPolicy修改为None dnsConfig: # 2. 增加dnsConfig配置 nameservers: - 1.2.3.4 # 使用yurt-tunnel-dns service的clusterIP替换 searches: - kube-system.svc.cluster.local - svc.cluster.local - cluster.local options: - name: ndots value: \"5\" containers: - command: - kube-apiserver ... - --kubelet-preferred-address-types=Hostname,InternalIP,ExternalIP # 3. 把Hostname放在第一位 ... 2. kube-controller-manager 禁用默认的 nodelifecycle 控制器，当节点断连时不驱逐pod。 nodelifecycle控制器主要用来根据node的status及lease的更新时间来决定是否要驱逐节点上的pod。为了让 yurt-controller-mamanger 能够正常工作，因此需要禁用controller的驱逐功能。 vim /etc/kubernetes/manifests/kube-controller-manager.yaml # 在--controllers=*,bootstrapsigner,tokencleaner后面添加,-nodelifecycle # 即参数为： --controllers=*,bootstrapsigner,tokencleaner,-nodelifecycle # 如果kube-controller-manager是以static pod部署，修改yaml文件后会自动重启。 3. CoreDNS 将coredns从deployment部署改为daemonset部署。 将deployment的coredns副本数调整为0。 kubectl scale --replicas=0 deployment/coredns -n kube-system 创建daemonset的coredns。 wget https://raw.githubusercontent.com/huweihuang/kubeadm-scripts/main/openyurt/yurt-tunnel/coredns.ds.yaml kubectl apply -f 支持流量拓扑： # 利用openyurt实现endpoint过滤 kubectl annotate svc kube-dns -n kube-system openyurt.io/topologyKeys='openyurt.io/nodepool' 4. kube-proxy 云边端场景下，边缘节点间很有可能无法互通，因此需要endpoints基于nodepool进行拓扑。直接将kube-proxy的kubeconfig配置删除，将apiserver请求经过yurthub即可解决服务拓扑问题。 kubectl edit cm -n kube-system kube-proxy 示例： apiVersion: v1 data: config.conf: |- apiVersion: kubeproxy.config.k8s.io/v1alpha1 bindAddress: 0.0.0.0 bindAddressHardFail: false clientConnection: acceptContentTypes: \"\" burst: 0 contentType: \"\" #kubeconfig: /var/lib/kube-proxy/kubeconfig.conf 参考： https://openyurt.io/zh/docs/installation/openyurt-prepare/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"edge/openyurt/code-analysis/_index.html":{"url":"edge/openyurt/code-analysis/_index.html","title":"OpenYurt源码分析","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"edge/openyurt/code-analysis/yurthub-code-analysis-1.html":{"url":"edge/openyurt/code-analysis/yurthub-code-analysis-1.html","title":"YurtHub源码分析（1)","keywords":"","body":"1. NewCmdStartYurtHub2. Run(ctx, yurtHubCfg)3. RunYurtHubServers3.1. YurtHubServerServing3.2. YurtHubProxyServerServing3.3. NewYurtReverseProxyHandler4. yurtReverseProxy5. LoadBalancer5.1. NewLoadBalancer5.2. loadBalancer.ServeHTTP5.3. errorHandler5.4. QueryCache5.5. 查询storage中的数据6. RemoteProxy7. LocalProxy7.1. localReqCache总结 本文分析yurthub源码，第一部分。 本文以commit id：180282663457080119a1bc6076cce20c922b5c50， 对应版本tag: v1.2.1 的源码分析yurthub的实现逻辑。 yurthub是部署在每个边缘节点上用来实现边缘自治的组件。在云边通信正常的情况下实现apiserver的请求转发，断网的情况下通过本地的缓存数据保证节点上容器的正常运行。 基本架构图： pkg包中yurthub代码目录结构； yurthub ├── cachemanager # cache 管理器， ├── certificate # 证书token管理 ├── filter ├── gc # GCManager ├── healthchecker # cloud apiserver 探火机制 ├── kubernetes # ├── metrics ├── network # 网络iptables配置 ├── otaupdate ├── poolcoordinator ├── proxy # 核心代码，反向代理机制，包括remote proxy和local proxy ├── server # yurthub server ├── storage # 本地存储的实现 ├── tenant ├── transport └── util 1. NewCmdStartYurtHub openyurt的代码风格与k8s的一致，由cmd为入口，pkg为主要的实现逻辑。 以下是cmd的main函数。 func main() { newRand := rand.New(rand.NewSource(time.Now().UnixNano())) newRand.Seed(time.Now().UnixNano()) cmd := app.NewCmdStartYurtHub(server.SetupSignalContext()) cmd.Flags().AddGoFlagSet(flag.CommandLine) if err := cmd.Execute(); err != nil { panic(err) } } main 函数主要创建 NewCmdStartYurtHub 对象。NewCmd的函数一般都包含以下的几个部分，运行顺序从上到下： NewYurtHubOptions：创建option参数对象，主要用于flag参数解析到option的结构体。 yurtHubOptions.AddFlags(cmd.Flags())：添加AddFlags，设置flag参数信息。 yurtHubOptions.Validate()：校验flag解析后的option的参数合法性。 yurtHubCfg, err := config.Complete(yurtHubOptions)：将option的参数转换为config的对象。 Run(ctx, yurtHubCfg)：基于config执行run函数，运行cmd的核心逻辑。 // NewCmdStartYurtHub creates a *cobra.Command object with default parameters func NewCmdStartYurtHub(ctx context.Context) *cobra.Command { yurtHubOptions := options.NewYurtHubOptions() cmd := &cobra.Command{ Use: projectinfo.GetHubName(), Short: \"Launch \" + projectinfo.GetHubName(), Long: \"Launch \" + projectinfo.GetHubName(), Run: func(cmd *cobra.Command, args []string) { if yurtHubOptions.Version { fmt.Printf(\"%s: %#v\\n\", projectinfo.GetHubName(), projectinfo.Get()) return } fmt.Printf(\"%s version: %#v\\n\", projectinfo.GetHubName(), projectinfo.Get()) cmd.Flags().VisitAll(func(flag *pflag.Flag) { klog.V(1).Infof(\"FLAG: --%s=%q\", flag.Name, flag.Value) }) if err := yurtHubOptions.Validate(); err != nil { klog.Fatalf(\"validate options: %v\", err) } yurtHubCfg, err := config.Complete(yurtHubOptions) if err != nil { klog.Fatalf(\"complete %s configuration error, %v\", projectinfo.GetHubName(), err) } klog.Infof(\"%s cfg: %#+v\", projectinfo.GetHubName(), yurtHubCfg) if err := Run(ctx, yurtHubCfg); err != nil { klog.Fatalf(\"run %s failed, %v\", projectinfo.GetHubName(), err) } }, } yurtHubOptions.AddFlags(cmd.Flags()) return cmd } 以上flag、option、config的构建函数此处不做分析，以下分析run函数的逻辑。 2. Run(ctx, yurtHubCfg) Run函数部分主要构建了几个manager，每个manager各司其职，负责对应的逻辑。有的manager在该函数中直接构建后执行manager.run的逻辑。有的则作为参数传入下一级函数中再执行manager.run函数。 主要包括以下的manager： transportManager cloudHealthChecker restConfigMgr cacheMgr gcMgr tenantMgr NetworkMgr 每个manager的实现细节此处暂不做分析。 此处先贴一下完整源码，避免读者还需要去翻代码。 代码：/cmd/yurthub/app/start.go // Run runs the YurtHubConfiguration. This should never exit func Run(ctx context.Context, cfg *config.YurtHubConfiguration) error { defer cfg.CertManager.Stop() trace := 1 klog.Infof(\"%d. new transport manager\", trace) // 构造NewTransportManager transportManager, err := transport.NewTransportManager(cfg.CertManager, ctx.Done()) if err != nil { return fmt.Errorf(\"could not new transport manager, %w\", err) } trace++ klog.Infof(\"%d. prepare cloud kube clients\", trace) cloudClients, err := createClients(cfg.HeartbeatTimeoutSeconds, cfg.RemoteServers, cfg.CoordinatorServerURL, transportManager) if err != nil { return fmt.Errorf(\"failed to create cloud clients, %w\", err) } trace++ var cloudHealthChecker healthchecker.MultipleBackendsHealthChecker if cfg.WorkingMode == util.WorkingModeEdge { klog.Infof(\"%d. create health checkers for remote servers and pool coordinator\", trace) cloudHealthChecker, err = healthchecker.NewCloudAPIServerHealthChecker(cfg, cloudClients, ctx.Done()) if err != nil { return fmt.Errorf(\"could not new cloud health checker, %w\", err) } } else { klog.Infof(\"%d. disable health checker for node %s because it is a cloud node\", trace, cfg.NodeName) // In cloud mode, cloud health checker is not needed. // This fake checker will always report that the cloud is healthy and pool coordinator is unhealthy. cloudHealthChecker = healthchecker.NewFakeChecker(true, make(map[string]int)) } trace++ klog.Infof(\"%d. new restConfig manager\", trace) restConfigMgr, err := hubrest.NewRestConfigManager(cfg.CertManager, cloudHealthChecker) if err != nil { return fmt.Errorf(\"could not new restConfig manager, %w\", err) } trace++ var cacheMgr cachemanager.CacheManager if cfg.WorkingMode == util.WorkingModeEdge { klog.Infof(\"%d. new cache manager with storage wrapper and serializer manager\", trace) cacheMgr = cachemanager.NewCacheManager(cfg.StorageWrapper, cfg.SerializerManager, cfg.RESTMapperManager, cfg.SharedFactory) } else { klog.Infof(\"%d. disable cache manager for node %s because it is a cloud node\", trace, cfg.NodeName) } trace++ if cfg.WorkingMode == util.WorkingModeEdge { klog.Infof(\"%d. new gc manager for node %s, and gc frequency is a random time between %d min and %d min\", trace, cfg.NodeName, cfg.GCFrequency, 3*cfg.GCFrequency) gcMgr, err := gc.NewGCManager(cfg, restConfigMgr, ctx.Done()) if err != nil { return fmt.Errorf(\"could not new gc manager, %w\", err) } // 直接运行manager gcMgr.Run() } else { klog.Infof(\"%d. disable gc manager for node %s because it is a cloud node\", trace, cfg.NodeName) } trace++ klog.Infof(\"%d. new tenant sa manager\", trace) tenantMgr := tenant.New(cfg.TenantNs, cfg.SharedFactory, ctx.Done()) trace++ var coordinatorHealthCheckerGetter func() healthchecker.HealthChecker = getFakeCoordinatorHealthChecker var coordinatorTransportManagerGetter func() transport.Interface = getFakeCoordinatorTransportManager var coordinatorGetter func() poolcoordinator.Coordinator = getFakeCoordinator if cfg.EnableCoordinator { klog.Infof(\"%d. start to run coordinator\", trace) trace++ coordinatorInformerRegistryChan := make(chan struct{}) // coordinatorRun will register secret informer into sharedInformerFactory, and start a new goroutine to periodically check // if certs has been got from cloud APIServer. It will close the coordinatorInformerRegistryChan if the secret channel has // been registered into informer factory. coordinatorHealthCheckerGetter, coordinatorTransportManagerGetter, coordinatorGetter = coordinatorRun(ctx, cfg, restConfigMgr, cloudHealthChecker, coordinatorInformerRegistryChan) // wait for coordinator informer registry klog.Infof(\"waiting for coordinator informer registry\") 除了上述的各种manager的构造及运行外，run函数中还构建了yurtProxyHandler，最终执行RunYurtHubServers运行一组不会退出的http server。以下先不对manager的实现做展开，而直接分析RunYurtHubServers的逻辑。RunYurtHubServers的代码在pkg包中。 3. RunYurtHubServers RunYurtHubServers就是一个传统的http server的运行逻辑，主要包括几个不同类型的http server。http server的运行逻辑可以概括如下： hubServerHandler := mux.NewRouter()： 新建路由创建handler registerHandlers(hubServerHandler, cfg, rest)： 注册路由 YurtHubServerServing.Serve：执行http server.Serve函数启动一个server服务。 http server分为两类： yurthub http server: yurthub metrics, healthz的接口。 yurthub proxy server: 代理kube-apiserver的请求。 3.1. YurtHubServerServing hubServerHandler := mux.NewRouter() registerHandlers(hubServerHandler, cfg, rest) // start yurthub http server for serving metrics, pprof. if cfg.YurtHubServerServing != nil { if err := cfg.YurtHubServerServing.Serve(hubServerHandler, 0, stopCh); err != nil { return err } } registerHandlers的路由内容如下： // registerHandler registers handlers for yurtHubServer, and yurtHubServer can handle requests like profiling, healthz, update token. func registerHandlers(c *mux.Router, cfg *config.YurtHubConfiguration, rest *rest.RestConfigManager) { // register handlers for update join token c.Handle(\"/v1/token\", updateTokenHandler(cfg.CertManager)).Methods(\"POST\", \"PUT\") // register handler for health check c.HandleFunc(\"/v1/healthz\", healthz).Methods(\"GET\") c.Handle(\"/v1/readyz\", readyz(cfg.CertManager)).Methods(\"GET\") // register handler for profile if cfg.EnableProfiling { profile.Install(c) } // register handler for metrics c.Handle(\"/metrics\", promhttp.Handler()) // register handler for ota upgrade c.Handle(\"/pods\", ota.GetPods(cfg.StorageWrapper)).Methods(\"GET\") c.Handle(\"/openyurt.io/v1/namespaces/{ns}/pods/{podname}/upgrade\", ota.HealthyCheck(rest, cfg.NodeName, ota.UpdatePod)).Methods(\"POST\") } 以上路由不做深入分析。 3.2. YurtHubProxyServerServing YurtHubProxyServerServing主要代理kube-apiserver的转发请求。 // start yurthub proxy servers for forwarding requests to cloud kube-apiserver if cfg.WorkingMode == util.WorkingModeEdge { proxyHandler = wrapNonResourceHandler(proxyHandler, cfg, rest) } if cfg.YurtHubProxyServerServing != nil { if err := cfg.YurtHubProxyServerServing.Serve(proxyHandler, 0, stopCh); err != nil { return err } } 以下分析yurtProxyHandler的逻辑。 3.3. NewYurtReverseProxyHandler NewYurtReverseProxyHandler主要创建了http handler 代理所有转发请求。 1、创建Load Balancer，主要用来转发apiserver的请求。 lb, err := remote.NewLoadBalancer( yurtHubCfg.LBMode, yurtHubCfg.RemoteServers, localCacheMgr, transportMgr, coordinatorGetter, cloudHealthChecker, yurtHubCfg.FilterManager, yurtHubCfg.WorkingMode, stopCh) 2、创建local Proxy，主要用来转发本地缓存的请求。 // When yurthub works in Edge mode, we may use local proxy or pool proxy to handle // the request when offline. localProxy = local.NewLocalProxy(localCacheMgr, cloudHealthChecker.IsHealthy, isCoordinatorHealthy, yurtHubCfg.MinRequestTimeout, ) localProxy = local.WithFakeTokenInject(localProxy, yurtHubCfg.SerializerManager) 3、创建yurtReverseProxy yurtProxy := &yurtReverseProxy{ resolver: resolver, loadBalancer: lb, cloudHealthChecker: cloudHealthChecker, coordinatorHealtCheckerGetter: coordinatorHealthCheckerGetter, localProxy: localProxy, poolProxy: poolProxy, maxRequestsInFlight: yurtHubCfg.MaxRequestInFlight, isCoordinatorReady: isCoordinatorReady, enablePoolCoordinator: yurtHubCfg.EnableCoordinator, tenantMgr: tenantMgr, workingMode: yurtHubCfg.WorkingMode, } return yurtProxy.buildHandlerChain(yurtProxy), nil 4. yurtReverseProxy yurtReverseProxy主要是作为实现反向代理的结构体。 type yurtReverseProxy struct { resolver apirequest.RequestInfoResolver loadBalancer remote.LoadBalancer cloudHealthChecker healthchecker.MultipleBackendsHealthChecker coordinatorHealtCheckerGetter func() healthchecker.HealthChecker localProxy http.Handler poolProxy http.Handler maxRequestsInFlight int tenantMgr tenant.Interface isCoordinatorReady func() bool workingMode hubutil.WorkingMode enablePoolCoordinator bool } 反向代理服务 func (p *yurtReverseProxy) ServeHTTP(rw http.ResponseWriter, req *http.Request) { if p.workingMode == hubutil.WorkingModeCloud { p.loadBalancer.ServeHTTP(rw, req) return } switch { case util.IsKubeletLeaseReq(req): p.handleKubeletLease(rw, req) case util.IsEventCreateRequest(req): p.eventHandler(rw, req) case util.IsPoolScopedResouceListWatchRequest(req): p.poolScopedResouceHandler(rw, req) case util.IsSubjectAccessReviewCreateGetRequest(req): p.subjectAccessReviewHandler(rw, req) default: // For resource request that do not need to be handled by pool-coordinator, // handling the request with cloud apiserver or local cache. if p.cloudHealthChecker.IsHealthy() { p.loadBalancer.ServeHTTP(rw, req) } else { p.localProxy.ServeHTTP(rw, req) } } } 核心逻辑：如果是云端apiserver可以访问的通，则通过loadbalaner来转发，否则就通过localproxy来转发读取本地节点的数据。 5. LoadBalancer LoadBalancer是个本地的负载均衡逻辑，通过轮询的方式去请求cloud的apiserver，当云边网络通信是正常的时候反向代理apiserver的请求，并做本地缓存持久化。断网的时候则读取本地的缓存数据。 backends：真实反向代理的后端 algo: 处理负载均衡策略，轮询或者按优先级 localCacheMgr: 本地缓存管理的manager 代码：/pkg/yurthub/proxy/remote/loadbalancer.go type loadBalancer struct { backends []*util.RemoteProxy algo loadBalancerAlgo localCacheMgr cachemanager.CacheManager filterManager *manager.Manager coordinatorGetter func() poolcoordinator.Coordinator workingMode hubutil.WorkingMode stopCh 5.1. NewLoadBalancer NewLoadBalancer构建一个remote的反向代理，主要包含添加romote server proxy和处理负载均衡策略两部分。 1、添加多个apiserver的地址，创建remote proxy实现反向代理操作。 backends := make([]*util.RemoteProxy, 0, len(remoteServers)) for i := range remoteServers { b, err := util.NewRemoteProxy(remoteServers[i], lb.modifyResponse, lb.errorHandler, transportMgr, stopCh) if err != nil { klog.Errorf(\"could not new proxy backend(%s), %v\", remoteServers[i].String(), err) continue } backends = append(backends, b) } 2、处理负载均衡策略： var algo loadBalancerAlgo switch lbMode { case \"rr\": algo = &rrLoadBalancerAlgo{backends: backends, checker: healthChecker} case \"priority\": algo = &priorityLoadBalancerAlgo{backends: backends, checker: healthChecker} default: algo = &rrLoadBalancerAlgo{backends: backends, checker: healthChecker} } 5.2. loadBalancer.ServeHTTP loadBalancer实现ServeHTTP的接口，通过负载均衡策略挑选出一个可用的反向代理backend。再调用backend的ServeHTTP方法实现具体的反向代理操作。 // pick a remote proxy based on the load balancing algorithm. rp := lb.algo.PickOne() rp.ServeHTTP(rw, req) 5.3. errorHandler 如果请求apiserver失败，当verb=get/list, 则读取cache中的内容。 func (lb *loadBalancer) errorHandler(rw http.ResponseWriter, req *http.Request, err error) { klog.Errorf(\"remote proxy error handler: %s, %v\", hubutil.ReqString(req), err) if lb.localCacheMgr == nil || !lb.localCacheMgr.CanCacheFor(req) { rw.WriteHeader(http.StatusBadGateway) return } ctx := req.Context() if info, ok := apirequest.RequestInfoFrom(ctx); ok { if info.Verb == \"get\" || info.Verb == \"list\" { # 读取cache内容 if obj, err := lb.localCacheMgr.QueryCache(req); err == nil { hubutil.WriteObject(http.StatusOK, obj, rw, req) return } } } rw.WriteHeader(http.StatusBadGateway) } 5.4. QueryCache // QueryCache get runtime object from backend storage for request func (cm *cacheManager) QueryCache(req *http.Request) (runtime.Object, error) { ctx := req.Context() info, ok := apirequest.RequestInfoFrom(ctx) if !ok || info == nil || info.Resource == \"\" { return nil, fmt.Errorf(\"failed to get request info for request %s\", util.ReqString(req)) } if !info.IsResourceRequest { return nil, fmt.Errorf(\"failed to QueryCache for getting non-resource request %s\", util.ReqString(req)) } # 根据verb查询storage中的数据 switch info.Verb { case \"list\": return cm.queryListObject(req) case \"get\", \"patch\", \"update\": return cm.queryOneObject(req) default: return nil, fmt.Errorf(\"failed to QueryCache, unsupported verb %s of request %s\", info.Verb, util.ReqString(req)) } } 5.5. 查询storage中的数据 func (cm *cacheManager) queryOneObject(req *http.Request) (runtime.Object, error) { ... klog.V(4).Infof(\"component: %s try to get key: %s\", comp, key.Key()) obj, err := cm.storage.Get(key) if err != nil { klog.Errorf(\"failed to get obj %s from storage, %v\", key.Key(), err) return nil, err } ... } 目前存储有两种接口实现，一个是本地磁盘存储，一个是etcd存储。以下以磁盘存储为例分析。 代码：/pkg/yurthub/storage/disk/storage.go // Get will get content from the regular file that specified by key. // If key points to a dir, return ErrKeyHasNoContent. func (ds *diskStorage) Get(key storage.Key) ([]byte, error) { if err := utils.ValidateKey(key, storageKey{}); err != nil { return []byte{}, storage.ErrKeyIsEmpty } storageKey := key.(storageKey) if !ds.lockKey(storageKey) { return nil, storage.ErrStorageAccessConflict } defer ds.unLockKey(storageKey) path := filepath.Join(ds.baseDir, storageKey.Key()) buf, err := ds.fsOperator.Read(path) switch err { case nil: return buf, nil case fs.ErrNotExists: return nil, storage.ErrStorageNotFound case fs.ErrIsNotFile: return nil, storage.ErrKeyHasNoContent default: return buf, fmt.Errorf(\"failed to read file at %s, %v\", path, err) } } 6. RemoteProxy RemoteProxy实现一个具体的反向代理操作。 字段说明： reverseProxy：http的ReverseProxy remoteServer：apiserver的地址 代码：/pkg/yurthub/proxy/util/remote.go // RemoteProxy is an reverse proxy for remote server type RemoteProxy struct { reverseProxy *httputil.ReverseProxy remoteServer *url.URL currentTransport http.RoundTripper bearerTransport http.RoundTripper upgradeHandler *proxy.UpgradeAwareHandler bearerUpgradeHandler *proxy.UpgradeAwareHandler stopCh 实现ReverseProxy的ServeHTTP接口。 func (rp *RemoteProxy) ServeHTTP(rw http.ResponseWriter, req *http.Request) { if httpstream.IsUpgradeRequest(req) { klog.V(5).Infof(\"get upgrade request %s\", req.URL) if isBearerRequest(req) { rp.bearerUpgradeHandler.ServeHTTP(rw, req) } else { rp.upgradeHandler.ServeHTTP(rw, req) } return } rp.reverseProxy.ServeHTTP(rw, req) } 实现错误处理的接口。 func (r *responder) Error(w http.ResponseWriter, req *http.Request, err error) { klog.Errorf(\"failed while proxying request %s, %v\", req.URL, err) http.Error(w, err.Error(), http.StatusInternalServerError) } 7. LocalProxy LocalProxy是一个当云边网络断开的时候，用于处理本地kubelet请求的数据的代理。 字段说明： cacheMgr：主要包含本地cache的一个处理管理器。 代码：/pkg/yurthub/proxy/local/local.go // LocalProxy is responsible for handling requests when remote servers are unhealthy type LocalProxy struct { cacheMgr manager.CacheManager isCloudHealthy IsHealthy isCoordinatorReady IsHealthy minRequestTimeout time.Duration } LocalProxy实现ServeHTTP接口，根据不同的k8s请求类型，执行不同的操作： watch：lp.localWatch(w, req) create：lp.localPost(w, req) delete, deletecollection: localDelete(w, req) list., get, update：lp.localReqCache(w, req) // ServeHTTP implements http.Handler for LocalProxy func (lp *LocalProxy) ServeHTTP(w http.ResponseWriter, req *http.Request) { var err error ctx := req.Context() if reqInfo, ok := apirequest.RequestInfoFrom(ctx); ok && reqInfo != nil && reqInfo.IsResourceRequest { klog.V(3).Infof(\"go into local proxy for request %s\", hubutil.ReqString(req)) switch reqInfo.Verb { case \"watch\": err = lp.localWatch(w, req) case \"create\": err = lp.localPost(w, req) case \"delete\", \"deletecollection\": err = localDelete(w, req) default: // list, get, update err = lp.localReqCache(w, req) } if err != nil { klog.Errorf(\"could not proxy local for %s, %v\", hubutil.ReqString(req), err) util.Err(err, w, req) } } else { klog.Errorf(\"local proxy does not support request(%s), requestInfo: %s\", hubutil.ReqString(req), hubutil.ReqInfoString(reqInfo)) util.Err(apierrors.NewBadRequest(fmt.Sprintf(\"local proxy does not support request(%s)\", hubutil.ReqString(req))), w, req) } } 7.1. localReqCache 当边缘网络断连的时候，kubelet执行get list的操作时，通过localReqCache请求本地缓存的数据，返回给kubelet对应的k8s元数据。 // localReqCache handles Get/List/Update requests when remote servers are unhealthy func (lp *LocalProxy) localReqCache(w http.ResponseWriter, req *http.Request) error { if !lp.cacheMgr.CanCacheFor(req) { klog.Errorf(\"can not cache for %s\", hubutil.ReqString(req)) return apierrors.NewBadRequest(fmt.Sprintf(\"can not cache for %s\", hubutil.ReqString(req))) } obj, err := lp.cacheMgr.QueryCache(req) if errors.Is(err, storage.ErrStorageNotFound) || errors.Is(err, hubmeta.ErrGVRNotRecognized) { klog.Errorf(\"object not found for %s\", hubutil.ReqString(req)) reqInfo, _ := apirequest.RequestInfoFrom(req.Context()) return apierrors.NewNotFound(schema.GroupResource{Group: reqInfo.APIGroup, Resource: reqInfo.Resource}, reqInfo.Name) } else if err != nil { klog.Errorf(\"failed to query cache for %s, %v\", hubutil.ReqString(req), err) return apierrors.NewInternalError(err) } else if obj == nil { klog.Errorf(\"no cache object for %s\", hubutil.ReqString(req)) return apierrors.NewInternalError(fmt.Errorf(\"no cache object for %s\", hubutil.ReqString(req))) } return util.WriteObject(http.StatusOK, obj, w, req) } 核心代码为： 查询本地缓存，返回缓存数据。 obj, err := lp.cacheMgr.QueryCache(req) return util.WriteObject(http.StatusOK, obj, w, req) 总结 yurthub是实现边缘断网自治的核心组件，核心逻辑是kubelet向apiserver的请求会通过yurhub进行转发，如果apiserver的接口可通，则将请求结果返回，并存储到本地，如果接口不可通，则读取本地的数据。 yurthub本质是一个反向代理的http server, 核心逻辑主要包括 ： - proxy: 反向代理的实现 - cachemanager：cache的实现 - storage：本地存储的实现 参考： https://github.com/openyurtio/openyurt Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"edge/openyurt/code-analysis/tunnel-server-code-analysis.html":{"url":"edge/openyurt/code-analysis/tunnel-server-code-analysis.html","title":"TunnelServer源码分析（1）","keywords":"","body":"1. Tunnel-server简介2. NewYurttunnelServerCommand3. Run(cfg.Complete(), stopCh)3. TunnelServer4. runAgentServer5. Interceptor 本文以commit id：180282663457080119a1bc6076cce20c922b5c50， 对应版本tag: v1.2.1 的源码分析tunnel-server的实现逻辑。 1. Tunnel-server简介 云与边一般位于不同网络平面，同时边缘节点普遍位于防火墙内部，采用云(中心)边协同架构，将导致原生 K8s 系统的运维监控能力面临如下挑战: K8s 原生运维能力缺失(如 kubectl logs/exec 等无法执行) 社区主流监控运维组件无法工作(如 Prometheus/metrics-server ) 在 OpenYurt 中，引入了专门的组件 YurtTunnel 负责解决云边通信问题。反向通道是解决跨网络通信的一种常见方式，而 YurtTunnel 的本质也是一个反向通道。 它是一个典型的C/S结构的组件，由部署于云端的 YurtTunnelServer 和部署于边缘节点上的 YurtTunnelAgent组成。 本文主要分析tunnel-server的代理逻辑。 以下是基本架构图： pkg中的目录结构： yurttunnel ├── agent # tunnel agent代码 ├── constants # 常量值 ├── handlerwrapper ├── informers ├── kubernetes # k8s clientset工具包 ├── server # 核心代码： tunnel server逻辑 ├── trafficforward # iptables和dns操作 └── util 2. NewYurttunnelServerCommand main函数入口： func main() { cmd := app.NewYurttunnelServerCommand(stop) cmd.Flags().AddGoFlagSet(flag.CommandLine) if err := cmd.Execute(); err != nil { klog.Fatalf(\"%s failed: %s\", projectinfo.GetServerName(), err) } } 以下是NewYurttunnelServerCommand构造函数，常见的三件套，不做展开： 读取参数：serverOptions.AddFlags(cmd.Flags()) 生成配置：cfg.Complete() 执行Run函数：核心逻辑 func NewYurttunnelServerCommand(stopCh 3. Run(cfg.Complete(), stopCh) Run函数最终是运行一个tunnelserver的常驻进程。在之前会做一些controller或manager的准备工作。 其中包括： DNS controller IP table manager certificate manager RegisterInformersForTunnelServer 首先是构建并运行上述的manager或controller， 源码中的注释也大概描述了主要流程： 注册tunnel所需的SharedInformerFactory 运行dns controller 运行ip table manager 给tunnel server创建certificate manager 给tunnel agent 创建certificate manager 创建handler wrappers 生成TLS 证书 运行tunnel server 以下是部分代码，已删除无关紧要的代码： // run starts the yurttunel-server func Run(cfg *config.CompletedConfig, stopCh 3. TunnelServer anpTunnelServer实现了TunnelServer的接口，以下分析TunnelServer.Run的部分。 run部分主要运行了三个server proxyServer： 主要是重定向apiserver的请求到tunnel server MasterServer： AgentServer：主要运行一个grpc server与tunnel agent连接，即云边反向隧道 代码参考：/pkg/yurttunnel/server/anpserver.go // Run runs the yurttunnel-server func (ats *anpTunnelServer) Run() error { proxyServer := anpserver.NewProxyServer(uuid.New().String(), []anpserver.ProxyStrategy{anpserver.ProxyStrategy(ats.proxyStrategy)}, ats.serverCount, &anpserver.AgentTokenAuthenticationOptions{}) // 1. start the proxier proxierErr := runProxier( &anpserver.Tunnel{Server: proxyServer}, ats.egressSelectorEnabled, ats.interceptorServerUDSFile, ats.tlsCfg) wrappedHandler, err := wh.WrapHandler( NewRequestInterceptor(ats.interceptorServerUDSFile, ats.proxyClientTlsCfg), ats.wrappers, ) // 2. start the master server masterServerErr := runMasterServer( wrappedHandler, ats.egressSelectorEnabled, ats.serverMasterAddr, ats.serverMasterInsecureAddr, ats.tlsCfg) // 3. start the agent server agentServerErr := runAgentServer(ats.tlsCfg, ats.serverAgentAddr, proxyServer) return nil } 4. runAgentServer runAgentServer主要运行一个grpc server与edge端的agent形成grpc连接。 // runAgentServer runs a grpc server that handles connections from the yurttunel-agent // NOTE agent server is responsible for managing grpc connection yurttunel-server // and yurttunnel-agent, and the proxy server is responsible for redirecting requests // to corresponding yurttunel-agent func runAgentServer(tlsCfg *tls.Config, agentServerAddr string, proxyServer *anpserver.ProxyServer) error { serverOption := grpc.Creds(credentials.NewTLS(tlsCfg)) ka := keepalive.ServerParameters{ // Ping the client if it is idle for `Time` seconds to ensure the // connection is still active Time: constants.YurttunnelANPGrpcKeepAliveTimeSec * time.Second, // Wait `Timeout` second for the ping ack before assuming the // connection is dead Timeout: constants.YurttunnelANPGrpcKeepAliveTimeoutSec * time.Second, } grpcServer := grpc.NewServer(serverOption, grpc.KeepaliveParams(ka)) anpagent.RegisterAgentServiceServer(grpcServer, proxyServer) listener, err := net.Listen(\"tcp\", agentServerAddr) klog.Info(\"start handling connection from agents\") if err != nil { return fmt.Errorf(\"fail to listen to agent on %s: %w\", agentServerAddr, err) } go grpcServer.Serve(listener) return nil } 5. Interceptor Interceptor（请求拦截器）拦截kube-apiserver的请求转发给tunnel，通过tunnel请求kubelet。 流程图： 代码地址：/pkg/yurttunnel/server/interceptor.go // NewRequestInterceptor creates a interceptor object that intercept request from kube-apiserver func NewRequestInterceptor(udsSockFile string, cfg *tls.Config) *RequestInterceptor { if len(udsSockFile) == 0 || cfg == nil { return nil } cfg.InsecureSkipVerify = true contextDialer := func(addr string, header http.Header, isTLS bool) (net.Conn, error) { klog.V(4).Infof(\"Sending request to %q.\", addr) proxyConn, err := net.Dial(\"unix\", udsSockFile) if err != nil { return nil, fmt.Errorf(\"dialing proxy %q failed: %w\", udsSockFile, err) } var connectHeaders string for _, h := range supportedHeaders { if v := header.Get(h); len(v) != 0 { connectHeaders = fmt.Sprintf(\"%s\\r\\n%s: %s\", connectHeaders, h, v) } } fmt.Fprintf(proxyConn, \"CONNECT %s HTTP/1.1\\r\\nHost: localhost%s\\r\\n\\r\\n\", addr, connectHeaders) br := newBufioReader(proxyConn) defer putBufioReader(br) res, err := http.ReadResponse(br, nil) if err != nil { proxyConn.Close() return nil, fmt.Errorf(\"reading HTTP response from CONNECT to %s via proxy %s failed: %w\", addr, udsSockFile, err) } if res.StatusCode != 200 { proxyConn.Close() return nil, fmt.Errorf(\"proxy error from %s while dialing %s, code %d: %v\", udsSockFile, addr, res.StatusCode, res.Status) } // if the request scheme is https, setup a tls connection over the // proxy tunnel (i.e. interceptor kubelet) if isTLS { tlsTunnelConn := tls.Client(proxyConn, cfg) if err := tlsTunnelConn.Handshake(); err != nil { proxyConn.Close() return nil, fmt.Errorf(\"fail to setup TLS handshake through the Tunnel: %w\", err) } klog.V(4).Infof(\"successfully setup TLS connection to %q with headers: %s\", addr, connectHeaders) return tlsTunnelConn, nil } klog.V(2).Infof(\"successfully setup connection to %q with headers: %q\", addr, connectHeaders) return proxyConn, nil } return &RequestInterceptor{ contextDialer: contextDialer, } } 参考： https://openyurt.io/zh/docs/core-concepts/yurttunnel Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"edge/openyurt/code-analysis/tennel-agent-code-analysis.html":{"url":"edge/openyurt/code-analysis/tennel-agent-code-analysis.html","title":"Tunnel-Agent源码分析","keywords":"","body":"1. Tunnel-Agent简介2. NewYurttunnelAgentCommand3. Run4. TunnelAgent5. anpTunnelAgent.Run1. Tunnel-Agent简介 tunnel-agent是通过daemonset部署在每个worker节点，通过grpc协议与云端的tunnel-server建立连接。以下分析tunnel-agent的源码逻辑。 常用的启动参数： - args: - --node-name=$(NODE_NAME) - --node-ip=$(POD_IP) - --tunnelserver-addr=tunnel-server-address[ip:port] - --v=2 command: - yurt-tunnel-agent 2. NewYurttunnelAgentCommand NewYurttunnelAgentCommand还是常用命令代码三板斧，此处不做展开，直接分析Run函数。 // NewYurttunnelAgentCommand creates a new yurttunnel-agent command func NewYurttunnelAgentCommand(stopCh 3. Run Run函数即启动一个agent服务，主要包含以下几个步骤： 先获取配置项tunnelserver-addr中的地址，如果地址不存在，则获取x-tunnel-server-svc的service 地址。（说明：一般情况下，tunnel-server跟agent不在同一个网络域，因此网络会不通，所以一般需要配置独立且可连通的地址，可以通过Nginx转发） agent通过host的网络模式运行在宿主机上，启动证书manager。并等待证书生成。 生成连接tunnel-server的证书。 启动 yurttunnel-agent。 启动meta server。 // Run starts the yurttunel-agent func Run(cfg *config.CompletedConfig, stopCh 4. TunnelAgent TunnelAgent与tunnel-server建立tunnel，接收server的请求，并转发给kubelet。 // TunnelAgent sets up tunnel to TunnelServer, receive requests // from tunnel, and forwards requests to kubelet type TunnelAgent interface { Run( 5. anpTunnelAgent.Run anpTunnelAgent使用apiserver-network-proxy包来实现tunnel逻辑。项目具体参考：https://github.com/kubernetes-sigs/apiserver-network-proxy) 代码：/pkg/yurttunnel/agent/anpagent.go // RunAgent runs the yurttunnel-agent which will try to connect yurttunnel-server func (ata *anpTunnelAgent) Run(stopChan 以下是apiserver-network-proxy的源码分析，待补充。 参考： /pkg/yurttunnel/agent/anpagent.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"kvm/vm-concept.html":{"url":"kvm/vm-concept.html","title":"虚拟化相关概念","keywords":"","body":"1. 虚拟化2. 虚拟化的类型3. hypervisor4. kvm5. qemu6. libvirt1. 虚拟化 借助虚拟化技术，用户能以单个物理硬件系统为基础，创建多个模拟环境或专用资源，并使用一款名为“Hypervisor”（虚拟机监控程序）的软件直接连接到硬件，从而将一个系统划分为不同、单独而安全的环境，即虚拟机 (VM)。 虚拟化技术可以重新划分IT资源，提高资源的利用率。 2. 虚拟化的类型 全虚拟化（Full virtualization） 全虚拟化使用未修改的guest操作系统版本，guest直接与CPU通信，是最快的虚拟化方法。 半虚拟化（Paravirtualization） 半虚拟化使用修改过的guest操作系统，guest与hypervisor通信，hypervisor将guest的调用传递给CPU和其他接口。因为通信经过hypervisor，因此比全虚拟化慢。 3. hypervisor hypervisor又称为 virtual machine monitor (VMM)，是一个创建和运行虚拟机的程序。被 hypervisor 用来运行一个或多个虚拟机的计算机称为宿主机（host machine），这些虚拟机则称为客户机（guest machine）。 4. kvm kvm(Kernel-based Virtual Machine)是Linux内核的虚拟化模块，可以利用Linux内核的功能来作为hypervisor。 KVM本身不进行模拟，而是暴露一个/dev/kvm接口。 使用KVM，可以在Linux的镜像上 5. qemu QEMU（quick emulator） 待补充 6. libvirt libvirt是一个硬件虚拟化的管理工具API，可用于KVM、QEMU等虚拟化技术， 参考： https://access.redhat.com/documentation/en-us/red_hat_enterprise_linux/6/html/virtualization_getting_started_guide/chap-virtualization_getting_started-what_is_it https://en.wikipedia.org/wiki/Hypervisor https://www.linux-kvm.org/page/Main_Page http://www.linux-kvm.org/page/Documents https://en.wikipedia.org/wiki/Kernel-based_Virtual_Machine https://wiki.qemu.org/Index.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"kvm/kubevirt/_index.html":{"url":"kvm/kubevirt/_index.html","title":"KubeVirt","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"kvm/kubevirt/kubevirt-introduction.html":{"url":"kvm/kubevirt/kubevirt-introduction.html","title":"KubeVirt的介绍","keywords":"","body":"1. kubevirt简介2. 架构图2.1. 组件说明2.2. 自定义CRD对象3. 创建虚拟机流程 本文主要由云原生虚拟化：基于 Kubevirt 构建边缘计算实例文章重新整理而成。 1. kubevirt简介 kubevirt是基于k8s之上，提供了一种通过k8s来编排和管理虚拟机的方式。 2. 架构图 图片 - arch 2.1. 组件说明 分类 组件 部署方式 功能说明 控制面 virt-api deployment 自定义API，开机、关机、重启等，作为apiserver的插件，业务通过k8s apiserver请求virt-api。 virt-controller deployment 管理和监控VMI对象的状态，控制VMI下的pod。 节点侧 virt-handler daemonset 类似kubelet，管理宿主机上的所有虚拟机实例。 virt-launcher virt-handler pod 调用libvirt和qemu创建虚拟机进程。 virt-launcher与libvirt逻辑： 2.2. 自定义CRD对象 分类 CRD对象 功能说明 虚机 VirtualMachineInstance（VMI） 代表运行的虚拟机实例 VirtualMachine（VM） 虚机对象，提供开机、关机、重启，管理VMI实例，与VMI的关系是1：1 3. 创建虚拟机流程 待补充 参考： https://github.com/kubevirt/kubevirt Architecture - KubeVirt User-Guide https://mp.weixin.qq.com/s/IwA1QcGaooZAL96YjvTqjA Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"kvm/kubevirt/kubevirt-installation.html":{"url":"kvm/kubevirt/kubevirt-installation.html","title":"KubeVirt的使用","keywords":"","body":"1. 安装kubevirt1.1. 修改镜像仓库1.2. 部署virt-operator1.3. 部署virtctl2. kubevirt部署产物3. 创建虚拟机1. 安装kubevirt 1.1. 修改镜像仓库 针对私有环境，需要将所需镜像上传到自己的镜像仓库中。 涉及的镜像组件有 virt-operator virt-api virt-controller virt-launcher 重命名镜像脚本如下: #!/bin/bash # kubevirt组件版本 version=$1 # 私有镜像仓库 registry=$2 # 私有镜像仓库的namespace namespace=$3 kubevirtRegistry=\"quay.io/kubevirt\" readonly APPLIST=( virt-operator virt-api virt-controller virt-launcher ) for app in \"${APPLIST[@]}\"; do # 拉取镜像 docker pull ${kubevirtRegistry}/${app}:${version} # 重命名 docker tag ${kubevirtRegistry}/${app}:${version} ${registry}/${namespace}/${app}:${version} # 推送镜像 docker push ${registry}/${namespace}/${app}:${version} done echo \"重新命名成功\" 1.2. 部署virt-operator 通过kubevirt operator安装kubevirt相关组件，选择指定版本，下载kubevirt-operator.yaml和kubevirt-cr.yaml文件，并创建k8s相关对象。 如果是私有镜像仓库，则需要将kubevirt-operator.yaml文件中镜像的名字替换为私有镜像仓库的地址，并提前按步骤1推送所需镜像到私有镜像仓库。 # Pick an upstream version of KubeVirt to install $ export RELEASE=v0.52.0 # Deploy the KubeVirt operator $ kubectl apply -f https://github.com/kubevirt/kubevirt/releases/download/${RELEASE}/kubevirt-operator.yaml # Create the KubeVirt CR (instance deployment request) which triggers the actual installation $ kubectl apply -f https://github.com/kubevirt/kubevirt/releases/download/${RELEASE}/kubevirt-cr.yaml # wait until all KubeVirt components are up $ kubectl -n kubevirt wait kv kubevirt --for condition=Available 1.3. 部署virtctl virtctl用来启动和关闭虚拟机。 VERSION=$(kubectl get kubevirt.kubevirt.io/kubevirt -n kubevirt -o=jsonpath=\"{.status.observedKubeVirtVersion}\") ARCH=$(uname -s | tr A-Z a-z)-$(uname -m | sed 's/x86_64/amd64/') || windows-amd64.exe echo ${ARCH} curl -L -o virtctl https://github.com/kubevirt/kubevirt/releases/download/${VERSION}/virtctl-${VERSION}-${ARCH} chmod +x virtctl sudo install virtctl /usr/local/bin 2. kubevirt部署产物 通过手动部署virt-operator，会自动部署以下组件 组件 部署方式 副本数 virt-api deployment 2 virt-controller deployment 2 virt-handler daemonset - 具体参考: #kg all -n kubevirt NAME READY STATUS RESTARTS AGE pod/virt-api-5fb5cffb7f-hgjjh 1/1 Running 0 23h pod/virt-api-5fb5cffb7f-jcp7x 1/1 Running 0 23h pod/virt-controller-844cd4f58c-h8vsx 1/1 Running 0 23h pod/virt-controller-844cd4f58c-vlxqs 1/1 Running 0 23h pod/virt-handler-lb5ft 1/1 Running 0 23h pod/virt-handler-mtr4d 1/1 Running 0 22h pod/virt-handler-sxd2t 1/1 Running 0 23h pod/virt-operator-8595f577cd-b9txg 1/1 Running 0 23h pod/virt-operator-8595f577cd-p2f69 1/1 Running 0 23h NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/kubevirt-operator-webhook ClusterIP 10.254.159.81 443/TCP 23h service/kubevirt-prometheus-metrics ClusterIP 10.254.7.231 443/TCP 23h service/virt-api ClusterIP 10.254.244.139 443/TCP 23h NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE daemonset.apps/virt-handler 3 3 3 3 3 kubernetes.io/os=linux 23h NAME READY UP-TO-DATE AVAILABLE AGE deployment.apps/virt-api 2/2 2 2 23h deployment.apps/virt-controller 2/2 2 2 23h deployment.apps/virt-operator 2/2 2 2 23h NAME DESIRED CURRENT READY AGE replicaset.apps/virt-api-5fb5cffb7f 2 2 2 23h replicaset.apps/virt-controller-844cd4f58c 2 2 2 23h replicaset.apps/virt-operator-8595f577cd 2 2 2 23h NAME AGE PHASE kubevirt.kubevirt.io/kubevirt 23h Deployed 3. 创建虚拟机 通过vm.yaml创建虚拟机 # 下载vm.yaml wget https://kubevirt.io/labs/manifests/vm.yaml # 创建虚拟机 kubectl apply -f https://kubevirt.io/labs/manifests/vm.yaml vm.yaml文件 apiVersion: kubevirt.io/v1 kind: VirtualMachine metadata: name: testvm spec: running: false template: metadata: labels: kubevirt.io/size: small kubevirt.io/domain: testvm spec: domain: devices: disks: - name: containerdisk disk: bus: virtio - name: cloudinitdisk disk: bus: virtio interfaces: - name: default masquerade: {} resources: requests: memory: 64M networks: - name: default pod: {} volumes: - name: containerdisk containerDisk: image: quay.io/kubevirt/cirros-container-disk-demo - name: cloudinitdisk cloudInitNoCloud: userDataBase64: SGkuXG4= 查看虚拟机 kubectl get vms kubectl get vms -o yaml testvm 启动或暂停虚拟机 # 启动虚拟机 virtctl start testvm # 关闭虚拟机 virtctl stop testvm # 进入虚拟机 virtctl console testvm 删除虚拟机 kubectl delete vm testvm 参考： Installation - KubeVirt User-Guide KubeVirt quickstart with Minikube | KubeVirt.io Use KubeVirt | KubeVirt.io Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"monitor/kubernetes-cluster-monitoring.html":{"url":"monitor/kubernetes-cluster-monitoring.html","title":"监控体系介绍","keywords":"","body":"1. 概述1.1. cAdvisor1.2. Heapster1.3. InfluxDB1.4. Grafana1.5. 总体架构图2. 部署与使用2.1. cAdvisor2.2. Service2.2.1. heapster-service2.2.2. influxdb-service2.2.3. grafana-service2.2.4. 创建service2.3. ReplicationController2.3.1. influxdb-grafana-controller2.3.2. heapster-controller2.3.3. 创建ReplicationController3. 查看界面及数据3.1. InfluxDB3.2. Grafana4. 容器化部署4.1. 拉取镜像4.2. 运行容器4.2.1. influxdb4.2.2. cadvisor4.2.3. grafana4.2.4. heapster4.3. 访问1. 概述 1.1. cAdvisor cAdvisor对Node机器上的资源及容器进行实时监控和性能数据采集，包括CPU使用情况、内存使用情况、网络吞吐量及文件系统使用情况，cAdvisor集成在Kubelet中，当kubelet启动时会自动启动cAdvisor，即一个cAdvisor仅对一台Node机器进行监控。kubelet的启动参数--cadvisor-port可以定义cAdvisor对外提供服务的端口，默认为4194。可以通过浏览器Node_IP:port访问。项目主页：http://github.com/google/cadvisor。 1.2. Heapster 是对集群中的各个Node、Pod的资源使用数据进行采集，通过访问每个Node上Kubelet的API，再通过Kubelet调用cAdvisor的API来采集该节点上所有容器的性能数据。由Heapster进行数据汇聚，保存到后端存储系统中，例如InfluxDB，Google Cloud Logging等。项目主页为：https://github.com/kubernetes/heapster。 1.3. InfluxDB 是分布式时序数据库（每条记录带有时间戳属性），主要用于实时数据采集、事件跟踪记录、存储时间图表、原始数据等。提供REST API用于数据的存储和查询。项目主页为http://InfluxDB.com。 1.4. Grafana 通过Dashboard将InfluxDB的时序数据展现成图表形式，便于查看集群运行状态。项目主页为http://Grafana.org。 1.5. 总体架构图 图片 - k8s监控架构图 其中当前Kubernetes中，Heapster、InfluxDB、Grafana均以Pod的形式启动和运行。Heapster与Master需配置安全连接。 2. 部署与使用 2.1. cAdvisor kubelet的启动参数--cadvisor-port可以定义cAdvisor对外提供服务的端口，默认为4194。可以通过浏览器Node_IP:port访问。也提供了REST API供客户端远程调用，API返回的格式为JSON，可以采用URL访问：http://`hostname`:`port`/api/`version`/`request`/ 例如：http://14.152.49.100:4194/api/v1.3/machine 获取主机信息。 2.2. Service 2.2.1. heapster-service heapster-service.yaml apiVersion:v1 kind:Service metadata: label: kubenetes.io/cluster-service:\"true\" kubernetes.io/name:Heapster name:heapster namespace:kube-system spec: ports: - port:80 targetPort:8082 selector: k8s-app:heapster 2.2.2. influxdb-service influxdb-service.yaml apiVersion:v1 kind:Service metadata: label:null name:monitoring-InfluxDB namespace:kube-system spec: type:Nodeport ports: - name:http port:80 targetPort:8083 - name:api port:8086 targetPort:8086 Nodeport:8086 selector: name:influxGrafana 2.2.3. grafana-service grafana-service.yaml apiVersion:v1 kind:Service metadata: label: kubenetes.io/cluster-service:\"true\" kubernetes.io/name:monitoring-Grafana name:monitoring-Grafana namespace:kube-system spec: type:Nodeport ports: port:80 targetPort:8080 Nodeport:8085 selector: name:influxGrafana 使用type=NodePort将InfluxDB和Grafana暴露在Node的端口上，以便通过浏览器进行访问。 2.2.4. 创建service kubectl create -f heapster-service.yaml kubectl create -f InfluxDB-service.yaml kubectl create -f Grafana-service.yaml 2.3. ReplicationController 2.3.1. influxdb-grafana-controller influxdb-grafana-controller-v3.yaml apiVersion:v1 kind:ReplicationController metadata: name:monitoring-influxdb-grafana-v3 namespace:kube-system labels: k8s-app:influxGrafana version:v3 kubernetes.io/cluster-service:\"true spec: replicas:1 selector: k8s-app:influxGrafana version:v3 template: metadata: labels: k8s-app:influxGrafana version:v3 kubernetes.io/cluster-service:\"true spec: containers: - image:gcr.io/google_containers/heapster_influxdb:v0.5 name:influxdb resources: limits: cpu:100m memory:500Mi requests: cpu:100m memory:500Mi ports: - containerPort:8083 - containerPort:8086 volumeMounts: -name:influxdb-persistent-storage mountPath:/data - image:grc.io/google_containers/heapster_grafana:v2.6.0-2 name:grafana resources: limits: cpu:100m memory:100Mi requests: cpu:100m memory:100Mi env: - name:INFLUXDB_SERVICE_URL value:http://monitoring-influxdb:8086 - name:GF_AUTH_BASIC_ENABLED value:\"false\" - name:GF_AUTH_ANONYMOUS_ENABLED value:\"true\" - name:GF_AUTH_ANONYMOUS_ORG_ROLE value:Admin - name:GF_SERVER_ROOT_URL value:/api/v1/proxy/namespace/kube-system/services/monitoring-grafana/ volumeMounts: - name:grafana-persistent-storage mountPath:/var volumes: - name:influxdb-persistent-storage emptyDir{} - name:grafana-persistent-storage emptyDir{} 2.3.2. heapster-controller heapster-controller.yaml apiVersion:v1 kind:ReplicationController metadata: labels: k8s-app:heapster name:heapster version:v6 name:heapster namespace:kube-system spec: replicas:1 selector: name:heapster k8s-app:heapster version:v6 template: metadata: labels: k8s-app:heapster version:v6 spec: containers: - image:gcr.io/google_containers/heapster:v0.17.0 name:heapster command: - /heapster - --source=kubernetes:http://192.168.1.128:8080?inClusterConfig=flase&kubeletHttps=true&useServiceAccount=true&auth= - --sink=InfluxDB:http://monitoring-InfluxDB:8086 Heapster设置启动参数说明： 1、–source 配置监控来源，本例中表示从k8s-Master获取各个Node的信息。在URL的参数部分，修改kubeletHttps、inClusterConfig、useServiceAccount的值。 2、–sink 配置后端的存储系统，本例中使用InfluxDB。URL中主机名的地址是InfluxDB的Service名字，需要DNS服务正常工作，如果没有配置DNS服务可使用Service的ClusterIP地址。 2.3.3. 创建ReplicationController kubelet create -f InfluxDB-Grafana-controller.yaml kubelet create -f heapster-controller.yaml 3. 查看界面及数据 3.1. InfluxDB 访问任意一台Node机器的30083端口。 3.2. Grafana 访问任意一台Node机器的30080端口。 4. 容器化部署 4.1. 拉取镜像 docker pull influxdb:latest docker pull cadvisor:latest docker pull grafana:latest docker pull heapster:latest 4.2. 运行容器 4.2.1. influxdb #influxdb docker run -d -p 8083:8083 -p 8086:8086 --expose 8090 --expose 8099 --volume=/opt/data/influxdb:/data --name influxsrv influxdb:latest 4.2.2. cadvisor #cadvisor docker run --volume=/:/rootfs:ro --volume=/var/run:/var/run:rw --volume=/sys:/sys:ro --volume=/var/lib/docker/:/var/lib/docker:ro --publish=8080:8080 --detach=true --link influxsrv:influxsrv --name=cadvisor cadvisor:latest -storage_driver=influxdb -storage_driver_db=cadvisor -storage_driver_host=influxsrv:8086 4.2.3. grafana #grafana docker run -d -p 3000:3000 -e INFLUXDB_HOST=influxsrv -e INFLUXDB_PORT=8086 -e INFLUXDB_NAME=cadvisor -e INFLUXDB_USER=root -e INFLUXDB_PASS=root --link influxsrv:influxsrv --name grafana grafana:latest 4.2.4. heapster docker run -d -p 8082:8082 --net=host heapster:canary --source=kubernetes:http://`k8s-server-ip`:8080?inClusterConfig=false/&useServiceAccount=false --sink=influxdb:http://`influxdb-ip`:8086 4.3. 访问 在浏览器输入IP:PORT Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"monitor/kube-promethus-stack.html":{"url":"monitor/kube-promethus-stack.html","title":"kube-prometheus-stack的使用","keywords":"","body":"1. kube-prometheus-stack简介2. 安装kube-prometheus-stack3. 查看安装结果4. 登录grafana1. kube-prometheus-stack简介 kube-prometheus-stack是prometheus监控k8s集群的套件，可以通过helm一键安装，同时带有监控的模板。 各组件包括 grafana kube-state-metrics prometheus alertmanager node-exporter 2. 安装kube-prometheus-stack 执行以下命令 helm repo add prometheus-community https://prometheus-community.github.io/helm-charts helm repo update helm install kube-prometheus-stack prometheus-community/kube-prometheus-stack -n prometheus 示例： # helm install kube-prometheus-stack prometheus-community/kube-prometheus-stack -n prometheus NAME: kube-prometheus-stack LAST DEPLOYED: Wed May 17 17:12:24 2023 NAMESPACE: prometheus STATUS: deployed REVISION: 1 NOTES: kube-prometheus-stack has been installed. Check its status by running: kubectl --namespace prometheus get pods -l \"release=kube-prometheus-stack\" Visit https://github.com/prometheus-operator/kube-prometheus for instructions on how to create & configure Alertmanager and Prometheus instances using the Operator. 3. 查看安装结果 deployment kube-prometheus-stack-grafana kube-prometheus-stack-kube-state-metrics kube-prometheus-stack-operator statefulset prometheus-kube-prometheus-stack-prometheus alertmanager-kube-prometheus-stack-alertmanager daemonset kube-prometheus-stack-prometheus-node-exporter # kg all -n prometheus NAME READY STATUS RESTARTS AGE pod/alertmanager-kube-prometheus-stack-alertmanager-0 2/2 Running 0 9m34s pod/kube-prometheus-stack-grafana-5bb7689dc8-lgrws 3/3 Running 0 9m35s pod/kube-prometheus-stack-kube-state-metrics-5d6578867c-25xbq 1/1 Running 0 9m35s pod/kube-prometheus-stack-operator-9c5fbdc68-nrn7h 1/1 Running 0 9m35s pod/kube-prometheus-stack-prometheus-node-exporter-8ghd8 1/1 Running 0 48s pod/kube-prometheus-stack-prometheus-node-exporter-brtp9 1/1 Running 0 29s pod/kube-prometheus-stack-prometheus-node-exporter-n4kdp 1/1 Running 0 88s pod/kube-prometheus-stack-prometheus-node-exporter-ttksv 1/1 Running 0 35s pod/prometheus-kube-prometheus-stack-prometheus-0 2/2 Running 0 9m34s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/alertmanager-operated ClusterIP None 9093/TCP,9094/TCP,9094/UDP 9m34s service/kube-prometheus-stack-alertmanager ClusterIP 10.99.108.180 9093/TCP 9m36s service/kube-prometheus-stack-grafana ClusterIP 10.110.62.28 80/TCP 9m36s service/kube-prometheus-stack-kube-state-metrics ClusterIP 10.110.105.139 8080/TCP 9m35s service/kube-prometheus-stack-operator ClusterIP 10.96.147.204 443/TCP 9m36s service/kube-prometheus-stack-prometheus ClusterIP 10.98.235.203 9090/TCP 9m36s service/kube-prometheus-stack-prometheus-node-exporter ClusterIP 10.105.99.77 9100/TCP 9m36s service/prometheus-operated ClusterIP None 9090/TCP 9m34s NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE daemonset.apps/kube-prometheus-stack-prometheus-node-exporter 4 4 4 4 4 9m35s NAME READY UP-TO-DATE AVAILABLE AGE deployment.apps/kube-prometheus-stack-grafana 1/1 1 1 9m35s deployment.apps/kube-prometheus-stack-kube-state-metrics 1/1 1 1 9m35s deployment.apps/kube-prometheus-stack-operator 1/1 1 1 9m35s NAME READY AGE statefulset.apps/alertmanager-kube-prometheus-stack-alertmanager 1/1 9m34s statefulset.apps/prometheus-kube-prometheus-stack-prometheus 1/1 9m34s 4. 登录grafana 默认账号密码 账号：admin 密码：prom-operator 默认账号密码位于secret中，通过base64解码可得上述密码。 kg secret -n prometheus kube-prometheus-stack-grafana -oyaml apiVersion: v1 data: admin-password: cHJvbS1vcGVyYXRvcg== admin-user: YWRtaW4= 模板内容： pod数据： 参考： https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack https://github.com/prometheus-operator/kube-prometheus Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"monitor/cadvisor-introduction.html":{"url":"monitor/cadvisor-introduction.html","title":"cAdvisor介绍","keywords":"","body":"1. cAdvisor简介2. cAdvisor结构图3. Metrics4. cAdvisor源码4.1. cAdvisor入口函数4.2. cAdvisor Client的使用4.2.1 client定义4.2.2. client方法1. cAdvisor简介 ​ cAdvisor对Node机器上的资源及容器进行实时监控和性能数据采集，包括CPU使用情况、内存使用情况、网络吞吐量及文件系统使用情况，cAdvisor集成在Kubelet中，当kubelet启动时会自动启动cAdvisor，即一个cAdvisor仅对一台Node机器进行监控。kubelet的启动参数--cadvisor-port可以定义cAdvisor对外提供服务的端口，默认为4194。可以通过浏览器访问。项目主页：http://github.com/google/cadvisor。 2. cAdvisor结构图 图片 - cAdvisor 3. Metrics 分类 字段 描述 cpu cpu_usage_total cpu_usage_system cpu_usage_user cpu_usage_per_cpu load_average Smoothed average of number of runnable threads x 1000 memory memory_usage Memory Usage memory_working_set Working set size network rx_bytes Cumulative count of bytes received rx_errors Cumulative count of receive errors encountered tx_bytes Cumulative count of bytes transmitted tx_errors Cumulative count of transmit errors encountered filesystem fs_device Filesystem device fs_limit Filesystem limit fs_usage Filesystem usage 4. cAdvisor源码 4.1. cAdvisor入口函数 cadvisor.go func main() { defer glog.Flush() flag.Parse() if *versionFlag { fmt.Printf(\"cAdvisor version %s (%s)/n\", version.Info[\"version\"], version.Info[\"revision\"]) os.Exit(0) } setMaxProcs() memoryStorage, err := NewMemoryStorage() if err != nil { glog.Fatalf(\"Failed to initialize storage driver: %s\", err) } sysFs, err := sysfs.NewRealSysFs() if err != nil { glog.Fatalf(\"Failed to create a system interface: %s\", err) } collectorHttpClient := createCollectorHttpClient(*collectorCert, *collectorKey) containerManager, err := manager.New(memoryStorage, sysFs, *maxHousekeepingInterval, *allowDynamicHousekeeping, ignoreMetrics.MetricSet, &collectorHttpClient) if err != nil { glog.Fatalf(\"Failed to create a Container Manager: %s\", err) } mux := http.NewServeMux() if *enableProfiling { mux.HandleFunc(\"/debug/pprof/\", pprof.Index) mux.HandleFunc(\"/debug/pprof/cmdline\", pprof.Cmdline) mux.HandleFunc(\"/debug/pprof/profile\", pprof.Profile) mux.HandleFunc(\"/debug/pprof/symbol\", pprof.Symbol) } // Register all HTTP handlers. err = cadvisorhttp.RegisterHandlers(mux, containerManager, *httpAuthFile, *httpAuthRealm, *httpDigestFile, *httpDigestRealm) if err != nil { glog.Fatalf(\"Failed to register HTTP handlers: %v\", err) } cadvisorhttp.RegisterPrometheusHandler(mux, containerManager, *prometheusEndpoint, nil) // Start the manager. if err := containerManager.Start(); err != nil { glog.Fatalf(\"Failed to start container manager: %v\", err) } // Install signal handler. installSignalHandler(containerManager) glog.Infof(\"Starting cAdvisor version: %s-%s on port %d\", version.Info[\"version\"], version.Info[\"revision\"], *argPort) addr := fmt.Sprintf(\"%s:%d\", *argIp, *argPort) glog.Fatal(http.ListenAndServe(addr, mux)) } 核心代码： memoryStorage, err := NewMemoryStorage() sysFs, err := sysfs.NewRealSysFs() #创建containerManager containerManager, err := manager.New(memoryStorage, sysFs, *maxHousekeepingInterval, *allowDynamicHousekeeping, ignoreMetrics.MetricSet, &collectorHttpClient) #启动containerManager err := containerManager.Start() 4.2. cAdvisor Client的使用 import \"github.com/google/cadvisor/client\" func main(){ client, err := client.NewClient(\"http://192.168.19.30:4194/\") //http://:/ } 4.2.1 client定义 cadvisor/client/client.go // Client represents the base URL for a cAdvisor client. type Client struct { baseUrl string } // NewClient returns a new v1.3 client with the specified base URL. func NewClient(url string) (*Client, error) { if !strings.HasSuffix(url, \"/\") { url += \"/\" } return &Client{ baseUrl: fmt.Sprintf(\"%sapi/v1.3/\", url), }, nil } 4.2.2. client方法 1）MachineInfo // MachineInfo returns the JSON machine information for this client. // A non-nil error result indicates a problem with obtaining // the JSON machine information data. func (self *Client) MachineInfo() (minfo *v1.MachineInfo, err error) { u := self.machineInfoUrl() ret := new(v1.MachineInfo) if err = self.httpGetJsonData(ret, nil, u, \"machine info\"); err != nil { return } minfo = ret return } 2）ContainerInfo // ContainerInfo returns the JSON container information for the specified // container and request. func (self *Client) ContainerInfo(name string, query *v1.ContainerInfoRequest) (cinfo *v1.ContainerInfo, err error) { u := self.containerInfoUrl(name) ret := new(v1.ContainerInfo) if err = self.httpGetJsonData(ret, query, u, fmt.Sprintf(\"container info for %q\", name)); err != nil { return } cinfo = ret return } 3）DockerContainer // Returns the JSON container information for the specified // Docker container and request. func (self *Client) DockerContainer(name string, query *v1.ContainerInfoRequest) (cinfo v1.ContainerInfo, err error) { u := self.dockerInfoUrl(name) ret := make(map[string]v1.ContainerInfo) if err = self.httpGetJsonData(&ret, query, u, fmt.Sprintf(\"Docker container info for %q\", name)); err != nil { return } if len(ret) != 1 { err = fmt.Errorf(\"expected to only receive 1 Docker container: %+v\", ret) return } for _, cont := range ret { cinfo = cont } return } 4）AllDockerContainers // Returns the JSON container information for all Docker containers. func (self *Client) AllDockerContainers(query *v1.ContainerInfoRequest) (cinfo []v1.ContainerInfo, err error) { u := self.dockerInfoUrl(\"/\") ret := make(map[string]v1.ContainerInfo) if err = self.httpGetJsonData(&ret, query, u, \"all Docker containers info\"); err != nil { return } cinfo = make([]v1.ContainerInfo, 0, len(ret)) for _, cont := range ret { cinfo = append(cinfo, cont) } return } Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"monitor/heapster-introduction.html":{"url":"monitor/heapster-introduction.html","title":"Heapster介绍","keywords":"","body":"1. heapster简介2. heapster部署与配置2.1. 注意事项2.2. 容器式部署2.3. 配置说明2.3.1. –source2.3.2. –sink3. Metrics4. Labels5. heapster API1. heapster简介 Heapster是容器集群监控和性能分析工具，天然的支持Kubernetes和CoreOS。 Kubernetes有个出名的监控agent—cAdvisor。在每个kubernetes Node上都会运行cAdvisor，它会收集本机以及容器的监控数据(cpu,memory,filesystem,network,uptime)。 2. heapster部署与配置 2.1. 注意事项 需同步部署机器和被采集机器的时间：ntpdate time.windows.com 加入定时任务，定期同步时间 crontab –e 30 5 * /usr/sbin/ntpdate time.windows.com //每天早晨5点半执行 2.2. 容器式部署 #拉取镜像 docker pull heapster:latest #运行容器 docker run -d -p 8082:8082 --net=host heapster:latest --source=kubernetes:http://:8080?inClusterConfig=false\\&useServiceAccount=false --sink=influxdb:http://:8086?db= 2.3. 配置说明 可以参考官方文档 2.3.1. –source –source: 指定数据获取源。这里指定kube-apiserver即可。 后缀参数： inClusterConfig: kubeletPort: 指定kubelet的使用端口，默认10255 kubeletHttps: 是否使用https去连接kubelets(默认：false) apiVersion: 指定K8S的apiversion insecure: 是否使用安全证书(默认：false) auth: 安全认证 useServiceAccount: 是否使用K8S的安全令牌 2.3.2. –sink –sink: 指定后端数据存储。这里指定influxdb数据库。 后缀参数： user: InfluxDB用户 pw: InfluxDB密码 db: 数据库名 secure: 安全连接到InfluxDB(默认：false) withfields： 使用InfluxDB fields(默认：false)。 3. Metrics 分类 Metric Name Description 备注 cpu cpu/limit CPU hard limit in millicores. CPU上限 cpu/node_capacity Cpu capacity of a node. Node节点的CPU容量 cpu/node_allocatable Cpu allocatable of a node. Node节点可分配的CPU cpu/node_reservation Share of cpu that is reserved on the node allocatable. cpu/node_utilization CPU utilization as a share of node allocatable. cpu/request CPU request (the guaranteed amount of resources) in millicores. cpu/usage Cumulative CPU usage on all cores. CPU总使用量 cpu/usage_rate CPU usage on all cores in millicores. filesystem filesystem/usage Total number of bytes consumed on a filesystem. 文件系统的使用量 filesystem/limit The total size of filesystem in bytes. 文件系统的使用上限 filesystem/available The number of available bytes remaining in a the filesystem 可用的文件系统容量 filesystem/inodes The number of available inodes in a the filesystem filesystem/inodes_free The number of free inodes remaining in a the filesystem memory memory/limit Memory hard limit in bytes. 内存上限 memory/major_page_faults Number of major page faults. memory/major_page_faults_rate Number of major page faults per second. memory/node_capacity Memory capacity of a node. memory/node_allocatable Memory allocatable of a node. memory/node_reservation Share of memory that is reserved on the node allocatable. memory/node_utilization Memory utilization as a share of memory allocatable. memory/page_faults Number of page faults. memory/page_faults_rate Number of page faults per second. memory/request Memory request (the guaranteed amount of resources) in bytes. memory/usage Total memory usage. memory/cache Cache memory usage. memory/rss RSS memory usage. memory/working_set Total working set usage. Working set is the memory being used and not easily dropped by the kernel. network network/rx Cumulative number of bytes received over the network. network/rx_errors Cumulative number of errors while receiving over the network. network/rx_errors_rate Number of errors while receiving over the network per second. network/rx_rate Number of bytes received over the network per second. network/tx Cumulative number of bytes sent over the network network/tx_errors Cumulative number of errors while sending over the network network/tx_errors_rate Number of errors while sending over the network network/tx_rate Number of bytes sent over the network per second. uptime Number of milliseconds since the container was started. - 4. Labels Label Name Description pod_id Unique ID of a Pod pod_name User-provided name of a Pod pod_namespace The namespace of a Pod container_base_image Base image for the container container_name User-provided name of the container or full cgroup name for system containers host_id Cloud-provider specified or user specified Identifier of a node hostname Hostname where the container ran labels Comma-separated(Default) list of user-provided labels. Format is 'key:value' namespace_id UID of the namespace of a Pod resource_id A unique identifier used to differentiate multiple metrics of the same type. e.x. Fs partitions under filesystem/usage 5. heapster API 见官方文档：https://github.com/kubernetes/heapster/blob/master/docs/model.md Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "},"monitor/influxdb-introduction.html":{"url":"monitor/influxdb-introduction.html","title":"Influxdb介绍","keywords":"","body":"1. InfluxDB简介2. InfluxDB安装1）安装2）启动3）访问4）docker image方式安装3. InfluxDB的基本概念3.1. 与传统数据库中的名词做比较3.2. InfluxDB中独有的概念3.2.1. Point3.2.2. series4. InfluxDB的基本操作4.1. InfluxDB数据库操作4.2. InfluxDB数据表操作1. InfluxDB简介 InfluxDB是一个当下比较流行的时序数据库，InfluxDB使用 Go 语言编写，无需外部依赖，安装配置非常方便，适合构建大型分布式系统的监控系统。 主要特色功能： 1）基于时间序列，支持与时间有关的相关函数（如最大，最小，求和等） 2）可度量性：你可以实时对大量数据进行计算 3）基于事件：它支持任意的事件数据 2. InfluxDB安装 1）安装 wget https://dl.influxdata.com/influxdb/releases/influxdb-0.13.0.x86_64.rpm yum localinstall influxdb-0.13.0.armhf.rpm 2）启动 service influxdb start 3）访问 http://服务器IP:8083 4）docker image方式安装 docker pull influxdb docker run -d -p 8083:8083 -p 8086:8086 --expose 8090 --expose 8099 --volume=/opt/data/influxdb:/data --name influxsrv influxdb:latest 3. InfluxDB的基本概念 3.1. 与传统数据库中的名词做比较 influxDB中的名词 传统数据库中的概念 database 数据库 measurement 数据库中的表 points 表里面的一行数据 3.2. InfluxDB中独有的概念 3.2.1. Point Point由时间戳（time）、数据（field）、标签（tags）组成。 Point相当于传统数据库里的一行数据，如下表所示： Point属性 传统数据库中的概念 time 每个数据记录时间，是数据库中的主索引(会自动生成) fields 各种记录值（没有索引的属性）也就是记录的值：温度， 湿度 tags 各种有索引的属性：地区，海拔 3.2.2. series 所有在数据库中的数据，都需要通过图表来展示，而这个series表示这个表里面的数据，可以在图表上画成几条线：通过tags排列组合算出来 show series from cpu 4. InfluxDB的基本操作 InfluxDB提供三种操作方式： 1）客户端命令行方式 2）HTTP API接口 3）各语言API库 4.1. InfluxDB数据库操作 操作 命令 显示数据库 show databases 创建数据库 create database db_name 删除数据库 drop database db_name 使用某个数据库 use db_name 4.2. InfluxDB数据表操作 操作 命令 说明 显示所有表 SHOW MEASUREMENTS 创建数据表 insert table_name,hostname=server01 value=442221834240i 1435362189575692182 其中 disk_free 就是表名，hostname是索引，value=xx是记录值，记录值可以有多个，最后是指定的时间 删除数据表 drop measurement table_name 查看表内容 select * from table_name 查看series show series from table_name series表示这个表里面的数据，可以在图表上画成几条线，series主要通过tags排列组合算出来 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:37:01 "}} \ No newline at end of file +{"./":{"url":"./","title":"序言","keywords":"","body":"Kubernetes 学习笔记微信公众号云原生体系 安装与配置 基本概念 核心原理 容器网络 容器存储 资源隔离 运维指南 开发指南 问题排查 源码分析 Runtime Etcd 多集群管理 边缘容器 虚拟化 监控体系 赞赏Kubernetes 学习笔记 本系列是 Kubernetes 学习笔记 更多的学习笔记请参考： Kubernetes 学习笔记 Kubernetes 源码分析笔记 Docker 学习笔记 Golang 学习笔记 Linux 学习笔记 数据结构学习笔记 个人博客： www.huweihuang.com blog.huweihuang.com k8s.huweihuang.com 微信公众号 微信公众号：容器云架构 云原生体系 12-Factor K8S知识体系 安装与配置 部署k8s集群 使用kubeadm安装生产环境kubernetes 使用kubespray安装kubernetes 使用minikube安装kubernetes 使用kind安装kubernetes 安装k8s dashboard kubeadm升级k8s集群 kubeadm管理证书 k8s证书及秘钥 k8s版本说明 k8s版本记录 基本概念 kubernetes架构 Kubernetes总架构图 基于Docker及Kubernetes技术构建容器云（PaaS）平台概述 kubernetes对象 理解kubernetes对象 kubernetes常用对象说明 Pod Pod介绍 Pod定义文件 Pod生命周期 Pod健康检查 Pod存储卷 Pod控制器 Pod伸缩与升级 配置 ConfigMap Workload 核心原理 核心组件 Api Server Controller Manager Scheduler Kubelet 流程图 Pod创建流程 PVC创建流程 容器网络 Docker网络 K8S网络 Pod的DNS策略 网络插件 Flannel介绍 CNI CNI接口介绍 Macvlan介绍 容器存储 存储卷概念 Volume Persistent Volume Persistent Volume Claim Storage Class Dynamic Volume Provisioning CSI csi-cephfs-plugin 部署csi-cephfs 部署cephfs-provisioner FlexVolume介绍 资源隔离 资源配额 Pod限额 资源服务质量 Lxcfs资源视图隔离 运维指南 kubernetes集群问题排查 kubectl工具 kubectl安装与配置 kubectl命令说明 kubectl命令别名 kubectl进入node shell helm工具 helm的使用 节点迁移 安全迁移节点 指定Node调度与隔离 镜像仓库 配置私有的镜像仓库 拉取私有镜像 访问控制 使用RBAC鉴权 版本发布 金丝雀发布 开发指南 client-go的使用及源码分析 CSI插件开发 nfs-client-provisioner源码分析 csi-provisioner源码分析 operator开发 kubebuilder的使用 如何开发一个Operator k8s社区开发指南 问题排查 节点相关问题 keycreate permission denied Cgroup不支持pid资源 Cgroup子系统无法挂载 Pod驱逐 镜像拉取失败问题 PVC Terminating 源码分析 Kubernetes源码分析笔记 kubelet NewKubeletCommand NewMainKubelet startKubelet syncLoopIteration syncPod kube-controller-manager NewControllerManagerCommand DeploymentController Informer机制 kube-scheduler NewSchedulerCommand registerAlgorithmProvider scheduleOne findNodesThatFit PrioritizeNodes preempt kube-apiserver NewAPIServerCommand Runtime Runtime Runc和Containerd概述 Containerd 安装Containerd Docker Docker学习笔记 Kata Container kata容器简介 kata配置 GPU nvidia-device-plugin介绍 Etcd Etcd介绍 Raft算法 Etcd启动配置参数 Etcd访问控制 etcdctl命令工具 etcdctl命令工具-V3 etcdctl命令工具-V2 Etcd中的k8s数据 Etcd-Operator的使用 多集群管理 k8s多集群管理的思考 Virtual Kubelet Virtual Kubelet介绍 Virtual Kubelet 命令 Karmada Karmada介绍 边缘容器 KubeEdge介绍 KubeEdge源码分析 cloudcore edgecore OpenYurt OpenYurt部署 OpenYurt部署之调整k8s配置 OpenYurt源码分析 YurtHub源码分析（1) TunnelServer源码分析（1） Tunnel-Agent源码分析 虚拟化 虚拟化相关概念 KubeVirt KubeVirt的介绍 KubeVirt的使用 监控体系 监控体系介绍 kube-prometheus-stack的使用 cAdvisor介绍 Heapster介绍 Influxdb介绍 赞赏 如果觉得文章有帮助的话，可以打赏一下，谢谢！ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2023-10-29 11:09:37 "},"paas/12-factor.html":{"url":"paas/12-factor.html","title":"12-Factor","keywords":"","body":"简介1. 基准代码1.1. 一份基准代码1.2. 多份部署2. 依赖3. 配置4. 后端服务5. 构建，发布，运行6. 进程7. 端口绑定8. 并发9. 易处理10. 开发环境与线上环境等价11. 日志12. 管理进程 以下主要介绍PaaS平台设计架构中使用到的方法论，统称为12-Factor(要素) 简介 软件通常会作为一种服务来交付，即软件即服务(SaaS)。12-Factor原则为构建SaaS应用提供了以下的方法论： 使用标准化流程自动配置，减少开发者的学习成本。 和操作系统解耦，使其可以在各个系统间提供最大的移植性。 适合部署在现代的云计算平台上，从而在服务器和系统管理方面节省资源。 将开发环境与生产环境的差异降至最低，并使用持续交付实施敏捷开发。 可以在工具、架构和开发流程不发生明显变化的前提下实现拓展 该理论适应于任何语言和后端服务(数据库、消息队列、缓存等)开发的应用程序。 1. 基准代码 一份基准代码，多份部署 应用代码使用版本控制系统来管理，常用的有Git、SVN等。一份用来跟踪代码所有修订版本的数据库称为代码库。 1.1. 一份基准代码 基准代码和应用之间总是保持一一对应的关系： 一旦有多个基准代码，则不能称之为一个应用，而是一个分布式系统。分布式系统中的每个组件都是一个应用，每个应用都可以使用12-Factor原则进行开发。 多个应用共享一份基准代码有悖于12-Factor原则。解决方法是将共享的代码拆成独立的类库，通过依赖管理去使用它们。 1.2. 多份部署 每个应用只对应一份基准代码，但可以同时存在多份的部署，每份部署相当于运行了一个应用的实例。 多份部署的区别在于： 可以存在不同的配置文件对应不同的环境。例如开发环境、预发布环境、生产环境等。 可以使用不同的版本。例如开发环境的版本可能高于预发布环境版本，还没同步到预发布环境版本，同理，预发布环境版本可能高于生产环境版本。 2. 依赖 显式声明依赖关系 大多数的编程语言都会提供一个包管理系统或工具，其中包含所有的依赖库，例如Golang的vendor目录存放了该应用的所有依赖包。 12-Factor原则下的应用会通过依赖清单来显式确切地声明所有的依赖项。在运行工程中通过依赖隔离工具来保证应用不会去调用系统中存在但依赖清单中未声明的依赖项。 显式声明依赖项的优点在于可以简化环境配置流程，开发者关注应用的基准代码，而依赖库则由依赖库管理工具来管理和配置。例如，Golang中的包管理工具dep等。 3. 配置 在环境中存储配置 通常，应用的配置在不同的发布环境中(例如：开发、预发布、生产环境)会有很大的差异，其中包括： 数据库、Redis等后端服务的配置 每份部署特有的配置，例如域名 第三方服务的证书等 12-Factor原则要求代码和配置严格分离，而不应该通过代码常量的形式写在代理里面。配置在不同的部署环境中存在大幅差异，但是代码却是完全一致的。 判断一个应用是否正确地将配置排除在代码外，可以看应用的基准代码是否可以立即开源而不担心暴露敏感信息。 12-Factor原则建议将应用的配置存储在环境变量中，环境变量可以方便在不同的部署环境中修改，而不侵入原有的代码。(例如，k8s的大部分代码配置是通过环境变量的方式来传入的)。 12-Factor应用中，环境变量的粒度要足够小且相对独立。当应用需要拓展时，可以平滑过渡。 4. 后端服务 把后端服务当作附加资源 后端服务指程序运行时所需要通过网络调用的各种服务，例如：数据库（MySQL，CouchDB），消息/队列系统（RabbitMQ，Beanstalkd），SMTP 邮件发送服务（Postfix），以及缓存系统（Memcached）。 其中可以根据管理对象分为本地服务(例如本地数据库)和第三方服务(例如Amason S3)。对于12-Factor应用来说都是附加资源，没有区别对待，当其中一份后端服务失效后，可以通过切换到原先备份的后端服务中，而不需要修改代码(但可能需要修改配置)。12-Factor应用与后端服务保持松耦合的关系。 5. 构建，发布，运行 严格分离构建和运行 基准代码转化成一份部署需要经过三个阶段： 构建阶段：指代码转化为可执行包的过程。构建过程会使用指定版本的代码，获取依赖项，编译生成二进制文件和资源文件。 发布阶段：将构建的结果与当前部署所需的配置结合，并可以在运行环境中使用。 运行阶段（运行时）：指针对指定的发布版本在执行环境中启动一系列应用程序的进程。 12-Factor应用严格区分构建、发布、运行三个步骤，每一个发布版本对应一个唯一的发布ID，可以使用时间戳或递增的版本序列号。 如果需要修改则需要产生一个新的发布版本，如果需要回退，则回退到之前指定的发布版本。 新代码部署之前，由开发人员触发构建操作，构建阶段可以相对复杂一些，方便错误信息可以展示出来得到妥善处理。运行阶段可以人为触发或自动运行，运行阶段应该保持尽可能少的模块。 6. 进程 以一个或多个无状态进程运行应用 12-Factor应有的进程必须是无状态且无共享的，任何需要持久化的数据存储在后端服务中，例如数据库。 内存区域和磁盘空间可以作为进程的缓存，12-Factor应用不需要关注这些缓存的持久化，而是允许其丢失，例如重启的时候。 进程的二进制文件应该在构建阶段执行编译而不是运行阶段。 当应用使用到粘性Session，即将用户的session数据缓存到进程的内存中，将同一用户的后续请求路由到同一个进程。12-Factor应用反对这种处理方式，而是建议将session的数据保存在redis/memcached带有过期时间的缓存中。 7. 端口绑定 通过端口绑定提供服务 应用通过端口绑定来提供服务，并监听发送至该端口的请求。端口绑定的方式意味着一个应用也可以成为另一个应用的后端服务，例如提供某些API请求。 8. 并发 通过进程模型进行扩展 12-Factor应用中，开发人员可以将不同的工作分配给不同类型进程，例如HTTP请求由web进程来处理，常驻的后台工作由worker进程来处理（k8s的设计中就经常用不同类型的manager来处理不同的任务）。 12-Factor应用的进程具备无共享、水平分区的特性，使得水平扩展较为容易。 12-Factor应用的进程不需要守护进程或是写入PID文件，而是通过进程管理器（例如 systemd）来管理输出流，响应崩溃的进程，以及处理用户触发的重启或关闭超级进程的操作。 9. 易处理 快速启动和优雅终止可最大化健壮性 12-Factor应用的进程是易处理的，即它们可以快速的开启或停止，这样有利于快速部署迭代和弹性伸缩实例。 进程应该追求最小的启动时间，这样可以敏捷发布，增加健壮性，当出现问题可以快速在别的机器部署一个实例。 进程一旦接收到终止信号(SIGTERM)就会优雅终止。优雅终止指停止监听服务的端口，拒绝所有新的请求，并继续执行当前已接收的请求，然后退出。 进程还需在面对突然挂掉的情况下保持健壮性，例如通过任务队列的方式来解决进程突然挂掉而没有完成处理的事情，所以应该设计为任务执行是幂等的，可以被重复执行，重复执行的结果是一致的。 10. 开发环境与线上环境等价 尽可能的保持开发，预发布，线上环境相同 不同的发布环境可能存在以下差异： 时间差异：开发到部署的周期较长。 人员差异：开发人员只负责开发，运维人员只负责部署。分工过于隔离。 工具差异：不同环境的配置和运行环境，使用的后端类型可能存在不同。 应尽量缩小本地与线上的差异，缩短上线周期，开发运维一体化，保证开发环境与线上运行的环境一致（例如，可以通过Docker容器的方式）。 11. 日志 把日志当作事件流 日志应该是事件流的汇总。12-Factor应用本身不考虑存储自己的日志输出流，不去写或管理日志文件，而是通过标准输出（stdout）的方式。 日志的标准输出流可以通过其他组件截获，整合其他的日志输出流，一并发给统一的日志中心处理，用于查看或存档。例如：日志收集开源工具Fluentd。 截获的日志流可以输出至文件，或者在终端实时查看。最重要的是可以发送到Splunk这样的日志索引及分析系统，提供后续的分析统计及监控告警等功能。例如： 找出过去一段时间的特殊事件。 图形化一个大规模的趋势，如每分钟的请求量。 根据用户定义的条件触发告警，如每分钟报错数超过某个警戒线。 12. 管理进程 后台管理任务当作一次性进程运行 开发人员经常需要执行一些管理或维护应用的一次性任务，一次性管理进程应该和常驻进程使用相同的运行环境，开发人员可以通过ssh方式来执行一次性脚本或任务。 参考： https://12factor.net/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"paas/k8s.html":{"url":"paas/k8s.html","title":"K8S知识体系","keywords":"","body":"1. k8s知识体系2. k8s重点开源项目1. k8s知识体系 以下整理了k8s涉及的相关知识体系。 图片 - k8s体系 思维导图：https://www.processon.com/view/link/5d7f7d08e4b03461a3a937e2 2. k8s重点开源项目 TODO Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"setup/installer/_index.html":{"url":"setup/installer/_index.html","title":"部署k8s集群","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"setup/installer/install-k8s-by-kubeadm.html":{"url":"setup/installer/install-k8s-by-kubeadm.html","title":"使用kubeadm安装生产环境kubernetes","keywords":"","body":"1. 环境准备1.0. master硬件配置1.1. 设置防火墙端口策略1.1.1. master节点端口配置1.1.2. worker节点端口配置1.2. 关闭​​swap​​分区1.3. 开启br_netfilter和bridge-nf-call-iptables2. 安装容器运行时2.1. Containerd2.2. Docker2.3. Container Socket3. 安装kubeadm,kubelet,kubectl4. 配置kubeadm config4.1. 配置项说明4.1.1. 配置类型4.1.2. Init配置4.2. Init配置示例5. 安装Master控制面5.1. 安装master5.2. 添加其他master6. 添加Node节点7. 安装网络插件7.1. 问题8. 部署dashboard9. 重置部署10. 问题排查10.1. kubeadm token过期10.2. 修改kubeadm join的master IP或端口10.3. conntrack not found10.4. Kubelet: unable to determine runtime API version 本文为基于kubeadm搭建生产环境级别高可用的k8s集群。 1. 环境准备 1.0. master硬件配置 参考： Master节点规格 高可靠推荐配置 - 容器服务 ACK - 阿里云 Kubernetes集群Master节点上运行着etcd、kube-apiserver、kube-controller等核心组件，对于Kubernetes集群的稳定性有着至关重要的影响，对于生产环境的集群，必须慎重选择Master规格。Master规格跟集群规模有关，集群规模越大，所需要的Master规格也越高。 说明 ：可从多个角度衡量集群规模，例如节点数量、Pod数量、部署频率、访问量。这里简单的认为集群规模就是集群里的节点数量。 对于常见的集群规模，可以参见如下的方式选择Master节点的规格（对于测试环境，规格可以小一些。下面的选择能尽量保证Master负载维持在一个较低的水平上）。 节点规模 Master规格 磁盘 1~5个节点 4核8 GB（不建议2核4 GB） 6~20个节点 4核16 GB 21~100个节点 8核32 GB 100~200个节点 16核64 GB 1000个节点 32核128GB 1T SSD 注意事项： 由于Etcd的性能瓶颈，Etcd的数据存储盘尽量选择SSD磁盘。 为了实现多机房容灾，可将三台master分布在一个可用区下三个不同机房。（机房之间的网络延迟在10毫秒及以下级别） 申请LB来做master节点的负载均衡实现高可用，LB作为apiserver的访问地址。 1.1. 设置防火墙端口策略 生产环境设置k8s节点的iptables端口访问规则。 1.1.1. master节点端口配置 协议 方向 端口范围 目的 使用者 TCP 入站 6443 Kubernetes API server 所有 TCP 入站 2379-2380 etcd server client API kube-apiserver, etcd TCP 入站 10250 Kubelet API 自身, 控制面 TCP 入站 10259 kube-scheduler 自身 TCP 入站 10257 kube-controller-manager 自身 1.1.2. worker节点端口配置 协议 方向 端口范围 目的 使用者 TCP 入站 10250 Kubelet API 自身, 控制面 TCP 入站 30000-32767 NodePort Services 所有 添加防火墙iptables规则 master节点开放6443、2379、2380端口。 iptables -A INPUT -p tcp -m multiport --dports 6443,2379,2380,10250 -j ACCEPT 1.2. 关闭​​swap​​分区 [root@master ~]#swapoff -a [root@master ~]# [root@master ~]# free -m total used free shared buff/cache available Mem: 976 366 135 6 474 393 Swap: 0 0 0 # swap 一栏为0，表示已经关闭了swap 1.3. 开启br_netfilter和bridge-nf-call-iptables 参考：https://imroc.cc/post/202105/why-enable-bridge-nf-call-iptables/ # 设置加载br_netfilter模块 cat 2. 安装容器运行时 在所有主机上安装容器运行时，推荐使用containerd为runtime。以下分别是containerd与docker的安装命令。 2.1. Containerd 1、参考：安装containerd # for ubuntu apt install -y containerd.io 2、生成默认配置 containerd config default > /etc/containerd/config.toml 3、修改CgroupDriver为systemd k8s官方推荐使用systemd类型的CgroupDriver。 [plugins.\"io.containerd.grpc.v1.cri\".containerd.runtimes.runc] ... [plugins.\"io.containerd.grpc.v1.cri\".containerd.runtimes.runc.options] SystemdCgroup = true 4、重启containerd systemctl restart containerd 2.2. Docker # for ubuntu apt install -y docker.io 官方建议配置cgroupdriver为systemd。 # 修改docker进程管理器 vi /etc/docker/daemon.json { \"exec-opts\": [\"native.cgroupdriver=systemd\"] } systemctl daemon-reload && systemctl restart docker docker info | grep -i cgroup 2.3. Container Socket 运行时 Unix 域套接字 Containerd unix:///var/run/containerd/containerd.sock CRI-O unix:///var/run/crio/crio.sock Docker Engine (使用 cri-dockerd) unix:///var/run/cri-dockerd.sock 3. 安装kubeadm,kubelet,kubectl 安装脚本可参考仓库：https://github.com/huweihuang/kubeadm-scripts.git 在所有主机上安装kubeadm，kubelet，kubectl。最好版本与需要安装的k8s的版本一致。 # 以Ubuntu系统为例 # 安装仓库依赖 sudo apt-get update sudo apt-get install -y apt-transport-https ca-certificates curl # use google registry sudo curl -fsSLo /usr/share/keyrings/kubernetes-archive-keyring.gpg https://packages.cloud.google.com/apt/doc/apt-key.gpg echo \"deb [signed-by=/usr/share/keyrings/kubernetes-archive-keyring.gpg] https://apt.kubernetes.io/ kubernetes-xenial main\" | sudo tee /etc/apt/sources.list.d/kubernetes.list # or use aliyun registry curl -s https://mirrors.aliyun.com/kubernetes/apt/doc/apt-key.gpg | sudo apt-key add - tee /etc/apt/sources.list.d/kubernetes.list 离线下载安装 #!/bin/bash Version=${Version:-1.24.2} wget https://dl.k8s.io/release/v${Version}/bin/linux/amd64/kubeadm wget https://dl.k8s.io/release/v${Version}/bin/linux/amd64/kubelet wget https://dl.k8s.io/release/v${Version}/bin/linux/amd64/kubectl chmod +x kubeadm kubelet kubectl cp kubeadm kubelet kubectl /usr/bin/ # add kubelet serivce cat > /lib/systemd/system/kubelet.service /etc/systemd/system/kubelet.service.d/10-kubeadm.conf 4. 配置kubeadm config 参考： kubeadm Configuration (v1beta3) | Kubernetes kubeadm Configuration (v1beta2) | Kubernetes 4.1. 配置项说明 4.1.1. 配置类型 kubeadm config支持以下几类配置。 apiVersion: kubeadm.k8s.io/v1beta3 kind: InitConfiguration apiVersion: kubeadm.k8s.io/v1beta3 kind: ClusterConfiguration apiVersion: kubelet.config.k8s.io/v1beta1 kind: KubeletConfiguration apiVersion: kubeproxy.config.k8s.io/v1alpha1 kind: KubeProxyConfiguration apiVersion: kubeadm.k8s.io/v1beta3 kind: JoinConfiguration 可以使用以下命令打印init和join的默认配置。 kubeadm config print init-defaults kubeadm config print join-defaults 4.1.2. Init配置 kubeadm init配置中只有InitConfiguration 和 ClusterConfiguration 是必须的。 InitConfiguration: apiVersion: kubeadm.k8s.io/v1beta3 kind: InitConfiguration bootstrapTokens: ... nodeRegistration: ... bootstrapTokens nodeRegistration criSocket：runtime的socket name：节点名称 localAPIEndpoint advertiseAddress：apiserver的广播IP bindPort：k8s控制面安全端口 ClusterConfiguration: apiVersion: kubeadm.k8s.io/v1beta3 kind: ClusterConfiguration networking: ... etcd: ... apiServer: extraArgs: ... extraVolumes: ... ... networking: podSubnet：Pod CIDR范围 serviceSubnet： service CIDR范围 dnsDomain etcd: dataDir：Etcd的数据存储目录 apiserver certSANs：设置额外的apiserver的域名签名证书 imageRepository：镜像仓库 controlPlaneEndpoint：控制面LB的域名 kubernetesVersion：k8s版本 4.2. Init配置示例 在master节点生成默认配置，并修改配置参数。 kubeadm config print init-defaults > kubeadm-config.yaml 修改配置内容 apiVersion: kubeadm.k8s.io/v1beta3 bootstrapTokens: - groups: - system:bootstrappers:kubeadm:default-node-token token: abcdef.0123456789abcdef ttl: 24h0m0s usages: - signing - authentication kind: InitConfiguration localAPIEndpoint: advertiseAddress: 1.2.3.4 # 修改为apiserver的IP 或者去掉localAPIEndpoint则会读取默认IP。 bindPort: 6443 nodeRegistration: criSocket: unix:///var/run/containerd/containerd.sock imagePullPolicy: IfNotPresent name: node taints: null --- apiServer: certSANs: - lb.k8s.domain # 添加额外的apiserver的域名 - timeoutForControlPlane: 4m0s apiVersion: kubeadm.k8s.io/v1beta3 certificatesDir: /etc/kubernetes/pki clusterName: kubernetes controllerManager: {} dns: {} # 默认为coredns etcd: local: dataDir: /data/etcd # 修改etcd的存储盘目录 imageRepository: k8s.gcr.io # 修改镜像仓库地址 controlPlaneEndpoint: lb.k8s.domain # 修改控制面域名 kind: ClusterConfiguration kubernetesVersion: 1.24.0 # k8s 版本 networking: dnsDomain: cluster.local serviceSubnet: 10.96.0.0/12 podSubnet: 10.244.0.0/16 # 设置pod的IP范围 scheduler: {} --- kind: KubeletConfiguration apiVersion: kubelet.config.k8s.io/v1beta1 cgroupDriver: systemd # 设置为systemd 安装完成后可以查看kubeadm config kubectl get cm -n kube-system kubeadm-config -oyaml 5. 安装Master控制面 提前拉取镜像： kubeadm config images pull 5.1. 安装master sudo kubeadm init --config kubeadm-config.yaml --upload-certs --node-name 部署参数说明： --control-plane-endpoint：指定控制面(kube-apiserver)的IP或DNS域名地址。 --apiserver-advertise-address：kube-apiserver的IP地址。 --pod-network-cidr：pod network范围，控制面会自动给每个节点分配CIDR。 --service-cidr：service的IP范围，default \"10.96.0.0/12\"。 --kubernetes-version：指定k8s的版本。 --image-repository：指定k8s镜像仓库地址。 --upload-certs ：标志用来将在所有控制平面实例之间的共享证书上传到集群。 --node-name：hostname-override，作为节点名称。 执行完毕会输出添加master和添加worker的命令如下： ... You can now join any number of control-plane node by running the following command on each as a root: kubeadm join 192.168.0.200:6443 --token 9vr73a.a8uxyaju799qwdjv --discovery-token-ca-cert-hash sha256:7c2e69131a36ae2a042a339b33381c6d0d43887e2de83720eff5359e26aec866 --control-plane --certificate-key f8902e114ef118304e561c3ecd4d0b543adc226b7a07f675f56564185ffe0c07 Please note that the certificate-key gives access to cluster sensitive data, keep it secret! As a safeguard, uploaded-certs will be deleted in two hours; If necessary, you can use kubeadm init phase upload-certs to reload certs afterward. Then you can join any number of worker nodes by running the following on each as root: kubeadm join 192.168.0.200:6443 --token 9vr73a.a8uxyaju799qwdjv --discovery-token-ca-cert-hash sha256:7c2e69131a36ae2a042a339b33381c6d0d43887e2de83720eff5359e26aec866 5.2. 添加其他master 添加master和添加worker的差别在于添加master多了--control-plane 参数来表示添加类型为master。 kubeadm join :6443 --token \\ --discovery-token-ca-cert-hash sha256: \\ --control-plane --certificate-key \\ --node-name 6. 添加Node节点 kubeadm join :6443 --token \\ --discovery-token-ca-cert-hash sha256: \\ --cri-socket /run/containerd/containerd.sock \\ --node-name 7. 安装网络插件 ## 如果安装之后node的状态都改为ready，即为成功 wget https://raw.githubusercontent.com/coreos/flannel/master/Documentation/kube-flannel.yml kubectl apply -f ./kube-flannel.yml kubectl get nodes 如果Pod CIDR的网段不是10.244.0.0/16，则需要加flannel配置中的网段更改为与Pod CIDR的网段一致。 7.1. 问题 Warning FailedCreatePodSandBox 4m6s kubelet Failed to create pod sandbox: rpc error: code = Unknown desc = failed to setup network for sandbox \"300d9b570cc1e23b6335c407b8e7d0ef2c74dc2fe5d7a110678c2dc919c62edf\": plugin type=\"flannel\" failed (add): failed to delegate add: failed to set bridge addr: \"cni0\" already has an IP address different from 10.244.3.1/24 原因： 宿主机节点有cni0网卡，且网卡的IP段与flannel的CIDR网段不同，因此需要删除该网卡，让其重建。 查看cni0网卡 # ifconfig cni0 |grep -w inet inet 10.244.5.1 netmask 255.255.255.0 broadcast 10.244.116.255 查看flannel配置 # cat /run/flannel/subnet.env FLANNEL_NETWORK=10.244.0.0/16 FLANNEL_SUBNET=10.244.116.1/24 FLANNEL_MTU=1450 FLANNEL_IPMASQ=true 发现cni0 IP与FLANNEL_SUBNET网段不一致，因此删除cni0重建。 解决： ifconfig cni0 down ip link delete cni0 8. 部署dashboard kubectl apply -f https://raw.githubusercontent.com/kubernetes/dashboard/v2.5.0/aio/deploy/recommended.yaml 镜像： kubernetesui/dashboard:v2.5.0 默认端口：8443 登录页面需要填入token或kubeconfig 参考：dashboard/creating-sample-user apiVersion: v1 kind: ServiceAccount metadata: name: admin-user namespace: kubernetes-dashboard --- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: admin-user roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: cluster-admin subjects: - kind: ServiceAccount name: admin-user namespace: kubernetes-dashboard 创建用户 kubectl -n kubernetes-dashboard create token admin-user 9. 重置部署 # kubeadm重置 kubeadm reset # 清空数据目录 rm -fr /data/etcd rm -fr /etc/kubernetes rm -fr ~/.kube/ 删除flannel ifconfig cni0 down ip link delete cni0 ifconfig flannel.1 down ip link delete flannel.1 rm -rf /var/lib/cni/ rm -f /etc/cni/net.d/* 10. 问题排查 10.1. kubeadm token过期 问题描述: 添加节点时报以下错误： [discovery] The cluster-info ConfigMap does not yet contain a JWS signature for token ID \"abcdef\", will try again 原因：token过期，初始化token后会在24小时候会被master删除。 解决办法： # 重新生成token kubeadm token create --print-join-command kubeadm token list # kubeadm token create oumnnc.aqlxuvdbntlvzoiv # 重新生成hash openssl x509 -pubkey -in /etc/kubernetes/pki/ca.crt | openssl rsa -pubin -outform der 2>/dev/null | openssl dgst -sha256 -hex | sed 's/^.* //' 基于新生成的token重新添加节点。 10.2. 修改kubeadm join的master IP或端口 kubeadm join命令会去kube-public命名空间获取名为cluster-info的ConfigMap。如果需要修改kubeadm join使用的master的IP或端口，则需要修改cluster-info的configmap。 # 查看cluster-info kubectl -n kube-public get configmaps cluster-info -o yaml # 修改cluster-info kubectl -n kube-public edit configmaps cluster-info 修改配置文件中的server字段 clusters: - cluster: certificate-authority-data: xxx server: https://lb.k8s.domain:36443 name: \"\" 执行kubeadm join的命令时指定新修改的master地址。 10.3. conntrack not found [preflight] Some fatal errors occurred: [ERROR FileExisting-conntrack]: conntrack not found in system path 解决方法： apt -y install conntrack 10.4. Kubelet: unable to determine runtime API version Error: failed to run Kubelet: unable to determine runtime API version: rpc error:code = Unavailable desc = connection error: desc = \"transport: Error while dialing dial unix: missing address\" 解决方法： 检查kubelet的启动参数，可以用二进制直接添加参数debug # 查看启动参数是否遗漏，比如10-kubeadm.conf 文件参数缺失 systemctl cat --no-pager kubelet cat /lib/systemd/system/kubelet.service cat /etc/systemd/system/kubelet.service.d/10-kubeadm.conf 参考： 利用 kubeadm 创建高可用集群 | Kubernetes 使用 kubeadm 创建集群 | Kubernetes 高可用拓扑选项 | Kubernetes kubeadm init | Kubernetes v1.24.2|kubeadm|v1beta3 Installing kubeadm | Kubernetes Ports and Protocols | Kubernetes 容器运行时 | Kubernetes https://github.com/Mirantis/cri-dockerd 配置 cgroup 驱动|Kubernetes GitHub: flannel is a network fabric for containers 部署和访问 Kubernetes 仪表板（Dashboard） | Kubernetes Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"setup/installer/install-k8s-by-kubespray.html":{"url":"setup/installer/install-k8s-by-kubespray.html","title":"使用kubespray安装kubernetes","keywords":"","body":"1. 环境准备1.1. 部署机器1.2. 配置管理机1.3. 配置部署机器1.4. 涉及镜像2. 部署集群2.1. 下载kubespary的源码2.2. 编辑配置文件2.2.1. hosts.ini2.2.2. k8s-cluster.yml2.3. 执行部署操作3. 确认部署结果3.1. ansible的部署结果3.2. k8s集群运行结果4. k8s集群扩容节点4.1. 修改hosts.ini文件4.2. 执行扩容命令4.3. 检查扩容结果5. 部署高可用集群6. 升级k8s集群7. troubles shooting7.1. python-netaddr未安装7.2. swap未关闭7.3. 部署机器内存过小7.4. kube-scheduler组件运行失败7.5. docker安装包冲突1. 环境准备 1.1. 部署机器 以下机器为虚拟机 机器IP 主机名 角色 系统版本 备注 172.16.94.140 kube-master-0 k8s master Centos 4.17.14 内存：3G 172.16.94.141 kube-node-41 k8s node Centos 4.17.14 内存：3G 172.16.94.142 kube-node-42 k8s node Centos 4.17.14 内存：3G 172.16.94.135 部署管理机 - 1.2. 配置管理机 管理机主要用来部署k8s集群，需要安装以下版本的软件，具体可参考： https://github.com/kubernetes-incubator/kubespray#requirements https://github.com/kubernetes-incubator/kubespray/blob/master/requirements.txt ansible>=2.4.0 jinja2>=2.9.6 netaddr pbr>=1.6 ansible-modules-hashivault>=3.9.4 hvac 1、安装及配置ansible 参考ansible的使用。 给部署机器配置SSH的免密登录权限，具体参考ssh免密登录。 2、安装python-netaddr # 安装pip yum -y install epel-release yum -y install python-pip # 安装python-netaddr pip install netaddr 3、升级Jinja # Jinja 2.9 (or newer) pip install --upgrade jinja2 1.3. 配置部署机器 部署机器即用来运行k8s集群的机器，包括Master和Node。 1、确认系统版本 本文采用centos7的系统，建议将系统内核升级到4.x.x以上。 2、关闭防火墙 systemctl stop firewalld systemctl disable firewalld iptables -F 3、关闭swap Kubespary v2.5.0的版本需要关闭swap，具体参考 https://github.com/kubernetes-incubator/kubespray/blob/02cd5418c22d51e40261775908d55bc562206023/roles/kubernetes/preinstall/tasks/verify-settings.yml#L75 - name: Stop if swap enabled assert: that: ansible_swaptotal_mb == 0 when: kubelet_fail_swap_on|default(true) ignore_errors: \"{{ ignore_assert_errors }}\" V2.6.0 版本去除了swap的检查，具体参考： https://github.com/kubernetes-incubator/kubespray/commit/b902602d161f8c147f3d155d2ac5360244577127#diff-b92ae64dd18d34a96fbeb7f7e48a6a9b 执行关闭swap命令swapoff -a。 [root@master ~]#swapoff -a [root@master ~]# [root@master ~]# free -m total used free shared buff/cache available Mem: 976 366 135 6 474 393 Swap: 0 0 0 # swap 一栏为0，表示已经关闭了swap 4、确认部署机器内存 由于本文采用虚拟机部署，内存可能存在不足的问题，因此将虚拟机内存调整为3G或以上；如果是物理机一般不会有内存不足的问题。具体参考： https://github.com/kubernetes-incubator/kubespray/blob/95f1e4634a1c50fa77312d058a2b713353f4307e/roles/kubernetes/preinstall/tasks/verify-settings.yml#L52 - name: Stop if memory is too small for masters assert: that: ansible_memtotal_mb >= 1500 ignore_errors: \"{{ ignore_assert_errors }}\" when: inventory_hostname in groups['kube-master'] - name: Stop if memory is too small for nodes assert: that: ansible_memtotal_mb >= 1024 ignore_errors: \"{{ ignore_assert_errors }}\" when: inventory_hostname in groups['kube-node'] 1.4. 涉及镜像 Docker版本为17.03.2-ce。 1、Master节点 镜像 版本 大小 镜像ID 备注 gcr.io/google-containers/hyperkube v1.9.5 620 MB a7e7fdbc5fee k8s quay.io/coreos/etcd v3.2.4 35.7 MB 498ffffcfd05 gcr.io/google_containers/pause-amd64 3.0 747 kB 99e59f495ffa quay.io/calico/node v2.6.8 282 MB e96a297310fd calico quay.io/calico/cni v1.11.4 70.8 MB 4c4cb67d7a88 calico quay.io/calico/ctl v1.6.3 44.4 MB 46d3aace8bc6 calico 2、Node节点 镜像 版本 大小 镜像ID 备注 gcr.io/google-containers/hyperkube v1.9.5 620 MB a7e7fdbc5fee k8s gcr.io/google_containers/pause-amd64 3.0 747 kB 99e59f495ffa quay.io/calico/node v2.6.8 282 MB e96a297310fd calico quay.io/calico/cni v1.11.4 70.8 MB 4c4cb67d7a88 calico quay.io/calico/ctl v1.6.3 44.4 MB 46d3aace8bc6 calico gcr.io/google_containers/k8s-dns-dnsmasq-nanny-amd64 1.14.8 40.9 MB c2ce1ffb51ed dns gcr.io/google_containers/k8s-dns-sidecar-amd64 1.14.8 42.2 MB 6f7f2dc7fab5 dns gcr.io/google_containers/k8s-dns-kube-dns-amd64 1.14.8 50.5 MB 80cc5ea4b547 dns gcr.io/google_containers/cluster-proportional-autoscaler-amd64 1.1.2 50.5 MB 78cf3f492e6b gcr.io/google_containers/kubernetes-dashboard-amd64 v1.8.3 102 MB 0c60bcf89900 dashboard nginx 1.13 109 MB ae513a47849c - 3、说明 镜像被墙并且全部镜像下载需要较多时间，建议提前下载到部署机器上。 hyperkube镜像主要用来运行k8s核心组件（例如kube-apiserver等）。 此处使用的网络组件为calico。 2. 部署集群 2.1. 下载kubespary的源码 git clone https://github.com/kubernetes-incubator/kubespray.git 2.2. 编辑配置文件 2.2.1. hosts.ini hosts.ini主要为部署节点机器信息的文件，路径为：kubespray/inventory/sample/hosts.ini。 cd kubespray # 复制一份配置进行修改 cp -rfp inventory/sample inventory/k8s vi inventory/k8s/hosts.ini 例如： hosts.ini文件可以填写部署机器的登录密码，也可以不填密码而设置ssh的免密登录。 # Configure 'ip' variable to bind kubernetes services on a # different ip than the default iface # 主机名 ssh登陆IP ssh用户名 ssh登陆密码 机器IP 子网掩码 kube-master-0 ansible_ssh_host=172.16.94.140 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.140 mask=/24 kube-node-41 ansible_ssh_host=172.16.94.141 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.141 mask=/24 kube-node-42 ansible_ssh_host=172.16.94.142 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.142 mask=/24 # configure a bastion host if your nodes are not directly reachable # bastion ansible_ssh_host=x.x.x.x [kube-master] kube-master-0 [etcd] kube-master-0 [kube-node] kube-node-41 kube-node-42 [k8s-cluster:children] kube-node kube-master [calico-rr] 2.2.2. k8s-cluster.yml k8s-cluster.yml主要为k8s集群的配置文件，路径为：kubespray/inventory/k8s/group_vars/k8s-cluster.yml。该文件可以修改安装的k8s集群的版本，参数为：kube_version: v1.9.5。具体可参考： https://github.com/kubernetes-incubator/kubespray/blob/master/inventory/sample/group_vars/k8s-cluster.yml#L22 2.3. 执行部署操作 涉及文件为cluster.yml。 # 进入主目录 cd kubespray # 执行部署命令 ansible-playbook -i inventory/k8s/hosts.ini cluster.yml -b -vvv -vvv 参数表示输出运行日志 如果需要重置可以执行以下命令： 涉及文件为reset.yml。 ansible-playbook -i inventory/k8s/hosts.ini reset.yml -b -vvv 3. 确认部署结果 3.1. ansible的部署结果 ansible命令执行完，出现以下日志，则说明部署成功，否则根据报错内容进行修改。 PLAY RECAP ***************************************************************************** kube-master-0 : ok=309 changed=30 unreachable=0 failed=0 kube-node-41 : ok=203 changed=8 unreachable=0 failed=0 kube-node-42 : ok=203 changed=8 unreachable=0 failed=0 localhost : ok=2 changed=0 unreachable=0 failed=0 以下为部分部署执行日志： kubernetes/preinstall : Update package management cache (YUM) --------------------23.96s /root/gopath/src/kubespray/roles/kubernetes/preinstall/tasks/main.yml:121 kubernetes/master : Master | wait for the apiserver to be running ----------------23.44s /root/gopath/src/kubespray/roles/kubernetes/master/handlers/main.yml:79 kubernetes/preinstall : Install packages requirements ----------------------------20.20s /root/gopath/src/kubespray/roles/kubernetes/preinstall/tasks/main.yml:203 kubernetes/secrets : Check certs | check if a cert already exists on node --------13.94s /root/gopath/src/kubespray/roles/kubernetes/secrets/tasks/check-certs.yml:17 gather facts from all instances --------------------------------------------------9.98s /root/gopath/src/kubespray/cluster.yml:25 kubernetes/node : install | Compare host kubelet with hyperkube container --------9.66s /root/gopath/src/kubespray/roles/kubernetes/node/tasks/install_host.yml:2 kubernetes-apps/ansible : Kubernetes Apps | Start Resources -----------------------9.27s /root/gopath/src/kubespray/roles/kubernetes-apps/ansible/tasks/main.yml:37 kubernetes-apps/ansible : Kubernetes Apps | Lay Down KubeDNS Template ------------8.47s /root/gopath/src/kubespray/roles/kubernetes-apps/ansible/tasks/kubedns.yml:3 download : Sync container ---------------------------------------------------------8.23s /root/gopath/src/kubespray/roles/download/tasks/main.yml:15 kubernetes-apps/network_plugin/calico : Start Calico resources --------------------7.82s /root/gopath/src/kubespray/roles/kubernetes-apps/network_plugin/calico/tasks/main.yml:2 download : Download items ---------------------------------------------------------7.67s /root/gopath/src/kubespray/roles/download/tasks/main.yml:6 download : Download items ---------------------------------------------------------7.48s /root/gopath/src/kubespray/roles/download/tasks/main.yml:6 download : Sync container ---------------------------------------------------------7.35s /root/gopath/src/kubespray/roles/download/tasks/main.yml:15 download : Download items ---------------------------------------------------------7.16s /root/gopath/src/kubespray/roles/download/tasks/main.yml:6 network_plugin/calico : Calico | Copy cni plugins from calico/cni container -------7.10s /root/gopath/src/kubespray/roles/network_plugin/calico/tasks/main.yml:62 download : Download items ---------------------------------------------------------7.04s /root/gopath/src/kubespray/roles/download/tasks/main.yml:6 download : Download items ---------------------------------------------------------7.01s /root/gopath/src/kubespray/roles/download/tasks/main.yml:6 download : Sync container ---------------------------------------------------------7.00s /root/gopath/src/kubespray/roles/download/tasks/main.yml:15 download : Download items ---------------------------------------------------------6.98s /root/gopath/src/kubespray/roles/download/tasks/main.yml:6 download : Download items ---------------------------------------------------------6.79s /root/gopath/src/kubespray/roles/download/tasks/main.yml:6 3.2. k8s集群运行结果 1、k8s组件信息 # kubectl get all --namespace=kube-system NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE ds/calico-node 3 3 3 3 3 2h NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE deploy/kube-dns 2 2 2 2 2h deploy/kubedns-autoscaler 1 1 1 1 2h deploy/kubernetes-dashboard 1 1 1 1 2h NAME DESIRED CURRENT READY AGE rs/kube-dns-79d99cdcd5 2 2 2 2h rs/kubedns-autoscaler-5564b5585f 1 1 1 2h rs/kubernetes-dashboard-69cb58d748 1 1 1 2h NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE ds/calico-node 3 3 3 3 3 2h NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE deploy/kube-dns 2 2 2 2 2h deploy/kubedns-autoscaler 1 1 1 1 2h deploy/kubernetes-dashboard 1 1 1 1 2h NAME DESIRED CURRENT READY AGE rs/kube-dns-79d99cdcd5 2 2 2 2h rs/kubedns-autoscaler-5564b5585f 1 1 1 2h rs/kubernetes-dashboard-69cb58d748 1 1 1 2h NAME READY STATUS RESTARTS AGE po/calico-node-22vsg 1/1 Running 0 2h po/calico-node-t7zgw 1/1 Running 0 2h po/calico-node-zqnx8 1/1 Running 0 2h po/kube-apiserver-kube-master-0 1/1 Running 0 22h po/kube-controller-manager-kube-master-0 1/1 Running 0 2h po/kube-dns-79d99cdcd5-f2t6t 3/3 Running 0 2h po/kube-dns-79d99cdcd5-gw944 3/3 Running 0 2h po/kube-proxy-kube-master-0 1/1 Running 2 22h po/kube-proxy-kube-node-41 1/1 Running 3 22h po/kube-proxy-kube-node-42 1/1 Running 3 22h po/kube-scheduler-kube-master-0 1/1 Running 0 2h po/kubedns-autoscaler-5564b5585f-lt9bb 1/1 Running 0 2h po/kubernetes-dashboard-69cb58d748-wmb9x 1/1 Running 0 2h po/nginx-proxy-kube-node-41 1/1 Running 3 22h po/nginx-proxy-kube-node-42 1/1 Running 3 22h NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE svc/kube-dns ClusterIP 10.233.0.3 53/UDP,53/TCP 2h svc/kubernetes-dashboard ClusterIP 10.233.27.24 443/TCP 2h 2、k8s节点信息 # kubectl get nodes NAME STATUS ROLES AGE VERSION kube-master-0 Ready master 22h v1.9.5 kube-node-41 Ready node 22h v1.9.5 kube-node-42 Ready node 22h v1.9.5 3、组件健康信息 # kubectl get cs NAME STATUS MESSAGE ERROR scheduler Healthy ok controller-manager Healthy ok etcd-0 Healthy {\"health\": \"true\"} 4. k8s集群扩容节点 4.1. 修改hosts.ini文件 如果需要扩容Node节点，则修改hosts.ini文件，增加新增的机器信息。例如，要增加节点机器kube-node-43（IP为172.16.94.143），修改后的文件内容如下： # Configure 'ip' variable to bind kubernetes services on a # different ip than the default iface # 主机名 ssh登陆IP ssh用户名 ssh登陆密码 机器IP 子网掩码 kube-master-0 ansible_ssh_host=172.16.94.140 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.140 mask=/24 kube-node-41 ansible_ssh_host=172.16.94.141 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.141 mask=/24 kube-node-42 ansible_ssh_host=172.16.94.142 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.142 mask=/24 kube-node-43 ansible_ssh_host=172.16.94.143 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.143 mask=/24 # configure a bastion host if your nodes are not directly reachable # bastion ansible_ssh_host=x.x.x.x [kube-master] kube-master-0 [etcd] kube-master-0 [kube-node] kube-node-41 kube-node-42 kube-node-43 [k8s-cluster:children] kube-node kube-master [calico-rr] 4.2. 执行扩容命令 涉及文件为scale.yml。 # 进入主目录 cd kubespray # 执行部署命令 ansible-playbook -i inventory/k8s/hosts.ini scale.yml -b -vvv 4.3. 检查扩容结果 1、ansible的执行结果 PLAY RECAP *************************************** kube-node-41 : ok=228 changed=11 unreachable=0 failed=0 kube-node-42 : ok=197 changed=6 unreachable=0 failed=0 kube-node-43 : ok=227 changed=69 unreachable=0 failed=0 # 新增Node节点 localhost : ok=2 changed=0 unreachable=0 failed=0 2、k8s的节点信息 # kubectl get nodes NAME STATUS ROLES AGE VERSION kube-master-0 Ready master 1d v1.9.5 kube-node-41 Ready node 1d v1.9.5 kube-node-42 Ready node 1d v1.9.5 kube-node-43 Ready node 1m v1.9.5 #该节点为新增Node节点 可以看到新增的kube-node-43节点已经扩容完成。 3、k8s组件信息 # kubectl get po --namespace=kube-system -o wide NAME READY STATUS RESTARTS AGE IP NODE calico-node-22vsg 1/1 Running 0 10h 172.16.94.140 kube-master-0 calico-node-8fz9x 1/1 Running 2 27m 172.16.94.143 kube-node-43 calico-node-t7zgw 1/1 Running 0 10h 172.16.94.142 kube-node-42 calico-node-zqnx8 1/1 Running 0 10h 172.16.94.141 kube-node-41 kube-apiserver-kube-master-0 1/1 Running 0 1d 172.16.94.140 kube-master-0 kube-controller-manager-kube-master-0 1/1 Running 0 10h 172.16.94.140 kube-master-0 kube-dns-79d99cdcd5-f2t6t 3/3 Running 0 10h 10.233.100.194 kube-node-41 kube-dns-79d99cdcd5-gw944 3/3 Running 0 10h 10.233.107.1 kube-node-42 kube-proxy-kube-master-0 1/1 Running 2 1d 172.16.94.140 kube-master-0 kube-proxy-kube-node-41 1/1 Running 3 1d 172.16.94.141 kube-node-41 kube-proxy-kube-node-42 1/1 Running 3 1d 172.16.94.142 kube-node-42 kube-proxy-kube-node-43 1/1 Running 0 26m 172.16.94.143 kube-node-43 kube-scheduler-kube-master-0 1/1 Running 0 10h 172.16.94.140 kube-master-0 kubedns-autoscaler-5564b5585f-lt9bb 1/1 Running 0 10h 10.233.100.193 kube-node-41 kubernetes-dashboard-69cb58d748-wmb9x 1/1 Running 0 10h 10.233.107.2 kube-node-42 nginx-proxy-kube-node-41 1/1 Running 3 1d 172.16.94.141 kube-node-41 nginx-proxy-kube-node-42 1/1 Running 3 1d 172.16.94.142 kube-node-42 nginx-proxy-kube-node-43 1/1 Running 0 26m 172.16.94.143 kube-node-43 5. 部署高可用集群 将hosts.ini文件中的master和etcd的机器增加到多台，执行部署命令。 ansible-playbook -i inventory/k8s/hosts.ini cluster.yml -b -vvv 例如： # Configure 'ip' variable to bind kubernetes services on a # different ip than the default iface # 主机名 ssh登陆IP ssh用户名 ssh登陆密码 机器IP 子网掩码 kube-master-0 ansible_ssh_host=172.16.94.140 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.140 mask=/24 kube-master-1 ansible_ssh_host=172.16.94.144 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.144 mask=/24 kube-master-2 ansible_ssh_host=172.16.94.145 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.145 mask=/24 kube-node-41 ansible_ssh_host=172.16.94.141 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.141 mask=/24 kube-node-42 ansible_ssh_host=172.16.94.142 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.142 mask=/24 kube-node-43 ansible_ssh_host=172.16.94.143 ansible_ssh_user=root ansible_ssh_pass=123 ip=172.16.94.143 mask=/24 # configure a bastion host if your nodes are not directly reachable # bastion ansible_ssh_host=x.x.x.x [kube-master] kube-master-0 kube-master-1 kube-master-2 [etcd] kube-master-0 kube-master-1 kube-master-2 [kube-node] kube-node-41 kube-node-42 kube-node-43 [k8s-cluster:children] kube-node kube-master [calico-rr] 6. 升级k8s集群 选择对应的k8s版本信息，执行升级命令。涉及文件为upgrade-cluster.yml。 ansible-playbook upgrade-cluster.yml -b -i inventory/k8s/hosts.ini -e kube_version=v1.10.4 -vvv 7. troubles shooting 在使用kubespary部署k8s集群时，主要遇到以下报错。 7.1. python-netaddr未安装 报错内容： fatal: [node1]: FAILED! => {\"failed\": true, \"msg\": \"The ipaddr filter requires python-netaddr be installed on the ansible controller\"} 解决方法： 需要安装 python-netaddr，具体参考上述[环境准备]内容。 7.2. swap未关闭 报错内容： fatal: [kube-master-0]: FAILED! => { \"assertion\": \"ansible_swaptotal_mb == 0\", \"changed\": false, \"evaluated_to\": false } fatal: [kube-node-41]: FAILED! => { \"assertion\": \"ansible_swaptotal_mb == 0\", \"changed\": false, \"evaluated_to\": false } fatal: [kube-node-42]: FAILED! => { \"assertion\": \"ansible_swaptotal_mb == 0\", \"changed\": false, \"evaluated_to\": false } 解决方法： 所有部署机器执行swapoff -a关闭swap，具体参考上述[环境准备]内容。 7.3. 部署机器内存过小 报错内容： TASK [kubernetes/preinstall : Stop if memory is too small for masters] ********************************************************************************************************************************************************************************************************* task path: /root/gopath/src/kubespray/roles/kubernetes/preinstall/tasks/verify-settings.yml:52 Friday 10 August 2018 21:50:26 +0800 (0:00:00.940) 0:01:14.088 ********* fatal: [kube-master-0]: FAILED! => { \"assertion\": \"ansible_memtotal_mb >= 1500\", \"changed\": false, \"evaluated_to\": false } TASK [kubernetes/preinstall : Stop if memory is too small for nodes] *********************************************************************************************************************************************************************************************************** task path: /root/gopath/src/kubespray/roles/kubernetes/preinstall/tasks/verify-settings.yml:58 Friday 10 August 2018 21:50:27 +0800 (0:00:00.570) 0:01:14.659 ********* fatal: [kube-node-41]: FAILED! => { \"assertion\": \"ansible_memtotal_mb >= 1024\", \"changed\": false, \"evaluated_to\": false } fatal: [kube-node-42]: FAILED! => { \"assertion\": \"ansible_memtotal_mb >= 1024\", \"changed\": false, \"evaluated_to\": false } to retry, use: --limit @/root/gopath/src/kubespray/cluster.retry 解决方法： 调大所有部署机器的内存，本示例中调整为3G或以上。 7.4. kube-scheduler组件运行失败 kube-scheduler组件运行失败，导致http://localhost:10251/healthz调用失败。 报错内容： FAILED - RETRYING: Master | wait for kube-scheduler (1 retries left). FAILED - RETRYING: Master | wait for kube-scheduler (1 retries left). fatal: [node1]: FAILED! => {\"attempts\": 60, \"changed\": false, \"content\": \"\", \"failed\": true, \"msg\": \"Status code was not [200]: Request failed: \", \"redirected\": false, \"status\": -1, \"url\": \"http://localhost:10251/healthz\"} 解决方法： 可能是内存不足导致，本示例中调大了部署机器的内存。 7.5. docker安装包冲突 报错内容： failed: [k8s-node-1] (item={u'name': u'docker-engine-1.13.1-1.el7.centos'}) => { \"attempts\": 4, \"changed\": false, ... \"item\": { \"name\": \"docker-engine-1.13.1-1.el7.centos\" }, \"msg\": \"Error: docker-ce-selinux conflicts with 2:container-selinux-2.66-1.el7.noarch\\n\", \"rc\": 1, \"results\": [ \"Loaded plugins: fastestmirror\\nLoading mirror speeds from cached hostfile\\n * elrepo: mirrors.tuna.tsinghua.edu.cn\\n * epel: mirrors.tongji.edu.cn\\nPackage docker-engine is obsoleted by docker-ce, trying to install docker-ce-17.03.2.ce-1.el7.centos.x86_64 instead\\nResolving Dependencies\\n--> Running transaction check\\n---> Package docker-ce.x86_64 0:17.03.2.ce-1.el7.centos will be installed\\n--> Processing Dependency: docker-ce-selinux >= 17.03.2.ce-1.el7.centos for package: docker-ce-17.03.2.ce-1.el7.centos.x86_64\\n--> Processing Dependency: libltdl.so.7()(64bit) for package: docker-ce-17.03.2.ce-1.el7.centos.x86_64\\n--> Running transaction check\\n---> Package docker-ce-selinux.noarch 0:17.03.2.ce-1.el7.centos will be installed\\n---> Package libtool-ltdl.x86_64 0:2.4.2-22.el7_3 will be installed\\n--> Processing Conflict: docker-ce-selinux-17.03.2.ce-1.el7.centos.noarch conflicts docker-selinux\\n--> Restarting Dependency Resolution with new changes.\\n--> Running transaction check\\n---> Package container-selinux.noarch 2:2.55-1.el7 will be updated\\n---> Package container-selinux.noarch 2:2.66-1.el7 will be an update\\n--> Processing Conflict: docker-ce-selinux-17.03.2.ce-1.el7.centos.noarch conflicts docker-selinux\\n--> Finished Dependency Resolution\\n You could try using --skip-broken to work around the problem\\n You could try running: rpm -Va --nofiles --nodigest\\n\" ] } 解决方法： 卸载旧的docker版本，由kubespary自动安装。 sudo yum remove -y docker \\ docker-client \\ docker-client-latest \\ docker-common \\ docker-latest \\ docker-latest-logrotate \\ docker-logrotate \\ docker-selinux \\ docker-engine-selinux \\ docker-engine 参考文章： https://github.com/kubernetes-incubator/kubespray https://github.com/kubernetes-incubator/kubespray/blob/master/docs/upgrades.md Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"setup/installer/install-k8s-by-minikube.html":{"url":"setup/installer/install-k8s-by-minikube.html","title":"使用minikube安装kubernetes","keywords":"","body":"1. 安装kubectl2. 安装minikube2.1 安装minikube2.2 minikube命令帮助3. 使用minikube安装k8s集群3.1. minikube start3.2. minikube status3.3. minikube stop3.4. minikube delete4. 查看部署结果4.1. 部署组件4.2. dashboard5. troubleshooting5.1. 没有安装VirtualBox5.2. 没有安装Docker 以下内容基于Linux系统，特别为Ubuntu系统 1. 安装kubectl curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/linux/amd64/kubectl && chmod +x kubectl && sudo mv kubectl /usr/local/bin/ 下载指定版本，例如下载v1.9.0版本 curl -LO https://storage.googleapis.com/kubernetes-release/release/v1.9.0/bin/linux/amd64/kubectl && chmod +x kubectl && sudo mv kubectl /usr/local/bin/ 2. 安装minikube minikube的源码地址：https://github.com/kubernetes/minikube 2.1 安装minikube 以下命令为安装latest版本的minikube。 curl -Lo minikube https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64 && chmod +x minikube && sudo mv minikube /usr/local/bin/ 安装指定版本可到https://github.com/kubernetes/minikube/releases下载对应版本。 例如：以下为安装v0.28.2版本 curl -Lo minikube https://storage.googleapis.com/minikube/releases/v0.28.2/minikube-linux-amd64 && chmod +x minikube && sudo mv minikube /usr/local/bin/ 2.2 minikube命令帮助 Minikube is a CLI tool that provisions and manages single-node Kubernetes clusters optimized for development workflows. Usage: minikube [command] Available Commands: addons Modify minikube's kubernetes addons cache Add or delete an image from the local cache. completion Outputs minikube shell completion for the given shell (bash or zsh) config Modify minikube config dashboard Opens/displays the kubernetes dashboard URL for your local cluster delete Deletes a local kubernetes cluster docker-env Sets up docker env variables; similar to '$(docker-machine env)' get-k8s-versions Gets the list of Kubernetes versions available for minikube when using the localkube bootstrapper ip Retrieves the IP address of the running cluster logs Gets the logs of the running localkube instance, used for debugging minikube, not user code mount Mounts the specified directory into minikube profile Profile sets the current minikube profile service Gets the kubernetes URL(s) for the specified service in your local cluster ssh Log into or run a command on a machine with SSH; similar to 'docker-machine ssh' ssh-key Retrieve the ssh identity key path of the specified cluster start Starts a local kubernetes cluster status Gets the status of a local kubernetes cluster stop Stops a running local kubernetes cluster update-check Print current and latest version number update-context Verify the IP address of the running cluster in kubeconfig. version Print the version of minikube Flags: --alsologtostderr log to standard error as well as files -b, --bootstrapper string The name of the cluster bootstrapper that will set up the kubernetes cluster. (default \"localkube\") --log_backtrace_at traceLocation when logging hits line file:N, emit a stack trace (default :0) --log_dir string If non-empty, write log files in this directory --loglevel int Log level (0 = DEBUG, 5 = FATAL) (default 1) --logtostderr log to standard error instead of files -p, --profile string The name of the minikube VM being used. This can be modified to allow for multiple minikube instances to be run independently (default \"minikube\") --stderrthreshold severity logs at or above this threshold go to stderr (default 2) -v, --v Level log level for V logs --vmodule moduleSpec comma-separated list of pattern=N settings for file-filtered logging Use \"minikube [command] --help\" for more information about a command. 3. 使用minikube安装k8s集群 3.1. minikube start 可以以Docker的方式运行k8s的组件，但需要先安装Docker(可参考Docker安装)，启动参数使用--vm-driver=none。 minikube start --vm-driver=none 例如： root@ubuntu:~# minikube start --vm-driver=none Starting local Kubernetes v1.10.0 cluster... Starting VM... Getting VM IP address... Moving files into cluster... Downloading kubeadm v1.10.0 Downloading kubelet v1.10.0 ^[[DFinished Downloading kubelet v1.10.0 Finished Downloading kubeadm v1.10.0 Setting up certs... Connecting to cluster... Setting up kubeconfig... Starting cluster components... Kubectl is now configured to use the cluster. =================== WARNING: IT IS RECOMMENDED NOT TO RUN THE NONE DRIVER ON PERSONAL WORKSTATIONS The 'none' driver will run an insecure kubernetes apiserver as root that may leave the host vulnerable to CSRF attacks When using the none driver, the kubectl config and credentials generated will be root owned and will appear in the root home directory. You will need to move the files to the appropriate location and then set the correct permissions. An example of this is below: sudo mv /root/.kube $HOME/.kube # this will write over any previous configuration sudo chown -R $USER $HOME/.kube sudo chgrp -R $USER $HOME/.kube sudo mv /root/.minikube $HOME/.minikube # this will write over any previous configuration sudo chown -R $USER $HOME/.minikube sudo chgrp -R $USER $HOME/.minikube This can also be done automatically by setting the env var CHANGE_MINIKUBE_NONE_USER=true Loading cached images from config file. 安装指定版本的kubernetes集群 # 查阅版本 minikube get-k8s-versions # 选择版本启动 minikube start --kubernetes-version v1.7.3 --vm-driver=none 3.2. minikube status $ minikube status minikube: Running cluster: Running kubectl: Correctly Configured: pointing to minikube-vm at 172.16.94.139 3.3. minikube stop minikube stop 命令可以用来停止集群。 该命令会关闭 minikube 虚拟机，但将保留所有集群状态和数据。 再次启动集群将恢复到之前的状态。 3.4. minikube delete minikube delete 命令可以用来删除集群。 该命令将关闭并删除 minikube 虚拟机。没有数据或状态会被保存下来。 4. 查看部署结果 4.1. 部署组件 root@ubuntu:~# kubectl get all --namespace=kube-system NAME READY STATUS RESTARTS AGE pod/etcd-minikube 1/1 Running 0 38m pod/kube-addon-manager-minikube 1/1 Running 0 38m pod/kube-apiserver-minikube 1/1 Running 1 39m pod/kube-controller-manager-minikube 1/1 Running 0 38m pod/kube-dns-86f4d74b45-bdfnx 3/3 Running 0 38m pod/kube-proxy-dqdvg 1/1 Running 0 38m pod/kube-scheduler-minikube 1/1 Running 0 38m pod/kubernetes-dashboard-5498ccf677-c2gnh 1/1 Running 0 38m pod/storage-provisioner 1/1 Running 0 38m NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/kube-dns ClusterIP 10.96.0.10 53/UDP,53/TCP 38m service/kubernetes-dashboard NodePort 10.104.48.227 80:30000/TCP 38m NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE daemonset.apps/kube-proxy 1 1 1 1 1 38m NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE deployment.apps/kube-dns 1 1 1 1 38m deployment.apps/kubernetes-dashboard 1 1 1 1 38m NAME DESIRED CURRENT READY AGE replicaset.apps/kube-dns-86f4d74b45 1 1 1 38m replicaset.apps/kubernetes-dashboard-5498ccf677 1 1 1 38m 4.2. dashboard 通过访问ip:port，例如：http://172.16.94.139:30000/，可以访问k8s的`dashboard`控制台。 5. troubleshooting 5.1. 没有安装VirtualBox [root@minikube ~]# minikube start Starting local Kubernetes v1.10.0 cluster... Starting VM... Downloading Minikube ISO 160.27 MB / 160.27 MB [============================================] 100.00% 0s E0727 15:47:08.655647 9407 start.go:174] Error starting host: Error creating host: Error executing step: Running precreate checks. : VBoxManage not found. Make sure VirtualBox is installed and VBoxManage is in the path. Retrying. E0727 15:47:08.656994 9407 start.go:180] Error starting host: Error creating host: Error executing step: Running precreate checks. : VBoxManage not found. Make sure VirtualBox is installed and VBoxManage is in the path ================================================================================ An error has occurred. Would you like to opt in to sending anonymized crash information to minikube to help prevent future errors? To opt out of these messages, run the command: minikube config set WantReportErrorPrompt false ================================================================================ Please enter your response [Y/n]: 解决方法，先安装VirtualBox。 5.2. 没有安装Docker [root@minikube ~]# minikube start --vm-driver=none Starting local Kubernetes v1.10.0 cluster... Starting VM... E0727 15:56:54.936706 9441 start.go:174] Error starting host: Error creating host: Error executing step: Running precreate checks. : docker cannot be found on the path for this machine. A docker installation is a requirement for using the none driver: exec: \"docker\": executable file not found in $PATH. Retrying. E0727 15:56:54.938930 9441 start.go:180] Error starting host: Error creating host: Error executing step: Running precreate checks. : docker cannot be found on the path for this machine. A docker installation is a requirement for using the none driver: exec: \"docker\": executable file not found in $PATH 解决方法，先安装Docker。 文章参考： https://github.com/kubernetes/minikube https://kubernetes.io/docs/setup/minikube/ https://kubernetes.io/docs/tasks/tools/install-minikube/ https://kubernetes.io/docs/tasks/tools/install-kubectl/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"setup/installer/install-k8s-by-kind.html":{"url":"setup/installer/install-k8s-by-kind.html","title":"使用kind安装kubernetes","keywords":"","body":"1. 安装kind2. 创建k8s集群3. kindest/node容器内进程1. 安装kind On mac or linux curl -Lo ./kind \"https://github.com/kubernetes-sigs/kind/releases/download/v0.7.0/kind-$(uname)-amd64\" chmod +x ./kind mv ./kind /some-dir-in-your-PATH/kind 2. 创建k8s集群 $ kind create cluster Creating cluster \"kind\" ... ✓ Ensuring node image (kindest/node:v1.17.0) 🖼 ✓ Preparing nodes 📦 ✓ Writing configuration 📜 ✓ Starting control-plane 🕹️ ✓ Installing CNI 🔌 ✓ Installing StorageClass 💾 Set kubectl context to \"kind-kind\" You can now use your cluster with: kubectl cluster-info --context kind-kind Not sure what to do next? 😅 Check out https://kind.sigs.k8s.io/docs/user/quick-start/ 查看集群信息 $ kubectl cluster-info --context kind-kind Kubernetes master is running at https://127.0.0.1:32768 KubeDNS is running at https://127.0.0.1:32768/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy 查看node $ kubectl get node -o wide NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME kind-control-plane Ready master 35h v1.17.0 172.17.0.2 Ubuntu 19.10 3.10.107-1-tlinux2_kvm_guest-0049 containerd://1.3.2 查看pod $ kubectl get po --all-namespaces -o wide NAMESPACE NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES kube-system coredns-6955765f44-lqk9v 1/1 Running 0 35h 10.244.0.4 kind-control-plane kube-system coredns-6955765f44-zpsmc 1/1 Running 0 35h 10.244.0.3 kind-control-plane kube-system etcd-kind-control-plane 1/1 Running 0 35h 172.17.0.2 kind-control-plane kube-system kindnet-8mt7d 1/1 Running 0 35h 172.17.0.2 kind-control-plane kube-system kube-apiserver-kind-control-plane 1/1 Running 0 35h 172.17.0.2 kind-control-plane kube-system kube-controller-manager-kind-control-plane 1/1 Running 0 35h 172.17.0.2 kind-control-plane kube-system kube-proxy-5w25s 1/1 Running 0 35h 172.17.0.2 kind-control-plane kube-system kube-scheduler-kind-control-plane 1/1 Running 0 35h 172.17.0.2 kind-control-plane local-path-storage local-path-provisioner-7745554f7f-dckzr 1/1 Running 0 35h 10.244.0.2 kind-control-plane docker ps $ docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 93b291f99dd4 kindest/node:v1.17.0 \"/usr/local/bin/entr…\" 2 minutes ago Up 2 minutes 127.0.0.1:32768->6443/tcp kind-control-plane 3. kindest/node容器内进程 $ docker exec -it 93b291f99dd4 bash root@kind-control-plane:/# ps auxw USER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND root 1 0.1 0.0 19512 7480 ? Ss 03:18 0:00 /sbin/init root 105 0.0 0.0 26396 7344 ? S root 1080 0.0 0.0 0 0 ? Z 03:18 0:00 [iptables-nft-sa] root 1241 0.0 0.0 112540 5156 ? Sl 03:19 0:00 /usr/local/bin/containerd-shim-runc-v2 -namespace k8s.io -id 5cbd7bbe186cf5847786c7a03aa4c6f82e6c805d0a189f0f3e8fb1750594260d -address /run/containerd/containerd.sock root 1262 0.0 0.0 1012 4 ? Ss 03:19 0:00 /pause root 1303 0.1 0.0 134372 14088 ? Ssl 03:19 0:00 local-path-provisioner --debug start --helper-image k8s.gcr.io/debian-base:v2.0.0 --config /etc/config/config.json root 1411 0.0 0.0 112540 4876 ? Sl 03:19 0:00 /usr/local/bin/containerd-shim-runc-v2 -namespace k8s.io -id 196b440345cb5ef47a6c31222323d35bbfef85d1d79c149ec0e3a6e22022a5f0 -address /run/containerd/containerd.sock root 1437 0.0 0.0 1012 4 ? Ss 03:19 0:00 /pause root 1450 0.0 0.0 112540 4380 ? Sl 03:19 0:00 /usr/local/bin/containerd-shim-runc-v2 -namespace k8s.io -id de7bdf052083978c78708383f842567d4fb38adff22a56792437a4de82425afe -address /run/containerd/containerd.sock root 1480 0.0 0.0 1012 4 ? Ss 03:19 0:00 /pause root 1530 0.1 0.1 144324 19056 ? Ssl 03:19 0:00 /coredns -conf /etc/coredns/Corefile root 1531 0.1 0.1 144580 19204 ? Ssl 03:19 0:00 /coredns -conf /etc/coredns/Corefile 参考： https://github.com/kubernetes-sigs/kind https://kind.sigs.k8s.io/docs/user/quick-start/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"setup/installer/install-dashboard.html":{"url":"setup/installer/install-dashboard.html","title":"安装k8s dashboard","keywords":"","body":"1. 部署dashboard2. 登录dashboard2.1. 创建超级管理员2.2. 创建Namespace管理员2.3. 创建只读账户3. 集成SSO登录1. 部署dashboard kubectl apply -f https://raw.githubusercontent.com/kubernetes/dashboard/v2.7.0/aio/deploy/recommended.yaml 镜像： kubernetesui/dashboard:v2.5.0 默认端口：8443 登录页面需要填入token或kubeconfig 2. 登录dashboard 2.1. 创建超级管理员 参考：dashboard/creating-sample-user 创建dashboard-adminuser.yaml文件如下： k8s 1.24+版本需要自行创建secret绑定serviceaccount apiVersion: v1 kind: ServiceAccount metadata: name: admin-user namespace: kubernetes-dashboard --- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: admin-user roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: cluster-admin subjects: - kind: ServiceAccount name: admin-user namespace: kubernetes-dashboard --- apiVersion: v1 kind: Secret metadata: name: admin-user-secret namespace: kubernetes-dashboard annotations: kubernetes.io/service-account.name: \"admin-user\" type: kubernetes.io/service-account-token 创建serviceaccount和ClusterRoleBinding，绑定cluster-admin的超级管理员的权限。 kubectl apply -f dashboard-adminuser.yaml 创建用户token kubectl -n kubernetes-dashboard create token admin-user --duration 8760h 或者通过secret查询token kubectl get secret admin-user-secret -n kubernetes-dashboard -o jsonpath={\".data.token\"} | base64 -d 移除账号 kubectl -n kubernetes-dashboard delete serviceaccount admin-user kubectl -n kubernetes-dashboard delete clusterrolebinding admin-user 2.2. 创建Namespace管理员 1、创建角色权限（role） kind: Role apiVersion: rbac.authorization.k8s.io/v1 metadata: namespace: name: -admin-role rules: - apiGroups: - '*' resources: - '*' verbs: - '*' 2、创建用户账号（ServiceAccount） apiVersion: v1 kind: ServiceAccount metadata: name: -admin-user namespace: 创建secret 可自动生成token apiVersion: v1 kind: Secret metadata: name: ${SecretName} namespace: ${ServiceAccountNS} annotations: kubernetes.io/service-account.name: \"${ServiceAccountName}\" type: kubernetes.io/service-account-token 3、创建角色绑定关系 apiVersion: rbac.authorization.k8s.io/v1 kind: RoleBinding metadata: name: -admin-user namespace: roleRef: apiGroup: rbac.authorization.k8s.io kind: Role name: -admin-role subjects: - kind: ServiceAccount name: -admin-user namespace: 4、生成token kubectl -n create token --duration 8760h 或者通过上述secret中的token获得 kubectl get secret ${SecretName} -n ${ServiceAccountNS} -o jsonpath={\".data.token\"} | base64 -d 2.3. 创建只读账户 集群默认提供了几种命名空间级别的权限，分别设置ClusterRole: [admin, edit, view], 将授权设置为ClusterRole为view即可。 apiVersion: rbac.authorization.k8s.io/v1 kind: RoleBinding metadata: name: -admin-user namespace: roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: view subjects: - kind: ServiceAccount name: -admin-user namespace: 3. 集成SSO登录 社区提供了添加Authorization header的方式来集成自定义的SSO登录。即在HTTP请求中增加Header: Authorization: Bearer 。该操作可以通过apisix或Nginx等插件注入Header。 参考： 部署和访问 Kubernetes 仪表板（Dashboard） | Kubernetes dashboard/creating-sample-user.md at master · kubernetes/dashboard · GitHub dashboard/docs/user/access-control at master · kubernetes/dashboard · GitHub Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"setup/kubeadm-upgrade.html":{"url":"setup/kubeadm-upgrade.html","title":"kubeadm升级k8s集群","keywords":"","body":"1. 版本注意事项1.1. 版本跨度过大1.2. 可升级的版本计划2. 版本升级步骤2.1. 准备工作2.2. 升级master节点2.2.1. 升级第一个master节点2.2.2. 手动升级你的 CNI 驱动插件。2.2.3. 升级其他master节点2.2.4. 升级master的kubelet组件2.3. 升级worker节点3. 升级版本回滚4. 问题排查本文主要说明如何使用kubeadm来升级k8s集群。 1. 版本注意事项 假设k8s的版本格式为x.y.z，那么使用kubeadm最多只能升级到y+1版本，或者是当前y版本的最新版本。例如你k8s集群的版本为1.24.x，那么你最大版本只能下载1.25.x的kubeadm来升级版本。 因此升级前需要执行以下命令来验证可升级的版本。 kubeadm upgrade plan 1.1. 版本跨度过大 如果出现以下报错，说明升级的版本跨度过大。 # kubeadm的版本为v1.26.7 ./kubeadm version kubeadm version: &version.Info{Major:\"1\", Minor:\"26\", GitVersion:\"v1.26.7\", GitCommit:\"84e1fc493a47446df2e155e70fca768d2653a398\", GitTreeState:\"clean\", BuildDate:\"2023-07-19T12:22:13Z\", GoVersion:\"go1.20.6\", Compiler:\"gc\", Platform:\"linux/amd64\"} # 当前k8s集群版本1.24.2 版本跨度过大。 ./kubeadm upgrade plan [upgrade/config] Making sure the configuration is correct: [upgrade/config] Reading configuration from the cluster... [upgrade/config] FYI: You can look at this config file with 'kubectl -n kube-system get cm kubeadm-config -o yaml' [upgrade/config] FATAL: this version of kubeadm only supports deploying clusters with the control plane version >= 1.25.0. Current version: v1.24.2 To see the stack trace of this error execute with --v=5 or higher 1.2. 可升级的版本计划 可升级的版本计划如下 当前的k8s版本为1.24.2，可升级的版本是1.24.16或1.25.12，其中etcd升级的版本为3.5.6-0。 ./kubeadm upgrade plan [upgrade/config] Making sure the configuration is correct: [upgrade/config] Reading configuration from the cluster... [upgrade/config] FYI: You can look at this config file with 'kubectl -n kube-system get cm kubeadm-config -o yaml' [upload-config] Storing the configuration used in ConfigMap \"kubeadm-config\" in the \"kube-system\" Namespace [preflight] Running pre-flight checks. [upgrade] Running cluster health checks [upgrade] Fetching available versions to upgrade to [upgrade/versions] Cluster version: v1.24.2 [upgrade/versions] kubeadm version: v1.25.12 I0815 21:41:34.096199 2934255 version.go:256] remote version is much newer: v1.27.4; falling back to: stable-1.25 [upgrade/versions] Target version: v1.25.12 [upgrade/versions] Latest version in the v1.24 series: v1.24.16 Components that must be upgraded manually after you have upgraded the control plane with 'kubeadm upgrade apply': COMPONENT CURRENT TARGET kubelet 4 x v1.24.2 v1.24.16 Upgrade to the latest version in the v1.24 series: COMPONENT CURRENT TARGET kube-apiserver v1.24.2 v1.24.16 kube-controller-manager v1.24.2 v1.24.16 kube-scheduler v1.24.2 v1.24.16 kube-proxy v1.24.2 v1.24.16 CoreDNS v1.8.6 v1.9.3 etcd 3.5.3-0 3.5.6-0 You can now apply the upgrade by executing the following command: kubeadm upgrade apply v1.24.16 _____________________________________________________________________ Components that must be upgraded manually after you have upgraded the control plane with 'kubeadm upgrade apply': COMPONENT CURRENT TARGET kubelet 4 x v1.24.2 v1.25.12 Upgrade to the latest stable version: COMPONENT CURRENT TARGET kube-apiserver v1.24.2 v1.25.12 kube-controller-manager v1.24.2 v1.25.12 kube-scheduler v1.24.2 v1.25.12 kube-proxy v1.24.2 v1.25.12 CoreDNS v1.8.6 v1.9.3 etcd 3.5.3-0 3.5.6-0 You can now apply the upgrade by executing the following command: kubeadm upgrade apply v1.25.12 _____________________________________________________________________ The table below shows the current state of component configs as understood by this version of kubeadm. Configs that have a \"yes\" mark in the \"MANUAL UPGRADE REQUIRED\" column require manual config upgrade or resetting to kubeadm defaults before a successful upgrade can be performed. The version to manually upgrade to is denoted in the \"PREFERRED VERSION\" column. API GROUP CURRENT VERSION PREFERRED VERSION MANUAL UPGRADE REQUIRED kubeproxy.config.k8s.io v1alpha1 v1alpha1 no kubelet.config.k8s.io v1beta1 v1beta1 no _____________________________________________________________________ 2. 版本升级步骤 2.1. 准备工作 下载指定版本的kubeadm二进制 查看升级计划：kubeadm upgrade plan 2.2. 升级master节点 2.2.1. 升级第一个master节点 kubeadm upgrade apply v1.25.x -f 升级结束会查看都以下输出： [upgrade/successful] SUCCESS! Your cluster was upgraded to \"v1.25.x\". Enjoy! [upgrade/kubelet] Now that your control plane is upgraded, please proceed with upgrading your kubelets if you haven't already done so. 2.2.2. 手动升级你的 CNI 驱动插件。 你的容器网络接口（CNI）驱动应该提供了程序自身的升级说明。 参阅插件页面查找你的 CNI 驱动， 并查看是否需要其他升级步骤。 如果 CNI 驱动作为 DaemonSet 运行，则在其他控制平面节点上不需要此步骤。 2.2.3. 升级其他master节点 下载指定版本的kubeadm组件。使用以下命令升级，注意区别于第一个master的升级命令。 kubeadm upgrade node 2.2.4. 升级master的kubelet组件 将节点标记为不可调度并驱逐所有负载，准备节点的维护： # 将 替换为你要腾空的控制面节点名称 kubectl drain --ignore-daemonsets 下载kubelet和kubectl # 用最新的补丁版本替换 1.27.x-00 中的 x apt-mark unhold kubelet kubectl && \\ apt-get update && apt-get install -y kubelet=1.27.x-00 kubectl=1.27.x-00 && \\ apt-mark hold kubelet kubectl 重启kubelet sudo systemctl daemon-reload sudo systemctl restart kubelet 解除节点保护 # 将 替换为你的节点名称 kubectl uncordon 2.3. 升级worker节点 同 2.2.4. 升级master的kubelet组件的步骤，worker节点只需要升级kubelet。 3. 升级版本回滚 kubeadm升级过程中会把相关目录备份到/etc/kubernetes/tmp目录，备份内容如下： tmp/ ├── kubeadm-backup-etcd-2023-08-16-14-50-50 │ └── etcd └── kubeadm-backup-manifests-2023-08-16-14-50-50 ├── etcd.yaml ├── kube-apiserver.yaml ├── kube-controller-manager.yaml └── kube-scheduler.yaml 如果 kubeadm upgrade 失败并且没有回滚，例如由于执行期间节点意外关闭， 你可以再次运行 kubeadm upgrade。 此命令是幂等的，并最终确保实际状态是你声明的期望状态。 要从故障状态恢复，你还可以运行 kubeadm upgrade apply --force 而无需更改集群正在运行的版本。 在升级期间，kubeadm 向 /etc/kubernetes/tmp 目录下的如下备份文件夹写入数据： kubeadm-backup-etcd-- kubeadm-backup-manifests-- kubeadm-backup-etcd 包含当前控制面节点本地 etcd 成员数据的备份。 如果 etcd 升级失败并且自动回滚也无法修复，则可以将此文件夹中的内容复制到 /var/lib/etcd 进行手工修复。如果使用的是外部的 etcd，则此备份文件夹为空。 kubeadm-backup-manifests 包含当前控制面节点的静态 Pod 清单文件的备份版本。 如果升级失败并且无法自动回滚，则此文件夹中的内容可以复制到 /etc/kubernetes/manifests 目录实现手工恢复。 如果由于某些原因，在升级前后某个组件的清单未发生变化，则 kubeadm 也不会为之生成备份版本。 4. 问题排查 在升级k8s 从1.25.12到1.26.7的过程中，遇到master节点的服务起不来，报错如下： \"CreatePodSandbox for pod failed\" err=\"open /run/systemd/resolve/resolv.conf: no such file or directory\" pod=\"kube-system/kube-apiserver\" 现象主要是静态pod起不来，包括etcd等。 具体解决方法参考：open /run/systemd/resolve/resolv.conf # 查看以下的resolv.conf是否存在 cat /var/lib/kubelet/config.yaml | grep resolvConf /run/systemd/resolve/resolv.conf # 如果不存在，检查systemd-resolved是否正常运行， systemctl status systemd-resolved # 如果没有运行，则运行该服务 systemctl start systemd-resolved # 或者新建文件/run/systemd/resolve/resolv.conf，并将其他master的文件拷贝过来。 参考： 升级 kubeadm 集群 | Kubernetes 升级 Linux 节点 | Kubernetes 安装扩展（Addon） | Kubernetes Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"setup/kubeadm-certs.html":{"url":"setup/kubeadm-certs.html","title":"kubeadm管理证书","keywords":"","body":"1. 检查证书是否过期2. 手动更新过期的证书2.1. 备份/etc/kubernetes目录2.2. 执行更新证书命令2.3. 重启k8s组件2.4. 更新默认的kubeconfig文件2.5. 配置kubelet证书轮转3. 修改kubeadm源码证书时间3.1. ca文件的有效期（默认为10年）3.2. 证书文件的有效期（默认为1年）通过kubeadm搭建的集群默认的证书时间是1年（由于官方期望每年更新一次k8s的版本，在更新的时候会默认更新证书），当你执行命令出现以下报错，说明你的证书已经到期了，则需要手动更新证书。 # kubectl get node Unable to connect to the server: x509: certificate has expired or is not yet valid: current time 2023-08-03T18:06:23+08:00 is after 2023-07-04T06:30:54Z # 或者出现以下报错 You must be logged in to the server(unauthorized) 以下说明手动更新证书的流程。 具体可以参考：使用 kubeadm 进行证书管理 | Kubernetes 1. 检查证书是否过期 kubeadm certs check-expiration 会输出以下的内容： CERTIFICATE EXPIRES RESIDUAL TIME CERTIFICATE AUTHORITY EXTERNALLY MANAGED admin.conf Dec 30, 2020 23:36 UTC 364d no apiserver Dec 30, 2020 23:36 UTC 364d ca no apiserver-etcd-client Dec 30, 2020 23:36 UTC 364d etcd-ca no apiserver-kubelet-client Dec 30, 2020 23:36 UTC 364d ca no controller-manager.conf Dec 30, 2020 23:36 UTC 364d no etcd-healthcheck-client Dec 30, 2020 23:36 UTC 364d etcd-ca no etcd-peer Dec 30, 2020 23:36 UTC 364d etcd-ca no etcd-server Dec 30, 2020 23:36 UTC 364d etcd-ca no front-proxy-client Dec 30, 2020 23:36 UTC 364d front-proxy-ca no scheduler.conf Dec 30, 2020 23:36 UTC 364d no CERTIFICATE AUTHORITY EXPIRES RESIDUAL TIME EXTERNALLY MANAGED ca Dec 28, 2029 23:36 UTC 9y no etcd-ca Dec 28, 2029 23:36 UTC 9y no front-proxy-ca Dec 28, 2029 23:36 UTC 9y no 2. 手动更新过期的证书 分别在master节点执行以下命令。 2.1. 备份/etc/kubernetes目录 cp -fr /etc/kubernetes /etc/kubernetes.bak cp -fr ~/.kube ~/.kube.bak 2.2. 执行更新证书命令 kubeadm certs renew all 输出如下： # kubeadm certs renew all [renew] Reading configuration from the cluster... [renew] FYI: You can look at this config file with 'kubectl -n kube-system get cm kubeadm-config -o yaml' [renew] Error reading configuration from the Cluster. Falling back to default configuration certificate embedded in the kubeconfig file for the admin to use and for kubeadm itself renewed certificate for serving the Kubernetes API renewed certificate the apiserver uses to access etcd renewed certificate for the API server to connect to kubelet renewed certificate embedded in the kubeconfig file for the controller manager to use renewed certificate for liveness probes to healthcheck etcd renewed certificate for etcd nodes to communicate with each other renewed certificate for serving etcd renewed certificate for the front proxy client renewed certificate embedded in the kubeconfig file for the scheduler manager to use renewed Done renewing certificates. You must restart the kube-apiserver, kube-controller-manager, kube-scheduler and etcd, so that they can use the new certificates. 2.3. 重启k8s组件 先重启etcd 注意事项：需要将三个master节点的证书都重新更新后，然后三个master的etcd服务一起重启，使得etcd集群使用新的证书可以正常运行，否则会导致kube-apiserver也启动失败。 crictl ps |grep \"etcd\"|awk '{print $1}'|xargs crictl stop 再重启kube-apiserver、kube-controller、kube-scheduler容器。 crictl ps |egrep \"kube-apiserver|kube-scheduler|kube-controller\"|awk '{print $1}'|xargs crictl stop 2.4. 更新默认的kubeconfig文件 cp -fr /etc/kubernetes/admin.conf $HOME/.kube/config 2.5. 配置kubelet证书轮转 由于kubelet默认支持证书轮转，当证书过期时，可以自动生成新的密钥，并从 Kubernetes API 申请新的证书。可以查看kubelet的配置检查是否已经开启。 # cat /var/lib/kubelet/config.yaml |grep rotate rotateCertificates: true 3. 修改kubeadm源码证书时间 由于社区不允许用户配置超过1年的证书，因此自定义证书时间的参数不被允许开发。 相关issue如下： https://github.com/kubernetes/kubernetes/issues/119350 如果要实现自定义参数设置证书时间，可参考一下pr： https://github.com/kubernetes/kubernetes/pull/100907/files 如果需要修改kubeadm源码证书可以参考如下代码修改。 kubeadm中跟证书相关的代码有： 3.1. ca文件的有效期（默认为10年） 代码文件：./staging/src/k8s.io/client-go/util/cert/cert.go 中 NewSelfSignedCACert 函数的NotAfter字段 代码如下： // NewSelfSignedCACert creates a CA certificate func NewSelfSignedCACert(cfg Config, key crypto.Signer) (*x509.Certificate, error) { now := time.Now() // returns a uniform random value in [0, max-1), then add 1 to serial to make it a uniform random value in [1, max). serial, err := cryptorand.Int(cryptorand.Reader, new(big.Int).SetInt64(math.MaxInt64-1)) if err != nil { return nil, err } serial = new(big.Int).Add(serial, big.NewInt(1)) notBefore := now.UTC() if !cfg.NotBefore.IsZero() { notBefore = cfg.NotBefore.UTC() } tmpl := x509.Certificate{ SerialNumber: serial, Subject: pkix.Name{ CommonName: cfg.CommonName, Organization: cfg.Organization, }, DNSNames: []string{cfg.CommonName}, NotBefore: notBefore, NotAfter: now.Add(duration365d * 10).UTC(), # 默认为10年 KeyUsage: x509.KeyUsageKeyEncipherment | x509.KeyUsageDigitalSignature | x509.KeyUsageCertSign, BasicConstraintsValid: true, IsCA: true, } certDERBytes, err := x509.CreateCertificate(cryptorand.Reader, &tmpl, &tmpl, key.Public(), key) if err != nil { return nil, err } return x509.ParseCertificate(certDERBytes) } 3.2. 证书文件的有效期（默认为1年） 代码文件：cmd/kubeadm/app/util/pkiutil/pki_helpers.go中 NewSignedCert 函数的 notAfter 字段 常量参数kubeadmconstants.CertificateValidity ： /cmd/kubeadm/app/constants/constants.go 代码如下： // NewSignedCert creates a signed certificate using the given CA certificate and key func NewSignedCert(cfg *CertConfig, key crypto.Signer, caCert *x509.Certificate, caKey crypto.Signer, isCA bool) (*x509.Certificate, error) { // returns a uniform random value in [0, max-1), then add 1 to serial to make it a uniform random value in [1, max). serial, err := cryptorand.Int(cryptorand.Reader, new(big.Int).SetInt64(math.MaxInt64-1)) if err != nil { return nil, err } serial = new(big.Int).Add(serial, big.NewInt(1)) if len(cfg.CommonName) == 0 { return nil, errors.New(\"must specify a CommonName\") } keyUsage := x509.KeyUsageKeyEncipherment | x509.KeyUsageDigitalSignature if isCA { keyUsage |= x509.KeyUsageCertSign } RemoveDuplicateAltNames(&cfg.AltNames) # 此处引用了一个常量 notAfter := time.Now().Add(kubeadmconstants.CertificateValidity).UTC() if cfg.NotAfter != nil { notAfter = *cfg.NotAfter } certTmpl := x509.Certificate{ Subject: pkix.Name{ CommonName: cfg.CommonName, Organization: cfg.Organization, }, DNSNames: cfg.AltNames.DNSNames, IPAddresses: cfg.AltNames.IPs, SerialNumber: serial, NotBefore: caCert.NotBefore, NotAfter: notAfter, KeyUsage: keyUsage, ExtKeyUsage: cfg.Usages, BasicConstraintsValid: true, IsCA: isCA, } certDERBytes, err := x509.CreateCertificate(cryptorand.Reader, &certTmpl, caCert, key.Public(), caKey) if err != nil { return nil, err } return x509.ParseCertificate(certDERBytes) } 其中常量文件为： /cmd/kubeadm/app/constants/constants.go 代码如下： # 常量默认证书为1年。 // CertificateValidity defines the validity for all the signed certificates generated by kubeadm CertificateValidity = time.Hour * 24 * 365 可以修改此处常量的值10年，例如： # 常量默认证书为1年。 // CertificateValidity defines the validity for all the signed certificates generated by kubeadm CertificateValidity = time.Hour * 24 * 365 * 10 修改源码后，就可以重新编译kubeadm二进制。生成10年的证书文件。 参考： 使用 kubeadm 进行证书管理 | Kubernetes Kubernetes v1.25 编译 kubeadm 修改证书有效期到 100 年 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"setup/k8s-cert.html":{"url":"setup/k8s-cert.html","title":"k8s证书及秘钥","keywords":"","body":"1. 证书分类1.1. k8s集群的证书分类2. CA证书及秘钥3. cfssl工具4. 创建 CA (Certificate Authority)4.1. 配置源文件4.2. 执行命令5. 创建 kubernetes 证书5.1. 配置源文件5.2. 执行命令6. 创建 admin 证书6.1. 配置源文件6.2. 执行命令7. 创建 kube-proxy 证书7.1. 配置源文件7.2. 执行命令8. 校验证书9. 分发证书1. 证书分类 服务器证书：server cert，用于客户端验证服务端的身份。 客户端证书：client cert，用于服务端验证客户端的身份。 对等证书：peer cert（既是server cert又是client cert），用户成员之间的身份验证，例如 etcd。 1.1. k8s集群的证书分类 etcd节点：需要标识自己服务的server cert，也需要client cert与etcd集群其他节点交互，因此需要一个对等证书。 master节点：需要标识 apiserver服务的server cert，也需要client cert连接etcd集群，也需要一个对等证书。 kubelet：需要标识自己服务的server cert，也需要client cert请求apiserver，也使用一个对等证书。 kubectl、kube-proxy、calico：需要client证书。 2. CA证书及秘钥 目录：/etc/kubernetes/ssl 分类 证书/秘钥 说明 组件 ca ca-key.pem ca.pem ca.csr Kubernetes kubernetes-key.pem kubernetes.pem kubernetes.csr Admin admin-key.pem admin.pem admin.csr Kubelet kubelet.crt kubelet.key 配置文件 分类 证书/秘钥 说明 ca ca-config.json ca-csr.json Kubernetes kubernetes-csr.json Admin admin-csr.json Kube-proxy kube-proxy-csr.json 3. cfssl工具 安装cfssl： # 下载cfssl $ curl https://pkg.cfssl.org/R1.2/cfssl_linux-amd64 -o /usr/local/bin/cfssl $ curl https://pkg.cfssl.org/R1.2/cfssljson_linux-amd64 -o /usr/local/bin/cfssljson $ curl https://pkg.cfssl.org/R1.2/cfssl-certinfo_linux-amd64 -o /usr/local/bin/cfssl-certinfo # 添加可执行权限 $ chmod +x /usr/local/bin/cfssl /usr/local/bin/cfssljson /usr/local/bin/cfssl-certinfo 4. 创建 CA (Certificate Authority) 4.1. 配置源文件 创建 CA 配置文件 ca-config.json cat ca-config.json { \"signing\": { \"default\": { \"expiry\": \"87600h\" }, \"profiles\": { \"kubernetes\": { \"usages\": [ \"signing\", \"key encipherment\", \"server auth\", \"client auth\" ], \"expiry\": \"876000h\" } } } } EOF 参数说明 ca-config.json：可以定义多个 profiles，分别指定不同的过期时间、使用场景等参数；后续在签名证书时使用某个 profile； signing：表示该证书可用于签名其它证书；生成的 ca.pem 证书中 CA=TRUE； server auth：表示client可以用该 CA 对server提供的证书进行验证； client auth：表示server可以用该CA对client提供的证书进行验证； 创建 CA 证书签名请求 ca-csr.json cat ca-csr.json { \"CN\": \"kubernetes\", \"key\": { \"algo\": \"rsa\", \"size\": 2048 }, \"names\": [ { \"C\": \"CN\", \"ST\": \"ShenZhen\", \"L\": \"ShenZhen\", \"O\": \"k8s\", \"OU\": \"System\" } ] } EOF 参数说明 ca-csr.json的参数 CN：Common Name，kube-apiserver 从证书中提取该字段作为请求的用户名 (User Name)；浏览器使用该字段验证网站是否合法； names中的字段： C : country，国家 ST: state，州或省份 L：location，城市 O：organization，组织，kube-apiserver 从证书中提取该字段作为请求用户所属的组 (Group) OU：organization unit 4.2. 执行命令 cfssl gencert -initca ca-csr.json | cfssljson -bare ca 输出如下： # cfssl gencert -initca ca-csr.json | cfssljson -bare ca 2019/12/13 14:35:52 [INFO] generating a new CA key and certificate from CSR 2019/12/13 14:35:52 [INFO] generate received request 2019/12/13 14:35:52 [INFO] received CSR 2019/12/13 14:35:52 [INFO] generating key: rsa-2048 2019/12/13 14:35:52 [INFO] encoded CSR 2019/12/13 14:35:52 [INFO] signed certificate with serial number 248379771349454958117219047414671162179070747780 生成以下文件： # 生成文件 -rw-r--r-- 1 root root 1005 12月 13 11:32 ca.csr -rw------- 1 root root 1675 12月 13 11:32 ca-key.pem -rw-r--r-- 1 root root 1363 12月 13 11:32 ca.pem # 配置源文件 -rw-r--r-- 1 root root 293 12月 13 11:31 ca-config.json -rw-r--r-- 1 root root 210 12月 13 11:31 ca-csr.json 5. 创建 kubernetes 证书 5.1. 配置源文件 创建 kubernetes 证书签名请求文件kubernetes-csr.json。 cat kubernetes-csr.json { \"CN\": \"kubernetes\", \"hosts\": [ \"127.0.0.1\", \"\", \"\", \"kubernetes\", \"kubernetes.default\", \"kubernetes.default.svc\", \"kubernetes.default.svc.cluster\", \"kubernetes.default.svc.cluster.local\" ], \"key\": { \"algo\": \"rsa\", \"size\": 2048 }, \"names\": [{ \"C\": \"\", \"ST\": \"\", \"L\": \"\", \"O\": \"\", \"OU\": \"\" }] } EOF 参数说明： MASTER_IP：master节点的IP或域名 MASTER_CLUSTER_IP：kube-apiserver 指定的 service-cluster-ip-range 网段的第一个IP，例如（10.254.0.1）。 5.2. 执行命令 $ cfssl gencert -ca=ca.pem -ca-key=ca-key.pem -config=ca-config.json -profile=kubernetes kubernetes-csr.json | cfssljson -bare kubernetes 输出如下： # cfssl gencert -ca=ca.pem -ca-key=ca-key.pem -config=ca-config.json -profile=kubernetes kubernetes-csr.json | cfssljson -bare kubernetes 2019/12/13 14:40:28 [INFO] generate received request 2019/12/13 14:40:28 [INFO] received CSR 2019/12/13 14:40:28 [INFO] generating key: rsa-2048 2019/12/13 14:40:28 [INFO] encoded CSR 2019/12/13 14:40:28 [INFO] signed certificate with serial number 392795299385191732458211386861696542628305189374 2019/12/13 14:40:28 [WARNING] This certificate lacks a \"hosts\" field. This makes it unsuitable for websites. For more information see the Baseline Requirements for the Issuance and Management of Publicly-Trusted Certificates, v.1.1.6, from the CA/Browser Forum (https://cabforum.org); specifically, section 10.2.3 (\"Information Requirements\"). 生成以下文件： # 生成文件 -rw-r--r-- 1 root root 1269 12月 13 14:40 kubernetes.csr -rw------- 1 root root 1679 12月 13 14:40 kubernetes-key.pem -rw-r--r-- 1 root root 1643 12月 13 14:40 kubernetes.pem # 配置源文件 -rw-r--r-- 1 root root 580 12月 13 14:40 kubernetes-csr.json 6. 创建 admin 证书 6.1. 配置源文件 创建 admin 证书签名请求文件 admin-csr.json： cat admin-csr.json { \"CN\": \"admin\", \"hosts\": [], \"key\": { \"algo\": \"rsa\", \"size\": 2048 }, \"names\": [ { \"C\": \"CN\", \"ST\": \"ShenZhen\", \"L\": \"ShenZhen\", \"O\": \"system:masters\", \"OU\": \"System\" } ] } EOF 6.2. 执行命令 $ cfssl gencert -ca=ca.pem -ca-key=ca-key.pem -config=ca-config.json -profile=kubernetes admin-csr.json | cfssljson -bare admin 输出如下： # cfssl gencert -ca=ca.pem -ca-key=ca-key.pem -config=ca-config.json -profile=kubernetes admin-csr.json | cfssljson -bare admin 2019/12/13 14:52:37 [INFO] generate received request 2019/12/13 14:52:37 [INFO] received CSR 2019/12/13 14:52:37 [INFO] generating key: rsa-2048 2019/12/13 14:52:37 [INFO] encoded CSR 2019/12/13 14:52:37 [INFO] signed certificate with serial number 465422983473444224050765004141217688748259757371 2019/12/13 14:52:37 [WARNING] This certificate lacks a \"hosts\" field. This makes it unsuitable for websites. For more information see the Baseline Requirements for the Issuance and Management of Publicly-Trusted Certificates, v.1.1.6, from the CA/Browser Forum (https://cabforum.org); specifically, section 10.2.3 (\"Information Requirements\"). 生成文件 # 生成文件 -rw-r--r-- 1 root root 1013 12月 13 14:52 admin.csr -rw------- 1 root root 1675 12月 13 14:52 admin-key.pem -rw-r--r-- 1 root root 1407 12月 13 14:52 admin.pem # 配置源文件 -rw-r--r-- 1 root root 231 12月 13 14:49 admin-csr.json 7. 创建 kube-proxy 证书 7.1. 配置源文件 创建 kube-proxy 证书签名请求文件 kube-proxy-csr.json： cat kube-proxy-csr.json { \"CN\": \"system:kube-proxy\", \"hosts\": [], \"key\": { \"algo\": \"rsa\", \"size\": 2048 }, \"names\": [ { \"C\": \"CN\", \"ST\": \"BeiJing\", \"L\": \"BeiJing\", \"O\": \"k8s\", \"OU\": \"System\" } ] } EOF 7.2. 执行命令 $ cfssl gencert -ca=ca.pem -ca-key=ca-key.pem -config=ca-config.json -profile=kubernetes kube-proxy-csr.json | cfssljson -bare kube-proxy 输出如下： # cfssl gencert -ca=ca.pem -ca-key=ca-key.pem -config=ca-config.json -profile=kubernetes kube-proxy-csr.json | cfssljson -bare kube-proxy 2019/12/13 19:37:48 [INFO] generate received request 2019/12/13 19:37:48 [INFO] received CSR 2019/12/13 19:37:48 [INFO] generating key: rsa-2048 2019/12/13 19:37:48 [INFO] encoded CSR 2019/12/13 19:37:48 [INFO] signed certificate with serial number 526712749765692443642491255093816136154324531741 2019/12/13 19:37:48 [WARNING] This certificate lacks a \"hosts\" field. This makes it unsuitable for websites. For more information see the Baseline Requirements for the Issuance and Management of Publicly-Trusted Certificates, v.1.1.6, from the CA/Browser Forum (https://cabforum.org); specifically, section 10.2.3 (\"Information Requirements\"). 生成文件： # 生成文件 -rw-r--r-- 1 root root 1009 12月 13 19:37 kube-proxy.csr -rw------- 1 root root 1675 12月 13 19:37 kube-proxy-key.pem -rw-r--r-- 1 root root 1407 12月 13 19:37 kube-proxy.pem # 配置源文件 -rw-r--r-- 1 root root 230 12月 13 19:37 kube-proxy-csr.json 8. 校验证书 openssl x509 -noout -text -in kubernetes.pem 输出如下： # openssl x509 -noout -text -in kubernetes.pem Certificate: Data: Version: 3 (0x2) Serial Number: 44:cd:8c:e6:a4:60:ff:3f:09:af:02:e7:68:5e:f2:0f:e6:a0:39:fe Signature Algorithm: sha256WithRSAEncryption Issuer: C=CN, ST=ShenZhen, L=ShenZhen, O=k8s, OU=System, CN=kubernetes Validity Not Before: Dec 13 06:35:00 2019 GMT Not After : Nov 19 06:35:00 2119 GMT Subject: C=CN, ST=ShenZhen, L=ShenZhen, O=k8s, OU=System, CN=kubernetes Subject Public Key Info: Public Key Algorithm: rsaEncryption Public-Key: (2048 bit) Modulus: 00:d7:91:4f:90:56:fb:ab:a9:de:c4:98:9e:d7:e6: 45:db:5a:14:9a:76:78:6a:4c:db:3c:47:3c:e7:1c: 3c:37:4e:8a:cf:9c:a1:8a:4c:51:4c:cd:45:b0:03: 87:06:b9:20:2c:3a:51:f9:21:55:1c:90:7c:f8:93: bc:6a:48:05:3d:8b:74:fd:f2:f1:e6:5e:ad:b4:a8: f6:6d:f9:63:9e:e4:b4:cc:68:9e:90:d7:ef:de:ce: c1:1d:1b:68:59:68:5e:5f:7d:5c:f3:49:4f:18:72: be:b5:c8:af:e2:8d:34:9c:d2:68:b7:8c:26:69:cc: a5:f4:ca:69:2d:d7:21:f5:19:2e:b2:b5:97:16:87: 9f:9c:fd:01:97:c2:0e:20:b4:88:27:9a:37:9a:af: 0a:cf:82:4f:26:24:cb:07:ac:8c:b1:34:20:42:22: 00:b2:b0:98:c5:53:01:fb:32:aa:15:1b:7e:39:44: ae:af:6e:c3:65:96:f6:38:7a:87:37:d0:31:63:d8: a4:15:13:f2:56:da:e6:09:45:2b:46:2c:cb:63:db: f7:ba:7f:44:0a:36:39:7c:cc:5b:42:e5:56:c7:7f: dd:64:5c:f2:4a:af:d3:a9:d1:6e:06:27:57:09:4d: db:08:62:87:66:c8:2c:36:00:41:f1:90:f6:5f:68: 20:3d Exponent: 65537 (0x10001) X509v3 extensions: X509v3 Key Usage: critical Digital Signature, Key Encipherment X509v3 Extended Key Usage: TLS Web Server Authentication, TLS Web Client Authentication X509v3 Basic Constraints: critical CA:FALSE X509v3 Subject Key Identifier: 3D:3F:FA:B8:36:D7:FE:B1:59:BE:B1:F5:C1:5D:88:3D:BC:78:9F:87 X509v3 Authority Key Identifier: keyid:40:A2:D4:30:22:12:2E:C2:FB:A2:55:2C:CB:F0:F6:3E:4D:B8:02:03 X509v3 Subject Alternative Name: DNS:kubernetes, DNS:kubernetes.default, DNS:kubernetes.default.svc, DNS:kubernetes.default.svc.cluster, DNS:kubernetes.default.svc.cluster.local, IP Address:127.0.0.1, IP Address:172.20.0.112, IP Address:172.20.0.113, IP Address:172.20.0.114, IP Address:172.20.0.115, IP Address:10.254.0.1 Signature Algorithm: sha256WithRSAEncryption 63:50:f6:2a:03:c7:35:dd:e9:10:8d:2f:b3:27:9a:64:f3:e1: 11:8a:18:1e:fa:6d:85:30:11:b4:59:a3:6c:86:cd:2b:5c:67: 17:4f:aa:0d:bb:4c:ee:c8:af:e7:3d:61:6d:03:9d:14:6f:00: 48:56:59:b5:76:13:a9:30:23:e0:b2:d2:12:64:0c:60:0d:76: ec:c6:4f:b1:bc:24:01:7a:48:c6:fd:9e:5d:68:da:b9:a1:ad: 30:7a:ba:90:e2:e3:4e:b4:92:1b:c5:f2:8c:c1:b0:3d:fc:14: d2:46:e8:f8:22:8f:d9:4d:85:4f:58:6b:0f:84:78:06:b4:b9: 92:b9:0d:bd:1d:95:e9:0d:42:d3:fd:dd:2a:59:60:3f:63:35: eb:07:25:d2:ea:0d:19:a6:f3:dc:92:8e:ee:73:04:15:5e:97: e8:da:51:c3:69:49:96:36:c7:cc:5b:e5:e5:cb:e5:ce:9f:21: 6f:6b:56:16:bf:85:ad:1c:8c:91:c1:91:0a:90:18:e2:4a:b0: 32:58:33:ef:55:8e:8f:4a:e3:0f:b8:f7:41:04:65:89:e1:1b: d8:41:28:6e:84:c3:1c:8e:a9:a0:8a:42:e4:fe:d7:fe:0e:24: dc:74:37:fa:5e:be:20:69:c5:9a:5a:e6:83:1c:0b:9e:e1:43: ef:4f:7a:37 字段说明： 确认 Issuer 字段的内容和 ca-csr.json 一致； 确认 Subject 字段的内容和 kubernetes-csr.json 一致； 确认 X509v3 Subject Alternative Name 字段的内容和 kubernetes-csr.json 一致； 确认 X509v3 Key Usage、Extended Key Usage 字段的内容和 ca-config.json 中 kubernetes profile 一致； 9. 分发证书 将生成的证书和秘钥文件（后缀名为.pem）拷贝到所有机器的 /etc/kubernetes/ssl 目录下。 mkdir -p /etc/kubernetes/ssl cp *.pem /etc/kubernetes/ssl 参考文章： https://kubernetes.io/docs/concepts/cluster-administration/certificates/ https://coreos.com/os/docs/latest/generate-self-signed-certificates.html https://jimmysong.io/kubernetes-handbook/practice/create-tls-and-secret-key.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"setup/k8s-version-release.html":{"url":"setup/k8s-version-release.html","title":"k8s版本说明","keywords":"","body":"1. k8s版本号说明2. 版本偏差策略2.1. 支持的版本偏差1）kube-apiserver2）kubelet3）kube-controller-manager、kube-scheduler 和 cloud-controller-manager4）kubectl5）kube-proxy2.2. 组件升级顺序3. k8s版本发布周期3.1. 发布周期参考：1. k8s版本号说明 k8s维护最新三个版本的发布分支（[2022.7.2]当前最新三个版本为1.24、1.23、1.22），Kubernetes 1.19 和更新的版本获得大约 1 年的补丁支持。 Kubernetes 版本表示为 x.y.z， 其中 x 是主要版本，y 是次要版本，z 是补丁版本。遵循语义化版本规范。 2. 版本偏差策略 2.1. 支持的版本偏差 总结： kubelet 版本不能比 kube-apiserver 版本新，最多只可落后两个次要版本。 kube-controller-manager、kube-scheduler 和 cloud-controller-manager 不能比 kube-apiserver 版本新。最多落后一个次要版本（允许实时升级）。 kubectl 在 kube-apiserver 的一个次要版本（较旧或较新）中支持。 kube-proxy 和节点上的 kubelet 必须是相同的次要版本。 1）kube-apiserver 在高可用性（HA）集群中， 最新版和最老版的 kube-apiserver 实例版本偏差最多为一个次要版本。 例如： 最新的 kube-apiserver 实例处于 1.24 版本 其他 kube-apiserver 实例支持 1.24 和 1.23 版本 2）kubelet kubelet 版本不能比 kube-apiserver 版本新，并且最多只可落后两个次要版本。 例如： kube-apiserver 处于 1.24 版本 kubelet 支持 1.24、1.23 和 1.22 版本 说明： 如果 HA 集群中的 kube-apiserver 实例之间存在版本偏差，这会缩小允许的 kubelet 版本范围。 例如： kube-apiserver 实例处于 1.24 和 1.23 版本 kubelet 支持 1.23 和 1.22 版本， （不支持 1.24 版本，因为这将比 kube-apiserver 1.23 版本的实例新） 3）kube-controller-manager、kube-scheduler 和 cloud-controller-manager kube-controller-manager、kube-scheduler 和 cloud-controller-manager 不能比与它们通信的 kube-apiserver 实例新。 它们应该与 kube-apiserver 次要版本相匹配，但可能最多旧一个次要版本（允许实时升级）。 例如： kube-apiserver 处于 1.24 版本 kube-controller-manager、kube-scheduler 和 cloud-controller-manager 支持 1.24 和 1.23 版本 说明： 如果 HA 集群中的 kube-apiserver 实例之间存在版本偏差， 并且这些组件可以与集群中的任何 kube-apiserver 实例通信（例如，通过负载均衡器），这会缩小这些组件所允许的版本范围。 例如： kube-apiserver 实例处于 1.24 和 1.23 版本 kube-controller-manager、kube-scheduler 和 cloud-controller-manager 与可以路由到任何 kube-apiserver 实例的负载均衡器通信 kube-controller-manager、kube-scheduler 和 cloud-controller-manager 支持 1.23 版本（不支持 1.24 版本，因为它比 1.23 版本的 kube-apiserver 实例新） 4）kubectl kubectl 在 kube-apiserver 的一个次要版本（较旧或较新）中支持。 例如： kube-apiserver 处于 1.24 版本 kubectl 支持 1.25、1.24 和 1.23 版本 说明： 如果 HA 集群中的 kube-apiserver 实例之间存在版本偏差，这会缩小支持的 kubectl 版本范围。 例如： kube-apiserver 实例处于 1.24 和 1.23 版本 kubectl 支持 1.24 和 1.23 版本（其他版本将与 kube-apiserver 组件之一相差不止一个的次要版本） 5）kube-proxy kube-proxy 和节点上的 kubelet 必须是相同的次要版本。 kube-proxy 版本不能比 kube-apiserver 版本新。 kube-proxy 最多只能比 kube-apiserver 落后两个次要版本。 例如： 如果 kube-proxy 版本处于 1.22 版本： kubelet 必须处于相同的次要版本 1.22。 kube-apiserver 版本必须介于 1.22 和 1.24 之间，包括两者。 2.2. 组件升级顺序 优先升级kube-apiserver，其他的组件按照上述的版本要求进行升级，最好保持一致的版本。 3. k8s版本发布周期 k8s每年大概发布三次，即3-4个月发布一次大版本（发布版本为 vX.Y 里程碑创建的 Git 分支 release-X.Y）。 发布过程可被认为具有三个主要阶段： 特性增强定义 实现 稳定 3.1. 发布周期 1）正常开发（第 1-11 周） /sig {name} /sig {name} /kind {type} /lgtm /approved 2）代码冻结（第 12-14 周） /milestone {v1.y} /sig {name} /kind {bug, failing-test} /lgtm /approved 3）发布后（第 14 周以上） 回到“正常开发”阶段要求： /sig {name} /kind {type} /lgtm /approved 参考： 发行版本 | Kubernetes kubernetes/CHANGELOG at master · GitHub sig-release/releases at master · kubernetes/sig-release · GitHub design-proposals-archive/versioning.md at main · kubernetes/design-proposals-archive · GitHub Kubernetes 发布周期 | Kubernetes Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"setup/k8s-changelog.html":{"url":"setup/k8s-changelog.html","title":"k8s版本记录","keywords":"","body":"1.27（一）重要更新k8s.gcr.io 重定向到 registry.k8s.io 相关说明原地调整 Pod 资源 (alpha)StatefulSet PVC 自动删除功能特性 Beta优化大型集群中 kube-proxy 的 iptables 模式性能Kubelet 事件驱动 PLEG 升级为 BetaPod 调度就绪态功能增强Deployment 滚动更新过程中的调度优化关于加快 Pod 启动的进展（二）弃用变更1.26（一）重要更新Kubelet Evented PLEG for Better Performance优化 kube-proxy 性能，它只发送在调用 iptables-restore 中更改的规则，而不是整个规则集PR#112200 client-go 的 SharedInformerFactory 增加 Shutdown 方法，来等待 Factory 内所有运行的 informer 都结束。（二）弃用变更1.25（一）重要更新cgroup v2 升级到 GACSI 内联存储卷正式发布GAEphemeral Containers进入稳定版本（二）弃用变更1.24（一）重要更新1）kubelet完全移除Dockershim【最重大更新】2）Beta API 默认关闭3）存储容量和卷扩展到GA4）避免 IP 分配给service的冲突（二）弃用变更1）kubeadm2）kube-apiserver3）kube-controller-manager4）kubelet1.23（一） 重要更新1）FlexVolume 已弃用2）IPv4/IPv6 双栈网络到 GA3）Horizo​​ntalPodAutoscaler v2 到 GA4）Scheduler简化多点插件配置（二）已知问题1.22（—）重要更新1）kubeadm2）etcd3）kubelet1.27 参考： Kubernetes 在 v1.27 中移除的特性和主要变更 https://github.com/kubernetes/kubernetes/blob/master/CHANGELOG/CHANGELOG-1.27.md#changelog-since-v1260 202304 | K8s 1.27 正式发布 - DaoCloud Enterprise （一）重要更新 k8s.gcr.io 重定向到 registry.k8s.io 相关说明 Kubernetes 项目为了托管其容器镜像，使用社区拥有的一个名为 registry.k8s.io. 的镜像仓库。从 3 月 20 日起，所有来自过期 k8s.gcr.io 仓库的流量将被重定向到 registry.k8s.io。 已弃用的 k8s.gcr.io 仓库最终将被淘汰。Kubernetes v1.27 版本不会发布到旧的仓库。 原地调整 Pod 资源 (alpha) 参考：Kubernetes 1.27: 原地调整 Pod 资源 (alpha) | Kubernetes 在 Kubernetes v1.27 中，添加了一个新的 alpha 特性，允许用户调整分配给 Pod 的 CPU 和内存资源大小，而无需重新启动容器。 首先，API 层面现在允许修改 Pod 容器中的 resources 字段下的 cpu 和 memory 资源。资源修改只需 patch 正在运行的 pod 规约即可。 StatefulSet PVC 自动删除功能特性 Beta 在 v1.23 中引入的 StatefulSetAutoDeletePVC 功能将在 1.27 版本中升级为 Beta，并默认开启。 然而，默认开启并不意味着所有 StatefulSet 的 PVC 都将自动删除。 优化大型集群中 kube-proxy 的 iptables 模式性能 功能 MinimizeIPTablesRestore 在 1.26 版本中引入，并在 1.27 版本中升级为 Beta 并默认启用。 该功能旨在改善大型集群中 kube-proxy 的 iptables 模式性能。 如果您遇到 Service 信息未正确同步到 iptables 的问题，您可以通过将 kube-proxy 启动参数设置为 --feature-gates=MinimizeIPTablesRestore=false 来禁用该功能（并向社区提交问题）。 您还可以查看 kube-proxy 的 metrics 信息中的 sync_proxy_rules_iptables_partial_restore_failures_total 指标来监控规则同步失败的次数。 Kubelet 事件驱动 PLEG 升级为 Beta 在节点 Pod 较多的情况下，通过容器运行时的 Event 驱动 Pod 状态更新，能够有效地提升效率。 在 1.27 中，该功能已经达到了 Beta 条件，基础的 E2E 测试任务已经添加。 之所以默认关闭该功能，是因为社区认为该功能还需要补充以下验证：压力测试、恢复测试和带退避逻辑的重试。 Pod 调度就绪态功能增强 调度就绪态功能 PodSchedulingReadiness，在 v1.26 作为 Alpha 功能引入，从 v1.27 开始该功能升级为 Beta，默认开启。 Deployment 滚动更新过程中的调度优化 在 v1.27 中，PodTopologySpread 调度策略可以区分调度 Pod 标签的值 （这里通常指 Pod 的 pod-template-hash 标签，不同 replica set 对应的 Pod 该标签的值不同）， 这样滚动更新后，新的 Pod 实例会被调度得更加均匀 。 关于加快 Pod 启动的进展 要启用并行镜像拉取，请在 kubelet 配置中将 serializeImagePulls 字段设置为 false。 当 serializeImagePulls 被禁用时，将立即向镜像服务发送镜像拉取请求，并可以并行拉取多个镜像。 为了在节点上具有多个 Pod 的场景中加快 Pod 启动，特别是在突然扩缩的情况下， kubelet 需要同步 Pod 状态并准备 ConfigMap、Secret 或卷。这就需要大带宽访问 kube-apiserver。在 v1.27 中，kubelet 为了提高 Pod 启动性能，将这些默认值分别提高到了 50 和 100。 （二）弃用变更 kubelet 移除了命令行参数 --container-runtime。 弃用的命令行参数 --pod-eviction-timeout 将被从 kube-controller-manager 中移除。 1.26 参考： Kubernetes 1.26 中的移除、弃用和主要变更 | Kubernetes https://github.com/kubernetes/kubernetes/blob/master/CHANGELOG/CHANGELOG-1.26.md#changelog-since-v1250 https://docs.daocloud.io/blogs/221209-k8s-1.26/ https://www.alibabacloud.com/help/zh/ack/product-overview/kubernetes-1-26-release-notes?spm=a2c63.p38356.0.0.64624df9xXfEkZ （一）重要更新 Kubelet Evented PLEG for Better Performance 该功能让 kubelet 在跟踪节点中 Pod 状态时，通过尽可能依赖容器运行时接口(CRI) 的通知来减少定期轮训，这会减少 kubelet 对 CPU 的使用 新增 Alpha Feature Gate —— EventedPLEG 来控制是否开启该功能。 优化 kube-proxy 性能，它只发送在调用 iptables-restore 中更改的规则，而不是整个规则集 PR#112200 client-go 的 SharedInformerFactory 增加 Shutdown 方法，来等待 Factory 内所有运行的 informer 都结束。 （二）弃用变更 Kubelet 不再支持 v1alpha2 版本的 CRI，接入的容器运行时必须实现 v1 版本的容器运行时接口。 Kubernetes v1.26 将不支持 containerd 1.5.x 及更早的版本；需要升级到 containerd 1.6.x 或更高版本后，才能将该节点的 kubelet 升级到 1.26。 1.25 参考： https://github.com/kubernetes/kubernetes/blob/master/CHANGELOG/CHANGELOG-1.25.md#whats-new-major-themes Kubernetes 1.25 的移除说明和主要变更 | Kubernetes （一）重要更新 cgroup v2 升级到 GA Kubernetes 1.25 将 cgroup v2 正式发布（GA）， 让kubelet使用最新的容器资源管理能力。一些 Kubernetes 特性专门使用 cgroup v2 来增强资源管理和隔离。 例如，MemoryQoS 特性提高了内存利用率并依赖 cgroup v2 功能来启用它。kubelet 中的新资源管理特性也将利用新的 cgroup v2 特性向前发展。 CSI 内联存储卷正式发布GA CSI 内联存储卷与其他类型的临时卷相似，如 configMap、downwardAPI 和 secret。 重要的区别是，存储是由 CSI 驱动提供的，它允许使用第三方供应商提供的临时存储。 卷被定义为 Pod 规约的一部分，并遵循 Pod 的生命周期，这意味着卷随着 Pod 的调度而创建，并随着 Pod 的销毁而销毁。 Ephemeral Containers进入稳定版本 当pod crash的时候，无法通过kubectl exec 进入容器，这个时候可以通过临时容器Ephemeral Containers) （二）弃用变更 Kubernetes v1.25 将移除 PodSecurityPolicy，取而代之的是 Pod Security Admission（即 PodSecurity 安全准入控制器）。 清理 IPTables 链的所有权 从 v1.25 开始，Kubelet 将逐渐迁移为不在 nat 表中创建以下 iptables 链： KUBE-MARK-DROP KUBE-MARK-MASQ KUBE-POSTROUTING 1.24 最新发行版本：1.24.2 (发布日期: 2022-06-15） 不再支持：2023-09-29 补丁版本： 1.24.1、 1.24.2 Complete 1.24 Schedule and Changelog Kubernetes 1.24 使用 go1.18构建，默认情况下将不再验证使用 SHA-1 哈希算法签名的证书。 （一）重要更新 1.24.0主要参考kubernetes/CHANGELOG-1.24.major-themes 1）kubelet完全移除Dockershim【最重大更新】 在 v1.20 中弃用后，dockershim 组件已从 kubelet 中删除。从 v1.24 开始，您将需要使用其他受支持的运行时之一（例如 containerd 或 CRI-O），或者如果您依赖 Docker 引擎作为容器运行时，则使用 cri-dockerd。有关确保您的集群已准备好进行此移除的更多信息，请参阅本指南)。 2）Beta API 默认关闭 默认情况下，不会在集群中启用新的 beta API。默认情况下，现有的 beta API 和现有 beta API 的新版本将继续启用。 3）存储容量和卷扩展到GA 存储容量跟踪支持通过 CSIStorageCapacity 对象公开当前可用的存储容量，并增强使用具有后期绑定的 CSI 卷的 pod 的调度。 卷扩展增加了对调整现有持久卷大小的支持。 4）避免 IP 分配给service的冲突 Kubernetes 1.24 引入了一项新的选择加入功能，允许您为服务的静态 IP 地址分配软预留范围。通过手动启用此功能，集群将更喜欢从服务 IP 地址池中自动分配，从而降低冲突风险。 可以分配 Service ClusterIP： 动态，这意味着集群将自动在配置的服务 IP 范围内选择一个空闲 IP。 静态，这意味着用户将在配置的服务 IP 范围内设置一个 IP。 Service ClusterIP 是唯一的，因此，尝试使用已分配的 ClusterIP 创建 Service 将返回错误。 （二）弃用变更 1）kubeadm kubeadm.k8s.io/v1beta2 已被弃用，并将在未来的版本中删除，可能在 3 个版本（一年）中。您应该开始将 kubeadm.k8s.io/v1beta3 用于新集群。要迁移磁盘上的旧配置文件，您可以使用 kubeadm config migrate 命令。 默认 k​​ubeadm 配置为 containerd 套接字（Unix：unix:///var/run/containerd/containerd.sock，Windows：npipe:////./pipe/containerd-containerd）而不是 Docker 的配置.如果在集群创建期间 Init|JoinConfiguration.nodeRegistration.criSocket 字段为空，并且在主机上发现多个套接字，则总是会抛出错误并要求用户通过设置字段中的值来指定要使用的套接字。使用 crictl 与 CRI 套接字进行所有通信，以执行诸如拉取图像和获取正在运行的容器列表等操作，而不是在 Docker 的情况下使用 docker CLI。 kubeadm 迁移到标签和污点中不再使用 master 一词。对于新的集群，标签 node-role.kubernetes.io/master 将不再添加到控制平面节点，只会添加标签 node-role.kubernetes.io/control-plane。 2）kube-apiserver 不安全的地址标志 --address、--insecure-bind-address、--port 和 --insecure-port（自 1.20 起惰性）被删除 弃用了--master-countflag 和--endpoint-reconciler-type=master-countreconciler，转而使用 lease reconciler。 已弃用Service.Spec.LoadBalancerIP。 3）kube-controller-manager kube-controller-manager 中的不安全地址标志 --address 和 --port 自 v1.20 起无效，并在 v1.24 中被删除。 4）kubelet --pod-infra-container-image kubelet 标志已弃用，将在未来版本中删除。 以下与 dockershim 相关的标志也与 dockershim 一起被删除 --experimental-dockershim-root-directory、--docker-endpoint、--image-pull-progress-deadline、--network-plugin、--cni-conf -dir，--cni-bin-dir，--cni-cache-dir，--network-plugin-mtu。(#106907, @cyclinder) 1.23 最新发行版本：1.23.8 (发布日期: 2022-06-15） 不再支持：2023-02-28 补丁版本： 1.23.1、 1.23.2、 1.23.3、 1.23.4、 1.23.5、 1.23.6、 1.23.7、 1.23.8 Complete 1.23 Schedule and Changelog Kubernetes 是使用 golang 1.17 构建的。此版本的 go 删除了使用 GODEBUG=x509ignoreCN=0 环境设置来重新启用将 X.509 服务证书的 CommonName 视为主机名的已弃用旧行为的能力。 （一） 重要更新 1）FlexVolume 已弃用 FlexVolume 已弃用。 Out-of-tree CSI 驱动程序是在 Kubernetes 中编写卷驱动程序的推荐方式。FlexVolume 驱动程序的维护者应实施 CSI 驱动程序并将 FlexVolume 的用户转移到 CSI。 FlexVolume 的用户应将其工作负载转移到 CSI 驱动程序。 2）IPv4/IPv6 双栈网络到 GA IPv4/IPv6 双栈网络从 GA 毕业。从 1.21 开始，Kubernetes 集群默认启用支持双栈网络。在 1.23 中，移除了 IPv6DualStack 功能门。双栈网络的使用不是强制性的。尽管启用了集群以支持双栈网络，但 Pod 和服务继续默认为单栈。要使用双栈网络：Kubernetes 节点具有可路由的 IPv4/IPv6 网络接口，使用支持双栈的 CNI 网络插件，Pod 配置为双栈，服务的 .spec.ipFamilyPolicy 字段设置为 PreferDualStack 或需要双栈。 3）Horizo​​ntalPodAutoscaler v2 到 GA Horizo​​ntalPodAutoscaler API 的第 2 版在 1.23 版本中逐渐稳定。 Horizo​​ntalPodAutoscaler autoscaling/v2beta2 API 已弃用，取而代之的是新的 autoscaling/v2 API，Kubernetes 项目建议将其用于所有用例。 4）Scheduler简化多点插件配置 kube-scheduler 正在为插件添加一个新的、简化的配置字段，以允许在一个位置启用多个扩展点。新的 multiPoint 插件字段旨在为管理员简化大多数调度程序设置。通过 multiPoint 启用的插件将自动为它们实现的每个单独的扩展点注册。例如，实现 Score 和 Filter 扩展的插件可以同时为两者启用。这意味着可以启用和禁用整个插件，而无需手动编辑单个扩展点设置。这些扩展点现在可以被抽象出来，因为它们与大多数用户无关。 （二）已知问题 在 1.22 Kubernetes 版本附带的 etcd v3.5.0 版本中发现了数据损坏问题。请阅读 etcd 的最新生产建议)。 运行etcd v3.5.2 v3.5.1和v3.5.0高负荷会导致数据损坏问题。如果etcd进程被杀,偶尔有些已提交的事务并不反映在所有的成员。建议升级到v3.5.3。 最低推荐etcd版本运行在生产3.3.18 + 3.4.2 + v3.5.3 +。 1.22 最新发行版本：1.22.11 (发布日期: 2022-06-15） 不再支持：2022-10-28 补丁版本： 1.22.1、 1.22.2、 1.22.3、 1.22.4、 1.22.5、 1.22.6、 1.22.7、 1.22.8、 1.22.9、 1.22.10、 1.22.11 Complete 1.22 Schedule and Changelog （—）重要更新 1）kubeadm 允许非root用户允许kubeadm。 现在V1beta3首选API版本;v1beta2 API也仍然是可用的,并没有弃用。 移除对docker cgroup driver的检查，kubeadm默认使用systemd cgroup driver，需要手动将runtime配置为systemd。 v1beta3中删除ClusterConfiguration.DNS字段，因为CoreDNS是唯一支持DNS类型。 2）etcd etcd使用v3.5.0版本。（但是在1.23版本中发现v3.5.0有数据损坏的问题） 3）kubelet 节点支持swap内存。 作为α特性,Kubernetes v1.22并且可以使用cgroup v2 API来控制内存分配和隔离。这个功能的目的是改善工作负载和节点可用性时对内存资源的争用。 参考： 发行版本 | Kubernetes kubernetes/CHANGELOG at master · GitHub sig-release/releases at master · kubernetes/sig-release · GitHub Kubernetes 1.24 正式发布，这里是更新功能总览 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/architecture/_index.html":{"url":"concepts/architecture/_index.html","title":"kubernetes架构","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/architecture/kubernetes-architecture.html":{"url":"concepts/architecture/kubernetes-architecture.html","title":"Kubernetes总架构图","keywords":"","body":"1. Kubernetes的总架构图2. Kubernetes各个组件介绍2.1 kube-master[控制节点]2.1.1 API Server[资源操作入口]2.1.2 Controller Manager[内部管理控制中心]2.1.3 Scheduler[集群分发调度器]2.2 kube-node[服务节点]2.2.1 Kubelet[节点上的Pod管家]2.2.2 Proxy[负载均衡、路由转发]2.2.3 kubectl[集群管理命令行工具集]1. Kubernetes的总架构图 2. Kubernetes各个组件介绍 2.1 kube-master[控制节点] master的工作流程图 Kubecfg将特定的请求，比如创建Pod，发送给Kubernetes Client。 Kubernetes Client将请求发送给API server。 API Server根据请求的类型，比如创建Pod时storage类型是pods，然后依此选择何种REST Storage API对请求作出处理。 REST Storage API对的请求作相应的处理。 将处理的结果存入高可用键值存储系统Etcd中。 在API Server响应Kubecfg的请求后，Scheduler会根据Kubernetes Client获取集群中运行Pod及Minion/Node信息。 依据从Kubernetes Client获取的信息，Scheduler将未分发的Pod分发到可用的Minion/Node节点上。 2.1.1 API Server[资源操作入口] 提供了资源对象的唯一操作入口，其他所有组件都必须通过它提供的API来操作资源数据，只有API Server与存储通信，其他模块通过API Server访问集群状态。 第一，是为了保证集群状态访问的安全。 第二，是为了隔离集群状态访问的方式和后端存储实现的方式：API Server是状态访问的方式，不会因为后端存储技术etcd的改变而改变。 作为kubernetes系统的入口，封装了核心对象的增删改查操作，以RESTFul接口方式提供给外部客户和内部组件调用。对相关的资源数据“全量查询”+“变化监听”，实时完成相关的业务功能。 更多API Server信息请参考：Kubernetes核心原理（一）之API Server 2.1.2 Controller Manager[内部管理控制中心] 实现集群故障检测和恢复的自动化工作，负责执行各种控制器，主要有： endpoint-controller：定期关联service和pod(关联信息由endpoint对象维护)，保证service到pod的映射总是最新的。 replication-controller：定期关联replicationController和pod，保证replicationController定义的复制数量与实际运行pod的数量总是一致的。 更多Controller Manager信息请参考：Kubernetes核心原理（二）之Controller Manager 2.1.3 Scheduler[集群分发调度器] Scheduler收集和分析当前Kubernetes集群中所有Minion/Node节点的资源(内存、CPU)负载情况，然后依此分发新建的Pod到Kubernetes集群中可用的节点。 实时监测Kubernetes集群中未分发和已分发的所有运行的Pod。 Scheduler也监测Minion/Node节点信息，由于会频繁查找Minion/Node节点，Scheduler会缓存一份最新的信息在本地。 最后，Scheduler在分发Pod到指定的Minion/Node节点后，会把Pod相关的信息Binding写回API Server。 更多Scheduler信息请参考：Kubernetes核心原理（三）之Scheduler 2.2 kube-node[服务节点] kubelet结构图 2.2.1 Kubelet[节点上的Pod管家] 负责Node节点上pod的创建、修改、监控、删除等全生命周期的管理 定时上报本Node的状态信息给API Server。 kubelet是Master API Server和Minion/Node之间的桥梁，接收Master API Server分配给它的commands和work，通过kube-apiserver间接与Etcd集群交互，读取配置信息。 具体的工作如下： 1) 设置容器的环境变量、给容器绑定Volume、给容器绑定Port、根据指定的Pod运行一个单一容器、给指定的Pod创建network 容器。 2) 同步Pod的状态、同步Pod的状态、从cAdvisor获取container info、 pod info、 root info、 machine info。 3) 在容器中运行命令、杀死容器、删除Pod的所有容器。 更多Kubelet信息请参考：Kubernetes核心原理（四）之Kubelet 2.2.2 Proxy[负载均衡、路由转发] Proxy是为了解决外部网络能够访问跨机器集群中容器提供的应用服务而设计的，运行在每个Minion/Node上。Proxy提供TCP/UDP sockets的proxy，每创建一种Service，Proxy主要从etcd获取Services和Endpoints的配置信息（也可以从file获取），然后根据配置信息在Minion/Node上启动一个Proxy的进程并监听相应的服务端口，当外部请求发生时，Proxy会根据Load Balancer将请求分发到后端正确的容器处理。 Proxy不但解决了同一主宿机相同服务端口冲突的问题，还提供了Service转发服务端口对外提供服务的能力，Proxy后端使用了随机、轮循负载均衡算法。 2.2.3 kubectl[集群管理命令行工具集] 通过客户端的kubectl命令集操作，API Server响应对应的命令结果，从而达到对kubernetes集群的管理。 参考文章： https://yq.aliyun.com/articles/47308?spm=5176.100240.searchblog.19.jF7FFa Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/architecture/paas-based-on-docker-and-kubernetes.html":{"url":"concepts/architecture/paas-based-on-docker-and-kubernetes.html","title":"基于Docker及Kubernetes技术构建容器云（PaaS）平台概述","keywords":"","body":"1. PaaS概述1.1. PaaS概念1.2. IaaS/PaaS/SaaS说明1.3. PaaS的特点（三种层次）2. App Engine概述2.1. App Engine概念2.2. 架构设计2.3. 组成模块说明3. 容器云平台技术栈4. Docker概述4.1. Docker介绍4.2. Docker的基本概念4.2.1. 镜像4.2.2. 容器4.2.3. 仓库4.3. Docker的优势5. Kubernetes概述5.1. Kubernetes介绍5.2. Kubernetes的基本概念5.2.1. Pod5.2.2. Replication Controller5.2.3. Service5.2.4. Label5.2.5. Node5.3. Kubernetes架构[编者的话] 目前很多的容器云平台通过Docker及Kubernetes等技术提供应用运行平台，从而实现运维自动化，快速部署应用、弹性伸缩和动态调整应用环境资源，提高研发运营效率。 从宏观到微观（从抽象到具体）的思路来理解：云计算→PaaS→ App Engine→XAE[XXX App Engine] （XAE泛指一类应用运行平台，例如GAE、SAE、BAE等）。 本文简要介绍了与容器云相关的几个重要概念：PaaS、App Engine、Dokcer、Kubernetes。 1. PaaS概述 1.1. PaaS概念 PaaS(Platform as a service)，平台即服务，指将软件研发的平台（或业务基础平台）作为一种服务，以SaaS的模式提交给用户。 PaaS是云计算服务的其中一种模式，云计算是一种按使用量付费的模式的服务，类似一种租赁服务，服务可以是基础设施计算资源（IaaS），平台（PaaS），软件（SaaS）。租用IT资源的方式来实现业务需要，如同水力、电力资源一样，计算、存储、网络将成为企业IT运行的一种被使用的资源，无需自己建设，可按需获得。 PaaS的实质是将互联网的资源服务化为可编程接口，为第三方开发者提供有商业价值的资源和服务平台。简而言之，IaaS就是卖硬件及计算资源，PaaS就是卖开发、运行环境，SaaS就是卖软件。 1.2. IaaS/PaaS/SaaS说明 类型 说明 比喻 例子 IaaS:Infrastructure-as-a-Service(基础设施即服务) 提供的服务是计算基础设施 地皮，需要自己盖房子 Amazon EC2（亚马逊弹性云计算） PaaS: Platform-as-a-Service(平台即服务) 提供的服务是软件研发的平台或业务基础平台 商品房，需要自己装修 GAE（谷歌开发者平台） SaaS: Software-as-a-Service(软件即服务) 提供的服务是运行在云计算基础设施上的应用程序 酒店套房，可以直接入住 谷歌的Gmail邮箱 1.3. PaaS的特点（三种层次） 特点 说明 平台即服务 PaaS提供的服务就是个基础平台，一个环境，而不是具体的应用 平台及服务 不仅提供平台，还提供对该平台的技术支持、优化等服务 平台级服务 “平台级服务”即强大稳定的平台和专业的技术支持团队，保障应用的稳定使用 2. App Engine概述 2.1. App Engine概念 App Engine是PaaS模式的一种实现方式，App Engine将应用运行所需的 IT 资源和基础设施以服务的方式提供给用户，包括了中间件服务、资源管理服务、弹性调度服务、消息服务等多种服务形式。App Engine的目标是对应用提供完整生命周期（包括设计、开发、测试和部署等阶段）的支持，从而减少了用户在购置和管理应用生命周期内所必须的软硬件以及部署应用和IT 基础设施的成本，同时简化了以上工作的复杂度。常见的App Engine有：GAE(Google App Engine)，SAE(Sina App Engine)，BAE(Baidu App Engine)。 App Engine利用虚拟化与自动化技术实现快速搭建部署应用运行环境和动态调整应用运行时环境资源这两个目标。一方面实现即时部署以及快速回收，降低了环境搭建时间，避免了手工配置错误，快速重复搭建环境，及时回收资源， 减少了低利用率硬件资源的空置。另一方面，根据应用运行时的需求对应用环境进行动态调整，实现了应用平台的弹性扩展和自优化，减少了非高峰时硬件资源的空置。 简而言之，App Engine主要目标是：Easy to maintain(维护), Easy to scale(扩容), Easy to build(构建)。 2.2. 架构设计 2.3. 组成模块说明 组成模块 模块说明 App Router[流量接入层] 接收用户请求，并转发到不同的App Runtime。 App Runtime[应用运行层] 应用运行环境，为各个应用提供基本的运行引擎，从而让app能够运行起来。 Services[基础服务层] 各个通用基础服务，主要是对主流的服务提供通用的接入，例如数据库等。 Platform Control[平台控制层] 整个平台的控制中心，实现业务调度，弹性扩容、资源审计、集群管理等相关工作。 Manage System[管理界面层] 提供友好可用的管理操作界面方便平台管理员来控制管理整个平台。 Platform Support[平台支持层] 为应用提供相关的支持，比如应用监控、问题定位、分布式日志重建、统计分析等。 Log Center[日志中心] 实时收集相关应用及系统的日志（日志收集），提供实时计算和分析平台（日志处理）。 Code Center[代码中心] 完成代码存储、部署上线相关的工作。 3. 容器云平台技术栈 功能组成部分 使用工具 应用载体 Docker 编排工具 Kubernetes 配置数据 Etcd 网络管理 Flannel 存储管理 Ceph 底层实现 Linux内核的Namespace[资源隔离]和CGroups[资源控制] Namespace[资源隔离] Namespaces机制提供一种资源隔离方案。PID,IPC,Network等系统资源不再是全局性的，而是属于某个特定的Namespace。每个namespace下的资源对于其他namespace下的资源都是透明，不可见的。 CGroups[资源控制] CGroup（control group）是将任意进程进行分组化管理的Linux内核功能。CGroup本身是提供将进程进行分组化管理的功能和接口的基础结构，I/O或内存的分配控制等具体的资源管理功能是通过这个功能来实现的。CGroups可以限制、记录、隔离进程组所使用的物理资源（包括：CPU、memory、IO等），为容器实现虚拟化提供了基本保证。CGroups本质是内核附加在程序上的一系列钩子（hooks），通过程序运行时对资源的调度触发相应的钩子以达到资源追踪和限制的目的。 4. Docker概述 更多详情请参考：Docker整体架构图 4.1. Docker介绍 Docker - Build, Ship, and Run Any App, Anywhere Docker是一种Linux容器工具集，它是为“构建（Build）、交付（Ship）和运行（Run）”分布式应用而设计的。 Docker相当于把应用以及应用所依赖的环境完完整整地打成了一个包，这个包拿到哪里都能原生运行。因此可以在开发、测试、运维中保证环境的一致性。 Docker的本质：Docker=LXC(Namespace+CGroups)+Docker Images，即在Linux内核的Namespace[资源隔离]和CGroups[资源控制]技术的基础上通过镜像管理机制来实现轻量化设计。 4.2. Docker的基本概念 4.2.1. 镜像 Docker 镜像就是一个只读的模板，可以把镜像理解成一个模子（模具），由模子（镜像）制作的成品（容器）都是一样的（除非在生成时加额外参数），修改成品（容器）本身并不会对模子（镜像）产生影响（除非将成品提交成一个模子），容器重启时，即由模子（镜像）重新制作成一个成品（容器），与其他由该模子制作成的成品并无区别。 例如：一个镜像可以包含一个完整的 ubuntu 操作系统环境，里面仅安装了 Apache 或用户需要的其它应用程序。镜像可以用来创建 Docker 容器。Docker 提供了一个很简单的机制来创建镜像或者更新现有的镜像，用户可以直接从其他人那里下载一个已经做好的镜像来直接使用。 4.2.2. 容器 Docker 利用容器来运行应用。容器是从镜像创建的运行实例。它可以被启动、开始、停止、删除。每个容器都是相互隔离的、保证安全的平台。可以把容器看做是一个简易版的 Linux 环境（包括root用户权限、进程空间、用户空间和网络空间等）和运行在其中的应用程序。 4.2.3. 仓库 仓库是集中存放镜像文件的场所。有时候会把仓库和仓库注册服务器（Registry）混为一谈，并不严格区分。实际上，仓库注册服务器上往往存放着多个仓库，每个仓库中又包含了多个镜像，每个镜像有不同的标签（tag）。 4.3. Docker的优势 容器的快速轻量 容器的启动，停止和销毁都是以秒或毫秒为单位的，并且相比传统的虚拟化技术，使用容器在CPU、内存，网络IO等资源上的性能损耗都有同样水平甚至更优的表现。 一次构建，到处运行 当将容器固化成镜像后，就可以非常快速地加载到任何环境中部署运行。而构建出来的镜像打包了应用运行所需的程序、依赖和运行环境， 这是一个完整可用的应用集装箱，在任何环境下都能保证环境一致性。 完整的生态链 容器技术并不是Docker首创，但是以往的容器实现只关注于如何运行，而Docker站在巨人的肩膀上进行整合和创新，特别是Docker镜像的设计，完美地解决了容器从构建、交付到运行，提供了完整的生态链支持。 5. Kubernetes概述 更多详情请参考：Kubernetes总架构图 5.1. Kubernetes介绍 Kubernetes是Google开源的容器集群管理系统。它构建Docker技术之上，为容器化的应用提供资源调度、部署运行、服务发现、扩容缩容等整一套功能，本质上可看作是基于容器技术的Micro-PaaS平台，即第三代PaaS的代表性项目。 5.2. Kubernetes的基本概念 5.2.1. Pod Pod是若干个相关容器的组合，是一个逻辑概念，Pod包含的容器运行在同一个宿主机上，这些容器使用相同的网络命名空间、IP地址和端口，相互之间能通过localhost来发现和通信，共享一块存储卷空间。在Kubernetes中创建、调度和管理的最小单位是Pod。一个Pod一般只放一个业务容器和一个用于统一网络管理的网络容器。 5.2.2. Replication Controller Replication Controller是用来控制管理Pod副本(Replica，或者称实例)，Replication Controller确保任何时候Kubernetes集群中有指定数量的Pod副本在运行，如果少于指定数量的Pod副本，Replication Controller会启动新的Pod副本，反之会杀死多余的以保证数量不变。另外Replication Controller是弹性伸缩、滚动升级的实现核心。 5.2.3. Service Service是真实应用服务的抽象，定义了Pod的逻辑集合和访问这个Pod集合的策略，Service将代理Pod对外表现为一个单一访问接口，外部不需要了解后端Pod如何运行，这给扩展或维护带来很大的好处，提供了一套简化的服务代理和发现机制。 5.2.4. Label Label是用于区分Pod、Service、Replication Controller的Key/Value键值对，实际上Kubernetes中的任意API对象都可以通过Label进行标识。每个API对象可以有多个Label，但是每个Label的Key只能对应一个Value。Label是Service和Replication Controller运行的基础，它们都通过Label来关联Pod，相比于强绑定模型，这是一种非常好的松耦合关系。 5.2.5. Node Kubernets属于主从的分布式集群架构，Kubernets Node（简称为Node，早期版本叫做Minion）运行并管理容器。Node作为Kubernetes的操作单元，将用来分配给Pod（或者说容器）进行绑定，Pod最终运行在Node上，Node可以认为是Pod的宿主机。 5.3. Kubernetes架构 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/object/_index.html":{"url":"concepts/object/_index.html","title":"kubernetes对象","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/object/understanding-kubernetes-objects.html":{"url":"concepts/object/understanding-kubernetes-objects.html","title":"理解kubernetes对象","keywords":"","body":"1. kubernetes对象概述2. Spec and Status3. 对象描述文件4. 必须字段1. kubernetes对象概述 kubernetes中的对象是一些持久化的实体，可以理解为是对集群状态的描述或期望。 包括： 集群中哪些node上运行了哪些容器化应用 应用的资源是否满足使用 应用的执行策略，例如重启策略、更新策略、容错策略等。 kubernetes的对象是一种意图（期望）的记录，kubernetes会始终保持预期创建的对象存在和集群运行在预期的状态下。 操作kubernetes对象（增删改查）需要通过kubernetes API，一般有以下几种方式： kubectl命令工具 Client library的方式，例如 client-go 2. Spec and Status 每个kubernetes对象的结构描述都包含spec和status两个部分。 spec：该内容由用户提供，描述用户期望的对象特征及集群状态。 status：该内容由kubernetes集群提供和更新，描述kubernetes对象的实时状态。 任何时候，kubernetes都会控制集群的实时状态status与用户的预期状态spec一致。 例如：当你定义Deployment的描述文件，指定集群中运行3个实例，那么kubernetes会始终保持集群中运行3个实例，如果任何实例挂掉，kubernetes会自动重建新的实例来保持集群中始终运行用户预期的3个实例。 3. 对象描述文件 当你要创建一个kubernetes对象的时候，需要提供该对象的描述信息spec，来描述你的对象在kubernetes中的预期状态。 一般使用kubernetes API来创建kubernetes对象，其中spec信息可以以JSON的形式存放在request body中，也可以以.yaml文件的形式通过kubectl工具创建。 例如，以下为Deployment对象对应的yaml文件： apiVersion: apps/v1beta2 # for versions before 1.8.0 use apps/v1beta1 kind: Deployment metadata: name: nginx-deployment spec: replicas: 3 selector: matchLabels: app: nginx template: metadata: labels: app: nginx spec: containers: - name: nginx image: nginx:1.7.9 ports: - containerPort: 80 执行kubectl create的命令 #create command kubectl create -f https://k8s.io/docs/user-guide/nginx-deployment.yaml --record #output deployment \"nginx-deployment\" created 4. 必须字段 在对象描述文件.yaml中，必须包含以下字段。 apiVersion：kubernetes API的版本 kind：kubernetes对象的类型 metadata：唯一标识该对象的元数据，包括name，UID，可选的namespace spec：标识对象的详细信息，不同对象的spec的格式不同，可以嵌套其他对象的字段。 文章参考： https://kubernetes.io/docs/concepts/overview/working-with-objects/kubernetes-objects/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/object/kubernetes-basic-concepts.html":{"url":"concepts/object/kubernetes-basic-concepts.html","title":"kubernetes常用对象说明","keywords":"","body":"1. Master1.1. Master的组件2. Node2.1. Node的组件2.2. Node相关命令2.3. describe命令的Node信息3. Pod4. Label5. Replication Controller(RC)6. Deployment7. Horizontal Pod Autoscaler(HPA)8. Service(服务)8.1. Service概述8.2. kubernetes的服务发现机制8.3. 外部系统访问Service的问题8.3.1. Node IP8.3.2. Pod IP8.3.3. Cluster IP8.3.4. 外部访问Kubernetes集群9. Volume(存储卷)9.1. Volume的功能9.2. Volume的特点9.3. Volume的使用方式9.4. Volume类型9.4.1. emptyDir9.4.2. hostPath9.4.3. gcePersistentDisk10. Persistent Volume11. Namespace12. Annotation(注解)1. Master 集群的控制节点，负责整个集群的管理和控制，kubernetes的所有的命令基本都是发给Master，由它来负责具体的执行过程。 1.1. Master的组件 kube-apiserver：资源增删改查的入口 kube-controller-manager：资源对象的大总管 kube-scheduler：负责资源调度（Pod调度） etcd Server:kubernetes的所有的资源对象的数据保存在etcd中。 2. Node Node是集群的工作负载节点，默认情况kubelet会向Master注册自己，一旦Node被纳入集群管理范围，kubelet会定时向Master汇报自身的情报，包括操作系统，Docker版本，机器资源情况等。 如果Node超过指定时间不上报信息，会被Master判断为“失联”，标记为Not Ready，随后Master会触发Pod转移。 2.1. Node的组件 kubelet:Pod的管家，与Master通信 kube-proxy：实现kubernetes Service的通信与负载均衡机制的重要组件 Docker：容器的创建和管理 2.2. Node相关命令 kubectl get nodes kuebctl describe node {node_name} 2.3. describe命令的Node信息 Node基本信息：名称、标签、创建时间等 Node当前的状态，Node启动后会进行自检工作，磁盘是否满，内存是否不足，若都正常则切换为Ready状态。 Node的主机地址与主机名 Node上的资源总量：CPU,内存，最大可调度Pod数量等 Node可分配资源量：当前Node可用于分配的资源量 主机系统信息：主机唯一标识符UUID，Linux kernel版本号，操作系统，kubernetes版本，kubelet与kube-proxy版本 当前正在运行的Pod列表及概要信息 已分配的资源使用概要，例如资源申请的最低、最大允许使用量占系统总量的百分比 Node相关的Event信息。 3. Pod Pod是Kubernetes中操作的基本单元。每个Pod中有个根容器(Pause容器)，Pause容器的状态代表整个容器组的状态，其他业务容器共享Pause的IP，即Pod IP，共享Pause挂载的Volume，这样简化了同个Pod中不同容器之间的网络问题和文件共享问题。 图片 - pod Kubernetes集群中，同宿主机的或不同宿主机的Pod之间要求能够TCP/IP直接通信，因此采用虚拟二层网络技术来实现，例如Flannel，Openvswitch(OVS)等，这样在同个集群中，不同的宿主机的Pod IP为不同IP段的IP，集群中的所有Pod IP都是唯一的，不同Pod之间可以直接通信。 Pod有两种类型：普通Pod和静态Pod。静态Pod即不通过K8S调度和创建，直接在某个具体的Node机器上通过具体的文件来启动。普通Pod则是由K8S创建、调度，同时数据存放在ETCD中。 Pod IP和具体的容器端口（ContainnerPort）组成一个具体的通信地址，即Endpoint。一个Pod中可以存在多个容器，可以有多个端口，Pod IP一样，即有多个Endpoint。 Pod Volume是定义在Pod之上，被各个容器挂载到自己的文件系统中，可以用分布式文件系统实现后端存储功能。 Pod中的Event事件可以用来排查问题，可以通过kubectl describe pod xxx 来查看对应的事件。 每个Pod可以对其能使用的服务器上的计算资源设置限额，一般为CPU和Memory。K8S中一般将千分之一个的CPU配置作为最小单位，用m表示，是一个绝对值，即100m对于一个Core的机器还是48个Core的机器都是一样的大小。Memory配额也是个绝对值，单位为内存字节数。 资源配额的两个参数 Requests:该资源的最小申请量，系统必须满足要求。 Limits:该资源最大允许使用量，当超过该量，K8S会kill并重启Pod。 图片 - pod2 4. Label Label是一个键值对，可以附加在任何对象上，比如Node,Pod,Service,RC等。Label和资源对象是多对多的关系，即一个Label可以被添加到多个对象上，一个对象也可以定义多个Label。 Label的作用主要用来实现精细的、多维度的资源分组管理，以便进行资源分配，调度，配置，部署等工作。 Label通俗理解就是“标签”，通过标签来过滤筛选指定的对象，进行具体的操作。k8s通过Label Selector(标签选择器)来筛选指定Label的资源对象，类似SQL语句中的条件查询（WHERE语句）。 Label Selector有基于等式和基于集合的两种表达方式，可以多个条件进行组合使用。 基于等式：name=redis-slave（匹配name=redis-slave的资源对象）;env!=product(匹配所有不具有标签env=product的资源对象) 基于集合：name in (redis-slave,redis-master);name not in (php-frontend)（匹配所有不具有标签name=php-frontend的资源对象） 使用场景 kube-controller进程通过资源对象RC上定义的Label Selector来筛选要监控的Pod副本数，从而实现副本数始终保持预期数目。 kube-proxy进程通过Service的Label Selector来选择对应Pod，自动建立每个Service到对应Pod的请求转发路由表，从而实现Service的智能负载均衡机制。 kube-scheduler实现Pod定向调度：对Node定义特定的Label，并且在Pod定义文件中使用NodeSelector标签调度策略。 5. Replication Controller(RC) RC是k8s系统中的核心概念，定义了一个期望的场景。 主要包括： Pod期望的副本数（replicas） 用于筛选目标Pod的Label Selector 用于创建Pod的模板（template） RC特性说明： Pod的缩放可以通过以下命令实现：kubectl scale rc redis-slave --replicas=3 删除RC并不会删除该RC创建的Pod，可以将副本数设置为0，即可删除对应Pod。或者通过kubectl stop /delete命令来一次性删除RC和其创建的Pod。 改变RC中Pod模板的镜像版本可以实现滚动升级（Rolling Update）。具体操作见https://kubernetes.io/docs/tasks/run-application/rolling-update-replication-controller/ Kubernetes1.2以上版本将RC升级为Replica Set，它与当前RC的唯一区别在于Replica Set支持基于集合的Label Selector(Set-based selector)，而旧版本RC只支持基于等式的Label Selector(equality-based selector)。 Kubernetes1.2以上版本通过Deployment来维护Replica Set而不是单独使用Replica Set。即控制流为：Delpoyment→Replica Set→Pod。即新版本的Deployment+Replica Set替代了RC的作用。 6. Deployment Deployment是kubernetes 1.2引入的概念，用来解决Pod的编排问题。Deployment可以理解为RC的升级版（RC+Reolicat Set）。特点在于可以随时知道Pod的部署进度，即对Pod的创建、调度、绑定节点、启动容器完整过程的进度展示。 使用场景 创建一个Deployment对象来生成对应的Replica Set并完成Pod副本的创建过程。 检查Deployment的状态来确认部署动作是否完成（Pod副本的数量是否达到预期值）。 更新Deployment以创建新的Pod(例如镜像升级的场景)。 如果当前Deployment不稳定，回退到上一个Deployment版本。 挂起或恢复一个Deployment。 可以通过kubectl describe deployment来查看Deployment控制的Pod的水平拓展过程。 7. Horizontal Pod Autoscaler(HPA) Horizontal Pod Autoscaler(HPA)即Pod横向自动扩容，与RC一样也属于k8s的资源对象。 HPA原理：通过追踪分析RC控制的所有目标Pod的负载变化情况，来确定是否针对性调整Pod的副本数。 Pod负载度量指标： CPUUtilizationPercentage：Pod所有副本自身的CPU利用率的平均值。即当前Pod的CPU使用量除以Pod Request的值。 应用自定义的度量指标，比如服务每秒内响应的请求数（TPS/QPS）。 8. Service(服务) 8.1. Service概述 图片 - service Service定义了一个服务的访问入口地址，前端应用通过这个入口地址访问其背后的一组由Pod副本组成的集群实例，Service与其后端的Pod副本集群之间是通过Label Selector来实现“无缝对接”。RC保证Service的Pod副本实例数目保持预期水平。 8.2. kubernetes的服务发现机制 主要通过kube-dns这个组件来进行DNS方式的服务发现。 8.3. 外部系统访问Service的问题 IP类型 说明 Node IP Node节点的IP地址 Pod IP Pod的IP地址 Cluster IP Service的IP地址 8.3.1. Node IP NodeIP是集群中每个节点的物理网卡IP地址，是真实存在的物理网络，kubernetes集群之外的节点访问kubernetes内的某个节点或TCP/IP服务的时候，需要通过NodeIP进行通信。 8.3.2. Pod IP Pod IP是每个Pod的IP地址，是Docker Engine根据docker0网桥的IP段地址进行分配的，是一个虚拟二层网络，集群中一个Pod的容器访问另一个Pod中的容器，是通过Pod IP进行通信的，而真实的TCP/IP流量是通过Node IP所在的网卡流出的。 8.3.3. Cluster IP Service的Cluster IP是一个虚拟IP，只作用于Service这个对象，由kubernetes管理和分配IP地址（来源于Cluster IP地址池）。 Cluster IP无法被ping通，因为没有一个实体网络对象来响应。 Cluster IP结合Service Port组成的具体通信端口才具备TCP/IP通信基础，属于kubernetes集群内，集群外访问该IP和端口需要额外处理。 k8s集群内Node IP 、Pod IP、Cluster IP之间的通信采取k8s自己的特殊的路由规则，与传统IP路由不同。 8.3.4. 外部访问Kubernetes集群 通过宿主机与容器端口映射的方式进行访问，例如：Service定位文件如下： 可以通过任意Node的IP 加端口访问该服务。也可以通过Nginx或HAProxy来设置负载均衡。 9. Volume(存储卷) 9.1. Volume的功能 Volume是Pod中能够被多个容器访问的共享目录，可以让容器的数据写到宿主机上或者写文件到网络存储中 可以实现容器配置文件集中化定义与管理，通过ConfigMap资源对象来实现。 9.2. Volume的特点 k8s中的Volume与Docker的Volume相似，但不完全相同。 k8s上Volume定义在Pod上，然后被一个Pod中的多个容器挂载到具体的文件目录下。 k8s的Volume与Pod生命周期相关而不是容器是生命周期，即容器挂掉，数据不会丢失但是Pod挂掉，数据则会丢失。 k8s中的Volume支持多种类型的Volume：Ceph、GlusterFS等分布式系统。 9.3. Volume的使用方式 先在Pod上声明一个Volume，然后容器引用该Volume并Mount到容器的某个目录。 9.4. Volume类型 9.4.1. emptyDir emptyDir Volume是在Pod分配到Node时创建的，初始内容为空，无须指定宿主机上对应的目录文件，由K8S自动分配一个目录，当Pod被删除时，对应的emptyDir数据也会永久删除。 作用： 临时空间，例如程序的临时文件，无须永久保留 长时间任务的中间过程CheckPoint的临时保存目录 一个容器需要从另一个容器中获取数据的目录（即多容器共享目录） 说明： 目前用户无法设置emptyVolume的使用介质，如果kubelet的配置使用硬盘则emptyDir将创建在该硬盘上。 9.4.2. hostPath hostPath是在Pod上挂载宿主机上的文件或目录。 作用： 容器应用日志需要持久化时，可以使用宿主机的高速文件系统进行存储 需要访问宿主机上Docker引擎内部数据结构的容器应用时，可以通过定义hostPath为宿主机/var/lib/docker目录，使容器内部应用可以直接访问Docker的文件系统。 注意点： 在不同的Node上具有相同配置的Pod可能会因为宿主机上的目录或文件不同导致对Volume上目录或文件的访问结果不一致。 如果使用了资源配额管理，则kubernetes无法将hostPath在宿主机上使用的资源纳入管理。 9.4.3. gcePersistentDisk 表示使用谷歌公有云提供的永久磁盘（Persistent Disk ,PD）存放Volume的数据，它与EmptyDir不同，PD上的内容会被永久保存。当Pod被删除时，PD只是被卸载时，但不会被删除。需要先创建一个永久磁盘，才能使用gcePersistentDisk。 使用gcePersistentDisk的限制条件： Node(运行kubelet的节点)需要是GCE虚拟机。 虚拟机需要与PD存在于相同的GCE项目中和Zone中。 10. Persistent Volume Volume定义在Pod上，属于“计算资源”的一部分，而Persistent Volume和Persistent Volume Claim是网络存储，简称PV和PVC，可以理解为k8s集群中某个网络存储中对应的一块存储。 PV是网络存储，不属于任何Node，但可以在每个Node上访问。 PV不是定义在Pod上，而是独立于Pod之外定义。 PV常见类型：GCE Persistent Disks、NFS、RBD等。 PV是有状态的对象，状态类型如下： Available:空闲状态 Bound:已经绑定到某个PVC上 Released:对应的PVC已经删除，但资源还没有回收 Failed:PV自动回收失败 11. Namespace Namespace即命名空间，主要用于多租户的资源隔离，通过将资源对象分配到不同的Namespace上，便于不同的分组在共享资源的同时可以被分别管理。 k8s集群启动后会默认创建一个“default”的Namespace。可以通过kubectl get namespaecs查看。 可以通过kubectl config use-context namespace配置当前k8s客户端的环境，通过kubectl get pods获取当前namespace的Pod。或者通过kubectl get pods --namespace=NAMESPACE来获取指定namespace的Pod。 Namespace yaml文件的定义 12. Annotation(注解) Annotation与Label类似，也使用key/value的形式进行定义，Label定义元数据（Metadata）,Annotation定义“附加”信息。 通常Annotation记录信息如下： build信息，release信息，Docker镜像信息等。 日志库、监控库等。 参考《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/pod/_index.html":{"url":"concepts/pod/_index.html","title":"Pod","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/pod/pod.html":{"url":"concepts/pod/pod.html","title":"Pod介绍","keywords":"","body":"1. Pod是什么（what）1.1. Pod概念1.1.1. Pod与Docker1.1.2. Pod中容器的运行方式1.2. Pod管理多个容器1.2.1. 网络1.2.2. 存储2. 为什么需要Pod(why)2.1. 管理需求2.2. 资源共享和通信2.3. Pod设计的优势3. Pod的使用(how)3.1. Controller3.2. Pod模板4. Pod的终止4.1. Pod终止的流程4.2. 强制删除Pod4.3. Pod特权模式1. Pod是什么（what） 1.1. Pod概念 Pod是kubernetes集群中最小的部署和管理的基本单元，协同寻址，协同调度。 Pod是一个或多个容器的集合，是一个或一组服务（进程）的抽象集合。 Pod中可以共享网络和存储（可以简单理解为一个逻辑上的虚拟机，但并不是虚拟机）。 Pod被创建后用一个UID来唯一标识，当Pod生命周期结束，被一个等价Pod替代，UID将重新生成。 1.1.1. Pod与Docker Docker是目前Pod最常用的容器环境，但仍支持其他容器环境。 Pod是一组被模块化的拥有共享命名空间和共享存储卷的容器，但并没有共享PID 命名空间（即同个Pod的不同容器中进程的PID是独立的，互相看不到非自己容器的进程）。 1.1.2. Pod中容器的运行方式 只运行一个单独的容器 即one-container-per-Pod模式，是最常用的模式，可以把这样的Pod看成单独的一个容器去管理。 运行多个强关联的容器 即sidecar模式，Pod 封装了一组紧耦合、共享资源、协同寻址的容器，将这组容器作为一个管理单元。 1.2. Pod管理多个容器 Pod是一组紧耦合的容器的集合，Pod内的容器作为一个整体以Pod形式进行协同寻址，协同调度、协同管理。相同Pod内的容器共享网络和存储。 1.2.1. 网络 每个Pod被分配了唯一的IP地址，该Pod内的所有容器共享一个网络空间，包括IP和端口。 同个Pod不同容器之间通过localhost通信，Pod内端口不能冲突。 不同Pod之间的通信则通过IP+端口的形式来访问到Pod内的具体服务（容器）。 1.2.2. 存储 可以在Pod中创建共享存储卷的方式来实现不同容器之间数据共享。 2. 为什么需要Pod(why) 2.1. 管理需求 Pod 是一种模式的抽象：互相协作的多个进程（容器）共同形成一个完整的服务。以一个或多个容器的方式组合成一个整体，作为管理的基本单元，通过Pod可以方便部署、水平扩展，协同调度等。 2.2. 资源共享和通信 Pod作为多个紧耦合的容器的集合，通过共享网络和存储的方式来简化紧耦合容器之间的通信，从这个角度，可以将Pod简单理解为一个逻辑上的“虚拟机”。而不同的Pod之间的通信则通过Pod的IP和端口的方式。 2.3. Pod设计的优势 调度器和控制器的可拔插性。 将Pod 的生存期从 controller 中剥离出来，从而减少相互影响。 高可用--在终止和删除 Pod 前，需要提前生成替代 Pod。 集群级别的功能和 Kubelet（Pod Controller） 级别的功能组合更加清晰。 3. Pod的使用(how) Pod一般是通过各种不同类型的Controller对Pod进行管理和控制，包括自我恢复（例如Pod因异常退出，则会再起一个相同的Pod替代该Pod，而该Pod则会被清除）。也可以不通过Controller单独创建一个Pod，但一般很少这么操作，因为这个Pod是一个孤立的实体，并不会被Controller管理。 3.1. Controller Controller是kubernetes中用于对Pod进行管理的控制器，通过该控制器让Pod始终维持在一个用户原本设定或期望的状态。如果节点宕机或者Pod因其他原因死亡，则会在其他节点起一个相同的Pod来替代该Pod。 常用的Controller有： Deployment StatefulSet DaemonSet Controller是通过用户提供的Pod模板来创建和控制Pod。 3.2. Pod模板 Pod模板用来定义Pod的各种属性，Controller通过Pod模板来生成对应的Pod。 Pod模板类似一个饼干模具，通过模具已经生成的饼干与原模具已经没有关系，即对原模具的修改不会影响已经生成的饼干，只会对通过修改后的模具生成的饼干有影响。这种方式可以更加方便地控制和管理Pod。 4. Pod的终止 用户发起一个删除Pod的请求，系统会先发送TERM信号给每个容器的主进程，如果在宽限期（默认30秒）主进程没有自主终止运行，则系统会发送KILL信号给该进程，接着Pod将被删除。 4.1. Pod终止的流程 用户发送一个删除 Pod 的命令， 并使用默认的宽限期（30s)。 把 API server 上的 pod 的时间更新成 Pod 与宽限期一起被认为 “dead” 之外的时间点。 使用客户端的命令，显示出的Pod的状态为 terminating。 （与第3步同时发生）Kubelet 发现某一个 Pod 由于时间超过第2步的设置而被标志成 terminating 状态时， Kubelet 将启动一个停止进程。 如果 pod 已经被定义成一个 preStop hook，这会在 pod 内部进行调用。如果宽限期已经过期但 preStop 锚依然还在运行，将调用第2步并在原来的宽限期上加一个小的时间窗口（2 秒钟）。 把 Pod 里的进程发送到 TERM信号。 （与第3步同时发生），Pod 被从终端的服务列表里移除，同时也不再被 replication controllers 看做时一组运行中的 pods。 在负载均衡（比如说 service proxy）会将它们从轮询中移除前， Pods 这种慢关闭的方式可以继续为流量提供服务。 当宽期限过期时， 任何还在 Pod 里运行的进程都会被 SIGKILL杀掉。 Kubelet 通过在 API server 把宽期限设置成0(立刻删除)的方式完成删除 Pod的过程。 这时 Pod 在 API 里消失，也不再能被用户看到。 4.2. 强制删除Pod 强制删除Pod是指从k8s集群状态和Etcd中立刻删除对应的Pod数据，API Server不会等待kubelet的确认信息。被强制删除后，即可重新创建一个相同名字的Pod。 删除默认的宽限期是30秒，通过将宽限期设置为0的方式可以强制删除Pod。 通过kubectl delete 命令后加--force和--grace-period=0的参数强制删除Pod。 kubectl delete pod --namespace= --force --grace-period=0 4.3. Pod特权模式 特权模式是指让Pod中的进程具有访问宿主机系统设备或使用网络栈操作等的能力，例如编写网络插件和卷插件。 通过将container spec中的SecurityContext设置为privileged即将该容器赋予了特权模式。特权模式的使用要求k8s版本高于v1.1。 参考文章： https://kubernetes.io/docs/concepts/workloads/pods/pod-overview/ https://kubernetes.io/docs/concepts/workloads/pods/pod/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/pod/pod-definition.html":{"url":"concepts/pod/pod-definition.html","title":"Pod定义文件","keywords":"","body":"1. Pod的基本用法1.1. 说明1.2. Pod相关命令2. Pod的定义文件3. 静态pod3.1. 通过配置文件方式1. Pod的基本用法 1.1. 说明 Pod实际上是容器的集合，在k8s中对运行容器的要求为：容器的主程序需要一直在前台运行，而不是后台运行。应用可以改造成前台运行的方式，例如Go语言的程序，直接运行二进制文件；java语言则运行主类；tomcat程序可以写个运行脚本。或者通过supervisor的进程管理工具，即supervisor在前台运行，应用程序由supervisor管理在后台运行。具体可参考supervisord。 当多个应用之间是紧耦合的关系时，可以将多个应用一起放在一个Pod中，同个Pod中的多个容器之间互相访问可以通过localhost来通信（可以把Pod理解成一个虚拟机，共享网络和存储卷）。 1.2. Pod相关命令 操作 命令 说明 创建 kubectl create -f frontend-localredis-pod.yaml 查询Pod运行状态 kubectl get pods --namespace= 查询Pod详情 kebectl describe pod --namespace= 该命令常用来排查问题，查看Event事件 删除 kubectl delete pod ;kubectl delete pod --all 更新 kubectl replace pod.yaml - 2. Pod的定义文件 apiVersion: v1 kind: Pod metadata: name: string namaspace: string labels: - name: string annotations: - name: string spec: containers: - name: string images: string imagePullPolice: [Always | Never | IfNotPresent] command: [string] args: [string] workingDir: string volumeMounts: - name: string mountPath: string readOnly: boolean ports: - name: string containerPort: int hostPort: int protocol: string env: - name: string value: string resources: limits: cpu: string memory: string requests: cpu: string memory: string livenessProbe: exec: command: [string] httpGet: path: string port: int host: string scheme: string httpHeaders: - name: string value: string tcpSocket: port: int initialDelaySeconds: number timeoutSeconds: number periodSeconds: number successThreshold: 0 failureThreshold: 0 securityContext: privileged: false restartPolicy: [Always | Never | OnFailure] nodeSelector: object imagePullSecrets: - name: string hostNetwork: false volumes: - name: string emptyDir: {} hostPath: path: string secret: secretName: string items: - key: string path: string configMap: name: string items: - key: string path: string 3. 静态pod 静态Pod是由kubelet进行管理，仅存在于特定Node上的Pod。它们不能通过API Server进行管理，无法与ReplicationController、Deployment或DaemonSet进行关联，并且kubelet也无法对其健康检查。 静态Pod总是由kubelet创建，并且总在kubelet所在的Node上运行。 创建静态Pod的方式： 3.1. 通过配置文件方式 需要设置kubelet的启动参数“–config”，指定kubelet需要监控的配置文件所在目录，kubelet会定期扫描该目录，并根据该目录的.yaml或.json文件进行创建操作。静态Pod无法通过API Server删除（若删除会变成pending状态），如需删除该Pod则将yaml或json文件从这个目录中删除。 例如： 配置目录为/etc/kubelet.d/，配置启动参数：--config=/etc/kubelet.d/，该目录下放入static-web.yaml。 apiVersion: v1 kind: Pod metadata: name: static-web labels: name: static-web spec: containers: - name: static-web image: nginx ports: - name: web containerPort: 80 参考文章 《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/pod/pod-lifecycle.html":{"url":"concepts/pod/pod-lifecycle.html","title":"Pod生命周期","keywords":"","body":"1. Pod phase2. Pod 状态3. 重启策略4. Pod的生命5. Pod状态转换5.1. 容器运行时内存超出限制5.2. 磁盘故障5.3. 运行节点挂掉1. Pod phase Pod的phase是Pod生命周期中的简单宏观描述，定义在Pod的PodStatus对象的phase 字段中。 phase有以下几种值： 状态值 说明 挂起（Pending） Pod 已被 Kubernetes 系统接受，但有一个或者多个容器镜像尚未创建。等待时间包括调度 Pod 的时间和通过网络下载镜像的时间。 运行中（Running） 该 Pod 已经绑定到了一个节点上，Pod 中所有的容器都已被创建。至少有一个容器正在运行，或者正处于启动或重启状态。 成功（Succeeded） Pod 中的所有容器都被成功终止，并且不会再重启。 失败（Failed） Pod 中的所有容器都已终止了，并且至少有一个容器是因为失败终止。也就是说，容器以非0状态退出或者被系统终止。 未知（Unknown） 因为某些原因无法取得 Pod 的状态，通常是因为与 Pod 所在主机通信失败。 2. Pod 状态 Pod 有一个 PodStatus 对象，其中包含一个 PodCondition 数组。 PodCondition包含以下以下字段： lastProbeTime：Pod condition最后一次被探测到的时间戳。 lastTransitionTime：Pod最后一次状态转变的时间戳。 message：状态转化的信息，一般为报错信息，例如：containers with unready status: [c-1]。 reason：最后一次状态形成的原因，一般为报错原因，例如：ContainersNotReady。 status：包含的值有 True、False 和 Unknown。 type：Pod状态的几种类型。 其中type字段包含以下几个值： PodScheduled：Pod已经被调度到运行节点。 Ready：Pod已经可以接收请求提供服务。 Initialized：所有的init container已经成功启动。 Unschedulable：无法调度该Pod，例如节点资源不够。 ContainersReady：Pod中的所有容器已准备就绪。 3. 重启策略 Pod通过restartPolicy字段指定重启策略，重启策略类型为：Always、OnFailure 和 Never，默认为 Always。 restartPolicy 仅指通过同一节点上的 kubelet 重新启动容器。 重启策略 说明 Always 当容器失效时，由kubelet自动重启该容器 OnFailure 当容器终止运行且退出码不为0时，由kubelet自动重启该容器 Never 不论容器运行状态如何，kubelet都不会重启该容器 说明： 可以管理Pod的控制器有Replication Controller，Job，DaemonSet，及kubelet（静态Pod）。 RC和DaemonSet：必须设置为Always，需要保证该容器持续运行。 Job：OnFailure或Never，确保容器执行完后不再重启。 kubelet：在Pod失效的时候重启它，不论RestartPolicy设置为什么值，并且不会对Pod进行健康检查。 4. Pod的生命 Pod的生命周期一般通过Controler 的方式管理，每种Controller都会包含PodTemplate来指明Pod的相关属性，Controller可以自动对pod的异常状态进行重新调度和恢复，除非通过Controller的方式删除其管理的Pod，不然kubernetes始终运行用户预期状态的Pod。 控制器的分类 使用 Job运行预期会终止的 Pod，例如批量计算。Job 仅适用于重启策略为 OnFailure 或 Never 的 Pod。 对预期不会终止的 Pod 使用 ReplicationController、ReplicaSet和 Deployment，例如 Web 服务器。 ReplicationController 仅适用于具有 restartPolicy 为 Always 的 Pod。 提供特定于机器的系统服务，使用 DaemonSet为每台机器运行一个 Pod 。 如果节点死亡或与集群的其余部分断开连接，则 Kubernetes 将应用一个策略将丢失节点上的所有 Pod 的 phase 设置为 Failed。 5. Pod状态转换 常见的状态转换 Pod的容器数 Pod当前状态 发生的事件 Pod结果状态 RestartPolicy=Always RestartPolicy=OnFailure RestartPolicy=Never 包含一个容器 Running 容器成功退出 Running Succeeded Succeeded 包含一个容器 Running 容器失败退出 Running Running Failure 包含两个容器 Running 1个容器失败退出 Running Running Running 包含两个容器 Running 容器被OOM杀掉 Running Running Failure 5.1. 容器运行时内存超出限制 容器以失败状态终止。 记录 OOM 事件。 如果restartPolicy为： Always：重启容器；Pod phase 仍为 Running。 OnFailure：重启容器；Pod phase 仍为 Running。 Never: 记录失败事件；Pod phase 仍为 Failed。 5.2. 磁盘故障 杀掉所有容器。 记录适当事件。 Pod phase 变成 Failed。 如果使用控制器来运行，Pod 将在别处重建。 5.3. 运行节点挂掉 节点控制器等待直到超时。 节点控制器将 Pod phase 设置为 Failed。 如果是用控制器来运行，Pod 将在别处重建。 参考文章： https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/pod/pod-probe.html":{"url":"concepts/pod/pod-probe.html","title":"Pod健康检查","keywords":"","body":"Pod健康检查1. 探针类型2. 探针使用场景3. 探针的配置3.1. 探针通用参数3.2. ReadinessProbe三种实现方式4. 探针相关源码4.1. Probe源码4.2. ProbeHandler源码4.3. ProbeActionPod健康检查 Pod的健康状态由两类探针来检查：LivenessProbe和ReadinessProbe。 1. 探针类型 1. livenessProbe(存活探针) 表明容器是否正在运行。 如果存活探测失败，则 kubelet 会杀死容器，并且容器将受到其 重启策略的影响。 如果容器不提供存活探针，则默认状态为 Success。 2. readinessProbe(就绪探针) 表明容器是否可以正常接受请求。 如果就绪探测失败，端点控制器将从与 Pod 匹配的所有 Service 的端点中删除该 Pod 的 IP 地址。 初始延迟之前的就绪状态默认为 Failure。 如果容器不提供就绪探针，则默认状态为 Success。 2. 探针使用场景 如果容器异常可以自动崩溃，则不一定要使用探针，可以由Pod的restartPolicy执行重启操作。 存活探针适用于希望容器探测失败后被杀死并重新启动，需要指定restartPolicy 为 Always 或 OnFailure。 就绪探针适用于希望Pod在不能正常接收流量的时候被剔除，并且在就绪探针探测成功后才接收流量。 探针是kubelet对容器执行定期的诊断，主要通过调用容器配置的四类Handler实现： Handler的类型： ExecAction：在容器内执行指定命令。如果命令退出时返回码为 0 则认为诊断成功。 TCPSocketAction：对指定端口上的容器的 IP 地址进行 TCP 检查。如果端口打开，则诊断被认为是成功的。 HTTPGetAction：对指定的端口和路径上的容器的 IP 地址执行 HTTP Get 请求。如果响应的状态码大于等于200 且小于 400，则诊断被认为是成功的。 GRPCAction：调用GRPC接口来判断服务是否健康。 如果响应的状态是 \"SERVING\"，则认为诊断成功。 探测结果为以下三种之一： 成功：容器通过了诊断。 失败：容器未通过诊断。 未知：诊断失败，因此不会采取任何行动。 3. 探针的配置 探针配置在pod的container结构体下，livenessProbe和readinessProbe参数基本一致。 3.1. 探针通用参数 常用的参数为timeoutSeconds、periodSeconds、periodSeconds，即接口超时时间，重试频率，重试次数三个值。 initialDelaySeconds：启动容器后首次进行健康检查的等待时间，单位为秒，默认值为0。 timeoutSeconds:健康检查接口超时响应的时间，默认为1秒，最小值为1秒。 periodSeconds：重试的频率，默认值为10秒，即10秒重试一次，最小值是1秒，建议可以设置为3-5秒。 failureThreshold：失败重试的次数，默认值为3，最小值为1。 successThreshold：最小探测成功次数，默认值为1，一般不设置。 除了以上的通用参数外，livenessProbe和readinessProbe参数基本一致。以下以readinessProbe为例说明探针的使用方式。 3.2. ReadinessProbe三种实现方式 3.2.1. HTTPGetAction 通过容器的IP地址、端口号及路径调用HTTP Get方法，如果响应的状态码大于等于200且小于等于400，则认为容器健康。 apiVersion: v1 kind: Pod metadata: name: pod-with-healthcheck spec: containers: - name: nginx image: nginx ports: - containnerPort: 80 readinessProbe: httpGet: path: /_status/healthz port: 80 scheme: HTTP initialDelaySeconds: 1 periodSeconds: 5 timeoutSeconds: 5 3.2.2. TCPSocketAction 通过容器IP地址和端口号执行TCP检查，如果能够建立TCP连接，则表明容器健康。 apiVersion: v1 kind: Pod metadata: name: pod-with-healthcheck spec: containers: - name: nginx image: nginx ports: - containnerPort: 80 readinessProbe: tcpSocket: port: 80 initialDelaySeconds: 1 timeoutSeconds: 5 3.2.3. ExecAction 在一个容器内部执行一个命令，如果该命令状态返回值为0，则表明容器健康。 apiVersion: v1 kind: Pod metadata: name: readiness-exec spec: containers: - name: readiness image: tomcagcr.io/google_containers/busybox args: - /bin/sh - -c - echo ok > /tmp/health;sleep 10;rm -fr /tmp/health;sleep 600 readinessreadinessProbe: exec: command: - cat - /tmp/health initialDelaySeconds: 1 timeoutSeconds: 5 4. 探针相关源码 探针配置在pod的container结构体下： // 存活探针 LivenessProbe *Probe `json:\"livenessProbe,omitempty\" protobuf:\"bytes,10,opt,name=livenessProbe\"` // 就绪探针 ReadinessProbe *Probe `json:\"readinessProbe,omitempty\" protobuf:\"bytes,11,opt,name=readinessProbe\"` 4.1. Probe源码 type Probe struct { // The action taken to determine the health of a container ProbeHandler `json:\",inline\" protobuf:\"bytes,1,opt,name=handler\"` // Number of seconds after the container has started before liveness probes are initiated. // More info: https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle#container-probes // +optional InitialDelaySeconds int32 `json:\"initialDelaySeconds,omitempty\" protobuf:\"varint,2,opt,name=initialDelaySeconds\"` // Number of seconds after which the probe times out. // Defaults to 1 second. Minimum value is 1. // More info: https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle#container-probes // +optional TimeoutSeconds int32 `json:\"timeoutSeconds,omitempty\" protobuf:\"varint,3,opt,name=timeoutSeconds\"` // How often (in seconds) to perform the probe. // Default to 10 seconds. Minimum value is 1. // +optional PeriodSeconds int32 `json:\"periodSeconds,omitempty\" protobuf:\"varint,4,opt,name=periodSeconds\"` // Minimum consecutive successes for the probe to be considered successful after having failed. // Defaults to 1. Must be 1 for liveness and startup. Minimum value is 1. // +optional SuccessThreshold int32 `json:\"successThreshold,omitempty\" protobuf:\"varint,5,opt,name=successThreshold\"` // Minimum consecutive failures for the probe to be considered failed after having succeeded. // Defaults to 3. Minimum value is 1. // +optional FailureThreshold int32 `json:\"failureThreshold,omitempty\" protobuf:\"varint,6,opt,name=failureThreshold\"` // Optional duration in seconds the pod needs to terminate gracefully upon probe failure. // The grace period is the duration in seconds after the processes running in the pod are sent // a termination signal and the time when the processes are forcibly halted with a kill signal. // Set this value longer than the expected cleanup time for your process. // If this value is nil, the pod's terminationGracePeriodSeconds will be used. Otherwise, this // value overrides the value provided by the pod spec. // Value must be non-negative integer. The value zero indicates stop immediately via // the kill signal (no opportunity to shut down). // This is a beta field and requires enabling ProbeTerminationGracePeriod feature gate. // Minimum value is 1. spec.terminationGracePeriodSeconds is used if unset. // +optional TerminationGracePeriodSeconds *int64 `json:\"terminationGracePeriodSeconds,omitempty\" protobuf:\"varint,7,opt,name=terminationGracePeriodSeconds\"` } 4.2. ProbeHandler源码 // ProbeHandler defines a specific action that should be taken in a probe. // One and only one of the fields must be specified. type ProbeHandler struct { // Exec specifies the action to take. // +optional Exec *ExecAction `json:\"exec,omitempty\" protobuf:\"bytes,1,opt,name=exec\"` // HTTPGet specifies the http request to perform. // +optional HTTPGet *HTTPGetAction `json:\"httpGet,omitempty\" protobuf:\"bytes,2,opt,name=httpGet\"` // TCPSocket specifies an action involving a TCP port. // +optional TCPSocket *TCPSocketAction `json:\"tcpSocket,omitempty\" protobuf:\"bytes,3,opt,name=tcpSocket\"` // GRPC specifies an action involving a GRPC port. // This is a beta field and requires enabling GRPCContainerProbe feature gate. // +featureGate=GRPCContainerProbe // +optional GRPC *GRPCAction `json:\"grpc,omitempty\" protobuf:\"bytes,4,opt,name=grpc\"` } 4.3. ProbeAction 4.3.1. HTTPGetAction // HTTPHeader describes a custom header to be used in HTTP probes type HTTPHeader struct { // The header field name Name string `json:\"name\" protobuf:\"bytes,1,opt,name=name\"` // The header field value Value string `json:\"value\" protobuf:\"bytes,2,opt,name=value\"` } // HTTPGetAction describes an action based on HTTP Get requests. type HTTPGetAction struct { // Path to access on the HTTP server. // +optional Path string `json:\"path,omitempty\" protobuf:\"bytes,1,opt,name=path\"` // Name or number of the port to access on the container. // Number must be in the range 1 to 65534. // Name must be an IANA_SVC_NAME. Port intstr.IntOrString `json:\"port\" protobuf:\"bytes,2,opt,name=port\"` // Host name to connect to, defaults to the pod IP. You probably want to set // \"Host\" in httpHeaders instead. // +optional Host string `json:\"host,omitempty\" protobuf:\"bytes,3,opt,name=host\"` // Scheme to use for connecting to the host. // Defaults to HTTP. // +optional Scheme URIScheme `json:\"scheme,omitempty\" protobuf:\"bytes,4,opt,name=scheme,casttype=URIScheme\"` // Custom headers to set in the request. HTTP allows repeated headers. // +optional HTTPHeaders []HTTPHeader `json:\"httpHeaders,omitempty\" protobuf:\"bytes,5,rep,name=httpHeaders\"` } // URIScheme identifies the scheme used for connection to a host for Get actions // +enum type URIScheme string const ( // URISchemeHTTP means that the scheme used will be http:// URISchemeHTTP URIScheme = \"HTTP\" // URISchemeHTTPS means that the scheme used will be https:// URISchemeHTTPS URIScheme = \"HTTPS\" ) 4.3.2. TCPSocketAction // TCPSocketAction describes an action based on opening a socket type TCPSocketAction struct { // Number or name of the port to access on the container. // Number must be in the range 1 to 65534. // Name must be an IANA_SVC_NAME. Port intstr.IntOrString `json:\"port\" protobuf:\"bytes,1,opt,name=port\"` // Optional: Host name to connect to, defaults to the pod IP. // +optional Host string `json:\"host,omitempty\" protobuf:\"bytes,2,opt,name=host\"` } 4.3.3. ExecAction // ExecAction describes a \"run in container\" action. type ExecAction struct { // Command is the command line to execute inside the container, the working directory for the // command is root ('/') in the container's filesystem. The command is simply exec'd, it is // not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use // a shell, you need to explicitly call out to that shell. // Exit status of 0 is treated as live/healthy and non-zero is unhealthy. // +optional Command []string `json:\"command,omitempty\" protobuf:\"bytes,1,rep,name=command\"` } 4.3.4. GRPCAction type GRPCAction struct { // Port number of the gRPC service. Number must be in the range 1 to 65534. Port int32 `json:\"port\" protobuf:\"bytes,1,opt,name=port\"` // Service is the name of the service to place in the gRPC HealthCheckRequest // (see https://github.com/grpc/grpc/blob/master/doc/health-checking.md). // // If this is not specified, the default behavior is defined by gRPC. // +optional // +default=\"\" Service *string `json:\"service\" protobuf:\"bytes,2,opt,name=service\"` } 参考文章： https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/ 配置存活、就绪和启动探针 | Kubernetes https://mp.weixin.qq.com/s/ApD8D0_UAPftUjw-0Txcyw 《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/pod/pod-volume.html":{"url":"concepts/pod/pod-volume.html","title":"Pod存储卷","keywords":"","body":"Pod VolumePod Volume 同一个Pod中的多个容器可以共享Pod级别的存储卷Volume,Volume可以定义为各种类型，多个容器各自进行挂载，将Pod的Volume挂载为容器内部需要的目录。 例如：Pod级别的Volume:\"app-logs\",用于tomcat向其中写日志文件，busybox读日志文件。 图片 - 这里写图片描述 pod-volumes-applogs.yaml apiVersion: v1 kind: Pod metadata: name: volume-pod spec: containers: - name: tomcat image: tomcat ports: - containerPort: 8080 volumeMounts: - name: app-logs mountPath: /usr/local/tomcat/logs - name: busybox image: busybox command: [\"sh\",\"-c\",\"tailf /logs/catalina*.log\"] volumeMounts: - name: app-logs mountPath: /logs volumes: - name: app-logs emptuDir: {} 查看日志 kubectl logs -c kubectl exec -it -c – tail /usr/local/tomcat/logs/catalina.xx.log 参考文章 《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/pod/pod-scheduler.html":{"url":"concepts/pod/pod-scheduler.html","title":"Pod控制器","keywords":"","body":"Pod调度1. RC、Deployment:全自动调度2. NodeSelector[定向调度]3. NodeAffinity[亲和性调度]4. DaemonSet：特定场景调度5. Job：批处理调度5.1. 批处理的三种模式5.2. Job的三种类型Pod调度 在kubernetes集群中，Pod（container）是应用的载体，一般通过RC、Deployment、DaemonSet、Job等对象来完成Pod的调度与自愈功能。 1. RC、Deployment:全自动调度 RC的功能即保持集群中始终运行着指定个数的Pod。 在调度策略上主要有： 系统内置调度算法[最优Node] NodeSelector[定向调度] NodeAffinity[亲和性调度] 2. NodeSelector[定向调度] k8s中kube-scheduler负责实现Pod的调度，内部系统通过一系列算法最终计算出最佳的目标节点。如果需要将Pod调度到指定Node上，则可以通过Node的标签（Label）和Pod的nodeSelector属性相匹配来达到目的。 1、kubectl label nodes {node-name} {label-key}={label-value} 2、nodeSelector: {label-key}:{label-value} 如果给多个Node打了相同的标签，则scheduler会根据调度算法从这组Node中选择一个可用的Node来调度。 如果Pod的nodeSelector的标签在Node中没有对应的标签，则该Pod无法被调度成功。 Node标签的使用场景： 对集群中不同类型的Node打上不同的标签，可控制应用运行Node的范围。例如role=frontend;role=backend;role=database。 3. NodeAffinity[亲和性调度] NodeAffinity意为Node亲和性调度策略，NodeSelector为精确匹配，NodeAffinity为条件范围匹配，通过In（属于）、NotIn（不属于）、Exists（存在一个条件）、DoesNotExist（不存在）、Gt（大于）、Lt（小于）等操作符来选择Node，使调度更加灵活。 RequiredDuringSchedulingRequiredDuringExecution：类似于NodeSelector，但在Node不满足条件时，系统将从该Node上移除之前调度上的Pod。 RequiredDuringSchedulingIgnoredDuringExecution：与上一个类似，区别是在Node不满足条件时，系统不一定从该Node上移除之前调度上的Pod。 PreferredDuringSchedulingIgnoredDuringExecution：指定在满足调度条件的Node中，哪些Node应更优先地进行调度。同时在Node不满足条件时，系统不一定从该Node上移除之前调度上的Pod。 如果同时设置了NodeSelector和NodeAffinity，则系统将需要同时满足两者的设置才能进行调度。 4. DaemonSet：特定场景调度 DaemonSet是kubernetes1.2版本新增的一种资源对象，用于管理在集群中每个Node上仅运行一份Pod的副本实例。 图片 - 这里写图片描述 该用法适用的应用场景： 在每个Node上运行一个GlusterFS存储或者Ceph存储的daemon进程。 在每个Node上运行一个日志采集程序：fluentd或logstach。 在每个Node上运行一个健康程序，采集该Node的运行性能数据，例如：Prometheus Node Exportor、collectd、New Relic agent或Ganglia gmond等。 DaemonSet的Pod调度策略与RC类似，除了使用系统内置算法在每台Node上进行调度，也可以通过NodeSelector或NodeAffinity来指定满足条件的Node范围进行调度。 5. Job：批处理调度 kubernetes从1.2版本开始支持批处理类型的应用，可以通过kubernetes Job资源对象来定义并启动一个批处理任务。批处理任务通常并行（或串行）启动多个计算进程去处理一批工作项（work item），处理完后，整个批处理任务结束。 5.1. 批处理的三种模式 图片 - 这里写图片描述 批处理按任务实现方式不同分为以下几种模式： Job Template Expansion模式 一个Job对象对应一个待处理的Work item，有几个Work item就产生几个独立的Job，通过适用于Work item数量少，每个Work item要处理的数据量比较大的场景。例如有10个文件（Work item）,每个文件（Work item）为100G。 Queue with Pod Per Work Item 采用一个任务队列存放Work item，一个Job对象作为消费者去完成这些Work item，其中Job会启动N个Pod，每个Pod对应一个Work item。 Queue with Variable Pod Count 采用一个任务队列存放Work item，一个Job对象作为消费者去完成这些Work item，其中Job会启动N个Pod，每个Pod对应一个Work item。但Pod的数量是可变的。 5.2. Job的三种类型 1）Non-parallel Jobs 通常一个Job只启动一个Pod,除非Pod异常才会重启该Pod,一旦此Pod正常结束，Job将结束。 2）Parallel Jobs with a fixed completion count 并行Job会启动多个Pod，此时需要设定Job的.spec.completions参数为一个正数，当正常结束的Pod数量达到该值则Job结束。 3）Parallel Jobs with a work queue 任务队列方式的并行Job需要一个独立的Queue，Work item都在一个Queue中存放，不能设置Job的.spec.completions参数。 此时Job的特性： 每个Pod能独立判断和决定是否还有任务项需要处理 如果某个Pod正常结束，则Job不会再启动新的Pod 如果一个Pod成功结束，则此时应该不存在其他Pod还在干活的情况，它们应该都处于即将结束、退出的状态 如果所有的Pod都结束了，且至少一个Pod成功结束，则整个Job算是成功结束 参考文章 《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/pod/pod-operation.html":{"url":"concepts/pod/pod-operation.html","title":"Pod伸缩与升级","keywords":"","body":"1. Pod伸缩1.1. 手动伸缩（scale）1.2. 自动伸缩（HPA）1.2.1. 通过kubectl autoscale创建1.2.2. 通过yaml配置文件创建2. Pod滚动升级2.1. 通过配置文件实现2.2. 通过kubectl rolling-update命令实现2.3. 升级回滚1. Pod伸缩 k8s中RC的用来保持集群中始终运行指定数目的实例，通过RC的scale机制可以完成Pod的扩容和缩容（伸缩）。 1.1. 手动伸缩（scale） kubectl scale rc redis-slave --replicas=3 1.2. 自动伸缩（HPA） Horizontal Pod Autoscaler（HPA）控制器用于实现基于CPU使用率进行自动Pod伸缩的功能。HPA控制器基于Master的kube-controller-manager服务启动参数--horizontal-pod-autoscaler-sync-period定义是时长（默认30秒），周期性监控目标Pod的CPU使用率，并在满足条件时对ReplicationController或Deployment中的Pod副本数进行调整，以符合用户定义的平均Pod CPU使用率。Pod CPU使用率来源于heapster组件，因此需安装该组件。 可以通过kubectl autoscale命令进行快速创建或者使用yaml配置文件进行创建。创建之前需已存在一个RC或Deployment对象，并且该RC或Deployment中的Pod必须定义resources.requests.cpu的资源请求值，以便heapster采集到该Pod的CPU。 1.2.1. 通过kubectl autoscale创建 例如： php-apache-rc.yaml apiVersion: v1 kind: ReplicationController metadata: name: php-apache spec: replicas: 1 template: metadata: name: php-apache labels: app: php-apache spec: containers: - name: php-apache image: gcr.io/google_containers/hpa-example resources: requests: cpu: 200m ports: - containerPort: 80 创建php-apache的RC kubectl create -f php-apache-rc.yaml php-apache-svc.yaml apiVersion: v1 kind: Service metadata: name: php-apache spec: ports: - port: 80 selector: app: php-apache 创建php-apache的Service kubectl create -f php-apache-svc.yaml 创建HPA控制器 kubectl autoscale rc php-apache --min=1 --max=10 --cpu-percent=50 1.2.2. 通过yaml配置文件创建 hpa-php-apache.yaml apiVersion: v1 kind: HorizontalPodAutoscaler metadata: name: php-apache spec: scaleTargetRef: apiVersion: v1 kind: ReplicationController name: php-apache minReplicas: 1 maxReplicas: 10 targetCPUUtilizationPercentage: 50 创建hpa kubectl create -f hpa-php-apache.yaml 查看hpa kubectl get hpa 2. Pod滚动升级 k8s中的滚动升级通过执行kubectl rolling-update命令完成，该命令创建一个新的RC（与旧的RC在同一个命名空间中），然后自动控制旧的RC中的Pod副本数逐渐减少为0，同时新的RC中的Pod副本数从0逐渐增加到附加值，但滚动升级中Pod副本数（包括新Pod和旧Pod）保持原预期值。 2.1. 通过配置文件实现 redis-master-controller-v2.yaml apiVersion: v1 kind: ReplicationController metadata: name: redis-master-v2 labels: name: redis-master version: v2 spec: replicas: 1 selector: name: redis-master version: v2 template: metadata: labels: name: redis-master version: v2 spec: containers: - name: master image: kubeguide/redis-master:2.0 ports: - containerPort: 6371 注意事项： RC的名字（name）不能与旧RC的名字相同 在selector中应至少有一个Label与旧的RC的Label不同，以标识其为新的RC。例如本例中新增了version的Label。 运行kubectl rolling-update kubectl rolling-update redis-master -f redis-master-controller-v2.yaml 2.2. 通过kubectl rolling-update命令实现 kubectl rolling-update redis-master --image=redis-master:2.0 与使用配置文件实现不同在于，该执行结果旧的RC被删除，新的RC仍使用旧的RC的名字。 2.3. 升级回滚 kubectl rolling-update加参数--rollback实现回滚操作 kubectl rolling-update redis-master --image=kubeguide/redis-master:2.0 --rollback 参考文章 《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/configmap/_index.html":{"url":"concepts/configmap/_index.html","title":"配置","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/configmap/pod-configmap.html":{"url":"concepts/configmap/pod-configmap.html","title":"ConfigMap","keywords":"","body":"Pod的配置管理1. ConfigMap：容器应用的配置管理2. 创建ConfigMap2.1. 通过yaml文件方式2.2. 通过kubectl命令行方式2.3. 通过环境变量的方式3. 使用ConfigMap的限制条件Pod的配置管理 Kubernetes v1.2的版本提供统一的集群配置管理方案–ConfigMap。 1. ConfigMap：容器应用的配置管理 使用场景： 生成为容器内的环境变量。 设置容器启动命令的启动参数（需设置为环境变量）。 以Volume的形式挂载为容器内部的文件或目录。 ConfigMap以一个或多个key:value的形式保存在kubernetes系统中供应用使用，既可以表示一个变量的值（例如：apploglevel=info），也可以表示完整配置文件的内容（例如：server.xml=...）。 可以通过yaml配置文件或者使用kubectl create configmap命令的方式创建ConfigMap。 2. 创建ConfigMap 2.1. 通过yaml文件方式 cm-appvars.yaml apiVersion: v1 kind: ConfigMap metadata: name: cm-appvars data: apploglevel: info appdatadir: /var/data 常用命令 kubectl create -f cm-appvars.yaml kubectl get configmap kubectl describe configmap cm-appvars kubectl get configmap cm-appvars -o yaml 2.2. 通过kubectl命令行方式 通过kubectl create configmap创建，使用参数--from-file或--from-literal指定内容，可以在一行中指定多个参数。 1）通过--from-file参数从文件中进行创建，可以指定key的名称，也可以在一个命令行中创建包含多个key的ConfigMap。 kubectl create configmap NAME --from-file=[key=]source --from-file=[key=]source 2）通过--from-file参数从目录中进行创建，该目录下的每个配置文件名被设置为key，文件内容被设置为value。 kubectl create configmap NAME --from-file=config-files-dir 3）通过--from-literal从文本中进行创建，直接将指定的key=value创建为ConfigMap的内容。 kubectl create configmap NAME --from-literal=key1=value1 --from-literal=key2=value2 容器应用对ConfigMap的使用有两种方法： 通过环境变量获取ConfigMap中的内容。 通过Volume挂载的方式将ConfigMap中的内容挂载为容器内部的文件或目录。 2.3. 通过环境变量的方式 ConfigMap的yaml文件:cm-appvars.yaml apiVersion: v1 kind: ConfigMap metadata: name: cm-appvars data: apploglevel: info appdatadir: /var/data Pod的yaml文件：cm-test-pod.yaml apiVersion: v1 kind: Pod metadata: name: cm-test-pod spec: containers: - name: cm-test image: busybox command: [\"/bin/sh\",\"-c\",\"env|grep APP\"] env: - name: APPLOGLEVEL valueFrom: configMapKeyRef: name: cm-appvars key: apploglevel - name: APPDATADIR valueFrom: configMapKeyRef: name: cm-appvars key: appdatadir 创建命令： kubectl create -f cm-test-pod.yaml kubectl get pods --show-all kubectl logs cm-test-pod 3. 使用ConfigMap的限制条件 ConfigMap必须在Pod之前创建 ConfigMap也可以定义为属于某个Namespace。只有处于相同Namespace中的Pod可以引用它。 kubelet只支持可以被API Server管理的Pod使用ConfigMap。静态Pod无法引用。 在Pod对ConfigMap进行挂载操作时，容器内只能挂载为“目录”，无法挂载为文件。 参考文章 《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"concepts/_index.html":{"url":"concepts/_index.html","title":"Workload","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"principle/component/_index.html":{"url":"principle/component/_index.html","title":"核心组件","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"principle/component/kubernetes-core-principle-api-server.html":{"url":"principle/component/kubernetes-core-principle-api-server.html","title":"Api Server","keywords":"","body":"1. API Server简介2. 如何访问kubernetes API2.1. 本地端口2.2. 安全端口2.3. 访问方式2.3.1. curl2.3.2. Kubectl Proxy2.3.3. kubectl客户端2.3.4. 编程方式调用3. 通过API Server访问Node、Pod和Service3.1. Node相关接口3.2. Pod相关接口3.3. Service相关接口4. 集群功能模块之间的通信4.1. kubelet与API Server交互4.2. kube-controller-manager与API Server交互4.3. kube-scheduler与API Server交互4.4. 特别说明1. API Server简介 k8s API Server提供了k8s各类资源对象（pod,RC,Service等）的增删改查及watch等HTTP Rest接口，是整个系统的数据总线和数据中心。 kubernetes API Server的功能： 提供了集群管理的REST API接口(包括认证授权、数据校验以及集群状态变更)； 提供其他模块之间的数据交互和通信的枢纽（其他模块通过API Server查询或修改数据，只有API Server才直接操作etcd）; 是资源配额控制的入口； 拥有完备的集群安全机制. kube-apiserver工作原理图 图片 - kube-apiserver 2. 如何访问kubernetes API k8s通过kube-apiserver这个进程提供服务，该进程运行在单个k8s-master节点上。默认有两个端口。 2.1. 本地端口 该端口用于接收HTTP请求； 该端口默认值为8080，可以通过API Server的启动参数“--insecure-port”的值来修改默认值； 默认的IP地址为“localhost”，可以通过启动参数“--insecure-bind-address”的值来修改该IP地址； 非认证或授权的HTTP请求通过该端口访问API Server。 2.2. 安全端口 该端口默认值为6443，可通过启动参数“--secure-port”的值来修改默认值； 默认IP地址为非本地（Non-Localhost）网络端口，通过启动参数“--bind-address”设置该值； 该端口用于接收HTTPS请求； 用于基于Tocken文件或客户端证书及HTTP Base的认证； 用于基于策略的授权； 默认不启动HTTPS安全访问控制。 2.3. 访问方式 Kubernetes REST API可参考https://kubernetes.io/docs/api-reference/v1.6/ 2.3.1. curl curl localhost:8080/api curl localhost:8080/api/v1/pods curl localhost:8080/api/v1/services curl localhost:8080/api/v1/replicationcontrollers 2.3.2. Kubectl Proxy Kubectl Proxy代理程序既能作为API Server的反向代理，也能作为普通客户端访问API Server的代理。通过master节点的8080端口来启动该代理程序。 kubectl proxy --port=8080 & 具体见kubectl proxy --help [root@node5 ~]# kubectl proxy --help To proxy all of the kubernetes api and nothing else, use: kubectl proxy --api-prefix=/ To proxy only part of the kubernetes api and also some static files: kubectl proxy --www=/my/files --www-prefix=/static/ --api-prefix=/api/ The above lets you 'curl localhost:8001/api/v1/pods'. To proxy the entire kubernetes api at a different root, use: kubectl proxy --api-prefix=/custom/ The above lets you 'curl localhost:8001/custom/api/v1/pods' Usage: kubectl proxy [--port=PORT] [--www=static-dir] [--www-prefix=prefix] [--api-prefix=prefix] [flags] Examples: # Run a proxy to kubernetes apiserver on port 8011, serving static content from ./local/www/ $ kubectl proxy --port=8011 --www=./local/www/ # Run a proxy to kubernetes apiserver on an arbitrary local port. # The chosen port for the server will be output to stdout. $ kubectl proxy --port=0 # Run a proxy to kubernetes apiserver, changing the api prefix to k8s-api # This makes e.g. the pods api available at localhost:8011/k8s-api/v1/pods/ $ kubectl proxy --api-prefix=/k8s-api Flags: --accept-hosts=\"^localhost$,^127//.0//.0//.1$,^//[::1//]$\": Regular expression for hosts that the proxy should accept. --accept-paths=\"^/.*\": Regular expression for paths that the proxy should accept. --api-prefix=\"/\": Prefix to serve the proxied API under. --disable-filter[=false]: If true, disable request filtering in the proxy. This is dangerous, and can leave you vulnerable to XSRF attacks, when used with an accessible port. -p, --port=8001: The port on which to run the proxy. Set to 0 to pick a random port. --reject-methods=\"POST,PUT,PATCH\": Regular expression for HTTP methods that the proxy should reject. --reject-paths=\"^/api/.*/exec,^/api/.*/run\": Regular expression for paths that the proxy should reject. -u, --unix-socket=\"\": Unix socket on which to run the proxy. -w, --www=\"\": Also serve static files from the given directory under the specified prefix. -P, --www-prefix=\"/static/\": Prefix to serve static files under, if static file directory is specified. Global Flags: --alsologtostderr[=false]: log to standard error as well as files --api-version=\"\": The API version to use when talking to the server --certificate-authority=\"\": Path to a cert. file for the certificate authority. --client-certificate=\"\": Path to a client key file for TLS. --client-key=\"\": Path to a client key file for TLS. --cluster=\"\": The name of the kubeconfig cluster to use --context=\"\": The name of the kubeconfig context to use --insecure-skip-tls-verify[=false]: If true, the server's certificate will not be checked for validity. This will make your HTTPS connections insecure. --kubeconfig=\"\": Path to the kubeconfig file to use for CLI requests. --log-backtrace-at=:0: when logging hits line file:N, emit a stack trace --log-dir=\"\": If non-empty, write log files in this directory --log-flush-frequency=5s: Maximum number of seconds between log flushes --logtostderr[=true]: log to standard error instead of files --match-server-version[=false]: Require server version to match client version --namespace=\"\": If present, the namespace scope for this CLI request. --password=\"\": Password for basic authentication to the API server. -s, --server=\"\": The address and port of the Kubernetes API server --stderrthreshold=2: logs at or above this threshold go to stderr --token=\"\": Bearer token for authentication to the API server. --user=\"\": The name of the kubeconfig user to use --username=\"\": Username for basic authentication to the API server. --v=0: log level for V logs --vmodule=: comma-separated list of pattern=N settings for file-filtered logging 2.3.3. kubectl客户端 命令行工具kubectl客户端，通过命令行参数转换为对API Server的REST API调用，并将调用结果输出。 命令格式：kubectl [command] [options] 具体可参考k8s常用命令 2.3.4. 编程方式调用 使用场景： 1、运行在Pod里的用户进程调用kubernetes API,通常用来实现分布式集群搭建的目标。 2、开发基于kubernetes的管理平台，比如调用kubernetes API来完成Pod、Service、RC等资源对象的图形化创建和管理界面。可以使用kubernetes提供的Client Library。 具体可参考https://github.com/kubernetes/client-go。 3. 通过API Server访问Node、Pod和Service k8s API Server最主要的REST接口是资源对象的增删改查，另外还有一类特殊的REST接口—k8s Proxy API接口，这类接口的作用是代理REST请求，即kubernetes API Server把收到的REST请求转发到某个Node上的kubelet守护进程的REST端口上，由该kubelet进程负责响应。 3.1. Node相关接口 关于Node相关的接口的REST路径为：/api/v1/proxy/nodes/{name}，其中{name}为节点的名称或IP地址。 /api/v1/proxy/nodes/{name}/pods/ #列出指定节点内所有Pod的信息 /api/v1/proxy/nodes/{name}/stats/ #列出指定节点内物理资源的统计信息 /api/v1/prxoy/nodes/{name}/spec/ #列出指定节点的概要信息 这里获取的Pod信息来自Node而非etcd数据库，两者时间点可能存在偏差。如果在kubelet进程启动时加--enable-debugging-handles=true参数，那么kubernetes Proxy API还会增加以下接口： /api/v1/proxy/nodes/{name}/run #在节点上运行某个容器 /api/v1/proxy/nodes/{name}/exec #在节点上的某个容器中运行某条命令 /api/v1/proxy/nodes/{name}/attach #在节点上attach某个容器 /api/v1/proxy/nodes/{name}/portForward #实现节点上的Pod端口转发 /api/v1/proxy/nodes/{name}/logs #列出节点的各类日志信息 /api/v1/proxy/nodes/{name}/metrics #列出和该节点相关的Metrics信息 /api/v1/proxy/nodes/{name}/runningpods #列出节点内运行中的Pod信息 /api/v1/proxy/nodes/{name}/debug/pprof #列出节点内当前web服务的状态，包括CPU和内存的使用情况 3.2. Pod相关接口 /api/v1/proxy/namespaces/{namespace}/pods/{name}/{path:*} #访问pod的某个服务接口 /api/v1/proxy/namespaces/{namespace}/pods/{name} #访问Pod #以下写法不同，功能一样 /api/v1/namespaces/{namespace}/pods/{name}/proxy/{path:*} #访问pod的某个服务接口 /api/v1/namespaces/{namespace}/pods/{name}/proxy #访问Pod 3.3. Service相关接口 /api/v1/proxy/namespaces/{namespace}/services/{name} Pod的proxy接口的作用：在kubernetes集群之外访问某个pod容器的服务（HTTP服务），可以用Proxy API实现，这种场景多用于管理目的，比如逐一排查Service的Pod副本，检查哪些Pod的服务存在异常问题。 4. 集群功能模块之间的通信 kubernetes API Server作为集群的核心，负责集群各功能模块之间的通信，集群内各个功能模块通过API Server将信息存入etcd，当需要获取和操作这些数据时，通过API Server提供的REST接口（GET/LIST/WATCH方法）来实现，从而实现各模块之间的信息交互。 4.1. kubelet与API Server交互 每个Node节点上的kubelet定期就会调用API Server的REST接口报告自身状态，API Server接收这些信息后，将节点状态信息更新到etcd中。kubelet也通过API Server的Watch接口监听Pod信息，从而对Node机器上的POD进行管理。 监听信息 kubelet动作 新的POD副本被调度绑定到本节点 执行POD对应的容器的创建和启动逻辑 POD对象被删除 删除本节点上相应的POD容器 修改POD信息 修改本节点的POD容器 4.2. kube-controller-manager与API Server交互 kube-controller-manager中的Node Controller模块通过API Server提供的Watch接口，实时监控Node的信息，并做相应处理。 4.3. kube-scheduler与API Server交互 Scheduler通过API Server的Watch接口监听到新建Pod副本的信息后，它会检索所有符合该Pod要求的Node列表，开始执行Pod调度逻辑。调度成功后将Pod绑定到目标节点上。 4.4. 特别说明 为了缓解各模块对API Server的访问压力，各功能模块都采用缓存机制来缓存数据，各功能模块定时从API Server获取指定的资源对象信息（LIST/WATCH方法），然后将信息保存到本地缓存，功能模块在某些情况下不直接访问API Server，而是通过访问缓存数据来间接访问API Server。 参考《kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"principle/component/kubernetes-core-principle-controller-manager.html":{"url":"principle/component/kubernetes-core-principle-controller-manager.html","title":"Controller Manager","keywords":"","body":"1. Controller Manager简介2. Replication Controller2.1. Replication Controller的职责2.2. Replication Controller使用场景3. Node Controller4. ResourceQuota Controller5. Namespace Controller6. Endpoint Controller7. Service Controller1. Controller Manager简介 Controller Manager作为集群内部的管理控制中心，负责集群内的Node、Pod副本、服务端点（Endpoint）、命名空间（Namespace）、服务账号（ServiceAccount）、资源定额（ResourceQuota）的管理，当某个Node意外宕机时，Controller Manager会及时发现并执行自动化修复流程，确保集群始终处于预期的工作状态。 图片 - controller manager 每个Controller通过API Server提供的接口实时监控整个集群的每个资源对象的当前状态，当发生各种故障导致系统状态发生变化时，会尝试将系统状态修复到“期望状态”。 2. Replication Controller 为了区分，将资源对象Replication Controller简称RC,而本文中是指Controller Manager中的Replication Controller，称为副本控制器。副本控制器的作用即保证集群中一个RC所关联的Pod副本数始终保持预设值。 只有当Pod的重启策略是Always的时候（RestartPolicy=Always），副本控制器才会管理该Pod的操作（创建、销毁、重启等）。 RC中的Pod模板就像一个模具，模具制造出来的东西一旦离开模具，它们之间就再没关系了。一旦Pod被创建，无论模板如何变化，也不会影响到已经创建的Pod。 Pod可以通过修改label来脱离RC的管控，该方法可以用于将Pod从集群中迁移，数据修复等调试。 删除一个RC不会影响它所创建的Pod，如果要删除Pod需要将RC的副本数属性设置为0。 不要越过RC创建Pod，因为RC可以实现自动化控制Pod，提高容灾能力。 2.1. Replication Controller的职责 确保集群中有且仅有N个Pod实例，N是RC中定义的Pod副本数量。 通过调整RC中的spec.replicas属性值来实现系统扩容或缩容。 通过改变RC中的Pod模板来实现系统的滚动升级。 2.2. Replication Controller使用场景 使用场景 说明 使用命令 重新调度 当发生节点故障或Pod被意外终止运行时，可以重新调度保证集群中仍然运行指定的副本数。 弹性伸缩 通过手动或自动扩容代理修复副本控制器的spec.replicas属性，可以实现弹性伸缩。 kubectl scale 滚动更新 创建一个新的RC文件，通过kubectl 命令或API执行，则会新增一个新的副本同时删除旧的副本，当旧副本为0时，删除旧的RC。 kubectl rolling-update 滚动升级，具体可参考kubectl rolling-update --help,官方文档：https://kubernetes.io/docs/tasks/run-application/rolling-update-replication-controller/ 3. Node Controller kubelet在启动时会通过API Server注册自身的节点信息，并定时向API Server汇报状态信息，API Server接收到信息后将信息更新到etcd中。 Node Controller通过API Server实时获取Node的相关信息，实现管理和监控集群中的各个Node节点的相关控制功能。流程如下 图片 - Node Controller 1、Controller Manager在启动时如果设置了--cluster-cidr参数，那么为每个没有设置Spec.PodCIDR的Node节点生成一个CIDR地址，并用该CIDR地址设置节点的Spec.PodCIDR属性，防止不同的节点的CIDR地址发生冲突。 2、具体流程见以上流程图。 3、逐个读取节点信息，如果节点状态变成非“就绪”状态，则将节点加入待删除队列，否则将节点从该队列删除。 4. ResourceQuota Controller 资源配额管理确保指定的资源对象在任何时候都不会超量占用系统物理资源。 支持三个层次的资源配置管理： 1）容器级别：对CPU和Memory进行限制 2）Pod级别：对一个Pod内所有容器的可用资源进行限制 3）Namespace级别：包括 Pod数量 Replication Controller数量 Service数量 ResourceQuota数量 Secret数量 可持有的PV（Persistent Volume）数量 说明： k8s配额管理是通过Admission Control（准入控制）来控制的； Admission Control提供两种配额约束方式：LimitRanger和ResourceQuota； LimitRanger作用于Pod和Container； ResourceQuota作用于Namespace上，限定一个Namespace里的各类资源的使用总额。 ResourceQuota Controller流程图： 图片 - ResourceQuota Controller 5. Namespace Controller 用户通过API Server可以创建新的Namespace并保存在etcd中，Namespace Controller定时通过API Server读取这些Namespace信息。 如果Namespace被API标记为优雅删除（即设置删除期限，DeletionTimestamp）,则将该Namespace状态设置为“Terminating”,并保存到etcd中。同时Namespace Controller删除该Namespace下的ServiceAccount、RC、Pod等资源对象。 6. Endpoint Controller Service、Endpoint、Pod的关系： 图片 - Endpoint Controller Endpoints表示了一个Service对应的所有Pod副本的访问地址，而Endpoints Controller负责生成和维护所有Endpoints对象的控制器。它负责监听Service和对应的Pod副本的变化。 如果监测到Service被删除，则删除和该Service同名的Endpoints对象； 如果监测到新的Service被创建或修改，则根据该Service信息获得相关的Pod列表，然后创建或更新Service对应的Endpoints对象。 如果监测到Pod的事件，则更新它对应的Service的Endpoints对象。 kube-proxy进程获取每个Service的Endpoints，实现Service的负载均衡功能。 7. Service Controller Service Controller是属于kubernetes集群与外部的云平台之间的一个接口控制器。Service Controller监听Service变化，如果是一个LoadBalancer类型的Service，则确保外部的云平台上对该Service对应的LoadBalancer实例被相应地创建、删除及更新路由转发表。 参考《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"principle/component/kubernetes-core-principle-scheduler.html":{"url":"principle/component/kubernetes-core-principle-scheduler.html","title":"Scheduler","keywords":"","body":"1. Scheduler简介2. 调度流程3. 预选策略3.1. NoDiskConflict3.2. PodFitsResources3.3. PodSelectorMatches3.4. PodFitsHost3.5. CheckNodeLabelPresence3.6. CheckServiceAffinity3.7. PodFitsPorts4. 优选策略4.1. LeastRequestedPriority4.2. CalculateNodeLabelPriority4.3. BalancedResourceAllocation1. Scheduler简介 Scheduler负责Pod调度。在整个系统中起\"承上启下\"作用，承上：负责接收Controller Manager创建的新的Pod，为其选择一个合适的Node；启下：Node上的kubelet接管Pod的生命周期。 Scheduler： 1）通过调度算法为待调度Pod列表的每个Pod从Node列表中选择一个最适合的Node，并将信息写入etcd中 2）kubelet通过API Server监听到kubernetes Scheduler产生的Pod绑定信息，然后获取对应的Pod清单，下载Image，并启动容器。 图片 - scheduler 2. 调度流程 1、预选调度过程，即遍历所有目标Node,筛选出符合要求的候选节点，kubernetes内置了多种预选策略（xxx Predicates）供用户选择 2、确定最优节点，在第一步的基础上采用优选策略（xxx Priority）计算出每个候选节点的积分，取最高积分。 调度流程通过插件式加载的“调度算法提供者”（AlgorithmProvider）具体实现，一个调度算法提供者就是包括一组预选策略与一组优选策略的结构体。 3. 预选策略 说明：返回true表示该节点满足该Pod的调度条件；返回false表示该节点不满足该Pod的调度条件。 3.1. NoDiskConflict 判断备选Pod的数据卷是否与该Node上已存在Pod挂载的数据卷冲突，如果是则返回false，否则返回true。 3.2. PodFitsResources 判断备选节点的资源是否满足备选Pod的需求，即节点的剩余资源满不满足该Pod的资源使用。 计算备选Pod和节点中已用资源（该节点所有Pod的使用资源）的总和。 获取备选节点的状态信息，包括节点资源信息。 如果（备选Pod+节点已用资源>该节点总资源）则返回false，即剩余资源不满足该Pod使用；否则返回true。 3.3. PodSelectorMatches 判断节点是否包含备选Pod的标签选择器指定的标签，即通过标签来选择Node。 如果Pod中没有指定spec.nodeSelector，则返回true。 否则获得备选节点的标签信息，判断该节点的标签信息中是否包含该Pod的spec.nodeSelector中指定的标签，如果包含返回true，否则返回false。 3.4. PodFitsHost 判断备选Pod的spec.nodeName所指定的节点名称与备选节点名称是否一致，如果一致返回true，否则返回false。 3.5. CheckNodeLabelPresence 检查备选节点中是否有Scheduler配置的标签，如果有返回true，否则返回false。 3.6. CheckServiceAffinity 判断备选节点是否包含Scheduler配置的标签，如果有返回true，否则返回false。 3.7. PodFitsPorts 判断备选Pod所用的端口列表中的端口是否在备选节点中已被占用，如果被占用返回false，否则返回true。 4. 优选策略 4.1. LeastRequestedPriority 优先从备选节点列表中选择资源消耗最小的节点（CPU+内存）。 4.2. CalculateNodeLabelPriority 优先选择含有指定Label的节点。 4.3. BalancedResourceAllocation 优先从备选节点列表中选择各项资源使用率最均衡的节点。 参考《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"principle/component/kubernetes-core-principle-kubelet.html":{"url":"principle/component/kubernetes-core-principle-kubelet.html","title":"Kubelet","keywords":"","body":"1. kubelet简介2. 节点管理3. Pod管理4. 容器健康检查5. cAdvisor资源监控1. kubelet简介 在kubernetes集群中，每个Node节点都会启动kubelet进程，用来处理Master节点下发到本节点的任务，管理Pod和其中的容器。kubelet会在API Server上注册节点信息，定期向Master汇报节点资源使用情况，并通过cAdvisor监控容器和节点资源。可以把kubelet理解成【Server-Agent】架构中的agent，是Node上的pod管家。 更多kubelet配置参数信息可参考kubelet --help 2. 节点管理 节点通过设置kubelet的启动参数“--register-node”，来决定是否向API Server注册自己，默认为true。可以通过kubelet --help或者查看kubernetes源码【cmd/kubelet/app/server.go中】来查看该参数。 kubelet的配置文件 默认配置文件在/etc/kubernetes/kubelet中，其中 --api-servers：用来配置Master节点的IP和端口。 --kubeconfig：用来配置kubeconfig的路径，kubeconfig文件常用来指定证书。 --hostname-override：用来配置该节点在集群中显示的主机名。 --node-status-update-frequency：配置kubelet向Master心跳上报的频率，默认为10s。 3. Pod管理 kubelet有几种方式获取自身Node上所需要运行的Pod清单。但本文只讨论通过API Server监听etcd目录，同步Pod列表的方式。 kubelet通过API Server Client使用WatchAndList的方式监听etcd中/registry/nodes/${当前节点名称}和/registry/pods的目录，将获取的信息同步到本地缓存中。 kubelet监听etcd，执行对Pod的操作，对容器的操作则是通过Docker Client执行，例如启动删除容器等。 kubelet创建和修改Pod流程： 为该Pod创建一个数据目录。 从API Server读取该Pod清单。 为该Pod挂载外部卷（External Volume） 下载Pod用到的Secret。 检查运行的Pod，执行Pod中未完成的任务。 先创建一个Pause容器，该容器接管Pod的网络，再创建其他容器。 Pod中容器的处理流程： 1）比较容器hash值并做相应处理。 2）如果容器被终止了且没有指定重启策略，则不做任何处理。 3）调用Docker Client下载容器镜像，调用Docker Client运行容器。 4. 容器健康检查 Pod通过探针的方式来检查容器的健康状态，具体可参考Pod详解#Pod健康检查。 5. cAdvisor资源监控 kubelet通过cAdvisor获取本节点信息及容器的数据。cAdvisor为谷歌开源的容器资源分析工具，默认集成到kubernetes中。 cAdvisor自动采集CPU,内存，文件系统，网络使用情况，容器中运行的进程，默认端口为4194。可以通过Node IP+Port访问。 更多参考：http://github.com/google/cadvisor 参考《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"principle/flow/_index.html":{"url":"principle/flow/_index.html","title":"流程图","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"principle/flow/pod-flow.html":{"url":"principle/flow/pod-flow.html","title":"Pod创建流程","keywords":"","body":"Pod创建基本流程图Pod创建完整流程图Pod创建基本流程图 Pod创建完整流程图 图片来源：https://fuckcloudnative.io/posts/what-happens-when-k8s/ 参考: https://fuckcloudnative.io/posts/what-happens-when-k8s/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"principle/flow/pvc-flow.html":{"url":"principle/flow/pvc-flow.html","title":"PVC创建流程","keywords":"","body":"pvc流程详细流程图pvc流程 流程如下： 用户创建了一个包含 PVC 的 Pod，该 PVC 要求使用动态存储卷； Scheduler 根据 Pod 配置、节点状态、PV 配置等信息，把 Pod 调度到一个合适的 Worker 节点上； PV 控制器 watch 到该 Pod 使用的 PVC 处于 Pending 状态，于是调用 Volume Plugin（in-tree）创建存储卷，并创建 PV 对象（out-of-tree 由 External Provisioner 来处理）； AD 控制器发现 Pod 和 PVC 处于待挂接状态，于是调用 Volume Plugin 挂接存储设备到目标 Worker 节点上 在 Worker 节点上，Kubelet 中的 Volume Manager 等待存储设备挂接完成，并通过 Volume Plugin 将设备挂载到全局目录：/var/lib/kubelet/pods/[pod uid]/volumes/kubernetes.io~iscsi/[PVname]（以 iscsi 为例）； Kubelet 通过 Docker 启动 Pod 的 Containers，用 bind mount 方式将已挂载到本地全局目录的卷映射到容器中。 详细流程图 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"network/docker-network.html":{"url":"network/docker-network.html","title":"Docker网络","keywords":"","body":"1. Docker的网络基础1.1. Network Namespace1.1.1. 网络命名空间的实现1.1.2. 网络命名空间的操作2. Docker的网络实现2.1. 容器网络2.2. 4类网络模式3. Docker网络模式3.1. bridge桥接模式3.1.1. 外部网络访问Docker容器3.1.2. Docker容器访问外部网络3.1.3. 缺点3.2. host模式3.3. container模式3.4. none模式1. Docker的网络基础 1.1. Network Namespace 不同的网络命名空间中，协议栈是独立的，完全隔离，彼此之间无法通信。同一个网络命名空间有独立的路由表和独立的Iptables/Netfilter来提供包的转发、NAT、IP包过滤等功能。 1.1.1. 网络命名空间的实现 将与网络协议栈相关的全局变量变成一个Net Namespace变量的成员，然后在调用协议栈函数中加入一个Namepace参数。 1.1.2. 网络命名空间的操作 1、创建网络命名空间 ip netns add name 2、在命名空间内执行命令 ip netns exec name command 3、进入命名空间 ip netns exec name bash 2. Docker的网络实现 2.1. 容器网络 Docker使用Linux桥接，在宿主机虚拟一个Docker容器网桥(docker0)，Docker启动一个容器时会根据Docker网桥的网段分配给容器一个IP地址，称为Container-IP，同时Docker网桥是每个容器的默认网关。因为在同一宿主机内的容器都接入同一个网桥，这样容器之间就能够通过容器的Container-IP直接通信。 Docker网桥是宿主机虚拟出来的，并不是真实存在的网络设备，外部网络是无法寻址到的，这也意味着外部网络无法通过直接Container-IP访问到容器。如果容器希望外部访问能够访问到，可以通过映射容器端口到宿主主机（端口映射），即docker run创建容器时候通过 -p 或 -P 参数来启用，访问容器的时候就通过[宿主机IP]:[容器端口]访问容器。 图片 - 这里写图片描述 2.2. 4类网络模式 Docker网络模式 配置 说明 host模式 --net=host 容器和宿主机共享Network namespace。 container模式 --net=container:NAME_or_ID 容器和另外一个容器共享Network namespace。 kubernetes中的pod就是多个容器共享一个Network namespace。 none模式 --net=none 容器有独立的Network namespace，但并没有对其进行任何网络设置，如分配veth pair 和网桥连接，配置IP等。 bridge模式 --net=bridge（默认为该模式） 桥接模式 3. Docker网络模式 3.1. bridge桥接模式 在bridge模式下，Docker可以使用独立的网络栈。实现方式是父进程在创建子进程的时候通过传入CLONE_NEWNET的参数创建出一个网络命名空间。 实现步骤： Docker Daemon首次启动时会创建一个虚拟网桥docker0，地址通常为172.x.x.x开头，在私有的网络空间中给这个网络分配一个子网。 由Docker创建处理的每个容器，都会创建一个虚拟以太设备对（veth pair），一端关联到网桥，另一端使用Namespace技术映射到容器内的eth0设备，然后从网桥的地址段内给eth0接口分配一个IP地址。 图片 - 这里写图片描述 一般情况，宿主机IP与docker0 IP、容器IP是不同的IP段，默认情况，外部看不到docker0和容器IP，对于外部来说相当于docker0和容器的IP为内网IP。 3.1.1. 外部网络访问Docker容器 外部访问docker容器可以通过端口映射(NAT)的方式，Docker使用NAT的方式将容器内部的服务与宿主机的某个端口port_1绑定。 外部访问容器的流程如下： 外界网络通过宿主机的IP和映射的端口port_1访问。 当宿主机收到此类请求，会通过DNAT将请求的目标IP即宿主机IP和目标端口即映射端口port_1替换成容器的IP和容器的端口port_0。 由于宿主机上可以识别容器IP，所以宿主机将请求发给veth pair。 veth pair将请求发送给容器内部的eth0，由容器内部的服务进行处理。 3.1.2. Docker容器访问外部网络 docker容器访问外部网络的流程： docker容器向外部目标IP和目标端口port_2发起请求，请求报文中的源IP为容器IP。 请求通过容器内部的eth0到veth pair的另一端docker0网桥。 docker0网桥通过数据报转发功能将请求转发到宿主机的eth0。 宿主机处理请求时通过SNAT将请求中的源IP换成宿主机eth0的IP。 处理后的报文通过请求的目标IP发送到外部网络。 3.1.3. 缺点 使用NAT的方式可能会带来性能的问题，影响网络传输效率。 3.2. host模式 host模式并没有给容器创建一个隔离的网络环境，而是和宿主机共用一个网络命名空间，容器使用宿主机的eth0和外界进行通信，同样容器也共用宿主机的端口资源，即分配端口可能存在与宿主机已分配的端口冲突的问题。 实现的方式即父进程在创建子进程的时候不传入CLONE_NEWNET的参数，从而和宿主机共享一个网络空间。 host模式没有通过NAT的方式进行转发因此性能上相对较好，但是不存在网络隔离性，可能产生端口冲突的问题。 3.3. container模式 container模式即docker容器可以使用其他容器的网络命名空间，即和其他容器处于同一个网络命名空间。 步骤： 查找其他容器的网络命名空间。 新创建的容器的网络命名空间使用其他容器的网络命名空间。 通过和其他容器共享网络命名空间的方式，可以让不同的容器之间处于相同的网络命名空间，可以直接通过localhost的方式进行通信，简化了强关联的多个容器之间的通信问题。 k8s中的pod的概念就是通过一组容器共享一个网络命名空间来达到pod内部的不同容器可以直接通过localhost的方式进行通信。 3.4. none模式 none模式即不为容器创建任何的网络环境，用户可以根据自己的需要手动去创建不同的网络定制配置。 参考： 《Docker源码分析》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"network/kubernetes-network.html":{"url":"network/kubernetes-network.html","title":"K8S网络","keywords":"","body":"1. kubernetes网络模型1.1. 基础原则1.2. k8s对集群的网络要求1.3. 网络架构图1.4. k8s集群IP概念汇总2. kubernetes的网络实现2.1. Pod网络2.2. Pod内部容器之间的通信2.3. Pod之间的通信2.3.1. 同Node的Pod之间的通信2.3.2. 不同Node的Pod之间的通信2.4. Service网络3. 开源的网络组件3.1. Flannel1. kubernetes网络模型 1.1. 基础原则 每个Pod都拥有一个独立的IP地址，而且假定所有Pod都在一个可以直接连通的、扁平的网络空间中，不管是否运行在同一Node上都可以通过Pod的IP来访问。 k8s中Pod的IP是最小粒度IP。同一个Pod内所有的容器共享一个网络堆栈，该模型称为IP-per-Pod模型。 Pod由docker0实际分配的IP，Pod内部看到的IP地址和端口与外部保持一致。同一个Pod内的不同容器共享网络，可以通过localhost来访问对方的端口，类似同一个VM内的不同进程。 IP-per-Pod模型从端口分配、域名解析、服务发现、负载均衡、应用配置等角度看，Pod可以看作是一台独立的VM或物理机。 1.2. k8s对集群的网络要求 所有容器都可以不用NAT的方式同别的容器通信。 所有节点都可以在不同NAT的方式下同所有容器通信，反之亦然。 容器的地址和别人看到的地址是同一个地址。 以上的集群网络要求可以通过第三方开源方案实现，例如flannel。 1.3. 网络架构图 图片 - 这里写图片描述 1.4. k8s集群IP概念汇总 由集群外部到集群内部： IP类型 说明 Proxy-IP 代理层公网地址IP，外部访问应用的网关服务器。[实际需要关注的IP] Service-IP Service的固定虚拟IP，Service-IP是内部，外部无法寻址到。 Node-IP 容器宿主机的主机IP。 Container-Bridge-IP 容器网桥（docker0）IP，容器的网络都需要通过容器网桥转发。 Pod-IP Pod的IP，等效于Pod中网络容器的Container-IP。 Container-IP 容器的IP，容器的网络是个隔离的网络空间。 2. kubernetes的网络实现 k8s网络场景 容器与容器之间的直接通信。 Pod与Pod之间的通信。 Pod到Service之间的通信。 集群外部与内部组件之间的通信。 2.1. Pod网络 Pod作为kubernetes的最小调度单元，Pod是容器的集合，是一个逻辑概念，Pod包含的容器都运行在同一个宿主机上，这些容器将拥有同样的网络空间，容器之间能够互相通信，它们能够在本地访问其它容器的端口。 实际上Pod都包含一个网络容器，它不做任何事情，只是用来接管Pod的网络，业务容器通过加入网络容器的网络从而实现网络共享。Pod网络本质上还是容器网络，所以Pod-IP就是网络容器的Container-IP。 一般将容器云平台的网络模型打造成一个扁平化网络平面，在这个网络平面内，Pod作为一个网络单元同Kubernetes Node的网络处于同一层级。 2.2. Pod内部容器之间的通信 同一个Pod之间的不同容器因为共享同一个网络命名空间，所以可以直接通过localhost直接通信。 2.3. Pod之间的通信 2.3.1. 同Node的Pod之间的通信 同一个Node内，不同的Pod都有一个全局IP，可以直接通过Pod的IP进行通信。Pod地址和docker0在同一个网段。 在pause容器启动之前，会创建一个虚拟以太网接口对（veth pair），该接口对一端连着容器内部的eth0 ，一端连着容器外部的vethxxx，vethxxx会绑定到容器运行时配置使用的网桥bridge0上，从该网络的IP段中分配IP给容器的eth0。 当同节点上的Pod-A发包给Pod-B时，包传送路线如下： pod-a的eth0—>pod-a的vethxxx—>bridge0—>pod-b的vethxxx—>pod-b的eth0 因为相同节点的bridge0是相通的，因此可以通过bridge0来完成不同pod直接的通信，但是不同节点的bridge0是不通的，因此不同节点的pod之间的通信需要将不同节点的bridge0给连接起来。 2.3.2. 不同Node的Pod之间的通信 不同的Node之间，Node的IP相当于外网IP，可以直接访问，而Node内的docker0和Pod的IP则是内网IP，无法直接跨Node访问。需要通过Node的网卡进行转发。 所以不同Node之间的通信需要达到两个条件： 对整个集群中的Pod-IP分配进行规划，不能有冲突（可以通过第三方开源工具来管理，例如flannel）。 将Node-IP与该Node上的Pod-IP关联起来，通过Node-IP再转发到Pod-IP。 不同节点的Pod之间的通信需要将不同节点的bridge0给连接起来。连接不同节点的bridge0的方式有好几种，主要有overlay和underlay，或常规的三层路由。 不同节点的bridge0需要不同的IP段，保证Pod IP分配不会冲突，节点的物理网卡eth0也要和该节点的网桥bridge0连接。因此，节点a上的pod-a发包给节点b上的pod-b，路线如下： 节点a上的pod-a的eth0—>pod-a的vethxxx—>节点a的bridge0—>节点a的eth0—> 节点b的eth0—>节点b的bridge0—>pod-b的vethxxx—>pod-b的eth0 图片 - 这里写图片描述 1. Pod间实现通信 例如：Pod1和Pod2（同主机），Pod1和Pod3(跨主机)能够通信 实现：因为Pod的Pod-IP是Docker网桥分配的，Pod-IP是同Node下全局唯一的。所以将不同Kubernetes Node的 Docker网桥配置成不同的IP网段即可。 2. Node与Pod间实现通信 例如：Node1和Pod1/ Pod2(同主机)，Pod3(跨主机)能够通信 实现：在容器集群中创建一个覆盖网络(Overlay Network)，联通各个节点，目前可以通过第三方网络插件来创建覆盖网络，比如Flannel和Open vSwitch等。 不同节点间的Pod访问也可以通过calico形成的Pod IP的路由表来解决。 2.4. Service网络 Service的就是在Pod之间起到服务代理的作用，对外表现为一个单一访问接口，将请求转发给Pod，Service的网络转发是Kubernetes实现服务编排的关键一环。Service都会生成一个虚拟IP，称为Service-IP， Kuberenetes Porxy组件负责实现Service-IP路由和转发，在容器覆盖网络之上又实现了虚拟转发网络。 Kubernetes Porxy实现了以下功能： 转发访问Service的Service-IP的请求到Endpoints(即Pod-IP)。 监控Service和Endpoints的变化，实时刷新转发规则。 负载均衡能力。 3. 开源的网络组件 3.1. Flannel 具体参考Flannel介绍 参考《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"network/pod-dns.html":{"url":"network/pod-dns.html","title":"Pod的DNS策略","keywords":"","body":"1. Pod的DNS策略2. Pod DNS的配置3. 自定义DNS服务3.1. 配置继承节点的DNS解析3.2. 配置CoreDNS3.3. 配置存根域和上游域名服务器4. 调试DNS问题4.1. 查看Coredns服务是否正常4.2. 查看/etc/resolv.conf4.3. nslookup查看解析报错1. Pod的DNS策略 可以在pod中定义dnsPolicy字段来设置dns的策略。 \"Default\": Pod 从运行所在的节点继承名称解析配置。就是该Pod的DNS配置会跟宿主机完全一致。 \"ClusterFirst\": 如果没有配置，即为默认的DNS策略。预先把kube-dns（或CoreDNS）的信息当作预设参数写入到该Pod内的DNS配置。与配置的集群域后缀不匹配的任何 DNS 查询（例如 \"www.kubernetes.io\"） 都会由 DNS 服务器转发到上游名称服务器。 \"ClusterFirstWithHostNet\": 对于以 hostNetwork 方式运行的 Pod，应将其 DNS 策略显式设置为 \"ClusterFirstWithHostNet\"。否则，以 hostNetwork 方式和 \"ClusterFirst\" 策略运行的 Pod 将会做出回退至 \"Default\" 策略的行为。 \"None\": 此设置允许 Pod 忽略 Kubernetes 环境中的 DNS 设置。Pod 会使用其 dnsConfig 字段所提供的 DNS 设置。 2. Pod DNS的配置 当 Pod 的 dnsPolicy 设置为 \"None\" 时，必须指定 dnsConfig 字段。 dnsConfig 字段中属性： nameservers：将用作于 Pod 的 DNS 服务器的 IP 地址列表。 最多可以指定 3 个 IP 地址。例如 coredns的Cluster IP。 searches：用于在 Pod 中查找主机名的 DNS 搜索域的列表。此属性是可选的。 options：可选的对象列表，其中每个对象可能具有 name 属性（必需）和 value 属性（可选）。 示例： apiVersion: v1 kind: Pod metadata: namespace: default name: dns-example spec: containers: - name: test image: nginx dnsPolicy: \"None\" dnsConfig: nameservers: - 1.2.3.4 searches: - ns1.svc.cluster-domain.example - my.dns.search.suffix options: - name: ndots value: \"2\" - name: edns0 通过以上配置，容器内的/etc/resolv.conf文件内容为： kubectl exec -it dns-example -- cat /etc/resolv.conf nameserver 1.2.3.4 search ns1.svc.cluster-domain.example my.dns.search.suffix options ndots:2 edns0 3. 自定义DNS服务 默认一般使用coredns来作为k8s的dns服务器。默认使用deployment的方式来运行coredns，会创建一个名为kube-dns的service，并用ClusterIP（默认为10.96.0.10）来作为集群内的pod的nameserver。 kubelet 使用 --cluster-dns= 标志将 DNS 解析器的信息传递给每个容器。使用 --cluster-domain= 标志配置本地域名。 可查看默认配置： # cat /var/lib/kubelet/config.yaml ... clusterDNS: - 10.96.0.10 clusterDomain: cluster.local 总结： 当没有给pod设置任何dns策略时，则默认使用ClusterFirst的策略，即nameserver的IP为coredns的ClusterIP。通过coredns来解析服务。 3.1. 配置继承节点的DNS解析 如果 Pod 的 dnsPolicy 设置为 default，则它将从 Pod 运行所在节点继承名称解析配置。 使用 kubelet 的 --resolv-conf 标志设置为宿主机的/etc/resolv.conf文件。 3.2. 配置CoreDNS 配置 apiVersion: v1 kind: ConfigMap metadata: name: coredns namespace: kube-system data: Corefi: | .:53 { errors health { lameduck 5s } ready kubernetes cluster.local in-addr.arpa ip6.arpa { pods insecure fallthrough in-addr.arpa ip6.arpa ttl 30 } prometheus :9153 forward . /etc/resolv.conf { max_concurrent 1000 } cache 30 loop reload loadbalance } 配置说明： Corefile 配置包括以下 CoreDNS 插件： errors：错误记录到标准输出。 health：在 http://localhost:8080/health 处提供 CoreDNS 的健康报告。 在这个扩展语法中，lameduck 会使此进程不健康，等待 5 秒后进程被关闭。 ready：在端口 8181 上提供的一个 HTTP 端点， 当所有能够表达自身就绪的插件都已就绪时，在此端点返回 200 OK。 kubernetes：CoreDNS 将基于服务和 Pod 的 IP 来应答 DNS 查询。 你可以在 CoreDNS 网站找到有关此插件的更多细节。 你可以使用 ttl 来定制响应的 TTL。默认值是 5 秒钟。TTL 的最小值可以是 0 秒钟， 最大值为 3600 秒。将 TTL 设置为 0 可以禁止对 DNS 记录进行缓存。 pods insecure 选项是为了与 kube-dns 向后兼容。 你可以使用 pods verified 选项，该选项使得仅在相同名字空间中存在具有匹配 IP 的 Pod 时才返回 A 记录。 如果你不使用 Pod 记录，则可以使用 pods disabled 选项。 prometheus：CoreDNS 的度量指标值以 Prometheus 格式（也称为 OpenMetrics）在 http://localhost:9153/metrics 上提供。 forward: 不在 Kubernetes 集群域内的任何查询都将转发到预定义的解析器 (/etc/resolv.conf)。 cache：启用前端缓存。 loop：检测简单的转发环，如果发现死循环，则中止 CoreDNS 进程。 reload：允许自动重新加载已更改的 Corefile。 编辑 ConfigMap 配置后，请等待两分钟，以使更改生效。 loadbalance：这是一个轮转式 DNS 负载均衡器， 它在应答中随机分配 A、AAAA 和 MX 记录的顺序。 3.3. 配置存根域和上游域名服务器 CoreDNS 能够使用 forward 插件配置存根域和上游域名服务器。 示例： 在 \"10.150.0.1\" 处运行了 Consul 域服务器， 且所有 Consul 名称都带有后缀 .consul.local。 consul.local:53 { errors cache 30 forward . 10.150.0.1 } 要显式强制所有非集群 DNS 查找通过特定的域名服务器（位于 172.16.0.1），可将 forward 指向该域名服务器，而不是 /etc/resolv.conf。 forward . 172.16.0.1 完整示例； apiVersion: v1 kind: ConfigMap metadata: name: coredns namespace: kube-system data: Corefile: | .:53 { errors health kubernetes cluster.local in-addr.arpa ip6.arpa { pods insecure fallthrough in-addr.arpa ip6.arpa } prometheus :9153 forward . 172.16.0.1 cache 30 loop reload loadbalance } consul.local:53 { errors cache 30 forward . 10.150.0.1 } 4. 调试DNS问题 创建一个调试的pod apiVersion: v1 kind: Pod metadata: name: dnsutils namespace: default spec: containers: - name: dnsutils image: registry.k8s.io/e2e-test-images/jessie-dnsutils:1.3 command: - sleep - \"infinity\" imagePullPolicy: IfNotPresent restartPolicy: Always 部署调试pod 4.1. 查看Coredns服务是否正常 kubectl get svc --namespace=kube-system NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE ... kube-dns ClusterIP 10.0.0.10 53/UDP,53/TCP 1h ... 4.2. 查看/etc/resolv.conf 查看容器内dns配置是否符合预期。 kubectl exec -ti dnsutils -- cat /etc/resolv.conf 4.3. nslookup查看解析报错 kubectl exec -i -t dnsutils -- nslookup kubernetes.default Server: 10.0.0.10 Address 1: 10.0.0.10 Name: kubernetes.default Address 1: 10.0.0.1 参考： Service 与 Pod 的 DNS | Kubernetes 自定义 DNS 服务 | Kubernetes 调试 DNS 问题 | Kubernetes 使用 CoreDNS 进行服务发现 | Kubernetes 自动扩缩集群 DNS 服务 | Kubernetes resolv.conf(5) - Linux manual page dns/specification.md at master · kubernetes/dns · GitHub deployment/CoreDNS-k8s_version.md at master · coredns/deployment · GitHub forward Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"network/flannel/_index.html":{"url":"network/flannel/_index.html","title":"网络插件","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"network/flannel/flannel-introduction.html":{"url":"network/flannel/flannel-introduction.html","title":"Flannel介绍","keywords":"","body":"1. flannel是什么（what）1.1. 概述1.2. 补充知识点1、覆盖网络[overlay network]2、路由2. 为什么使用flannel（why）3. 如何实现flannel（how）3.1. flannel原理图3.2. 实现说明1、UDP封装2、为docker分配不同的IP段3、路由规则3.3. flannel的安装与配置1、安装2、配置3、初始化flannel的etcd配置1. flannel是什么（what） 1.1. 概述 Flannel是CoreOS团队针对Kubernetes设计的一个网络规划服务，简单来说，它的功能是让集群中的不同节点主机创建的Docker容器都具有全集群唯一的虚拟IP地址。 Flannel官网：https://github.com/coreos/flannel 1.2. 补充知识点 1、覆盖网络[overlay network] 运行在一个网上的网（应用层网络），并不依靠ip地址来传递消息，而是采用一种映射机制，把ip地址和identifiers做映射来资源定位。 2、路由 互联网是由路由器连接的网络组合而成，路由器按照路由表、路由协议等机制实现对数据包正确地转发，从而到达目标主机。路由器根据数据包中目标主机的IP地址和路由控制表比较得出下一个接收数据的路由器。 1）静态路由：事先设置好路由器和主机中的路由表信息。 2）动态路由：让路由协议在运行中自动修改并设置路由表信息。 图片 - 动态路由 2. 为什么使用flannel（why） 在默认的Docker配置中，每个节点上的Docker服务会分别负责所在节点容器的IP分配。这样导致的一个问题是，不同节点上容器可能获得相同的内外IP地址。 Flannel的设计目的就是为集群中的所有节点重新规划IP地址的使用规则，从而使得不同节点上的容器能够获得“同属一个内网”且”不重复的”IP地址，并让属于不同节点上的容器能够直接通过内网IP通信。 3. 如何实现flannel（how） Flannel实质上是一种“覆盖网络(overlay network)”，也就是将TCP数据包装在另一种网络包里面进行路由转发和通信，目前已经支持UDP、VxLAN、AWS VPC和GCE路由等数据转发方式，默认的节点间数据通信方式是UDP转发。 3.1. flannel原理图 图片 - flannel 数据从源容器中发出后，经由所在主机的docker0虚拟网卡转发到flannel0虚拟网卡，这是个P2P的虚拟网卡，flanneld服务监听在网卡的另外一端。 Flannel通过Etcd服务维护了一张节点间的路由表。 源主机的flanneld服务将原本的数据内容UDP封装后根据自己的路由表投递给目的节点的flanneld服务，数据到达以后被解包，然后直 接进入目的节点的flannel0虚拟网卡，然后被转发到目的主机的docker0虚拟网卡，最后就像本机容器通信一下的有docker0路由到达目标容 器。 3.2. 实现说明 1、UDP封装 原始数据是在起始节点的Flannel服务上进行UDP封装的，投递到目的节点后就被另一端的Flannel服务还原成了原始的数据包，两边的Docker服务都感觉不到这个过程的存在。 UDP的数据内容部分其实是另一个ICMP（也就是ping命令）的数据包。 图片 - UDP封装 2、为docker分配不同的IP段 Flannel通过Etcd分配了每个节点可用的IP地址段后，偷偷的修改了Docker的启动参数。 图片 - docker启动参数 注意其中的“--bip=172.17.18.1/24”这个参数，它限制了所在节点容器获得的IP范围。 这个IP范围是由Flannel自动分配的，由Flannel通过保存在Etcd服务中的记录确保它们不会重复。 3、路由规则 1）数据发送节点的路由表 图片 - 数据发送节点路由表 2）数据接收节点的路由表 图片 - 数据接收节点路由表 例如现在有一个数据包要从IP为172.17.18.2的容器发到IP为172.17.46.2的容器。根据数据发送节点的路由表，它只与 172.17.0.0/16匹配这条记录匹配，因此数据从docker0出来以后就被投递到了flannel0。同理在目标节点，由于投递的地址是一个容 器，因此目的地址一定会落在docker0对于的172.17.46.0/24这个记录上，自然的被投递到了docker0网卡。 3.3. flannel的安装与配置 1、安装 wget http:///flannel/flannel-0.2.0-10.el7.x86_64.rpm yum localinstall -y flannel-0.2.0-10.el7.x86_64.rpm 2、配置 vi /etc/sysconfig/flanneld # Flanneld configuration options # etcd url location. Point this to the server where etcd runs FLANNEL_ETCD=\"http://127.0.0.1:4001\" # etcd config key. This is the configuration key that flannel queries # For address range assignment FLANNEL_ETCD_KEY=\"/xxx/flannel/product/network\" # Any additional options that you want to pass FLANNEL_OPTIONS=\" -iface=eth0\" 3、初始化flannel的etcd配置 etcdctl set /xxx/flannel/network/config '{ \"Network\": \"10.0.0.0/16\", \"Backend\": { \"Type\": \"vxlan\" } }' Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"network/cni/_index.html":{"url":"network/cni/_index.html","title":"CNI","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"network/cni/cni.html":{"url":"network/cni/cni.html","title":"CNI接口介绍","keywords":"","body":"1. CNI（Container Network Interface）2. CNI配置文件格式2.1. 主配置的字段2.2. 插件配置字段2.3. 配置文件示例3. CNI插件3.1. 安装插件3.2. 插件分类4. CNI插件接口4.1. ADD接口：添加容器网络4.2. DEL接口：删除容器网络4.3. CHECK接口：检查容器网络4.4. VERSION接口：输出CNI的版本1. CNI（Container Network Interface） CNI（Container Network Interface）即容器网络接口，通过约定统一的容器网络接口，从而kubelet可以通过这个标准的API来调用不同的网络插件实现不同的网络功能。 kubelet启动参数--network-plugin=cni来指定CNI插件，kubelet从--cni-conf-dir （默认是 /etc/cni/net.d） 读取文件并使用 该文件中的 CNI 配置来设置各个 Pod 的网络。 CNI 配置文件必须与 CNI 规约 匹配，并且配置所引用的所有所需的 CNI 插件都应存在于 --cni-bin-dir（默认是 /opt/cni/bin）下。如果有多个CNI配置文件，kubelet 将会使用按文件名的字典顺序排列 的第一个作为配置文件。 CNI规范定义： 网络配置文件的格式 容器runtime与CNI插件的通信协议 基于提供的配置执行网络插件的步骤 网络插件调用其他功能插件的步骤 插件返回给runtime结果的数据格式 2. CNI配置文件格式 CNI配置文件的格式为JSON格式，配置文件的默认路径：/etc/cni/net.d。插件二进制默认的路径为：/opt/cni/bin。 2.1. 主配置的字段 cniVersion (string)：CNI规范使用的版本，例如版本为0.4.0。 name (string)：目标网络的名称。 disableCheck (boolean)：关闭CHECK操作。 plugins (list)：CNI插件列表及插件配置。 2.2. 插件配置字段 根据不同的插件，插件配置所需的字段不同。 必选字段： type (string)：节点上插件二进制的名称，比如bridge，sriov，macvlan等。 可选字段： capabilities (dictionary) ipMasq (boolean)：为目标网络配上Outbound Masquerade(地址伪装)，即：由容器内部通过网关向外发送数据包时，对数据包的源IP地址进行修改。 当我们的容器以宿主机作为网关时，这个参数是必须要设置的。否则，从容器内部发出的数据包就没有办法通过网关路由到其他网段。因为容器内部的IP地址无法被目标网段识别，所以这些数据包最终会被丢弃掉。 ipam (dictionary)：IPAM(IP Adderss Management)即IP地址管理，提供了一系列方法用于对IP和路由进行管理。它对应的是由CNI提供的一组标准IPAM插件，比如像host-local，dhcp，static等。比如文中用到的bridge插件，会调用我们所指定的IPAM插件，实现对网络设备IP地址的分配和管理。**如果是自己开发的ipam插件，则相关的入参可以自己定义和实现。 以下以host-local为例说明。 type：指定所用IPAM插件的名称，在我们的例子里，用的是host-local。 subnet：为目标网络分配网段，包括网络ID和子网掩码，以CIDR形式标记。在我们的例子里为10.15.10.0/24，也就是目标网段为10.15.10.0，子网掩码为255.255.255.0。 routes：用于指定路由规则，插件会为我们在容器的路由表里生成相应的规则。其中，dst表示希望到达的目标网段，以CIDR形式标记。gw对应网关的IP地址，也就是要到达目标网段所要经过的“next hop(下一跳)”。如果省略gw的话，那么插件会自动帮我们选择默认网关。在我们的例子里，gw选择的是默认网关，而dst为0.0.0.0/0则代表“任何网络”，表示数据包将通过默认网关发往任何网络。实际上，这对应的是一条默认路由规则，即：当所有其他路由规则都不匹配时，将选择该路由。 rangeStart：允许分配的IP地址范围的起始值 rangeEnd：允许分配的IP地址范围的结束值 gateway：为网关（也就是我们将要在宿主机上创建的bridge）指定的IP地址。如果省略的话，那么插件会自动从允许分配的IP地址范围内选择起始值作为网关的IP地址。 dns (dictionary, optional)：dns配置 nameservers (list of strings, optional) domain (string, optional) search (list of strings, optional) options (list of strings, optional) 2.3. 配置文件示例 $ mkdir -p /etc/cni/net.d $ cat >/etc/cni/net.d/10-mynet.conf 3. CNI插件 3.1. 安装插件 安装CNI二进制插件，插件下载地：https://github.com/containernetworking/plugins/releases # 下载二进制 wget https://github.com/containernetworking/plugins/releases/download/v1.1.0/cni-plugins-linux-amd64-v1.1.0.tgz # 解压文件 tar -zvxf cni-plugins-linux-amd64-v1.1.0.tgz -C /opt/cni/bin/ # 查看解压文件 # ll -h 总用量 63M -rwxr-xr-x 1 root root 3.7M 2月 24 01:01 bandwidth -rwxr-xr-x 1 root root 4.1M 2月 24 01:01 bridge -rwxr-xr-x 1 root root 9.3M 2月 24 01:01 dhcp -rwxr-xr-x 1 root root 4.2M 2月 24 01:01 firewall -rwxr-xr-x 1 root root 3.7M 2月 24 01:01 host-device -rwxr-xr-x 1 root root 3.1M 2月 24 01:01 host-local -rwxr-xr-x 1 root root 3.8M 2月 24 01:01 ipvlan -rwxr-xr-x 1 root root 3.2M 2月 24 01:01 loopback -rwxr-xr-x 1 root root 3.8M 2月 24 01:01 macvlan -rwxr-xr-x 1 root root 3.6M 2月 24 01:01 portmap -rwxr-xr-x 1 root root 4.0M 2月 24 01:01 ptp -rwxr-xr-x 1 root root 3.4M 2月 24 01:01 sbr -rwxr-xr-x 1 root root 2.7M 2月 24 01:01 static -rwxr-xr-x 1 root root 3.3M 2月 24 01:01 tuning -rwxr-xr-x 1 root root 3.8M 2月 24 01:01 vlan -rwxr-xr-x 1 root root 3.4M 2月 24 01:01 vrf 3.2. 插件分类 参考：https://www.cni.dev/plugins/current/ 分类 插件 说明 main bridge Creates a bridge, adds the host and the container to it ipvlan Adds an ipvlan interface in the container macvlan Creates a new MAC address, forwards all traffic to that to the container ptp Creates a veth pair host-device Moves an already-existing device into a container vlan Creates a vlan interface off a master IPAM dhcp Runs a daemon on the host to make DHCP requests on behalf of a container host-local Maintains a local database of allocated IPs static Allocates static IPv4/IPv6 addresses to containers meta tuning Changes sysctl parameters of an existing interface portmap An iptables-based portmapping plugin. Maps ports from the host’s address space to the container bandwidth Allows bandwidth-limiting through use of traffic control tbf (ingress/egress) sbr A plugin that configures source based routing for an interface (from which it is chained) firewall A firewall plugin which uses iptables or firewalld to add rules to allow traffic to/from the container 4. CNI插件接口 具体可参考：https://github.com/containernetworking/cni/blob/master/SPEC.md#cni-operations CNI定义的接口操作有： ADD：添加容器网络，在容器启动时调用。 DEL：删除容器网络，在容器删除时调用。 CHECK：检查容器网络是否正常。 VERSION：显示插件版本。 这些操作通过CNI_COMMAND环境变量来传递给CNI插件二进制。 其中环境变量包括： CNI_COMMAND：命令操作，包括 ADD, DEL, CHECK, or VERSION。 CNI_CONTAINERID:容器的ID，有runtime分配，不为空。 CNI_NETNS:容器的网络命名空间，命名空间路径，例如：/run/netns/[nsname] CNI_IFNAME:容器内的网卡名称。 CNI_ARGS:其他参数。 CNI_PATH:CNI插件二进制的路径。 4.1. ADD接口：添加容器网络 在容器的网络命名空间CNI_NETNS中创建CNI_IFNAME网卡设备，或者调整网卡配置。 必选参数： CNI_COMMAND CNI_CONTAINERID CNI_NETNS CNI_IFNAME 可选参数： CNI_ARGS CNI_PATH 4.2. DEL接口：删除容器网络 删除容器网络命名空间CNI_NETNS中的容器网卡CNI_IFNAME，或者撤销ADD修改操作。 必选参数： CNI_COMMAND CNI_CONTAINERID CNI_IFNAME 可选参数： CNI_NETNS CNI_ARGS CNI_PATH 4.3. CHECK接口：检查容器网络 4.4. VERSION接口：输出CNI的版本 参考： https://www.cni.dev/docs/spec/ https://github.com/containernetworking/cni https://github.com/containernetworking/cni/blob/spec-v0.4.0/SPEC.md https://kubernetes.io/zh/docs/concepts/extend-kubernetes/compute-storage-net/network-plugins/ https://github.com/containernetworking/plugins/tree/master/plugins https://www.cni.dev/plugins/current/ https://cloud.tencent.com/developer/news/600713 配置CNI插件 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"network/cni/macvlan.html":{"url":"network/cni/macvlan.html","title":"Macvlan介绍","keywords":"","body":"1. 简介2. 四种工作模式2.1. VEPA (Virtual Ethernet Port Aggregator)2.2. Bridge mode2.3. Private mode2.4. Passthru mode1. 简介 macvlan可以看做是物理接口eth（父接口）的子接口，每个macvlan都拥有独立的mac地址，可以被绑定IP作为正常的网卡接口使用。通过这个特性，可以实现在一个物理网络设备绑定多个IP，每个IP拥有独立的mac地址。该特性经常被应用在容器虚拟化中（容器可以配置macvlan的网络，将macvlan interface移动到容器的namespace中）。 示意图： 2. 四种工作模式 2.1. VEPA (Virtual Ethernet Port Aggregator) VEPA为默认的工作模式，该模式下，所有macvlan发出的流量都会经过父接口，不管目的地是否与该macvlan共用一个父接口。 2.2. Bridge mode 该bridge模式类似于传统的网桥模式，拥有相同父接口的macvlan可以直接进行通信，不需要将数据发给父接口转发。该模式下不需要交换机支持hairpin模式，性能比VEPA模式好。另外相对于传统的网桥模式，该模式不需要学习mac地址，不需要STP，使得其性能比传统的网桥性能好得多。但是如果父接口down掉，则所有子接口也会down，同时无法通信。 2.3. Private mode 该模式是VEPA模式的增强版， 2.4. Passthru mode . 待完善 参考： https://backreference.org/2014/03/20/some-notes-on-macvlanmacvtap/ https://github.com/containernetworking/plugins/tree/master/plugins/main/macvlan https://github.com/containernetworking/plugins/blob/master/plugins/main/macvlan/macvlan.go http://wikibon.org/wiki/v/Edge_Virtual_Bridging Linux 虚拟网卡技术：Macvlan http://hicu.be/bridge-vs-macvlan http://hicu.be/docker-networking-macvlan-bridge-mode-configuration Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"storage/volume/_index.html":{"url":"storage/volume/_index.html","title":"存储卷概念","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"storage/volume/volume.html":{"url":"storage/volume/volume.html","title":"Volume","keywords":"","body":"1. volume概述2. volume类型2.1. emptyDir2.2. hostPath2.2.1. 同步文件变化到容器内2.3. configMap2.4. cephfs2.5. nfs2.6. persistentVolumeClaim1. volume概述 容器上的文件生命周期同容器的生命周期一致，即容器挂掉之后，容器将会以最初镜像中的文件系统内容启动，之前容器运行时产生的文件将会丢失。 Pod的volume的生命周期同Pod的生命周期一致，当Pod被删除的时候，对应的volume才会被删除。即Pod中的容器重启时，之前的文件仍可以保存。 容器中的进程看到的是由其 Docker 镜像和卷组成的文件系统视图。 Pod volume的使用方式 Pod 中的每个容器都必须独立指定每个卷的挂载位置，需要给Pod配置volume相关参数。 Pod的volume关键字段如下： spec.volumes：提供怎样的数据卷 spec.containers.volumeMounts：挂载到容器的什么路径 2. volume类型 2.1. emptyDir 1、特点 会创建emptyDir对应的目录，默认为空（如果该目录原来有文件也会被重置为空） Pod中的不同容器可以在目录中读写相同文件（即Pod中的不同容器可以通过该方式来共享文件） 当Pod被删除，emptyDir 中的数据将被永久删除，如果只是Pod挂掉该数据还会保留 2、使用场景 不同容器之间共享文件（例如日志采集等） 暂存空间，例如用于基于磁盘的合并排序 用作长时间计算崩溃恢复时的检查点 3、示例 apiVersion: v1 kind: Pod metadata: name: test-pd spec: containers: - image: k8s.gcr.io/test-webserver name: test-container volumeMounts: - mountPath: /cache name: cache-volume volumes: - name: cache-volume emptyDir: {} 2.2. hostPath 1、特点 会将宿主机的目录或文件挂载到Pod中 2、使用场景 运行需要访问 Docker 内部的容器；使用 /var/lib/docker 的 hostPath 在容器中运行 cAdvisor；使用 /dev/cgroups 的 hostPath 其他使用到宿主机文件的场景 hostPath的type字段 值 行为 空字符串（默认）用于向后兼容，这意味着在挂载 hostPath 卷之前不会执行任何检查。 DirectoryOrCreate 如果在给定的路径上没有任何东西存在，那么将根据需要在那里创建一个空目录，权限设置为 0755，与 Kubelet 具有相同的组和所有权。 Directory 给定的路径下必须存在目录 FileOrCreate 如果在给定的路径上没有任何东西存在，那么会根据需要创建一个空文件，权限设置为 0644，与 Kubelet 具有相同的组和所有权。 File 给定的路径下必须存在文件 Socket 给定的路径下必须存在 UNIX 套接字 CharDevice 给定的路径下必须存在字符设备 BlockDevice 给定的路径下必须存在块设备 注意事项 由于每个节点上的文件都不同，具有相同配置的 pod 在不同节点上的行为可能会有所不同 当 Kubernetes 按照计划添加资源感知调度时，将无法考虑 hostPath 使用的资源 在底层主机上创建的文件或目录只能由 root 写入。您需要在特权容器中以 root 身份运行进程，或修改主机上的文件权限以便写入 hostPath 卷 3、示例 apiVersion: v1 kind: Pod metadata: name: test-pd spec: containers: - image: k8s.gcr.io/test-webserver name: test-container volumeMounts: - mountPath: /test-pd name: test-volume volumes: - name: test-volume hostPath: # directory location on host path: /data # this field is optional type: Directory 2.2.1. 同步文件变化到容器内 hostPath的挂载方式可以挂载目录和文件两种格式，如果使用文件挂载的方式，通过简单的vi等命令修改宿主机的文件，并不会实时同步到容器内的映射文件。而需要对容器进行重启的操作才可以把文件的修改内容同步到文件中，但生产的容器一般不建议执行重启的操作。因此我们可以通过以下的方式来避免这个问题的发生。 以上文件不同步的本质原因是容器在初次挂载的时候使用了宿主机的文件的inode number进行标识，而vi等操作会导致文件的inode number发生变化，所以当宿主机文件的inode number发生变化，容器内并不会发生变化，为了保持文件内容一致，则需要保持修改文件的同时文件的inode number不变。那么我们可以使用 cat 或echo 命令覆盖文件的内容则inode number不会发生变化。 示例： containers: volumeMounts: - mountPath: /etc/hosts name: hosts readOnly: true volumes: - hostPath: path: /etc/hosts type: FileOrCreate name: hosts 例如，以上的案例是通过挂载宿主机的/etc/hosts文件来映射到容器，如果想修改宿主机的hosts文件来同步容器内的hosts文件，可以通过以下的方式: # 查看文件的inode ls -i /etc/hosts 39324780 /etc/hosts # 追加记录 echo \"1.1.1.1 xxx.com\" >> /etc/hosts # 替换内容 sed 's/1.1.1.1/2.2.2.2/g' /etc/hosts > temp.txt cat temp.txt > /etc/hosts # 查看宿主机和容器内的inode号都没有发生变化 # crictl exec -it 20891de31a4a6 sh /var/www/html # ls -i /etc/hosts 39324780 /etc/hosts 2.3. configMap configMap提供了一种给Pod注入配置文件的方式，配置文件内容存储在configMap对象中，如果Pod使用configMap作为volume的类型，需要先创建configMap的对象。 示例 apiVersion: v1 kind: Pod metadata: name: configmap-pod spec: containers: - name: test image: busybox volumeMounts: - name: config-vol mountPath: /etc/config volumes: - name: config-vol configMap: name: log-config items: - key: log_level path: log_level 2.4. cephfs cephfs的方式将Pod的存储挂载到ceph集群中，通过外部存储的方式持久化Pod的数据（即当Pod被删除数据仍可以存储在ceph集群中），前提是先部署和维护好一个ceph集群。 示例 apiVersion: v1 kind: Pod metadata: name: cephfs spec: containers: - name: cephfs-rw image: kubernetes/pause volumeMounts: - mountPath: \"/mnt/cephfs\" name: cephfs volumes: - name: cephfs cephfs: monitors: - 10.16.154.78:6789 - 10.16.154.82:6789 - 10.16.154.83:6789 # by default the path is /, but you can override and mount a specific path of the filesystem by using the path attribute # path: /some/path/in/side/cephfs user: admin secretFile: \"/etc/ceph/admin.secret\" readOnly: true 更多可参考 CephFS 示例。 2.5. nfs nfs的方式类似cephfs，即将Pod数据存储到NFS集群中，具体可参考NFS示例。 2.6. persistentVolumeClaim persistentVolumeClaim 卷用于将PersistentVolume挂载到容器中。PersistentVolumes 是在用户不知道特定云环境的细节的情况下“声明”持久化存储（例如 GCE PersistentDisk 或 iSCSI 卷）的一种方式。 参考文章： https://kubernetes.io/docs/concepts/storage/volumes/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"storage/volume/persistent-volume.html":{"url":"storage/volume/persistent-volume.html","title":"Persistent Volume","keywords":"","body":"1. PV概述2. PV和PVC的生命周期2.1. 配置（Provision）2.2. 绑定2.3. 使用2.4. 回收3. PV的类型4. PV的属性4.1. Capacity4.2. Volume Mode4.3. Access Modes4.4. Class4.5. Reclaim Policy4.6. Mount Options4.7. Phase1. PV概述 PersistentVolume（简称PV） 是 Volume 之类的卷插件，也是集群中的资源，但独立于Pod的生命周期（即不会因Pod删除而被删除），不归属于某个Namespace。 2. PV和PVC的生命周期 2.1. 配置（Provision） 有两种方式来配置 PV：静态或动态。 1、静态 手动创建PV，可供k8s集群中的对象消费。 2、动态 可以通过StorageClass和具体的Provisioner（例如nfs-client-provisioner）来动态地创建和删除PV。 2.2. 绑定 在动态配置的情况下，用户创建了特定的PVC，k8s会监听新的PVC，并寻找匹配的PV绑定。一旦绑定后，这种绑定是排他性的，PVC和PV的绑定是一对一的映射。 2.3. 使用 Pod 使用PVC作为卷。集群检查PVC以查找绑定的卷并为集群挂载该卷。用户通过在 Pod 的 volume 配置中包含 persistentVolumeClaim 来调度 Pod 并访问用户声明的 PV。 2.4. 回收 PV的回收策略可以设定PVC在释放后如何处理对应的Volume，目前有 Retained， Recycled和Deleted三种策略。 1、保留（Retain） 保留策略允许手动回收资源，当删除PVC的时候，PV仍然存在，可以通过以下步骤回收卷： 删除PV 手动清理外部存储的数据资源 手动删除或重新使用关联的存储资产 2、回收（Resycle） 该策略已废弃，推荐使用dynamic provisioning 回收策略会在 volume上执行基本擦除（rm -rf / thevolume / *），可被再次声明使用。 3、删除（Delete） 删除策略，当发生删除操作的时候，会从k8s集群中删除PV对象，并执行外部存储资源的删除操作（根据不同的provisioner定义的删除逻辑不同，有的是重命名）。 动态配置的卷继承其StorageClass的回收策略，默认为Delete，即当用户删除PVC的时候，会自动执行PV的删除策略。 如果要修改PV的回收策略，可执行以下命令： # Get pv kubectl get pv # Change policy to Retaion kubectl patch pv -p ‘{“spec”:{“persistentVolumeReclaimPolicy”:“Retain”}}’ 3. PV的类型 PersistentVolume 类型以插件形式实现。以下仅列部分常用类型： GCEPersistentDisk AWSElasticBlockStore NFS RBD (Ceph Block Device) CephFS Glusterfs 4. PV的属性 每个PV 配置中都包含一个 sepc规格字段和一个 status 卷状态字段。 apiVersion: v1 kind: PersistentVolume metadata: annotations: pv.kubernetes.io/provisioned-by: fuseim.pri/ifs creationTimestamp: 2018-07-12T06:46:48Z name: default-test-web-0-pvc-58cf5ec1-859f-11e8-bb61-005056b83985 resourceVersion: \"100163256\" selfLink: /api/v1/persistentvolumes/default-test-web-0-pvc-58cf5ec1-859f-11e8-bb61-005056b83985 uid: 59796ba3-859f-11e8-9c50-c81f66bcff65 spec: accessModes: - ReadWriteOnce capacity: storage: 2Gi volumeMode: Filesystem claimRef: apiVersion: v1 kind: PersistentVolumeClaim name: test-web-0 namespace: default resourceVersion: \"100163248\" uid: 58cf5ec1-859f-11e8-bb61-005056b83985 nfs: path: /data/nfs-storage/default-test-web-0-pvc-58cf5ec1-859f-11e8-bb61-005056b83985 server: 172.16.201.54 persistentVolumeReclaimPolicy: Delete storageClassName: managed-nfs-storage mountOptions: - hard - nfsvers=4.1 status: phase: Bound 4.1. Capacity 给PV设置特定的存储容量，更多 capacity 可参考Kubernetes 资源模型 。 4.2. Volume Mode volumeMode 的有效值可以是Filesystem或Block。如果未指定，volumeMode 将默认为Filesystem。 4.3. Access Modes 访问模式包括： ReadWriteOnce——该卷可以被单个节点以读/写模式挂载 ReadOnlyMany——该卷可以被多个节点以只读模式挂载 ReadWriteMany——该卷可以被多个节点以读/写模式挂载 在命令行中，访问模式缩写为： RWO - ReadWriteOnce ROX - ReadOnlyMany RWX - ReadWriteMany 一个卷一次只能使用一种访问模式挂载，即使它支持很多访问模式。 以下只列举部分常用插件： Volume 插件 ReadWriteOnce ReadOnlyMany ReadWriteMany AWSElasticBlockStore ✓ - - CephFS ✓ ✓ ✓ GCEPersistentDisk ✓ ✓ - Glusterfs ✓ ✓ ✓ HostPath ✓ - - NFS ✓ ✓ ✓ RBD ✓ ✓ - ... - 4.4. Class PV可以指定一个StorageClass来动态绑定PV和PVC，其中通过 storageClassName 属性来指定具体的StorageClass，如果没有指定该属性的PV，它只能绑定到不需要特定类的 PVC。 4.5. Reclaim Policy 回收策略包括： Retain（保留）——手动回收 Recycle（回收）——基本擦除（rm -rf /thevolume/*） Delete（删除）——关联的存储资产（例如 AWS EBS、GCE PD、Azure Disk 和 OpenStack Cinder 卷）将被删除 当前，只有 NFS 和 HostPath 支持回收策略。AWS EBS、GCE PD、Azure Disk 和 Cinder 卷支持删除策略。 4.6. Mount Options Kubernetes 管理员可以指定在节点上为挂载持久卷指定挂载选项。 注意：不是所有的持久化卷类型都支持挂载选项。 支持挂载选项常用的类型有： GCEPersistentDisk AWSElasticBlockStore AzureFile AzureDisk NFS RBD （Ceph Block Device） CephFS Cinder （OpenStack 卷存储） Glusterfs 4.7. Phase PV可以处于以下的某种状态： Available（可用）——一块空闲资源还没有被任何声明绑定 Bound（已绑定）——卷已经被声明绑定 Released（已释放）——声明被删除，但是资源还未被集群重新声明 Failed（失败）——该卷的自动回收失败 命令行会显示绑定到 PV 的 PVC 的名称。 参考文章： https://kubernetes.io/docs/concepts/storage/persistent-volumes/ https://kubernetes.io/docs/tasks/administer-cluster/change-pv-reclaim-policy/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"storage/volume/persistent-volume-claim.html":{"url":"storage/volume/persistent-volume-claim.html","title":"Persistent Volume Claim","keywords":"","body":"1. PVC概述2. PVC的属性2.1. accessModes2.2. volumeMode2.3. resources2.4. selector2.5. storageClassName3. 将PVC作为Volume1. PVC概述 PersistentVolumeClaim（简称PVC）是用户存储的请求，PVC消耗PV的资源，可以请求特定的大小和访问模式，需要指定归属于某个Namespace，在同一个Namespace的Pod才可以指定对应的PVC。 当需要不同性质的PV来满足存储需求时，可以使用StorageClass来实现。 每个 PVC 中都包含一个 spec 规格字段和一个 status 声明状态字段。 kind: PersistentVolumeClaim apiVersion: v1 metadata: name: myclaim spec: accessModes: - ReadWriteOnce volumeMode: Filesystem resources: requests: storage: 8Gi storageClassName: slow selector: matchLabels: release: \"stable\" matchExpressions: - {key: environment, operator: In, values: [dev]} 2. PVC的属性 2.1. accessModes 对应存储的访问模式，例如：ReadWriteOnce。 2.2. volumeMode 对应存储的数据卷模式，例如：Filesystem。 2.3. resources 声明可以请求特定数量的资源。相同的资源模型适用于Volume和PVC。 2.4. selector 声明label selector，只有标签与选择器匹配的卷可以绑定到声明。 matchLabels：volume 必须有具有该值的标签 matchExpressions：条件列表，通过条件表达式筛选匹配的卷。有效的运算符包括 In、NotIn、Exists 和 DoesNotExist。 2.5. storageClassName 通过storageClassName参数来指定使用对应名字的StorageClass，只有所请求的类与 PVC 具有相同 storageClassName 的 PV 才能绑定到 PVC。 PVC可以不指定storageClassName，或者将该值设置为空，如果打开了准入控制插件，并且指定一个默认的 StorageClass，则PVC会使用默认的StorageClass，否则就绑定到没有StorageClass的 PV上。 之前使用注解 volume.beta.kubernetes.io/storage-class 而不是 storageClassName 属性。这个注解仍然有效，但是在未来的 Kubernetes 版本中不会支持。 3. 将PVC作为Volume 将PVC作为Pod的Volume，PVC与Pod需要在同一个命名空间下，其实Pod的声明如下： kind: Pod apiVersion: v1 metadata: name: mypod spec: containers: - name: myfrontend image: dockerfile/nginx volumeMounts: - mountPath: \"/var/www/html\" name: mypd volumes: - name: mypd persistentVolumeClaim: # 使用PVC claimName: myclaim PersistentVolumes 绑定是唯一的，并且由于 PersistentVolumeClaims 是命名空间对象，因此只能在一个命名空间内挂载具有“多个”模式（ROX、RWX）的PVC。 参考文章： https://kubernetes.io/docs/concepts/storage/persistent-volumes/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"storage/volume/storage-class.html":{"url":"storage/volume/storage-class.html","title":"Storage Class","keywords":"","body":"1. StorageClass概述2. StorageClass的属性2.1. Provisioner（存储分配器）2.2. Reclaim Policy（回收策略）2.3. Mount Options（挂载选项）2.4. 参数1. StorageClass概述 StorageClass提供了一种描述存储类（class）的方法，不同的class可能会映射到不同的服务质量等级和备份策略或其他策略等。 StorageClass 对象中包含 provisioner、parameters 和 reclaimPolicy 字段，当需要动态分配 PersistentVolume 时会使用到。当创建 StorageClass 对象时，设置名称和其他参数，一旦创建了对象就不能再对其更新。也可以为没有申请绑定到特定 class 的 PVC 指定一个默认的 StorageClass 。 StorageClass对象文件 kind: StorageClass apiVersion: storage.k8s.io/v3 metadata: name: standard provisioner: kubernetes.io/aws-ebs parameters: type: gp2 reclaimPolicy: Retain mountOptions: - debug 2. StorageClass的属性 2.1. Provisioner（存储分配器） Storage class 有一个分配器（provisioner），用来决定使用哪个卷插件分配 PV，该字段必须指定。可以指定内部分配器，也可以指定外部分配器。外部分配器的代码地址为： kubernetes-incubator/external-storage，其中包括NFS和Ceph等。 2.2. Reclaim Policy（回收策略） 可以通过reclaimPolicy字段指定创建的Persistent Volume的回收策略，回收策略包括：Delete 或者 Retain，没有指定默认为Delete。 2.3. Mount Options（挂载选项） 由 storage class 动态创建的 Persistent Volume 将使用 class 中 mountOptions 字段指定的挂载选项。 2.4. 参数 Storage class 具有描述属于 storage class 卷的参数。取决于分配器，可以接受不同的参数。 当参数被省略时，会使用默认值。 例如以下使用Ceph RBD kind: StorageClass apiVersion: storage.k8s.io/v3 metadata: name: fast provisioner: kubernetes.io/rbd parameters: monitors: 30.36.353.305:6789 adminId: kube adminSecretName: ceph-secret adminSecretNamespace: kube-system pool: kube userId: kube userSecretName: ceph-secret-user fsType: ext4 imageFormat: \"2\" imageFeatures: \"layering\" 对应的参数说明 monitors：Ceph monitor，逗号分隔。该参数是必需的。 adminId：Ceph 客户端 ID，用于在池（ceph pool）中创建映像。 默认是 “admin”。 adminSecretNamespace：adminSecret 的 namespace。默认是 “default”。 adminSecret：adminId 的 Secret 名称。该参数是必需的。 提供的 secret 必须有值为 “kubernetes.io/rbd” 的 type 参数。 pool: Ceph RBD 池. 默认是 “rbd”。 userId：Ceph 客户端 ID，用于映射 RBD 镜像（RBD image）。默认与 adminId 相同。 userSecretName：用于映射 RBD 镜像的 userId 的 Ceph Secret 的名字。 它必须与 PVC 存在于相同的 namespace 中。该参数是必需的。 提供的 secret 必须具有值为 “kubernetes.io/rbd” 的 type 参数，例如以这样的方式创建： kubectl create secret generic ceph-secret --type=\"kubernetes.io/rbd\" \\ --from-literal=key='QVFEQ1pMdFhPUnQrSmhBQUFYaERWNHJsZ3BsMmNjcDR6RFZST0E9PQ==' \\ --namespace=kube-system fsType：Kubernetes 支持的 fsType。默认：\"ext4\"。 imageFormat：Ceph RBD 镜像格式，”1” 或者 “2”。默认值是 “1”。 imageFeatures：这个参数是可选的，只能在你将 imageFormat 设置为 “2” 才使用。 目前支持的功能只是 layering。 默认是 ““，没有功能打开。 参考文章： https://kubernetes.io/docs/concepts/storage/storage-classes/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"storage/volume/dynamic-provisioning.html":{"url":"storage/volume/dynamic-provisioning.html","title":"Dynamic Volume Provisioning","keywords":"","body":"Dynamic Volume Provisioning1. 开启Dynamic Provisioning2. 使用Dynamic Provisioning3. 默认的StorageClassDynamic Volume Provisioning Dynamic volume provisioning允许用户按需自动创建存储卷，这种方式可以让用户不需要关心存储的复杂性和差别，又可以选择不同的存储类型。 1. 开启Dynamic Provisioning 需要先提前创建StorageClass对象，StorageClass中定义了使用哪个provisioner，并且在provisioner被调用时传入哪些参数，具体可参考StorageClass介绍。 例如： 磁盘类存储 apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: slow provisioner: kubernetes.io/gce-pd parameters: type: pd-standard SSD类存储 apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: fast provisioner: kubernetes.io/gce-pd parameters: type: pd-ssd 2. 使用Dynamic Provisioning 创建一个PVC对象，并且在其中storageClassName字段指明需要用到的StorageClass的名称，例如： apiVersion: v1 kind: PersistentVolumeClaim metadata: name: claim1 spec: accessModes: - ReadWriteOnce storageClassName: fast resources: requests: storage: 30Gi 当使用到PVC的时候会自动创建对应的外部存储，当PVC被删除的时候，会自动销毁（或备份）外部存储。 3. 默认的StorageClass 当没有对应的StorageClass配置时，可以设定默认的StorageClass，需要执行以下操作： 在API Server开启DefaultStorageClass admission controller 。 设置默认的StorageClass对象。 可以通过添加storageclass.kubernetes.io/is-default-class注解的方式设置某个StorageClass为默认的StorageClass。当用户创建了一个PersistentVolumeClaim，但没有指定storageClassName的时候，会自动将该PVC的storageClassName指向默认的StorageClass。 参考文章： https://kubernetes.io/docs/concepts/storage/dynamic-provisioning/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"storage/csi/_index.html":{"url":"storage/csi/_index.html","title":"CSI","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"storage/csi/ceph/csi-cephfs-plugin.html":{"url":"storage/csi/ceph/csi-cephfs-plugin.html","title":"csi-cephfs-plugin","keywords":"","body":"1. 编译CSI CephFS plugin1.1. 编译二进制1.2. 编译Docker镜像2. 配置项2.1. 命令行参数2.2. volume参数2.3. provisionVolume2.3.1. 管理员密钥认证2.3.2. 普通用户密钥认证1. 编译CSI CephFS plugin CSI CephFS plugin用来提供CephFS存储卷和挂载存储卷，源码参考：https://github.com/ceph/ceph-csi 。 1.1. 编译二进制 $ make cephfsplugin 1.2. 编译Docker镜像 $ make image-cephfsplugin 2. 配置项 2.1. 命令行参数 Option Default value Description --endpoint unix://tmp/csi.sock CSI endpoint, must be a UNIX socket --drivername csi-cephfsplugin name of the driver (Kubernetes: provisioner field in StorageClass must correspond to this value) --nodeid empty This node’s ID --volumemounter empty default volume mounter. Available options are kernel and fuse. This is the mount method used if volume parameters don’t specify otherwise. If left unspecified, the driver will first probe for ceph-fuse in system’s path and will choose Ceph kernel client if probing failed. 2.2. volume参数 Parameter Required Description monitors yes Comma separated list of Ceph monitors (e.g. 192.168.100.1:6789,192.168.100.2:6789,192.168.100.3:6789) mounter no Mount method to be used for this volume. Available options are kernel for Ceph kernel client and fuse for Ceph FUSE driver. Defaults to “default mounter”, see command line arguments. provisionVolume yes Mode of operation. BOOL value. If true, a new CephFS volume will be provisioned. If false, an existing CephFS will be used. pool for provisionVolume=true Ceph pool into which the volume shall be created rootPath for provisionVolume=false Root path of an existing CephFS volume csiProvisionerSecretName, csiNodeStageSecretName for Kubernetes name of the Kubernetes Secret object containing Ceph client credentials. Both parameters should have the same value csiProvisionerSecretNamespace, csiNodeStageSecretNamespace for Kubernetes namespaces of the above Secret objects 2.3. provisionVolume 2.3.1. 管理员密钥认证 当provisionVolume=true时，必要的管理员认证参数如下： adminID: ID of an admin client adminKey: key of the admin client 2.3.2. 普通用户密钥认证 当provisionVolume=false时，必要的用户认证参数如下： userID: ID of a user client userKey: key of a user client 参考文章： https://github.com/ceph/ceph-csi/blob/master/docs/deploy-cephfs.md Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"storage/csi/ceph/deploy-csi-cephfs.html":{"url":"storage/csi/ceph/deploy-csi-cephfs.html","title":"部署csi-cephfs","keywords":"","body":"0. 说明涉及镜像1. 部署RBAC1.1. csi-attacher-rbac.yaml1.2. csi-provisioner-rbac.yaml1.3. csi-nodeplugin-rbac.yaml2. 部署CSI sidecar containers2.1. csi-cephfsplugin-provisioner.yaml2.2. csi-cephfsplugin-attacher.yaml3. 部署CSI-CephFS-driver(plugin)3.1. csi-cephfsplugin.yaml4. 确认部署结果0. 说明 要求Kubernetes的版本在1.11及以上，k8s集群必须允许特权Pod（privileged pods），即apiserver和kubelet需要设置--allow-privileged为true。节点的Docker daemon需要允许挂载共享卷。 涉及镜像 quay.io/k8scsi/csi-provisioner:v0.3.0 quay.io/k8scsi/csi-attacher:v0.3.0 quay.io/k8scsi/driver-registrar:v0.3.0 quay.io/cephcsi/cephfsplugin:v0.3.0 1. 部署RBAC 部署service accounts, cluster roles 和 cluster role bindings，这些可供RBD和CephFS CSI plugins共同使用，他们拥有相同的权限。 $ kubectl create -f csi-attacher-rbac.yaml $ kubectl create -f csi-provisioner-rbac.yaml $ kubectl create -f csi-nodeplugin-rbac.yaml 1.1. csi-attacher-rbac.yaml apiVersion: v1 kind: ServiceAccount metadata: name: csi-attacher --- kind: ClusterRole apiVersion: rbac.authorization.k8s.io/v1 metadata: name: external-attacher-runner rules: - apiGroups: [\"\"] resources: [\"events\"] verbs: [\"get\", \"list\", \"watch\", \"update\"] - apiGroups: [\"\"] resources: [\"persistentvolumes\"] verbs: [\"get\", \"list\", \"watch\", \"update\"] - apiGroups: [\"\"] resources: [\"nodes\"] verbs: [\"get\", \"list\", \"watch\"] - apiGroups: [\"storage.k8s.io\"] resources: [\"volumeattachments\"] verbs: [\"get\", \"list\", \"watch\", \"update\"] --- kind: ClusterRoleBinding apiVersion: rbac.authorization.k8s.io/v1 metadata: name: csi-attacher-role subjects: - kind: ServiceAccount name: csi-attacher namespace: default roleRef: kind: ClusterRole name: external-attacher-runner apiGroup: rbac.authorization.k8s.io 1.2. csi-provisioner-rbac.yaml apiVersion: v1 kind: ServiceAccount metadata: name: csi-provisioner --- kind: ClusterRole apiVersion: rbac.authorization.k8s.io/v1 metadata: name: external-provisioner-runner rules: - apiGroups: [\"\"] resources: [\"secrets\"] verbs: [\"get\", \"list\"] - apiGroups: [\"\"] resources: [\"persistentvolumes\"] verbs: [\"get\", \"list\", \"watch\", \"create\", \"delete\"] - apiGroups: [\"\"] resources: [\"persistentvolumeclaims\"] verbs: [\"get\", \"list\", \"watch\", \"update\"] - apiGroups: [\"storage.k8s.io\"] resources: [\"storageclasses\"] verbs: [\"get\", \"list\", \"watch\"] - apiGroups: [\"\"] resources: [\"events\"] verbs: [\"list\", \"watch\", \"create\", \"update\", \"patch\"] --- kind: ClusterRoleBinding apiVersion: rbac.authorization.k8s.io/v1 metadata: name: csi-provisioner-role subjects: - kind: ServiceAccount name: csi-provisioner namespace: default roleRef: kind: ClusterRole name: external-provisioner-runner apiGroup: rbac.authorization.k8s.io 1.3. csi-nodeplugin-rbac.yaml apiVersion: v1 kind: ServiceAccount metadata: name: csi-nodeplugin --- kind: ClusterRole apiVersion: rbac.authorization.k8s.io/v1 metadata: name: csi-nodeplugin rules: - apiGroups: [\"\"] resources: [\"nodes\"] verbs: [\"get\", \"list\", \"update\"] - apiGroups: [\"\"] resources: [\"namespaces\"] verbs: [\"get\", \"list\"] - apiGroups: [\"\"] resources: [\"persistentvolumes\"] verbs: [\"get\", \"list\", \"watch\", \"update\"] - apiGroups: [\"storage.k8s.io\"] resources: [\"volumeattachments\"] verbs: [\"get\", \"list\", \"watch\", \"update\"] --- kind: ClusterRoleBinding apiVersion: rbac.authorization.k8s.io/v1 metadata: name: csi-nodeplugin subjects: - kind: ServiceAccount name: csi-nodeplugin namespace: default roleRef: kind: ClusterRole name: csi-nodeplugin apiGroup: rbac.authorization.k8s.io 2. 部署CSI sidecar containers 通过StatefulSet的方式部署external-attacher和external-provisioner供CSI CephFS使用。 $ kubectl create -f csi-cephfsplugin-attacher.yaml $ kubectl create -f csi-cephfsplugin-provisioner.yaml 2.1. csi-cephfsplugin-provisioner.yaml kind: Service apiVersion: v1 metadata: name: csi-cephfsplugin-provisioner labels: app: csi-cephfsplugin-provisioner spec: selector: app: csi-cephfsplugin-provisioner ports: - name: dummy port: 12345 --- kind: StatefulSet apiVersion: apps/v1beta1 metadata: name: csi-cephfsplugin-provisioner spec: serviceName: \"csi-cephfsplugin-provisioner\" replicas: 1 template: metadata: labels: app: csi-cephfsplugin-provisioner spec: serviceAccount: csi-provisioner containers: - name: csi-provisioner image: quay.io/k8scsi/csi-provisioner:v0.3.0 args: - \"--provisioner=csi-cephfsplugin\" - \"--csi-address=$(ADDRESS)\" - \"--v=5\" env: - name: ADDRESS value: /var/lib/kubelet/plugins/csi-cephfsplugin/csi.sock imagePullPolicy: \"IfNotPresent\" volumeMounts: - name: socket-dir mountPath: /var/lib/kubelet/plugins/csi-cephfsplugin volumes: - name: socket-dir hostPath: path: /var/lib/kubelet/plugins/csi-cephfsplugin type: DirectoryOrCreate 2.2. csi-cephfsplugin-attacher.yaml kind: Service apiVersion: v1 metadata: name: csi-cephfsplugin-attacher labels: app: csi-cephfsplugin-attacher spec: selector: app: csi-cephfsplugin-attacher ports: - name: dummy port: 12345 --- kind: StatefulSet apiVersion: apps/v1beta1 metadata: name: csi-cephfsplugin-attacher spec: serviceName: \"csi-cephfsplugin-attacher\" replicas: 1 template: metadata: labels: app: csi-cephfsplugin-attacher spec: serviceAccount: csi-attacher containers: - name: csi-cephfsplugin-attacher image: quay.io/k8scsi/csi-attacher:v0.3.0 args: - \"--v=5\" - \"--csi-address=$(ADDRESS)\" env: - name: ADDRESS value: /var/lib/kubelet/plugins/csi-cephfsplugin/csi.sock imagePullPolicy: \"IfNotPresent\" volumeMounts: - name: socket-dir mountPath: /var/lib/kubelet/plugins/csi-cephfsplugin volumes: - name: socket-dir hostPath: path: /var/lib/kubelet/plugins/csi-cephfsplugin type: DirectoryOrCreate 3. 部署CSI-CephFS-driver(plugin) csi-cephfs-plugin 的作用类似nfs-client，部署在所有node节点上，执行ceph的挂载等相关任务。 通过DaemonSet的方式部署，其中包括两个容器：CSI driver-registrar 和 CSI CephFS driver。 $ kubectl create -f csi-cephfsplugin.yaml 3.1. csi-cephfsplugin.yaml kind: DaemonSet apiVersion: apps/v1beta2 metadata: name: csi-cephfsplugin spec: selector: matchLabels: app: csi-cephfsplugin template: metadata: labels: app: csi-cephfsplugin spec: serviceAccount: csi-nodeplugin hostNetwork: true # to use e.g. Rook orchestrated cluster, and mons' FQDN is # resolved through k8s service, set dns policy to cluster first dnsPolicy: ClusterFirstWithHostNet containers: - name: driver-registrar image: quay.io/k8scsi/driver-registrar:v0.3.0 args: - \"--v=5\" - \"--csi-address=$(ADDRESS)\" - \"--kubelet-registration-path=$(DRIVER_REG_SOCK_PATH)\" env: - name: ADDRESS value: /var/lib/kubelet/plugins/csi-cephfsplugin/csi.sock - name: DRIVER_REG_SOCK_PATH value: /var/lib/kubelet/plugins/csi-cephfsplugin/csi.sock - name: KUBE_NODE_NAME valueFrom: fieldRef: fieldPath: spec.nodeName volumeMounts: - name: socket-dir mountPath: /var/lib/kubelet/plugins/csi-cephfsplugin - name: registration-dir mountPath: /registration - name: csi-cephfsplugin securityContext: privileged: true capabilities: add: [\"SYS_ADMIN\"] allowPrivilegeEscalation: true image: quay.io/cephcsi/cephfsplugin:v0.3.0 args : - \"--nodeid=$(NODE_ID)\" - \"--endpoint=$(CSI_ENDPOINT)\" - \"--v=5\" - \"--drivername=csi-cephfsplugin\" env: - name: NODE_ID valueFrom: fieldRef: fieldPath: spec.nodeName - name: CSI_ENDPOINT value: unix://var/lib/kubelet/plugins/csi-cephfsplugin/csi.sock imagePullPolicy: \"IfNotPresent\" volumeMounts: - name: plugin-dir mountPath: /var/lib/kubelet/plugins/csi-cephfsplugin - name: pods-mount-dir mountPath: /var/lib/kubelet/pods mountPropagation: \"Bidirectional\" - mountPath: /sys name: host-sys - name: lib-modules mountPath: /lib/modules readOnly: true - name: host-dev mountPath: /dev volumes: - name: plugin-dir hostPath: path: /var/lib/kubelet/plugins/csi-cephfsplugin type: DirectoryOrCreate - name: registration-dir hostPath: path: /var/lib/kubelet/plugins/ type: Directory - name: pods-mount-dir hostPath: path: /var/lib/kubelet/pods type: Directory - name: socket-dir hostPath: path: /var/lib/kubelet/plugins/csi-cephfsplugin type: DirectoryOrCreate - name: host-sys hostPath: path: /sys - name: lib-modules hostPath: path: /lib/modules - name: host-dev hostPath: path: /dev 4. 确认部署结果 $ kubectl get all NAME READY STATUS RESTARTS AGE pod/csi-cephfsplugin-attacher-0 1/1 Running 0 26s pod/csi-cephfsplugin-provisioner-0 1/1 Running 0 25s pod/csi-cephfsplugin-rljcv 2/2 Running 0 24s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/csi-cephfsplugin-attacher ClusterIP 10.104.116.218 12345/TCP 27s service/csi-cephfsplugin-provisioner ClusterIP 10.101.78.75 12345/TCP 26s ... 参考文档： https://github.com/ceph/ceph-csi https://github.com/ceph/ceph-csi/blob/master/docs/deploy-cephfs.md https://github.com/ceph/ceph-csi/tree/master/deploy/cephfs/kubernetes Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"storage/csi/provisioner/cephfs-provisioner.html":{"url":"storage/csi/provisioner/cephfs-provisioner.html","title":"部署cephfs-provisioner","keywords":"","body":"1. 安装cephfs客户端2. 部署RBAC2.1. ClusterRole2.2. ClusterRoleBinding2.3. Role2.4. RoleBinding2.5. ServiceAccount3. 部署 cephfs-provisioner4. 部署storageclass5. 部署statefulset6. 日志6.1. cephfs-provisoner 执行日志6.2. debug 日志1. 安装cephfs客户端 所有node节点安装cephfs客户端，主要用来和ceph集群挂载使用。 yum install -y ceph-common 2. 部署RBAC 2.1. ClusterRole kind: ClusterRole apiVersion: rbac.authorization.k8s.io/v1 metadata: name: cephfs-provisioner namespace: cephfs rules: - apiGroups: [\"\"] resources: [\"persistentvolumes\"] verbs: [\"get\", \"list\", \"watch\", \"create\", \"delete\"] - apiGroups: [\"\"] resources: [\"persistentvolumeclaims\"] verbs: [\"get\", \"list\", \"watch\", \"update\"] - apiGroups: [\"storage.k8s.io\"] resources: [\"storageclasses\"] verbs: [\"get\", \"list\", \"watch\"] - apiGroups: [\"\"] resources: [\"events\"] verbs: [\"create\", \"update\", \"patch\"] - apiGroups: [\"\"] resources: [\"services\"] resourceNames: [\"kube-dns\",\"coredns\"] verbs: [\"list\", \"get\"] 2.2. ClusterRoleBinding kind: ClusterRoleBinding apiVersion: rbac.authorization.k8s.io/v1 metadata: name: cephfs-provisioner subjects: - kind: ServiceAccount name: cephfs-provisioner namespace: cephfs roleRef: kind: ClusterRole name: cephfs-provisioner apiGroup: rbac.authorization.k8s.io 2.3. Role apiVersion: rbac.authorization.k8s.io/v1 kind: Role metadata: name: cephfs-provisioner namespace: cephfs rules: - apiGroups: [\"\"] resources: [\"secrets\"] verbs: [\"create\", \"get\", \"delete\"] - apiGroups: [\"\"] resources: [\"endpoints\"] verbs: [\"get\", \"list\", \"watch\", \"create\", \"update\", \"patch\"] 2.4. RoleBinding apiVersion: rbac.authorization.k8s.io/v1 kind: RoleBinding metadata: name: cephfs-provisioner namespace: cephfs roleRef: apiGroup: rbac.authorization.k8s.io kind: Role name: cephfs-provisioner subjects: - kind: ServiceAccount name: cephfs-provisioner 2.5. ServiceAccount apiVersion: v1 kind: ServiceAccount metadata: name: cephfs-provisioner namespace: cephfs 3. 部署 cephfs-provisioner apiVersion: extensions/v1beta1 kind: Deployment metadata: name: cephfs-provisioner namespace: cephfs spec: replicas: 1 strategy: type: Recreate template: metadata: labels: app: cephfs-provisioner spec: containers: - name: cephfs-provisioner image: \"quay.io/external_storage/cephfs-provisioner:latest\" resources: limits: cpu: 500m memory: 512Mi requests: cpu: 100m memory: 64Mi env: - name: PROVISIONER_NAME # 与storageclass的provisioner参数相同 value: ceph.com/cephfs - name: PROVISIONER_SECRET_NAMESPACE # 与rbac的namespace相同 value: cephfs command: - \"/usr/local/bin/cephfs-provisioner\" args: - \"-id=cephfs-provisioner-1\" serviceAccount: cephfs-provisioner 4. 部署storageclass apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: cephfs-provisioner-sc provisioner: ceph.com/cephfs volumeBindingMode: WaitForFirstConsumer parameters: monitors: 192.168.27.43:6789,192.168.27.44:6789,192.168.27.45:6789 adminId: admin adminSecretName: csi-cephfs-secret adminSecretNamespace: \"kube-csi\" claimRoot: /pvc-volumes 5. 部署statefulset apiVersion: apps/v1 kind: StatefulSet metadata: name: cephfs-provisioner-nginx spec: serviceName: \"nginx\" replicas: 1 selector: matchLabels: app: nginx template: metadata: labels: app: nginx spec: containers: - name: nginx image: nginx:latest #nginx的镜像 imagePullPolicy: IfNotPresent volumeMounts: - mountPath: \"/mnt\" #容器里面的挂载目录，该目录挂载到NFS的共享目录上 name: test volumeClaimTemplates: - metadata: name: test spec: accessModes: [ \"ReadWriteOnce\" ] resources: requests: storage: 2Gi storageClassName: cephfs-provisioner-sc 6. 日志 6.1. cephfs-provisoner 执行日志 I0327 07:18:19.742239 1 controller.go:987] provision \"default/test-cephfs-ngx-wait-22-0\" class \"cephfs-provisioner-sc\": started I0327 07:18:19.745239 1 event.go:221] Event(v1.ObjectReference{Kind:\"PersistentVolumeClaim\", Namespace:\"default\", Name:\"test-cephfs-ngx-wait-22-0\", UID:\"7f6b60d5-5060-11e9-9a9c-c81f66bcff65\", APIVersion:\"v1\", ResourceVersion:\"347214256\", FieldPath:\"\"}): type: 'Normal' reason: 'Provisioning' External provisioner is provisioning volume for claim \"default/test-cephfs-ngx-wait-22-0\" I0327 07:18:23.281277 1 cephfs-provisioner.go:222] successfully created CephFS share &CephFSPersistentVolumeSource{Monitors:[192.168.27.43:6789 192.168.27.44:6789 192.168.27.45:6789],Path:/pvc-volumes/kubernetes/kubernetes-dynamic-pvc-7f7cb62f-5060-11e9-85c0-0adb8ef08100,User:kubernetes-dynamic-user-7f7cb69f-5060-11e9-85c0-0adb8ef08100,SecretFile:,SecretRef:&SecretReference{Name:ceph-kubernetes-dynamic-user-7f7cb69f-5060-11e9-85c0-0adb8ef08100-secret,Namespace:default,},ReadOnly:false,} I0327 07:18:23.281371 1 controller.go:1087] provision \"default/test-cephfs-ngx-wait-22-0\" class \"cephfs-provisioner-sc\": volume \"pvc-7f6b60d5-5060-11e9-9a9c-c81f66bcff65\" provisioned I0327 07:18:23.281415 1 controller.go:1101] provision \"default/test-cephfs-ngx-wait-22-0\" class \"cephfs-provisioner-sc\": trying to save persistentvvolume \"pvc-7f6b60d5-5060-11e9-9a9c-c81f66bcff65\" I0327 07:18:23.284621 1 controller.go:1108] provision \"default/test-cephfs-ngx-wait-22-0\" class \"cephfs-provisioner-sc\": persistentvolume \"pvc-7f6b60d5-5060-11e9-9a9c-c81f66bcff65\" saved I0327 07:18:23.284723 1 controller.go:1149] provision \"default/test-cephfs-ngx-wait-22-0\" class \"cephfs-provisioner-sc\": succeeded I0327 07:18:23.284810 1 event.go:221] Event(v1.ObjectReference{Kind:\"PersistentVolumeClaim\", Namespace:\"default\", Name:\"test-cephfs-ngx-wait-22-0\", UID:\"7f6b60d5-5060-11e9-9a9c-c81f66bcff65\", APIVersion:\"v1\", ResourceVersion:\"347214256\", FieldPath:\"\"}): type: 'Normal' reason: 'ProvisioningSucceeded' Successfully provisioned volume pvc-7f6b60d5-5060-11e9-9a9c-c81f66bcff65 6.2. debug 日志 I0327 08:08:11.789608 1 controller.go:987] provision \"default/test-cephfs-ngx-wait-44-0\" class \"cephfs-sc-wait\": started I0327 08:08:11.793258 1 event.go:221] Event(v1.ObjectReference{Kind:\"PersistentVolumeClaim\", Namespace:\"default\", Name:\"test-cephfs-ngx-wait-44-0\", UID:\"81846859-5067-11e9-9a9c-c81f66bcff65\", APIVersion:\"v1\", ResourceVersion:\"347237916\", FieldPath:\"\"}): type: 'Normal' reason: 'Provisioning' External provisioner is provisioning volume for claim \"default/test-cephfs-ngx-wait-44-0\" E0327 08:08:12.164705 1 cephfs-provisioner.go:158] failed to provision share \"kubernetes-dynamic-pvc-76ecdc5a-5067-11e9-9421-2a1b1be1aeef\" for \"kubernetes-dynamic-user-76ecdcee-5067-11e9-9421-2a1b1be1aeef\", err: exit status 1, output: Traceback (most recent call last): File \"/usr/local/bin/cephfs_provisioner\", line 364, in main() File \"/usr/local/bin/cephfs_provisioner\", line 358, in main print cephfs.create_share(share, user, size=size) File \"/usr/local/bin/cephfs_provisioner\", line 228, in create_share volume = self.volume_client.create_volume(volume_path, size=size, namespace_isolated=not self.ceph_namespace_isolation_disabled) File \"/usr/local/bin/cephfs_provisioner\", line 112, in volume_client self._volume_client.connect(None) File \"/lib/python2.7/site-packages/ceph_volume_client.py\", line 458, in connect self.rados.connect() File \"rados.pyx\", line 895, in rados.Rados.connect (/home/jenkins-build/build/workspace/ceph-build/ARCH/x86_64/AVAILABLE_ARCH/x86_64/AVAILABLE_DIST/centos7/DIST/centos7/MACHINE_SIZE/huge/release/13.2.1/rpm/el7/BUILD/ceph-13.2.1/build/src/pybind/rados/pyrex/rados.c:9815) rados.IOError: [errno 5] error connecting to the cluster W0327 08:08:12.164908 1 controller.go:746] Retrying syncing claim \"default/test-cephfs-ngx-wait-44-0\" because failures 2 参考 https://github.com/kubernetes-incubator/external-storage/tree/master/ceph/cephfs/deploy Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"storage/csi/flexvolume.html":{"url":"storage/csi/flexvolume.html","title":"FlexVolume介绍","keywords":"","body":"1. FlexVolume介绍2. 使用方式3. FlexVolume接口3.1. init3.2. attach3.3. detach3.4. waitforattach3.5. isattached3.6. mountdevice3.7. unmountdevice3.8. mount3.9. unmount3.10. 插件输出4. 示例4.1. pod的yaml文件内容4.2. 插件脚本1. FlexVolume介绍 Flexvolume提供了一种扩展k8s存储插件的方式，用户可以自定义自己的存储插件。类似的功能的实现还有CSI的方式。Flexvolume在k8s 1.8+以上版本提供GA功能版本。 2. 使用方式 在每个node节点安装存储插件二进制，该二进制实现flexvolume的相关接口，默认存储插件的存放路径为/usr/libexec/kubernetes/kubelet-plugins/volume/exec//。 其中vendor~driver的名字需要和pod中flexVolume.driver的字段名字匹配，该字段名字通过/替换~。 例如： path:/usr/libexec/kubernetes/kubelet-plugins/volume/exec/foo~cifs/cifs pod中flexVolume.driver:foo/cifs 3. FlexVolume接口 节点上的存储插件需要实现以下的接口。 3.1. init init 3.2. attach attach 3.3. detach detach 3.4. waitforattach waitforattach 3.5. isattached isattached 3.6. mountdevice mountdevice 3.7. unmountdevice unmountdevice 3.8. mount mount 3.9. unmount unmount 3.10. 插件输出 { \"status\": \"\", \"message\": \"\", \"device\": \"\" \"volumeName\": \"\" \"attached\": \"capabilities\": { \"attach\": } } 4. 示例 4.1. pod的yaml文件内容 nginx-nfs.yaml 相关参数为flexVolume.driver等。 apiVersion: v1 kind: Pod metadata: name: nginx-nfs namespace: default spec: containers: - name: nginx-nfs image: nginx volumeMounts: - name: test mountPath: /data ports: - containerPort: 80 volumes: - name: test flexVolume: driver: \"k8s/nfs\" fsType: \"nfs\" options: server: \"172.16.0.25\" share: \"dws_nas_scratch\" 4.2. 插件脚本 nfs脚本实现了flexvolume的接口。 /usr/libexec/kubernetes/kubelet-plugins/volume/exec/k8s~nfs/nfs。 #!/bin/bash # Copyright 2015 The Kubernetes Authors. # # Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Notes: # - Please install \"jq\" package before using this driver. usage() { err \"Invalid usage. Usage: \" err \"\\t$0 init\" err \"\\t$0 mount \" err \"\\t$0 unmount \" exit 1 } err() { echo -ne $* 1>&2 } log() { echo -ne $* >&1 } ismounted() { MOUNT=`findmnt -n ${MNTPATH} 2>/dev/null | cut -d' ' -f1` if [ \"${MOUNT}\" == \"${MNTPATH}\" ]; then echo \"1\" else echo \"0\" fi } domount() { MNTPATH=$1 NFS_SERVER=$(echo $2 | jq -r '.server') SHARE=$(echo $2 | jq -r '.share') if [ $(ismounted) -eq 1 ] ; then log '{\"status\": \"Success\"}' exit 0 fi mkdir -p ${MNTPATH} &> /dev/null mount -t nfs ${NFS_SERVER}:/${SHARE} ${MNTPATH} &> /dev/null if [ $? -ne 0 ]; then err \"{ \\\"status\\\": \\\"Failure\\\", \\\"message\\\": \\\"Failed to mount ${NFS_SERVER}:${SHARE} at ${MNTPATH}\\\"}\" exit 1 fi log '{\"status\": \"Success\"}' exit 0 } unmount() { MNTPATH=$1 if [ $(ismounted) -eq 0 ] ; then log '{\"status\": \"Success\"}' exit 0 fi umount ${MNTPATH} &> /dev/null if [ $? -ne 0 ]; then err \"{ \\\"status\\\": \\\"Failed\\\", \\\"message\\\": \\\"Failed to unmount volume at ${MNTPATH}\\\"}\" exit 1 fi log '{\"status\": \"Success\"}' exit 0 } op=$1 if ! command -v jq >/dev/null 2>&1; then err \"{ \\\"status\\\": \\\"Failure\\\", \\\"message\\\": \\\"'jq' binary not found. Please install jq package before using this driver\\\"}\" exit 1 fi if [ \"$op\" = \"init\" ]; then log '{\"status\": \"Success\", \"capabilities\": {\"attach\": false}}' exit 0 fi if [ $# -lt 2 ]; then usage fi shift case \"$op\" in mount) domount $* ;; unmount) unmount $* ;; *) log '{\"status\": \"Not supported\"}' exit 0 esac exit 1 参考： https://github.com/kubernetes/community/blob/master/contributors/devel/sig-storage/flexvolume.md https://github.com/kubernetes/examples/tree/master/staging/volumes/flexvolume https://github.com/kubernetes/examples/blob/master/staging/volumes/flexvolume/nginx-nfs.yaml https://github.com/kubernetes/examples/blob/master/staging/volumes/flexvolume/nfs Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"resource/resource-quota.html":{"url":"resource/resource-quota.html","title":"资源配额","keywords":"","body":"资源配额（ResourceQuota）1. 启动资源配额2. 计算资源配额3. 存储资源配额4. 对象数量的配额5. 限额的作用域6. request和limit7. 查看和设置配额8. 配额和集群容量资源配额（ResourceQuota） ResourceQuota对象用来定义某个命名空间下所有资源的使用限额，其实包括： 计算资源的配额 存储资源的配额 对象数量的配额 如果集群的总容量小于命名空间的配额总额，可能会产生资源竞争。这时会按照先到先得来处理。 资源竞争和配额的更新都不会影响已经创建好的资源。 1. 启动资源配额 Kubernetes 的众多发行版本默认开启了资源配额的支持。当在apiserver的--admission-control配置中添加ResourceQuota参数后，便启用了。 当一个命名空间中含有ResourceQuota对象时，资源配额将强制执行。 2. 计算资源配额 可以在给定的命名空间中限制可以请求的计算资源（compute resources）的总量。 资源名称 描述 cpu 非终止态的所有pod, cpu请求总量不能超出此值。 limits.cpu 非终止态的所有pod， cpu限制总量不能超出此值。 limits.memory 非终止态的所有pod, 内存限制总量不能超出此值。 memory 非终止态的所有pod, 内存请求总量不能超出此值。 requests.cpu 非终止态的所有pod, cpu请求总量不能超出此值。 requests.memory 非终止态的所有pod, 内存请求总量不能超出此值。 3. 存储资源配额 可以在给定的命名空间中限制可以请求的存储资源（storage resources）的总量。 资源名称 描述 requests.storage 所有PVC, 存储请求总量不能超出此值。 persistentvolumeclaims 命名空间中可以存在的PVC（persistent volume claims）总数。 .storageclass.storage.k8s.io/requests.storage 和该存储类关联的所有PVC, 存储请求总和不能超出此值。 .storageclass.storage.k8s.io/persistentvolumeclaims 和该存储类关联的所有PVC，命名空间中可以存在的PVC（persistent volume claims）总数。 4. 对象数量的配额 资源名称 描述 congfigmaps 命名空间中可以存在的配置映射的总数。 persistentvolumeclaims 命名空间中可以存在的PVC总数。 pods 命名空间中可以存在的非终止态的pod总数。如果一个pod的status.phase 是 Failed, Succeeded, 则该pod处于终止态。 replicationcontrollers 命名空间中可以存在的rc总数。 resourcequotas 命名空间中可以存在的资源配额（resource quotas）总数。 services 命名空间中可以存在的服务总数量。 services.loadbalancers 命名空间中可以存在的服务的负载均衡的总数量。 services.nodeports 命名空间中可以存在的服务的主机接口的总数量。 secrets 命名空间中可以存在的secrets的总数量。 例如：可以定义pod的限额来避免某用户消耗过多的Pod IPs。 5. 限额的作用域 作用域 描述 Terminating 匹配 spec.activeDeadlineSeconds >= 0 的pod NotTerminating 匹配 spec.activeDeadlineSeconds is nil 的pod BestEffort 匹配具有最佳服务质量的pod NotBestEffort 匹配具有非最佳服务质量的pod 6. request和limit 当分配计算资源时，每个容器可以为cpu或者内存指定一个请求值和一个限度值。可以配置限额值来限制它们中的任何一个值。 如果指定了requests.cpu 或者 requests.memory的限额值，那么就要求传入的每一个容器显式的指定这些资源的请求。如果指定了limits.cpu或者limits.memory，那么就要求传入的每一个容器显式的指定这些资源的限度。 7. 查看和设置配额 # 创建namespace $ kubectl create namespace myspace # 创建resourcequota $ cat compute-resources.yaml apiVersion: v1 kind: ResourceQuota metadata: name: compute-resources spec: hard: pods: \"4\" requests.cpu: \"1\" requests.memory: 1Gi limits.cpu: \"2\" limits.memory: 2Gi EOF $ kubectl create -f ./compute-resources.yaml --namespace=myspace # 查询resourcequota $ kubectl get quota --namespace=myspace NAME AGE compute-resources 30s # 查询resourcequota的详细信息 $ kubectl describe quota compute-resources --namespace=myspace Name: compute-resources Namespace: myspace Resource Used Hard -------- ---- ---- limits.cpu 0 2 limits.memory 0 2Gi pods 0 4 requests.cpu 0 1 requests.memory 0 1Gi 8. 配额和集群容量 资源配额对象与集群容量无关，它们以绝对单位表示。即增加节点的资源并不会增加已经配置的namespace的资源。 参考文章： https://kubernetes.io/docs/concepts/policy/resource-quotas/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"resource/limit-range.html":{"url":"resource/limit-range.html","title":"Pod限额","keywords":"","body":"Pod限额（LimitRange）1. 为namespace配置CPU和内存的默认值1.1. namespace的内存默认值1.2. namespace的CPU默认值1.3 说明2. 为namespace配置CPU和内存的最大最小值2.1. 内存的最大最小值2.2. CPU的最大最小值2.3. 说明Pod限额（LimitRange） ResourceQuota对象是限制某个namespace下所有Pod(容器)的资源限额 LimitRange对象是限制某个namespace单个Pod(容器)的资源限额 LimitRange对象用来定义某个命名空间下某种资源对象的使用限额，其中资源对象包括：Pod、Container、PersistentVolumeClaim。 1. 为namespace配置CPU和内存的默认值 如果在一个拥有默认内存或CPU限额的命名空间中创建一个容器，并且这个容器未指定它自己的内存或CPU的limit， 它会被分配这个默认的内存或CPU的limit。既没有设置pod的limit和request才会分配默认的内存或CPU的request。 1.1. namespace的内存默认值 # 创建namespace $ kubectl create namespace default-mem-example # 创建LimitRange $ cat memory-defaults.yaml apiVersion: v1 kind: LimitRange metadata: name: mem-limit-range spec: limits: - default: memory: 512Mi defaultRequest: memory: 256Mi type: Container $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/memory-defaults.yaml --namespace=default-mem-example # 创建Pod,未指定内存的limit和request $ cat memory-defaults-pod.yaml apiVersion: v1 kind: Pod metadata: name: default-mem-demo spec: containers: - name: default-mem-demo-ctr image: nginx $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/memory-defaults-pod.yaml --namespace=default-mem-example # 查看Pod $ kubectl get pod default-mem-demo --output=yaml --namespace=default-mem-example containers: - image: nginx imagePullPolicy: Always name: default-mem-demo-ctr resources: limits: memory: 512Mi requests: memory: 256Mi 1.2. namespace的CPU默认值 # 创建namespace $ kubectl create namespace default-cpu-example # 创建LimitRange $ cat cpu-defaults.yaml apiVersion: v1 kind: LimitRange metadata: name: cpu-limit-range spec: limits: - default: cpu: 1 defaultRequest: cpu: 0.5 type: Container $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/cpu-defaults.yaml --namespace=default-cpu-example # 创建Pod，未指定CPU的limit和request $ cat cpu-defaults-pod.yaml apiVersion: v1 kind: Pod metadata: name: default-cpu-demo spec: containers: - name: default-cpu-demo-ctr image: nginx $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/cpu-defaults-pod.yaml --namespace=default-cpu-example # 查看Pod $ kubectl get pod default-cpu-demo --output=yaml --namespace=default-cpu-example containers: - image: nginx imagePullPolicy: Always name: default-cpu-demo-ctr resources: limits: cpu: \"1\" requests: cpu: 500m 1.3 说明 如果没有指定pod的request和limit，则创建的pod会使用LimitRange对象定义的默认值（request和limit） 如果指定pod的limit但未指定request，则创建的pod的request值会取limit的值，而不会取LimitRange对象定义的request默认值。 如果指定pod的request但未指定limit，则创建的pod的limit值会取LimitRange对象定义的limit默认值。 默认Limit和request的动机 如果命名空间具有资源配额（ResourceQuota）, 它为内存限额（CPU限额）设置默认值是有意义的。 以下是资源配额对命名空间施加的两个限制： 在命名空间运行的每一个容器必须有它自己的内存限额（CPU限额）。 在命名空间中所有的容器使用的内存总量（CPU总量）不能超出指定的限额。 如果一个容器没有指定它自己的内存限额（CPU限额），它将被赋予默认的限额值，然后它才可以在被配额限制的命名空间中运行。 2. 为namespace配置CPU和内存的最大最小值 2.1. 内存的最大最小值 创建LimitRange # 创建namespace $ kubectl create namespace constraints-mem-example # 创建LimitRange $ cat memory-constraints.yaml apiVersion: v1 kind: LimitRange metadata: name: mem-min-max-demo-lr spec: limits: - max: memory: 1Gi min: memory: 500Mi type: Container $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/memory-constraints.yaml --namespace=constraints-mem-example # 查看LimitRange $ kubectl get limitrange cpu-min-max-demo --namespace=constraints-mem-example --output=yaml ... limits: - default: memory: 1Gi defaultRequest: memory: 1Gi max: memory: 1Gi min: memory: 500Mi type: Container ... # LimitRange设置了最大最小值，但没有设置默认值，也会被自动设置默认值。 创建符合要求的Pod # 创建符合要求的Pod $ cat memory-constraints-pod.yaml apiVersion: v1 kind: Pod metadata: name: constraints-mem-demo spec: containers: - name: constraints-mem-demo-ctr image: nginx resources: limits: memory: \"800Mi\" requests: memory: \"600Mi\" $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/memory-constraints-pod.yaml --namespace=constraints-mem-example # 查看Pod $ kubectl get pod constraints-mem-demo --output=yaml --namespace=constraints-mem-example ... resources: limits: memory: 800Mi requests: memory: 600Mi ... 创建超过最大内存limit的pod $ cat memory-constraints-pod-2.yaml apiVersion: v1 kind: Pod metadata: name: constraints-mem-demo-2 spec: containers: - name: constraints-mem-demo-2-ctr image: nginx resources: limits: memory: \"1.5Gi\" # 超过最大值 1Gi requests: memory: \"800Mi\" $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/memory-constraints-pod-2.yaml --namespace=constraints-mem-example # Pod创建失败，因为容器指定的limit过大 Error from server (Forbidden): error when creating \"docs/tasks/administer-cluster/memory-constraints-pod-2.yaml\": pods \"constraints-mem-demo-2\" is forbidden: maximum memory usage per Container is 1Gi, but limit is 1536Mi. 创建小于最小内存request的Pod $ cat memory-constraints-pod-3.yaml apiVersion: v1 kind: Pod metadata: name: constraints-mem-demo-3 spec: containers: - name: constraints-mem-demo-3-ctr image: nginx resources: limits: memory: \"800Mi\" requests: memory: \"100Mi\" # 小于最小值500Mi $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/memory-constraints-pod-3.yaml --namespace=constraints-mem-example # Pod创建失败，因为容器指定的内存request过小 Error from server (Forbidden): error when creating \"docs/tasks/administer-cluster/memory-constraints-pod-3.yaml\": pods \"constraints-mem-demo-3\" is forbidden: minimum memory usage per Container is 500Mi, but request is 100Mi. 创建没有指定任何内存limit和request的pod $ cat memory-constraints-pod-4.yaml apiVersion: v1 kind: Pod metadata: name: constraints-mem-demo-4 spec: containers: - name: constraints-mem-demo-4-ctr image: nginx $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/memory-constraints-pod-4.yaml --namespace=constraints-mem-example # 查看Pod $ kubectl get pod constraints-mem-demo-4 --namespace=constraints-mem-example --output=yaml ... resources: limits: memory: 1Gi requests: memory: 1Gi ... 容器没有指定自己的 CPU 请求和限制，所以它将从 LimitRange 获取默认的 CPU 请求和限制值。 2.2. CPU的最大最小值 创建LimitRange # 创建namespace $ kubectl create namespace constraints-cpu-example # 创建LimitRange $ cat cpu-constraints.yaml apiVersion: v1 kind: LimitRange metadata: name: cpu-min-max-demo-lr spec: limits: - max: cpu: \"800m\" min: cpu: \"200m\" type: Container $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/cpu-constraints.yaml --namespace=constraints-cpu-example # 查看LimitRange $ kubectl get limitrange cpu-min-max-demo-lr --output=yaml --namespace=constraints-cpu-example ... limits: - default: cpu: 800m defaultRequest: cpu: 800m max: cpu: 800m min: cpu: 200m type: Container ... 创建符合要求的Pod $ cat cpu-constraints-pod.yaml apiVersion: v1 kind: Pod metadata: name: constraints-cpu-demo spec: containers: - name: constraints-cpu-demo-ctr image: nginx resources: limits: cpu: \"800m\" requests: cpu: \"500m\" $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/cpu-constraints-pod.yaml --namespace=constraints-cpu-example # 查看Pod $ kubectl get pod constraints-cpu-demo --output=yaml --namespace=constraints-cpu-example ... resources: limits: cpu: 800m requests: cpu: 500m ... 创建超过最大CPU limit的Pod $ cat cpu-constraints-pod-2.yaml apiVersion: v1 kind: Pod metadata: name: constraints-cpu-demo-2 spec: containers: - name: constraints-cpu-demo-2-ctr image: nginx resources: limits: cpu: \"1.5\" requests: cpu: \"500m\" $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/cpu-constraints-pod-2.yaml --namespace=constraints-cpu-example # Pod创建失败，因为容器指定的CPU limit过大 Error from server (Forbidden): error when creating \"docs/tasks/administer-cluster/cpu-constraints-pod-2.yaml\": pods \"constraints-cpu-demo-2\" is forbidden: maximum cpu usage per Container is 800m, but limit is 1500m. 创建小于最小CPU request的Pod $ cat cpu-constraints-pod-3.yaml apiVersion: v1 kind: Pod metadata: name: constraints-cpu-demo-4 spec: containers: - name: constraints-cpu-demo-4-ctr image: nginx resources: limits: cpu: \"800m\" requests: cpu: \"100m\" $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/cpu-constraints-pod-3.yaml --namespace=constraints-cpu-example # Pod创建失败，因为容器指定的CPU request过小 Error from server (Forbidden): error when creating \"docs/tasks/administer-cluster/cpu-constraints-pod-3.yaml\": pods \"constraints-cpu-demo-4\" is forbidden: minimum cpu usage per Container is 200m, but request is 100m. 创建没有指定任何CPU limit和request的pod $ cat cpu-constraints-pod-4.yaml apiVersion: v1 kind: Pod metadata: name: constraints-cpu-demo-4 spec: containers: - name: constraints-cpu-demo-4-ctr image: vish/stress $ kubectl create -f https://k8s.io/docs/tasks/administer-cluster/cpu-constraints-pod-4.yaml --namespace=constraints-cpu-example # 查看Pod kubectl get pod constraints-cpu-demo-4 --namespace=constraints-cpu-example --output=yaml ... resources: limits: cpu: 800m requests: cpu: 800m ... 容器没有指定自己的 CPU 请求和限制，所以它将从 LimitRange 获取默认的 CPU 请求和限制值。 2.3. 说明 LimitRange 在 namespace 中施加的最小和最大内存（CPU）限制只有在创建和更新 Pod 时才会被应用。改变 LimitRange 不会对之前创建的 Pod 造成影响。 Kubernetes 都会执行下列步骤： 如果容器没有指定自己的内存（CPU）请求（request）和限制（limit），系统将会为其分配默认值。 验证容器的内存（CPU）请求大于等于最小值。 验证容器的内存（CPU）限制小于等于最大值。 参考文章： https://kubernetes.io/docs/tasks/administer-cluster/manage-resources/memory-default-namespace/ https://kubernetes.io/docs/tasks/administer-cluster/manage-resources/cpu-default-namespace/ https://kubernetes.io/docs/tasks/administer-cluster/manage-resources/memory-constraint-namespace/ https://kubernetes.io/docs/tasks/administer-cluster/manage-resources/cpu-constraint-namespace/ https://kubernetes.io/docs/tasks/administer-cluster/manage-resources/quota-memory-cpu-namespace/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"resource/quality-of-service.html":{"url":"resource/quality-of-service.html","title":"资源服务质量","keywords":"","body":"Resource Quality of Service1. 资源QoS简介2. Requests and Limits2.1. 可压缩的资源2.2. 不可压缩的资源3. QoS 级别3.1. Guaranteed3.2. Burstable3.3. Best-EffortResource Quality of Service 1. 资源QoS简介 request值表示容器保证可被分配到资源。limit表示容器可允许使用的最大资源。Pod级别的request和limit是其所有容器的request和limit之和。 2. Requests and Limits Pod可以指定request和limit资源。其中0 Node Allocatable & request 。调度是基于request而不是limit，即如果Pod被成功调度，那么可以保证Pod分配到指定的 request的资源。Pod使用的资源能否超过指定的limit值取决于该资源是否可被压缩。 2.1. 可压缩的资源 目前只支持CPU pod可以保证获得它们请求的CPU数量，它们可能会也可能不会获得额外的CPU时间(取决于正在运行的其他作业)。因为目前CPU隔离是在容器级别而不是pod级别。 2.2. 不可压缩的资源 目前只支持内存 pod将获得它们请求的内存数量，如果超过了它们的内存请求，它们可能会被杀死(如果其他一些pod需要内存)，但如果pod消耗的内存小于请求的内存，那么它们将不会被杀死(除非在系统任务或守护进程需要更多内存的情况下)。 3. QoS 级别 在机器资源超卖的情况下（limit的总量大于机器的资源容量），即CPU或内存耗尽，将不得不杀死部分不重要的容器。因此对容器分成了3个QoS的级别：Guaranteed,Burstable, Best-Effort，三个级别的优先级依次递减。 当CPU资源无法满足，pod不会被杀死可能被短暂控制。 内存是不可压缩的资源，当内存耗尽的情况下，会依次杀死优先级低的容器。Guaranteed的级别最高，不会被杀死，除非容器使用量超过limit限值或者资源耗尽，已经没有更低级别的容器可驱逐。 3.1. Guaranteed 所有的容器的limit值和request值被配置且两者相等（如果只配置limit没有request，则request取值于limit）。 例如： # 示例1 containers: name: foo resources: limits: cpu: 10m memory: 1Gi name: bar resources: limits: cpu: 100m memory: 100Mi # 示例2 containers: name: foo resources: limits: cpu: 10m memory: 1Gi requests: cpu: 10m memory: 1Gi name: bar resources: limits: cpu: 100m memory: 100Mi requests: cpu: 100m memory: 100Mi 3.2. Burstable 如果一个或多个容器的limit和request值被配置且两者不相等。 例如： # 示例1 containers: name: foo resources: limits: cpu: 10m memory: 1Gi requests: cpu: 10m memory: 1Gi name: bar # 示例2 containers: name: foo resources: limits: memory: 1Gi name: bar resources: limits: cpu: 100m # 示例3 containers: name: foo resources: requests: cpu: 10m memory: 1Gi name: bar 3.3. Best-Effort 所有的容器的limit和request值都没有配置。 例如： containers: name: foo resources: name: bar resources: 参考文章： https://github.com/kubernetes/community/blob/master/contributors/design-proposals/node/resource-qos.md Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"resource/lxcfs/lxcfs.html":{"url":"resource/lxcfs/lxcfs.html","title":"Lxcfs资源视图隔离","keywords":"","body":"1. 资源视图隔离2. Lxcfs简介3. Lxcfs原理4. 使用方式4.1. 安装lxcfs4.2. 运行lxcfs4.3. 挂载容器内/proc下的文件目录4.4. 验证容器内CPU和内存5. 使用k8s集群部署5.1. lxcfs-image5.2. daemonset5.3. lxcfs-admission-webhook1. 资源视图隔离 容器中的执行top、free等命令展示出来的CPU，内存等信息是从/proc目录中的相关文件里读取出来的。而容器并没有对/proc，/sys等文件系统做隔离，因此容器中读取出来的CPU和内存的信息是宿主机的信息，与容器实际分配和限制的资源量不同。 /proc/cpuinfo /proc/diskstats /proc/meminfo /proc/stat /proc/swaps /proc/uptime 为了实现让容器内部的资源视图更像虚拟机，使得应用程序可以拿到真实的CPU和内存信息，就需要通过文件挂载的方式将cgroup的真实的容器资源信息挂载到容器内/proc下的文件，使得容器内执行top、free等命令时可以拿到真实的CPU和内存信息。 2. Lxcfs简介 lxcfs是一个FUSE文件系统，使得Linux容器的文件系统更像虚拟机。lxcfs是一个常驻进程运行在宿主机上，从而来自动维护宿主机cgroup中容器的真实资源信息与容器内/proc下文件的映射关系。 lxcfs的命令信息如下： #/usr/local/bin/lxcfs -h Usage: lxcfs [-f|-d] -u -l -n [-p pidfile] mountpoint -f running foreground by default; -d enable debug output -l use loadavg -u no swap Default pidfile is /run/lxcfs.pid lxcfs -h lxcfs的源码：https://github.com/lxc/lxcfs 3. Lxcfs原理 lxcfs实现的基本原理是通过文件挂载的方式，把cgroup中容器相关的信息读取出来，存储到lxcfs相关的目录下，并将相关目录映射到容器内的/proc目录下，从而使得容器内执行top,free等命令时拿到的/proc下的数据是真实的cgroup分配给容器的CPU和内存数据。 原理图 图片 - lxcfs 映射目录 类别 容器内目录 宿主机lxcfs目录 cpu /proc/cpuinfo /var/lib/lxcfs/{container_id}/proc/cpuinfo 内存 /proc/meminfo /var/lib/lxcfs/{container_id}/proc/meminfo /proc/diskstats /var/lib/lxcfs/{container_id}/proc/diskstats /proc/stat /var/lib/lxcfs/{container_id}/proc/stat /proc/swaps /var/lib/lxcfs/{container_id}/proc/swaps /proc/uptime /var/lib/lxcfs/{container_id}/proc/uptime /proc/loadavg /var/lib/lxcfs/{container_id}/proc/loadavg /sys/devices/system/cpu/online /var/lib/lxcfs/{container_id}/sys/devices/system/cpu/online 4. 使用方式 4.1. 安装lxcfs 环境准备 yum install -y fuse fuse-lib fuse-devel 源码编译安装 git clone git://github.com/lxc/lxcfs cd lxcfs ./bootstrap.sh ./configure make make install 或者通过rpm包安装 wget https://copr-be.cloud.fedoraproject.org/results/ganto/lxc3/epel-7-x86_64/01041891-lxcfs/lxcfs-3.1.2-0.2.el7.x86_64.rpm; rpm -ivh lxcfs-3.1.2-0.2.el7.x86_64.rpm --force --nodeps 查看是否安装成功 lxcfs -h 4.2. 运行lxcfs 运行lxcfs主要执行两条命令。 sudo mkdir -p /var/lib/lxcfs sudo lxcfs /var/lib/lxcfs 可以通过systemd运行。 lxcfs.service文件： cat > /usr/lib/systemd/system/lxcfs.service 运行命令 systemctl daemon-reload && systemctl enable lxcfs && systemctl start lxcfs && systemctl status lxcfs 4.3. 挂载容器内/proc下的文件目录 docker run -it --rm -m 256m --cpus 2 \\ -v /var/lib/lxcfs/proc/cpuinfo:/proc/cpuinfo:rw \\ -v /var/lib/lxcfs/proc/diskstats:/proc/diskstats:rw \\ -v /var/lib/lxcfs/proc/meminfo:/proc/meminfo:rw \\ -v /var/lib/lxcfs/proc/stat:/proc/stat:rw \\ -v /var/lib/lxcfs/proc/swaps:/proc/swaps:rw \\ -v /var/lib/lxcfs/proc/uptime:/proc/uptime:rw \\ nginx:latest /bin/sh 4.4. 验证容器内CPU和内存 # cpu grep -c processor /proc/cpuinfo cat /proc/cpuinfo # memory free -g cat /proc/meminfo 5. 使用k8s集群部署 使用k8s集群部署与systemd部署方式同理，需要解决2个问题： 在每个node节点上部署lxcfs常驻进程，lxcfs需要通过镜像来运行，可以通过daemonset来部署。 实现将lxcfs维护的目录自动挂载到pod内的/proc目录。 具体可参考：https://github.com/denverdino/lxcfs-admission-webhook 5.1. lxcfs-image Dockerfile FROM centos:7 as build RUN yum -y update RUN yum -y install fuse-devel pam-devel wget install gcc automake autoconf libtool make ENV LXCFS_VERSION 3.1.2 RUN wget https://linuxcontainers.org/downloads/lxcfs/lxcfs-$LXCFS_VERSION.tar.gz && \\ mkdir /lxcfs && tar xzvf lxcfs-$LXCFS_VERSION.tar.gz -C /lxcfs --strip-components=1 && \\ cd /lxcfs && ./configure && make FROM centos:7 STOPSIGNAL SIGINT COPY --from=build /lxcfs/lxcfs /usr/local/bin/lxcfs COPY --from=build /lxcfs/.libs/liblxcfs.so /usr/local/lib/lxcfs/liblxcfs.so COPY --from=build /lxcfs/lxcfs /lxcfs/lxcfs COPY --from=build /lxcfs/.libs/liblxcfs.so /lxcfs/liblxcfs.so COPY --from=build /usr/lib64/libfuse.so.2.9.2 /usr/lib64/libfuse.so.2.9.2 COPY --from=build /usr/lib64/libulockmgr.so.1.0.1 /usr/lib64/libulockmgr.so.1.0.1 RUN ln -s /usr/lib64/libfuse.so.2.9.2 /usr/lib64/libfuse.so.2 && \\ ln -s /usr/lib64/libulockmgr.so.1.0.1 /usr/lib64/libulockmgr.so.1 COPY start.sh / CMD [\"/start.sh\"] star.sh #!/bin/bash # Cleanup nsenter -m/proc/1/ns/mnt fusermount -u /var/lib/lxcfs 2> /dev/null || true nsenter -m/proc/1/ns/mnt [ -L /etc/mtab ] || \\ sed -i \"/^lxcfs \\/var\\/lib\\/lxcfs fuse.lxcfs/d\" /etc/mtab # Prepare mkdir -p /usr/local/lib/lxcfs /var/lib/lxcfs # Update lxcfs cp -f /lxcfs/lxcfs /usr/local/bin/lxcfs cp -f /lxcfs/liblxcfs.so /usr/local/lib/lxcfs/liblxcfs.so # Mount exec nsenter -m/proc/1/ns/mnt /usr/local/bin/lxcfs /var/lib/lxcfs/ 5.2. daemonset lxcfs-daemonset.yaml apiVersion: apps/v1 kind: DaemonSet metadata: name: lxcfs labels: app: lxcfs spec: selector: matchLabels: app: lxcfs template: metadata: labels: app: lxcfs spec: hostPID: true tolerations: - key: node-role.kubernetes.io/master effect: NoSchedule containers: - name: lxcfs image: registry.cn-hangzhou.aliyuncs.com/denverdino/lxcfs:3.1.2 imagePullPolicy: Always securityContext: privileged: true volumeMounts: - name: cgroup mountPath: /sys/fs/cgroup - name: lxcfs mountPath: /var/lib/lxcfs mountPropagation: Bidirectional - name: usr-local mountPath: /usr/local volumes: - name: cgroup hostPath: path: /sys/fs/cgroup - name: usr-local hostPath: path: /usr/local - name: lxcfs hostPath: path: /var/lib/lxcfs type: DirectoryOrCreate 5.3. lxcfs-admission-webhook lxcfs-admission-webhook实现了一个动态的准入webhook，更准确的讲是实现了一个修改性质的webhook，即监听pod的创建，然后对pod执行patch的操作，从而将lxcfs与容器内的目录映射关系植入到pod创建的yaml中从而实现自动挂载。 deployment apiVersion: apps/v1 kind: Deployment metadata: name: lxcfs-admission-webhook-deployment labels: app: lxcfs-admission-webhook spec: replicas: 1 selector: matchLabels: app: lxcfs-admission-webhook template: metadata: labels: app: lxcfs-admission-webhook spec: containers: - name: lxcfs-admission-webhook image: registry.cn-hangzhou.aliyuncs.com/denverdino/lxcfs-admission-webhook:v1 imagePullPolicy: IfNotPresent args: - -tlsCertFile=/etc/webhook/certs/cert.pem - -tlsKeyFile=/etc/webhook/certs/key.pem - -alsologtostderr - -v=4 - 2>&1 volumeMounts: - name: webhook-certs mountPath: /etc/webhook/certs readOnly: true volumes: - name: webhook-certs secret: secretName: lxcfs-admission-webhook-certs 具体部署参考:install.sh #!/bin/bash ./deployment/webhook-create-signed-cert.sh kubectl get secret lxcfs-admission-webhook-certs kubectl create -f deployment/deployment.yaml kubectl create -f deployment/service.yaml cat ./deployment/mutatingwebhook.yaml | ./deployment/webhook-patch-ca-bundle.sh > ./deployment/mutatingwebhook-ca-bundle.yaml kubectl create -f deployment/mutatingwebhook-ca-bundle.yaml 执行命令 /deployment/install.sh 参考： https://github.com/lxc/lxcfs https://linuxcontainers.org/lxcfs/ https://github.com/denverdino/lxcfs-admission-webhook https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/kubernetes-troubleshooting.html":{"url":"operation/kubernetes-troubleshooting.html","title":"kubernetes集群问题排查","keywords":"","body":"1. 查看系统Event事件1.1. Pod1.2. NODE1.3. RC1.4. NAMESPACE1.5. Service2. 查看容器日志3. 查看k8s服务日志3.1. journalctl3.2. 日志文件4. 常见问题4.1. Pod状态一直为Pending4.2. Pod创建后不断重启1. 查看系统Event事件 kubectl describe pod --namespace= 该命令可以显示Pod创建时的配置定义、状态等信息和最近的Event事件，事件信息可用于排错。例如当Pod状态为Pending，可通过查看Event事件确认原因，一般原因有几种： 没有可用的Node可调度 开启了资源配额管理并且当前Pod的目标节点上恰好没有可用的资源 正在下载镜像（镜像拉取耗时太久）或镜像下载失败。 kubectl describe还可以查看其它k8s对象：NODE,RC,Service,Namespace,Secrets。 1.1. Pod kubectl describe pod --namespace= 以下是容器的启动命令非阻塞式导致容器挂掉，被k8s频繁重启所产生的事件。 kubectl describe pod --namespace= Events: FirstSeen LastSeen Count From SubobjectPath Reason Message ───────── ──────── ───── ──── ───────────── ────── ─────── 7m 7m 1 {scheduler } Scheduled Successfully assigned yangsc-1-0-0-index0 to 10.8.216.19 7m 7m 1 {kubelet 10.8.216.19} containers{infra} Pulled Container image \"gcr.io/kube-system/pause:0.8.0\" already present on machine 7m 7m 1 {kubelet 10.8.216.19} containers{infra} Created Created with docker id 84f133c324d0 7m 7m 1 {kubelet 10.8.216.19} containers{infra} Started Started with docker id 84f133c324d0 7m 7m 1 {kubelet 10.8.216.19} containers{yangsc0} Started Started with docker id 3f9f82abb145 7m 7m 1 {kubelet 10.8.216.19} containers{yangsc0} Created Created with docker id 3f9f82abb145 7m 7m 1 {kubelet 10.8.216.19} containers{yangsc0} Created Created with docker id fb112e4002f4 7m 7m 1 {kubelet 10.8.216.19} containers{yangsc0} Started Started with docker id fb112e4002f4 6m 6m 1 {kubelet 10.8.216.19} containers{yangsc0} Created Created with docker id 613b119d4474 6m 6m 1 {kubelet 10.8.216.19} containers{yangsc0} Started Started with docker id 613b119d4474 6m 6m 1 {kubelet 10.8.216.19} containers{yangsc0} Created Created with docker id 25cb68d1fd3d 6m 6m 1 {kubelet 10.8.216.19} containers{yangsc0} Started Started with docker id 25cb68d1fd3d 5m 5m 1 {kubelet 10.8.216.19} containers{yangsc0} Started Started with docker id 7d9ee8610b28 5m 5m 1 {kubelet 10.8.216.19} containers{yangsc0} Created Created with docker id 7d9ee8610b28 3m 3m 1 {kubelet 10.8.216.19} containers{yangsc0} Started Started with docker id 88b9e8d582dd 3m 3m 1 {kubelet 10.8.216.19} containers{yangsc0} Created Created with docker id 88b9e8d582dd 7m 1m 7 {kubelet 10.8.216.19} containers{yangsc0} Pulling Pulling image \"gcr.io/test/tcp-hello:1.0.0\" 1m 1m 1 {kubelet 10.8.216.19} containers{yangsc0} Started Started with docker id 089abff050e7 1m 1m 1 {kubelet 10.8.216.19} containers{yangsc0} Created Created with docker id 089abff050e7 7m 1m 7 {kubelet 10.8.216.19} containers{yangsc0} Pulled Successfully pulled image \"gcr.io/test/tcp-hello:1.0.0\" 6m 7s 34 {kubelet 10.8.216.19} containers{yangsc0} Backoff Back-off restarting failed docker container 1.2. NODE kubectl describe node 10.8.216.20 [root@FC-43745A-10 ~]# kubectl describe node 10.8.216.20 Name: 10.8.216.20 Labels: kubernetes.io/hostname=10.8.216.20,namespace/bcs-cc=true,namespace/myview=true CreationTimestamp: Mon, 17 Apr 2017 11:32:52 +0800 Phase: Conditions: Type Status LastHeartbeatTime LastTransitionTime Reason Message ──── ────── ───────────────── ────────────────── ────── ─────── Ready True Fri, 18 Aug 2017 09:38:33 +0800 Tue, 02 May 2017 17:40:58 +0800 KubeletReady kubelet is posting ready status OutOfDisk False Fri, 18 Aug 2017 09:38:33 +0800 Mon, 17 Apr 2017 11:31:27 +0800 KubeletHasSufficientDisk kubelet has sufficient disk space available Addresses: 10.8.216.20,10.8.216.20 Capacity: cpu: 32 memory: 67323039744 pods: 40 System Info: Machine ID: 723bafc7f6764022972b3eae1ce6b198 System UUID: 4C4C4544-0042-4210-8044-C3C04F595631 Boot ID: da01f2e3-987a-425a-9ca7-1caaec35d1e5 Kernel Version: 3.10.0-327.28.3.el7.x86_64 OS Image: CentOS Linux 7 (Core) Container Runtime Version: docker://1.13.1 Kubelet Version: v1.1.1-xxx2-13.1+79c90c68bfb72f-dirty Kube-Proxy Version: v1.1.1-xxx2-13.1+79c90c68bfb72f-dirty ExternalID: 10.8.216.20 Non-terminated Pods: (6 in total) Namespace Name CPU Requests CPU Limits Memory Requests Memory Limits ───────── ──── ──────────── ────────── ─────────────── ───────────── bcs-cc bcs-cc-api-0-0-1364-index0 1 (3%) 1 (3%) 4294967296 (6%) 4294967296 (6%) bcs-cc bcs-cc-api-0-0-1444-index0 1 (3%) 1 (3%) 4294967296 (6%) 4294967296 (6%) fw fw-demo2-0-0-1519-index0 1 (3%) 1 (3%) 4294967296 (6%) 4294967296 (6%) myview myview-api-0-0-1362-index0 1 (3%) 1 (3%) 4294967296 (6%) 4294967296 (6%) myview myview-api-0-0-1442-index0 1 (3%) 1 (3%) 4294967296 (6%) 4294967296 (6%) qa-ts-dna ts-dna-console3-0-0-1434-index0 1 (3%) 1 (3%) 4294967296 (6%) 4294967296 (6%) Allocated resources: (Total limits may be over 100%, i.e., overcommitted. More info: http://releases.k8s.io/HEAD/docs/user-guide/compute-resources.md) CPU Requests CPU Limits Memory Requests Memory Limits ──────────── ────────── ─────────────── ───────────── 6 (18%) 6 (18%) 25769803776 (38%) 25769803776 (38%) No events. 1.3. RC kubectl describe rc mytest-1-0-0 --namespace=test [root@FC-43745A-10 ~]# kubectl describe rc mytest-1-0-0 --namespace=test Name: mytest-1-0-0 Namespace: test Image(s): gcr.io/test/mywebcalculator:1.0.1 Selector: app=mytest,appVersion=1.0.0 Labels: app=mytest,appVersion=1.0.0,env=ts,zone=inner Replicas: 1 current / 1 desired Pods Status: 1 Running / 0 Waiting / 0 Succeeded / 0 Failed No volumes. Events: FirstSeen LastSeen Count From SubobjectPath Reason Message ───────── ──────── ───── ──── ───────────── ────── ─────── 20h 19h 9 {replication-controller } FailedCreate Error creating: Pod \"mytest-1-0-0-index0\" is forbidden: limited to 10 pods 20h 17h 7 {replication-controller } FailedCreate Error creating: pods \"mytest-1-0-0-index0\" already exists 20h 17h 4 {replication-controller } SuccessfulCreate Created pod: mytest-1-0-0-index0 1.4. NAMESPACE kubectl describe namespace test [root@FC-43745A-10 ~]# kubectl describe namespace test Name: test Labels: Status: Active Resource Quotas Resource Used Hard --- --- --- cpu 5 20 memory 1342177280 53687091200 persistentvolumeclaims 0 10 pods 4 10 replicationcontrollers 8 20 resourcequotas 1 1 secrets 3 10 services 8 20 No resource limits. 1.5. Service kubectl describe service xxx-containers-1-1-0 --namespace=test [root@FC-43745A-10 ~]# kubectl describe service xxx-containers-1-1-0 --namespace=test Name: xxx-containers-1-1-0 Namespace: test Labels: app=xxx-containers,appVersion=1.1.0,env=ts,zone=inner Selector: app=xxx-containers,appVersion=1.1.0 Type: ClusterIP IP: 10.254.46.42 Port: port-dna-tcp-35913 35913/TCP Endpoints: 10.0.92.17:35913 Port: port-l7-tcp-8080 8080/TCP Endpoints: 10.0.92.17:8080 Session Affinity: None No events. 2. 查看容器日志 1、查看指定pod的日志 kubectl logs kubectl logs -f #类似tail -f的方式查看 2、查看上一个pod的日志 kubectl logs -p 3、查看指定pod中指定容器的日志 kubectl logs -c 4、kubectl logs --help [root@node5 ~]# kubectl logs --help Print the logs for a container in a pod. If the pod has only one container, the container name is optional. Usage: kubectl logs [-f] [-p] POD [-c CONTAINER] [flags] Aliases: logs, log Examples: # Return snapshot logs from pod nginx with only one container $ kubectl logs nginx # Return snapshot of previous terminated ruby container logs from pod web-1 $ kubectl logs -p -c ruby web-1 # Begin streaming the logs of the ruby container in pod web-1 $ kubectl logs -f -c ruby web-1 # Display only the most recent 20 lines of output in pod nginx $ kubectl logs --tail=20 nginx # Show all logs from pod nginx written in the last hour $ kubectl logs --since=1h nginx 3. 查看k8s服务日志 3.1. journalctl 在Linux系统上systemd系统来管理kubernetes服务，并且journal系统会接管服务程序的输出日志，可以通过systemctl status 或journalctl -u -f来查看kubernetes服务的日志。 其中kubernetes组件包括： k8s组件 涉及日志内容 备注 kube-apiserver kube-controller-manager Pod扩容相关或RC相关 kube-scheduler Pod扩容相关或RC相关 kubelet Pod生命周期相关：创建、停止等 etcd 3.2. 日志文件 也可以通过指定日志存放目录来保存和查看日志 --logtostderr=false：不输出到stderr --log-dir=/var/log/kubernetes:日志的存放目录 --alsologtostderr=false:设置为true表示日志输出到文件也输出到stderr --v=0:glog的日志级别 --vmodule=gfs=2,test=4：glog基于模块的详细日志级别 4. 常见问题 4.1. Pod状态一直为Pending kubectl describe --namespace= 查看该POD的事件。 正在下载镜像但拉取不下来（镜像拉取耗时太久）[一般都是该原因] 没有可用的Node可调度 开启了资源配额管理并且当前Pod的目标节点上恰好没有可用的资源 解决方法： 查看该POD所在宿主机与镜像仓库之间的网络是否有问题，可以手动拉取镜像 删除POD实例，让POD调度到别的宿主机上 4.2. Pod创建后不断重启 kubectl get pods中Pod状态一会running，一会不是，且RESTARTS次数不断增加。 一般原因为容器启动命令不是阻塞式命令，导致容器运行后马上退出。 非阻塞式命令： 本身CMD指定的命令就是非阻塞式命令 将服务启动方式设置为后台运行 解决方法： 1、将命令改为阻塞式命令（前台运行），例如：zkServer.sh start-foreground 2、java运行程序的启动脚本将 nohup xxx &的nobup和&去掉，例如： nohup JAVA_HOME/bin/java JAVA_OPTS -cp $CLASSPATH com.cnc.open.processor.Main & 改为： JAVA_HOME/bin/java JAVA_OPTS -cp $CLASSPATH com.cnc.open.processor.Main 文章参考《Kubernetes权威指南》 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/kubectl/_index.html":{"url":"operation/kubectl/_index.html","title":"kubectl工具","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/kubectl/install-kubectl.html":{"url":"operation/kubectl/install-kubectl.html","title":"kubectl安装与配置","keywords":"","body":"1. kubectl的安装2. 配置k8s集群环境2.1. 命令行方式2.1.1 非安全方式2.1.2 安全方式2.1.3 查询当前配置环境2.2. 添加配置文件的方式3. kubectl config4. shell自动补齐1. kubectl的安装 curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/linux/amd64/kubectl && chmod +x kubectl && sudo mv kubectl /usr/local/bin/ 安装指定版本的kubectl，例如：v1.9.0 curl -LO https://storage.googleapis.com/kubernetes-release/release/v1.9.0/bin/linux/amd64/kubectl && chmod +x kubectl && sudo mv kubectl /usr/local/bin/ 2. 配置k8s集群环境 2.1. 命令行方式 2.1.1 非安全方式 kubectl config set-cluster k8s --server=http:// kubectl config set-context --cluster=k8s --namespace= kubectl config use-context 2.1.2 安全方式 kubectl config set-cluster k8s --server=https:// --insecure-skip-tls-verify=true kubectl config set-credentials k8s-user --username= --password= kubectl config set-context --cluster=k8s --user=k8s-user --namespace= kubectl config use-context 2.1.3 查询当前配置环境 [root@test ]# kubectl cluster-info Kubernetes master is running at http://192.168.10.3:8081 2.2. 添加配置文件的方式 当没有指定--kubeconfig参数和$KUBECONFIG的环境变量的时候，会默认读取${HOME}/.kube/config。 因此创建${HOME}/.kube/config文件，并在`${HOME}/.kube/ssl目录下创建ca.pem、cert.pem、key.pem文件。 内容如下： apiVersion: v1 kind: Config clusters: - name: local cluster: certificate-authority: ./ssl/ca.pem server: https://192.168.10.3:6443 users: - name: kubelet user: client-certificate: ./ssl/cert.pem client-key: ./ssl/key.pem contexts: - context: cluster: local user: kubelet name: kubelet-cluster.local current-context: kubelet-cluster.local 3. kubectl config kubectl config命令说明 $ kubectl config --help Modify kubeconfig files using subcommands like \"kubectl config set current-context my-context\" The loading order follows these rules: 1. If the --kubeconfig flag is set, then only that file is loaded. The flag may only be set once and no merging takes place. 2. If $KUBECONFIG environment variable is set, then it is used a list of paths (normal path delimitting rules for your system). These paths are merged. When a value is modified, it is modified in the file that defines the stanza. When a value is created, it is created in the first file that exists. If no files in the chain exist, then it creates the last file in the list. 3. Otherwise, ${HOME}/.kube/config is used and no merging takes place. Available Commands: current-context Displays the current-context delete-cluster Delete the specified cluster from the kubeconfig delete-context Delete the specified context from the kubeconfig get-clusters Display clusters defined in the kubeconfig get-contexts Describe one or many contexts rename-context Renames a context from the kubeconfig file. set Sets an individual value in a kubeconfig file set-cluster Sets a cluster entry in kubeconfig set-context Sets a context entry in kubeconfig set-credentials Sets a user entry in kubeconfig unset Unsets an individual value in a kubeconfig file use-context Sets the current-context in a kubeconfig file view Display merged kubeconfig settings or a specified kubeconfig file Usage: kubectl config SUBCOMMAND [options] Use \"kubectl --help\" for more information about a given command. Use \"kubectl options\" for a list of global command-line options (applies to all commands). 4. shell自动补齐 source > ~/.bashrc 如果出现以下报错 # kubectl自动补齐失败 kubectl _get_comp_words_by_ref : command not found 解决方法： yum install bash-completion -y source /etc/profile.d/bash_completion.sh 参考文章： https://kubernetes.io/docs/tasks/tools/install-kubectl/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/kubectl/kubectl-commands.html":{"url":"operation/kubectl/kubectl-commands.html","title":"kubectl命令说明","keywords":"","body":"1. kubectl命令介绍2. 操作的常用资源对象3. kubectl命令分类[command]3.1 增3.2 删3.3 改3.4 查4. Pod相关命令4.1 查询Pod4.2 进入Pod4.3 删除Pod4.4 日志查看5. 常用命令5.1. Node隔离与恢复5.2. kubectl label5.3. 升级镜像5.4. 调整资源值5.5. 调整readiness probe5.6. 调整tolerations属性5.7. 查看所有节点的IP5.8. 查看当前k8s组件leader节点5.9. 修改副本数5.10. 批量删除pod5.11. 各种查看命令5.12. 拷贝文件5.13. 强制删除namespace6. kubectl日志级别1. kubectl命令介绍 kubectl的命令语法 kubectl [command] [TYPE] [NAME] [flags] 其中command，TYPE，NAME，和flags分别是： command: 指定要在一个或多个资源进行操作，例如create，get，describe，delete。 TYPE：指定资源类型。资源类型区分大小写，您可以指定单数，复数或缩写形式。例如，以下命令产生相同的输出： kubectl get pod pod1 kubectl get pods pod1 kubectl get po pod1 NAME：指定资源的名称。名称区分大小写。如果省略名称，则会显示所有资源的详细信息,比如$ kubectl get pods。 按类型和名称指定多种资源： * 要分组资源，如果它们都是相同的类型：`TYPE1 name1 name2 name`. 例: `$ kubectl get pod example-pod1 example-pod2` * 要分别指定多种资源类型: `TYPE1/name1 TYPE1/name2 TYPE2/name3 TYPE/name`. 例: `$ kubectl get pod/example-pod1 replicationcontroller/example-rc1` flags：指定可选标志。例如，您可以使用-s或--serverflags来指定Kubernetes API服务器的地址和端口。 更多命令介绍： [root@node5 ~]# kubectl kubectl controls the Kubernetes cluster manager. Find more information at https://github.com/kubernetes/kubernetes. Basic Commands (Beginner): create Create a resource from a file or from stdin. expose Take a replication controller, service, deployment or pod and expose it as a new Kubernetes Service run Run a particular image on the cluster set Set specific features on objects run-container Run a particular image on the cluster. This command is deprecated, use \"run\" instead Basic Commands (Intermediate): get Display one or many resources explain Documentation of resources edit Edit a resource on the server delete Delete resources by filenames, stdin, resources and names, or by resources and label selector Deploy Commands: rollout Manage the rollout of a resource rolling-update Perform a rolling update of the given ReplicationController scale Set a new size for a Deployment, ReplicaSet, Replication Controller, or Job autoscale Auto-scale a Deployment, ReplicaSet, or ReplicationController Cluster Management Commands: certificate Modify certificate resources. cluster-info Display cluster info top Display Resource (CPU/Memory/Storage) usage. cordon Mark node as unschedulable uncordon Mark node as schedulable drain Drain node in preparation for maintenance taint Update the taints on one or more nodes Troubleshooting and Debugging Commands: describe Show details of a specific resource or group of resources logs Print the logs for a container in a pod attach Attach to a running container exec Execute a command in a container port-forward Forward one or more local ports to a pod proxy Run a proxy to the Kubernetes API server cp Copy files and directories to and from containers. auth Inspect authorization Advanced Commands: apply Apply a configuration to a resource by filename or stdin patch Update field(s) of a resource using strategic merge patch replace Replace a resource by filename or stdin convert Convert config files between different API versions Settings Commands: label Update the labels on a resource annotate Update the annotations on a resource completion Output shell completion code for the specified shell (bash or zsh) Other Commands: api-versions Print the supported API versions on the server, in the form of \"group/version\" config Modify kubeconfig files help Help about any command plugin Runs a command-line plugin version Print the client and server version information Use \"kubectl --help\" for more information about a given command. Use \"kubectl options\" for a list of global command-line options (applies to all commands). 2. 操作的常用资源对象 Node Podes Replication Controllers Services Namespace Deployment StatefulSet 具体对象类型及缩写： * all * certificatesigningrequests (aka 'csr') * clusterrolebindings * clusterroles * componentstatuses (aka 'cs') * configmaps (aka 'cm') * controllerrevisions * cronjobs * customresourcedefinition (aka 'crd') * daemonsets (aka 'ds') * deployments (aka 'deploy') * endpoints (aka 'ep') * events (aka 'ev') * horizontalpodautoscalers (aka 'hpa') * ingresses (aka 'ing') * jobs * limitranges (aka 'limits') * namespaces (aka 'ns') * networkpolicies (aka 'netpol') * nodes (aka 'no') * persistentvolumeclaims (aka 'pvc') * persistentvolumes (aka 'pv') * poddisruptionbudgets (aka 'pdb') * podpreset * pods (aka 'po') * podsecuritypolicies (aka 'psp') * podtemplates * replicasets (aka 'rs') * replicationcontrollers (aka 'rc') * resourcequotas (aka 'quota') * rolebindings * roles * secrets * serviceaccounts (aka 'sa') * services (aka 'svc') * statefulsets (aka 'sts') * storageclasses (aka 'sc') 3. kubectl命令分类[command] 3.1 增 1）create:[Create a resource by filename or stdin] 2）run:[ Run a particular image on the cluster] 3）apply:[Apply a configuration to a resource by filename or stdin] 4）proxy:[Run a proxy to the Kubernetes API server ] 3.2 删 1）delete:[Delete resources ] 3.3 改 1）scale:[Set a new size for a Replication Controller] 2）exec:[Execute a command in a container] 3）attach:[Attach to a running container] 4）patch:[Update field(s) of a resource by stdin] 5）edit:[Edit a resource on the server] 6） label:[Update the labels on a resource] 7）annotate:[Auto-scale a replication controller] 8）replace:[Replace a resource by filename or stdin] 9）config:[config modifies kubeconfig files] 3.4 查 1）get:[Display one or many resources] 2）describe:[Show details of a specific resource or group of resources] 3）log:[Print the logs for a container in a pod] 4）cluster-info:[Display cluster info] 5） version:[Print the client and server version information] 6）api-versions:[Print the supported API versions] 4. Pod相关命令 4.1 查询Pod kubectl get pod -o wide --namespace= 4.2 进入Pod kubectl exec -it /bin/bash --namespace= # 进入Pod中指定容器 kubectl exec -it -c /bin/bash --namespace= 4.3 删除Pod kubectl delete pod --namespace= # 强制删除Pod，当Pod一直处于Terminating状态 kubectl delete pod --namespace= --force --grace-period=0 # 删除某个namespace下某个类型的所有对象 kubectl delete deploy --all --namespace=test 4.4 日志查看 $ 查看运行容器日志 kubectl logs --namespace= $ 查看上一个挂掉的容器日志 kubectl logs -p --namespace= 5. 常用命令 5.1. Node隔离与恢复 说明：Node设置隔离之后，原先运行在该Node上的Pod不受影响，后续的Pod不会调度到被隔离的Node上。 1. Node隔离 # cordon命令 kubectl cordon # 或者 kubectl patch node -p '{\"spec\":{\"unschedulable\":true}}' 2. Node恢复 # uncordon kubectl uncordon # 或者 kubectl patch node -p '{\"spec\":{\"unschedulable\":false}}' 5.2. kubectl label 1. 固定Pod到指定机器 kubectl label node namespace/=true 2. 取消Pod固定机器 kubectl label node namespace/- 5.3. 升级镜像 # 升级镜像 kubectl set image deployment/nginx nginx=nginx:1.15.12 -n nginx # 查看滚动升级情况 kubectl rollout status deployment/nginx -n nginx 5.4. 调整资源值 # 调整指定容器的资源值 kubectl set resources sts nginx-0 -c=agent --limits=memory=512Mi -n nginx 5.5. 调整readiness probe # 批量查看readiness probe timeoutSeconds kubectl get statefulset -o=jsonpath='{range .items[*]}{.metadata.name}{\"\\t\"}{.spec.template.spec.containers[0].readinessProbe.timeoutSeconds}{\"\\n\"}{end}' # 调整readiness probe timeoutSeconds参数 kubectl patch statefulset nginx-sts --type='json' -p='[{\"op\": \"replace\", \"path\": \"/spec/template/spec/containers/0/readinessProbe/timeoutSeconds\", \"value\":5}]' -n nginx 5.6. 调整tolerations属性 kubectl patch statefulset nginx-sts --patch '{\"spec\": {\"template\": {\"spec\": {\"tolerations\": [{\"effect\": \"NoSchedule\",\"key\": \"dedicated\",\"operator\": \"Equal\",\"value\": \"nginx\"}]}}}}' -n nginx 5.7. 查看所有节点的IP kubectl get nodes -o=jsonpath='{range .items[*]}{.metadata.name}{\"\\t\"}{.status.addresses[0].address}{\"\\n\"}{end}' 5.8. 查看当前k8s组件leader节点 当k8s集群高可用部署的时候，kube-controller-manager和kube-scheduler只能一个服务处于实际逻辑运行状态，通过参数--leader-elect=true来开启选举操作。以下提供查询leader节点的命令。 $ kubectl get endpoints kube-controller-manager --namespace=kube-system -o yaml apiVersion: v1 kind: Endpoints metadata: annotations: control-plane.alpha.kubernetes.io/leader: '{\"holderIdentity\":\"xxx.xxx.xxx.xxx_6537b938-7f5a-11e9-8487-00220d338975\",\"leaseDurationSeconds\":15,\"acquireTime\":\"2019-05-26T02:03:18Z\",\"renewTime\":\"2019-05-26T02:06:08Z\",\"leaderTransitions\":1}' creationTimestamp: \"2019-05-26T01:52:39Z\" name: kube-controller-manager namespace: kube-system resourceVersion: \"1965\" selfLink: /api/v1/namespaces/kube-system/endpoints/kube-controller-manager uid: f1755fc5-7f58-11e9-b4c4-00220d338975 以上表示\"holderIdentity\":\"xxx.xxx.xxx.xxx为kube-controller-manager的leader节点。 同理，可以通过以下命令查看kube-scheduler的leader节点。 kubectl get endpoints kube-scheduler --namespace=kube-system -o yaml 5.9. 修改副本数 kubectl scale deployment.v1.apps/nginx-deployment --replicas=10 5.10. 批量删除pod kubectl get po -n default |grep Evicted |awk '{print $1}' |xargs -I {} kubectl delete po {} -n default 5.11. 各种查看命令 # 不使用外部工具来输出解码后的 Secret kubectl get secret my-secret -o go-template='{{range $k,$v := .data}}{{\"### \"}}{{$k}}{{\"\\n\"}}{{$v|base64decode}}{{\"\\n\\n\"}}{{end}}' # 列出事件（Events），按时间戳排序 kubectl get events --sort-by=.metadata.creationTimestamp 5.12. 拷贝文件 从pod拷贝到本地 注意事项： pod的目录是workdir的相对路径，可以将文件拷贝到workdir下再拷贝出来 文件绝对路径前面不能加 / 文件目标位置不能为文件夹，必须为文件路径 kubectl cp -n -c : # 示例： # 将pod workdir下的prometheus.env.yaml文件拷贝到本地 kubectl cp -n prometheus -c prometheus prometheus-0:prometheus.env.yaml ./prometheus.env.yaml 从本地拷贝到pod 注意事项： 如果没有加路径，默认拷贝到pod内workdir路径。 kubectl cp -n -c : # 示例： kubectl cp ./prometheus.env.yaml -n prometheus -c prometheus prometheus-0:prometheus.env.yaml 5.13. 强制删除namespace 如果强制删除ns失败，可以使用以下命令删除，将以下的calico-system改为需要删除的namespace。 kubectl get namespaces calico-system -o json \\ | tr -d \"\\n\" | sed \"s/\\\"finalizers\\\": \\[[^]]\\+\\]/\\\"finalizers\\\": []/\" \\ | kubectl replace --raw /api/v1/namespaces/calico-system/finalize -f - 6. kubectl日志级别 Kubectl 日志输出详细程度是通过 -v 或者 --v 来控制的，参数后跟一个数字表示日志的级别。 Kubernetes 通用的日志习惯和相关的日志级别在 这里 有相应的描述。 详细程度 描述 --v=0 用于那些应该 始终 对运维人员可见的信息，因为这些信息一般很有用。 --v=1 如果您不想要看到冗余信息，此值是一个合理的默认日志级别。 --v=2 输出有关服务的稳定状态的信息以及重要的日志消息，这些信息可能与系统中的重大变化有关。这是建议大多数系统设置的默认日志级别。 --v=3 包含有关系统状态变化的扩展信息。 --v=4 包含调试级别的冗余信息。 --v=5 跟踪级别的详细程度。 --v=6 显示所请求的资源。 --v=7 显示 HTTP 请求头。 --v=8 显示 HTTP 请求内容。 --v=9 显示 HTTP 请求内容而且不截断内容。 参考文章： https://kubernetes.io/docs/reference/kubectl/overview/ https://kubernetes.io/docs/tasks/access-application-cluster/configure-access-multiple-clusters/ https://kubernetes.io/zh/docs/reference/kubectl/cheatsheet/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/kubectl/kubectl-alias.html":{"url":"operation/kubectl/kubectl-alias.html","title":"kubectl命令别名","keywords":"","body":"1. kubectl-aliases2. 示例3. 安装1. kubectl-aliases kubectl-aliases开源工具是由脚本通过拼接各种kubectl相关元素组成的alias命令别名列表，其中命令别名拼接元素如下： base [system?] [operation] [resource] [flags] kubectl -n=kube-system get describe rm:delete logs exec apply pods deployment secret ingress node svc ns cm oyaml ojson owide all watch file l k=kubectl sys=--namespace kube-system commands: g=get d=describe rm=delete a:apply -f ex: exec -i -t lo: logs -f resources: po=pod dep=deployment ing=ingress svc=service cm=configmap sec=secret ns=namespace no=node flags: output format: oyaml, ojson, owide all: --all or --all-namespaces depending on the command sl: --show-labels w=-w/--watch value flags (should be at the end): f=-f/--filename l=-l/--selector 2. 示例 # 示例1 kd → kubectl describe # 示例2 kgdepallw → kubectl get deployment —all-namespaces —watch alias get示例： alias k='kubectl' alias kg='kubectl get' alias kgpo='kubectl get pods' alias kgpoojson='kubectl get pods -o=json' alias kgpon='kubectl get pods --namespace' alias ksysgpooyamll='kubectl --namespace=kube-system get pods -o=yaml -l' 3. 安装 # 将 .kubectl_aliases下载到 home 目录 cd ~ && wget https://raw.githubusercontent.com/ahmetb/kubectl-aliases/master/.kubectl_aliases # 将以下内容添加到 .bashrc中，并执行 source .bashrc [ -f ~/.kubectl_aliases ] && source ~/.kubectl_aliases function kubectl() { command kubectl $@; } # 如果需要提示别名的完整命令，则将以下内容添加到 .bashrc中，并执行 source .bashrc [ -f ~/.kubectl_aliases ] && source ~/.kubectl_aliases function kubectl() { echo \"+ kubectl $@\"; command kubectl $@; } 参考： https://ahmet.im/blog/kubectl-aliases/ https://github.com/ahmetb/kubectl-aliases Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/kubectl/kubectl-node-shell.html":{"url":"operation/kubectl/kubectl-node-shell.html","title":"kubectl进入node shell","keywords":"","body":"1. 安装krew node-shell1.1. 安装krew1.2. 安装node-shell2. 进入节点的shell2.1. 登录node2.2. 退出node3. 原理3.1. Pod.yaml本文介绍如何通过kubectl进入节点的shell环境。 1. 安装krew node-shell 1.1. 安装krew ( set -x; cd \"$(mktemp -d)\" && OS=\"$(uname | tr '[:upper:]' '[:lower:]')\" && ARCH=\"$(uname -m | sed -e 's/x86_64/amd64/' -e 's/\\(arm\\)\\(64\\)\\?.*/\\1\\2/' -e 's/aarch64$/arm64/')\" && KREW=\"krew-${OS}_${ARCH}\" && curl -fsSLO \"https://github.com/kubernetes-sigs/krew/releases/latest/download/${KREW}.tar.gz\" && tar zxvf \"${KREW}.tar.gz\" && ./\"${KREW}\" install krew ) 在~/.bashrc或~/.zshrc添加以下命令 export PATH=\"${KREW_ROOT:-$HOME/.krew}/bin:$PATH\" 1.2. 安装node-shell node-shell的代码参考：kubectl-node-shell/kubectl-node_shell at master · kvaps/kubectl-node-shell · GitHub kubectl krew install node-shell 示例： # kubectl krew install node-shell Updated the local copy of plugin index. Installing plugin: node-shell Installed plugin: node-shell \\ | Use this plugin: | kubectl node-shell | Documentation: | https://github.com/kvaps/kubectl-node-shell | Caveats: | \\ | | You need to be allowed to start privileged pods in the cluster | / / WARNING: You installed plugin \"node-shell\" from the krew-index plugin repository. These plugins are not audited for security by the Krew maintainers. Run them at your own risk. 2. 进入节点的shell 2.1. 登录node 创建一个临时的特权容器，登录容器即登录node shell。 kubectl node-shell 示例： # kubectl node-shell node1 spawning \"nsenter-9yqytp\" on \"node1\" If you don't see a command prompt, try pressing enter. groups: cannot find name for group ID 11 To run a command as administrator (user \"root\"), use \"sudo \". See \"man sudo_root\" for details. root@node1:/# 2.2. 退出node 退出容器，容器会被自动删除。 # exit logout pod default/nsenter-9yqytp terminated (Error) pod \"nsenter-9yqytp\" deleted 3. 原理 容器是弱隔离，共享节点的内核，通过cgroup和namespace来实现进程级别的隔离。那么通过在特权容器里执行nsenter的命令，则可以通过登录特权容器来实现登录node的shell环境。 创建一个特权容器，进入node shell的命令为： nsenter --target 1 --mount --uts --ipc --net --pid -- bash -l 进入 node shell 的权限： hostPID: true 共享 host 的 pid hostNetwork: true 共享 host 的网络 privileged: true: PSP 权限策略是 privileged, 即完全无限制。 3.1. Pod.yaml apiVersion: v1 kind: Pod metadata: labels: run: nsenter-9yqytp name: nsenter-9yqytp namespace: default spec: containers: - command: - nsenter - --target - \"1\" - --mount - --uts - --ipc - --net - --pid - -- - bash - -l image: docker.io/library/alpine imagePullPolicy: Always name: nsenter resources: limits: cpu: 100m memory: 256Mi requests: cpu: 100m memory: 256Mi securityContext: privileged: true stdin: true stdinOnce: true tty: true volumeMounts: - mountPath: /var/run/secrets/kubernetes.io/serviceaccount name: kube-api-access-4ktlf readOnly: true enableServiceLinks: true hostNetwork: true hostPID: true nodeName: node1 preemptionPolicy: PreemptLowerPriority priority: 0 restartPolicy: Never schedulerName: default-scheduler securityContext: {} serviceAccount: default serviceAccountName: default tolerations: - key: CriticalAddonsOnly operator: Exists - effect: NoExecute operator: Exists volumes: - name: kube-api-access-4ktlf projected: defaultMode: 420 sources: - serviceAccountToken: expirationSeconds: 3607 path: token - configMap: items: - key: ca.crt path: ca.crt name: kube-root-ca.crt - downwardAPI: items: - fieldRef: apiVersion: v1 fieldPath: metadata.namespace path: namespace 创建完容器后，直接登录容器即可登录节点的shell kubectl exec -it nsenter-9yqytp bash 参考： 如何通过 kubectl 进入 node shell - 东风微鸣技术博客 GitHub - kvaps/kubectl-node-shell: Exec into node via kubectl https://krew.sigs.k8s.io/docs/user-guide/setup/install/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/helm/_index.html":{"url":"operation/helm/_index.html","title":"helm工具","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/helm/helm-usage.html":{"url":"operation/helm/helm-usage.html","title":"helm的使用","keywords":"","body":"1. 安装helm2. 基本概念3. helm命令4. 常用命令4.1. helm search4.2. helm install/uninstall4.3. helm upgrade4.4. helm rollback4.5. helm repo4.6. helm pull5. 创建chart5.1. 初始化chart5.2. 调试模板1. 安装helm curl https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash 2. 基本概念 Helm是用来管理k8s集群上的软件包。 Chart:代表helm软件包 Repository：软件包的存放仓库 Release:运行在k8s上的一个发布实例。 3. helm命令 Usage: helm [command] Available Commands: completion generate autocompletion scripts for the specified shell create create a new chart with the given name dependency manage a chart's dependencies env helm client environment information get download extended information of a named release help Help about any command history fetch release history install install a chart lint examine a chart for possible issues list list releases package package a chart directory into a chart archive plugin install, list, or uninstall Helm plugins pull download a chart from a repository and (optionally) unpack it in local directory push push a chart to remote registry login to or logout from a registry repo add, list, remove, update, and index chart repositories rollback roll back a release to a previous revision search search for a keyword in charts show show information of a chart status display the status of the named release template locally render templates test run tests for a release uninstall uninstall a release upgrade upgrade a release verify verify that a chart at the given path has been signed and is valid version print the client version information 4. 常用命令 4.1. helm search helm search hub：从 Artifact Hub 中查找并列出 helm charts。支持模糊匹配。 helm search hub wordpress helm search repo：基于指定仓库进行搜索。 helm repo add brigade https://brigadecore.github.io/charts helm search repo brigade # 列出所有版本 helm search repo apisix -l 4.2. helm install/uninstall helm install # 示例 helm install happy-panda bitnami/wordpress - # uninstall helm uninstall RELEASE_NAME 安装自定义chart helm install -f values.yaml bitnami/wordpress --generate-name # 本地 chart 压缩包 helm install foo foo-0.1.1.tgz # 解压后的 chart 目录 helm install foo path/to/foo # 完整的 URL helm install foo https://example.com/charts/foo-1.2.3.tgz 4.3. helm upgrade helm upgrade happy-panda bitnami/wordpress 4.4. helm rollback helm rollback [REVISION] [flags] 4.5. helm repo helm repo add dev https://example.com/dev-charts helm repo list helm repo remove 4.6. helm pull 从仓库下载并（可选）在本地目录解压。 helm pull [chart URL | repo/chartname] helm pull [chart URL | repo/chartname] --version 5. 创建chart 5.1. 初始化chart helm create mychart 查看生成的文件目录： mychart |-- charts # 目录用于存放所依赖的子chart |-- Chart.yaml # 描述这个 Chart 的相关信息、包括名字、描述信息、版本等 |-- templates | |-- deployment.yaml | |-- _helpers.tpl # 模板助手文件，定义的值可在模板中使用 | |-- hpa.yaml | |-- ingress.yaml | |-- NOTES.txt # Chart 部署到集群后的一些信息，例如：如何使用、列出缺省值 | |-- serviceaccount.yaml | |-- service.yaml | `-- tests | `-- test-connection.yaml `-- values.yaml # 模板的值文件，这些值会在安装时应用到 GO 模板生成部署文件 移除默认模板文件，并添加自己的模板文件。 rm -rf mysubchart/templates/* 5.2. 调试模板 helm lint 是验证chart是否遵循最佳实践的首选工具。 helm template --debug 在本地测试渲染chart模板。 helm install --dry-run --debug：我们已经看到过这个技巧了，这是让服务器渲染模板的好方法，然后返回生成的清单文件。 helm get manifest: 这是查看安装在服务器上的模板的好方法。 参考： Helm | 安装Helm Helm | 使用Helm Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/node/_index.html":{"url":"operation/node/_index.html","title":"节点迁移","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/node/safely-drain-node.html":{"url":"operation/node/safely-drain-node.html","title":"安全迁移节点","keywords":"","body":"1. 迁移Pod1.1. 设置节点是否可调度1.2. 执行kubectl drain命令1.3. 特别说明2. kubectl drain 流程图3. TroubleShooting4. kubectl drain1. 迁移Pod 1.1. 设置节点是否可调度 确定需要迁移和被迁移的节点，将不允许被迁移的节点设置为不可调度。 # 查看节点 kubectl get nodes # 设置节点为不可调度 kubectl cordon # 设置节点为可调度 kubectl uncordon 1.2. 执行kubectl drain命令 # 驱逐节点的所有pod kubectl drain --force --ignore-daemonsets # 驱逐指定节点的pod kubectl drain --ignore-daemonsets --pod-selector=pod-template-hash=88964949c 示例： $ kubectl drain bjzw-prek8sredis-99-40 --force --ignore-daemonsets node \"bjzw-prek8sredis-99-40\" already cordoned WARNING: Deleting pods not managed by ReplicationController, ReplicaSet, Job, DaemonSet or StatefulSet: kube-proxy-bjzw-prek8sredis-99-40; Ignoring DaemonSet-managed pods: calicoopsmonitor-mfpqs, arachnia-agent-j56n8 pod \"pre-test-pro2-r-0-redis-2-8-19-1\" evicted pod \"pre-test-hwh1-r-8-redis-2-8-19-2\" evicted pod \"pre-eos-hdfs-vector-eos-hdfs-redis-2-8-19-0\" evicted 1.3. 特别说明 对于statefulset创建的Pod，kubectl drain的说明如下： kubectl drain操作会将相应节点上的旧Pod删除，并在可调度节点上面起一个对应的Pod。当旧Pod没有被正常删除的情况下，新Pod不会起来。例如：旧Pod一直处于Terminating状态。 对应的解决方式是通过重启相应节点的kubelet，或者强制删除该Pod。 示例： # 重启发生`Terminating`节点的kubelet systemctl restart kubelet # 强制删除`Terminating`状态的Pod kubectl delete pod --namespace= --force --grace-period=0 2. kubectl drain 流程图 3. TroubleShooting 1、存在不是通过ReplicationController, ReplicaSet, Job, DaemonSet 或者StatefulSet创建的Pod（即静态pod，通过文件方式创建的），所以需要设置强制执行的参数--force。 $ kubectl drain bjzw-prek8sredis-99-40 node \"bjzw-prek8sredis-99-40\" already cordoned error: unable to drain node \"bjzw-prek8sredis-99-40\", aborting command... There are pending nodes to be drained: bjzw-prek8sredis-99-40 error: DaemonSet-managed pods (use --ignore-daemonsets to ignore): calicoopsmonitor-mfpqs, arachnia-agent-j56n8; pods not managed by ReplicationController, ReplicaSet, Job, DaemonSet or StatefulSet (use --force to override): kube-proxy-bjzw-prek8sredis-99-40 2、存在DaemonSet方式管理的Pod，需要设置--ignore-daemonsets参数忽略报错。 $ kubectl drain bjzw-prek8sredis-99-40 --force node \"bjzw-prek8sredis-99-40\" already cordoned error: unable to drain node \"bjzw-prek8sredis-99-40\", aborting command... There are pending nodes to be drained: bjzw-prek8sredis-99-40 error: DaemonSet-managed pods (use --ignore-daemonsets to ignore): calicoopsmonitor-mfpqs, arachnia-agent-j56n8 4. kubectl drain $ kubectl drain --help Drain node in preparation for maintenance. The given node will be marked unschedulable to prevent new pods from arriving. 'drain' evicts the pods if the API server supports https://kubernetes.io/docs/concepts/workloads/pods/disruptions/ . Otherwise, it will use normal DELETE to delete the pods. The 'drain' evicts or deletes all pods except mirror pods (which cannot be deleted through the API server). If there are daemon set-managed pods, drain will not proceed without --ignore-daemonsets, and regardless it will not delete any daemon set-managed pods, because those pods would be immediately replaced by the daemon set controller, which ignores unschedulable markings. If there are any pods that are neither mirror pods nor managed by a replication controller, replica set, daemon set, stateful set, or job, then drain will not delete any pods unless you use --force. --force will also allow deletion to proceed if the managing resource of one or more pods is missing. 'drain' waits for graceful termination. You should not operate on the machine until the command completes. When you are ready to put the node back into service, use kubectl uncordon, which will make the node schedulable again. https://kubernetes.io/images/docs/kubectl_drain.svg Examples: # Drain node \"foo\", even if there are pods not managed by a replication controller, replica set, job, daemon set or stateful set on it kubectl drain foo --force # As above, but abort if there are pods not managed by a replication controller, replica set, job, daemon set or stateful set, and use a grace period of 15 minutes kubectl drain foo --grace-period=900 Options: --chunk-size=500: Return large lists in chunks rather than all at once. Pass 0 to disable. This flag is beta and may change in the future. --delete-emptydir-data=false: Continue even if there are pods using emptyDir (local data that will be deleted when the node is drained). --disable-eviction=false: Force drain to use delete, even if eviction is supported. This will bypass checking PodDisruptionBudgets, use with caution. --dry-run='none': Must be \"none\", \"server\", or \"client\". If client strategy, only print the object that would be sent, without sending it. If server strategy, submit server-side request without persisting the resource. --force=false: Continue even if there are pods not managed by a ReplicationController, ReplicaSet, Job, DaemonSet or StatefulSet. --grace-period=-1: Period of time in seconds given to each pod to terminate gracefully. If negative, the default value specified in the pod will be used. --ignore-daemonsets=false: Ignore DaemonSet-managed pods. --ignore-errors=false: Ignore errors occurred between drain nodes in group. --pod-selector='': Label selector to filter pods on the node -l, --selector='': Selector (label query) to filter on --skip-wait-for-delete-timeout=0: If pod DeletionTimestamp older than N seconds, skip waiting for the pod. Seconds must be greater than 0 to skip. --timeout=0s: The length of time to wait before giving up, zero means infinite Usage: kubectl drain NODE [options] Use \"kubectl options\" for a list of global command-line options (applies to all commands). 参考文档： https://kubernetes.io/docs/tasks/administer-cluster/safely-drain-node/ https://kubernetes.io/docs/tasks/run-application/configure-pdb/ https://kubernetes.io/docs/reference/generated/kubectl/kubectl-commands#drain Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/node/nodeselector-and-taint.html":{"url":"operation/node/nodeselector-and-taint.html","title":"指定Node调度与隔离","keywords":"","body":"1. NodeSelector1.1. 概念1.2. 使用方式1.2.1. 给Node打标签1.2.2. 给Pod设置NodeSelector1.3. 亲和性（Affinity）和反亲和性（Anti-affinity）2. Taint 和 Toleration2.1. 概念2.2. 使用方式2.2.1. kubectl taint2.2.2. 匹配规则：2.2.3. effect的类型2.3. 使用场景2.3.1. 专用节点2.3.2. 特殊硬件节点2.3.3. 基于taint驱逐1. NodeSelector 1.1. 概念 如果需要限制Pod到指定的Node上运行，则可以给Node打标签并给Pod配置NodeSelector。 1.2. 使用方式 1.2.1. 给Node打标签 # get node的name kubectl get nodes # 设置Label kubectl label nodes = # 例如 kubectl label nodes node-1 disktype=ssd # 查看Node的Label kubectl get nodes --show-labels # 删除Node的label kubectl label node - 1.2.2. 给Pod设置NodeSelector apiVersion: v1 kind: Pod metadata: name: nginx labels: env: test spec: containers: - name: nginx image: nginx imagePullPolicy: IfNotPresent nodeSelector: disktype: ssd # 对应Node的Label 1.3. 亲和性（Affinity）和反亲和性（Anti-affinity） 待补充 2. Taint 和 Toleration 2.1. 概念 nodeSelector可以通过打标签的形式让Pod被调度到指定的Node上，Taint则相反，它使节点能够排斥一类特定的Pod，除非Pod被指定了toleration的标签。（taint即污点，Node被打上污点；只有容忍[toleration]这些污点的Pod才可能被调度到该Node）。 2.2. 使用方式 2.2.1. kubectl taint # 给节点增加一个taint，它的key是，value是，effect是NoSchedule。 kubectl taint nodes =:NoSchedule 只有拥有和这个taint相匹配的toleration的pod才能够被分配到 node_name 这个节点。 例如，在 PodSpec 中定义 pod 的 toleration： tolerations: - key: \"key\" operator: \"Equal\" value: \"value\" effect: \"NoSchedule\" tolerations: - key: \"key\" operator: \"Exists\" effect: \"NoSchedule\" 2.2.2. 匹配规则： 一个 toleration 和一个 taint 相“匹配”是指它们有一样的 key 和 effect ，并且： 如果 operator 是 Exists （此时 toleration 不能指定 value） 如果 operator 是 Equal ，则它们的 value 应该相等 特殊情况： 如果一个 toleration 的 key 为空且 operator 为 Exists ，表示这个 toleration 与任意的 key 、 value 和 effect 都匹配，即这个 toleration 能容忍任意 taint。 tolerations: - operator: \"Exists\" 如果一个 toleration 的 effect 为空，则 key 值与之相同的相匹配 taint 的 effect 可以是任意值。 tolerations: - key: \"key\" operator: \"Exists\" 一个节点可以设置多个taint，一个pod也可以设置多个toleration。Kubernetes 处理多个 taint 和 toleration 的过程就像一个过滤器：从一个节点的所有 taint 开始遍历，过滤掉那些 pod 中存在与之相匹配的 toleration 的 taint。余下未被过滤的 taint 的 effect 值决定了 pod 是否会被分配到该节点，特别是以下情况： 如果未被过滤的 taint 中存在一个以上 effect 值为 NoSchedule 的 taint，则 Kubernetes 不会将 pod 分配到该节点。 如果未被过滤的 taint 中不存在 effect 值为 NoSchedule 的 taint，但是存在 effect 值为 PreferNoSchedule 的 taint，则 Kubernetes 会尝试将 pod 分配到该节点。 如果未被过滤的 taint 中存在一个以上 effect 值为 NoExecute 的 taint，则 Kubernetes 不会将 pod 分配到该节点（如果 pod 还未在节点上运行），或者将 pod 从该节点驱逐（如果 pod 已经在节点上运行）。 2.2.3. effect的类型 NoSchedule：只有拥有和这个 taint 相匹配的 toleration 的 pod 才能够被分配到这个节点。 PreferNoSchedule：系统会尽量避免将 pod 调度到存在其不能容忍 taint 的节点上，但这不是强制的。 NoExecute ：任何不能忍受这个 taint 的 pod 都会马上被驱逐，任何可以忍受这个 taint 的 pod 都不会被驱逐。Pod可指定属性 tolerationSeconds 的值，表示pod 还能继续在节点上运行的时间。 tolerations: - key: \"key1\" operator: \"Equal\" value: \"value1\" effect: \"NoExecute\" tolerationSeconds: 3600 2.3. 使用场景 2.3.1. 专用节点 kubectl taint nodes dedicated=:NoSchedule 先给Node添加taint，然后给Pod添加相对应的 toleration，则该Pod可调度到taint的Node，也可调度到其他节点。 如果想让Pod只调度某些节点且某些节点只接受对应的Pod，则需要在Node上添加Label（例如：dedicated=groupName），同时给Pod的nodeSelector添加对应的Label。 2.3.2. 特殊硬件节点 如果某些节点配置了特殊硬件（例如CPU），希望不使用这些特殊硬件的Pod不被调度该Node，以便保留必要资源。即可给Node设置taint和label，同时给Pod设置toleration和label来使得这些Node专门被指定Pod使用。 # kubectl taint kubectl taint nodes nodename special=true:NoSchedule # 或者 kubectl taint nodes nodename special=true:PreferNoSchedule 2.3.3. 基于taint驱逐 effect 值 NoExecute ，它会影响已经在节点上运行的 pod，即根据策略对Pod进行驱逐。 如果 pod 不能忍受effect 值为 NoExecute 的 taint，那么 pod 将马上被驱逐 如果 pod 能够忍受effect 值为 NoExecute 的 taint，但是在 toleration 定义中没有指定 tolerationSeconds，则 pod 还会一直在这个节点上运行。 如果 pod 能够忍受effect 值为 NoExecute 的 taint，而且指定了 tolerationSeconds，则 pod 还能在这个节点上继续运行这个指定的时间长度。 参考： https://kubernetes.io/docs/concepts/configuration/assign-pod-node/ https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/registry/_index.html":{"url":"operation/registry/_index.html","title":"镜像仓库","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/registry/config-private-registry.html":{"url":"operation/registry/config-private-registry.html","title":"配置私有的镜像仓库","keywords":"","body":"1. 镜像仓库的基本操作1.1. 登录镜像仓库1.2. 拉取镜像1.3. 推送镜像1.4. 重命名镜像2. docker.xxx.com镜像仓库2.1. 所有节点配置insecure-registries2.2. 所有节点配置/var/lib/kubelet/config.json2.3. 创建docker.xxx.com镜像的pod1. 镜像仓库的基本操作 1.1. 登录镜像仓库 docker login -u -p 1.2. 拉取镜像 docker pull https://registry.xxx.com/dev/nginx:latest 1.3. 推送镜像 docker push https://registry.xxx.com/dev/nginx:latest 1.4. 重命名镜像 docker tag 2. docker.xxx.com镜像仓库 使用docker.xxx.com镜像仓库。 2.1. 所有节点配置insecure-registries #cat /etc/docker/daemon.json { \"data-root\": \"/data/docker\", \"debug\": false, \"insecure-registries\": [ ... \"docker.xxx.com:8080\" ], ... } 2.2. 所有节点配置/var/lib/kubelet/config.json 具体参考：configuring-nodes-to-authenticate-to-a-private-registry 在某个节点登录docker.xxx.com:8080镜像仓库，会更新 $HOME/.docker/config.json 检查$HOME/.docker/config.json是否有该镜像仓库的auth信息。 #cat ~/.docker/config.json { \"auths\": { \"docker.xxx.com:8080\": { \"auth\": \"\" } }, \"HttpHeaders\": { \"User-Agent\": \"Docker-Client/18.09.9 (linux)\" } } 将$HOME/.docker/config.json拷贝到所有的Node节点上的/var/lib/kubelet/config.json。 # 获取所有节点的IP nodes=$(kubectl get nodes -o jsonpath='{range .items[*].status.addresses[?(@.type==\"ExternalIP\")]}{.address} {end}') # 拷贝到所有节点 for n in $nodes; do scp ~/.docker/config.json root@$n:/var/lib/kubelet/config.json; done 2.3. 创建docker.xxx.com镜像的pod 指定镜像为：docker.xxx.com:8080/public/2048:latest 完整pod.yaml apiVersion: apps/v1beta2 kind: Deployment metadata: annotations: deployment.kubernetes.io/revision: \"1\" generation: 1 labels: k8s-app: dockeroa-hub qcloud-app: dockeroa-hub name: dockeroa-hub namespace: test spec: progressDeadlineSeconds: 600 replicas: 3 revisionHistoryLimit: 10 selector: matchLabels: k8s-app: dockeroa-hub qcloud-app: dockeroa-hub strategy: rollingUpdate: maxSurge: 25% maxUnavailable: 25% type: RollingUpdate template: metadata: labels: k8s-app: dockeroa-hub qcloud-app: dockeroa-hub spec: containers: - image: docker.xxx.com:8080/public/2048:latest imagePullPolicy: Always name: game resources: limits: cpu: 500m memory: 1Gi requests: cpu: 250m memory: 256Mi terminationMessagePath: /dev/termination-log terminationMessagePolicy: File dnsPolicy: ClusterFirst restartPolicy: Always nodeName: 192.168.1.1 schedulerName: default-scheduler securityContext: {} terminationGracePeriodSeconds: 30 查看pod状态 #kgpoowide -n game NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES docker-oa-757bbbddb5-h6j7m 1/1 Running 0 14m 192.168.2.51 192.168.1.1 docker-oa-757bbbddb5-jp5dw 1/1 Running 0 14m 192.168.1.32 192.168.1.2 docker-oa-757bbbddb5-nlw9f 1/1 Running 0 14m 192.168.0.43 192.168.1.3 参考： https://kubernetes.io/docs/concepts/containers/images/#configuring-nodes-to-authenticate-to-a-private-registry Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/registry/ImagePullSecrets.html":{"url":"operation/registry/ImagePullSecrets.html","title":"拉取私有镜像","keywords":"","body":"1. 创建secret2. 添加ImagePullSecrets到serviceAccount3. 创建带有imagePullSecrets的pod4. 说明 本文介绍通过pod指定 ImagePullSecrets来拉取私有镜像仓库的镜像 1. 创建secret secret是namespace级别的，创建时候需要指定namespace。 kubectl create secret docker-registry --docker-server=DOCKER_REGISTRY_SERVER --docker-username=DOCKER_USER --docker-password=DOCKER_PASSWORD -n 2. 添加ImagePullSecrets到serviceAccount 可以通过将ImagePullSecrets到serviceAccount的方式来自动给pod添加imagePullSecrets参数值。 serviceAccount同样是namespace级别，只对该namespace生效。 #kubectl get secrets -n dev NAME TYPE DATA AGE docker.xxxx.com kubernetes.io/dockerconfigjson 1 6h23m 将ImagePullSecrets添加到serviceAccount对象中。 默认serviceAccount对象如下 #kubectl get serviceaccount default -n dev -o yaml apiVersion: v1 kind: ServiceAccount metadata: creationTimestamp: \"2020-02-27T03:30:38Z\" name: default namespace: dev resourceVersion: \"11651567\" selfLink: /api/v1/namespaces/dev/serviceaccounts/default uid: 85bcdd31-5911-11ea-9429-6c92bf3b7c33 secrets: - name: default-token-s7wfn 编辑或修改serviceAccount内容，增加imagePullSecrets字段。 imagePullSecrets: - name: docker.xxxx.com kubectl edit serviceaccount default -n dev 修改后内容为： apiVersion: v1 kind: ServiceAccount metadata: creationTimestamp: \"2020-02-27T03:30:38Z\" name: default namespace: dev resourceVersion: \"11651567\" selfLink: /api/v1/namespaces/dev/serviceaccounts/default uid: 85bcdd31-5911-11ea-9429-6c92bf3b7c33 secrets: - name: default-token-s7wfn imagePullSecrets: - name: docker.xxxx.com 其中imagePullSecrets字段是一个数组，可以配置多个镜像仓库的账号密码。 例如： apiVersion: v1 kind: ServiceAccount ... imagePullSecrets: - name: docker.xxxx.com - name: docker.test.xxxx.com 3. 创建带有imagePullSecrets的pod 如果已经执行了第二步操作，添加ImagePullSecrets到serviceAccount，则无需在pod中指定imagePullSecrets参数，默认会自动添加。 如果没有添加ImagePullSecrets到serviceAccount，则在pod中指定imagePullSecrets参数引用创建的镜像仓库的secret。 spec: imagePullSecrets: - name: docker.xxxx.com 4. 说明 由于secret和serviceaccount对象是对namespace级别生效，因此不同的namespace需要再次创建和更新这两个对象。该场景适合不同用户具有独立的镜像仓库的密码，可以通过该方式创建不同的镜像密码使用的secret来拉取不同的镜像部署。 参考： https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod https://kubernetes.io/docs/tasks/configure-pod-container/configure-service-account/#add-imagepullsecrets-to-a-service-account Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/access/_index.html":{"url":"operation/access/_index.html","title":"访问控制","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/access/rbac-auth.html":{"url":"operation/access/rbac-auth.html","title":"使用RBAC鉴权","keywords":"","body":"1. RBAC介绍2. API对象3. 权限3.1. 命名空间权限[Role]3.2. 集群级别权限[ClusterRole]3.3. 默认权限角色4. 用户4.1. ServiceAccount4.2. Secret5. 授权5.1. 授权命名空间权限[RoleBinding]5.2. 授权集群级别权限[ClusterRoleBinding] 本文基于https://kubernetes.io/zh-cn/docs/reference/access-authn-authz/rbac/ 整理。 1. RBAC介绍 基于角色的访问控制【Role-based access control (RBAC)】是一种基于组织中用户的角色来调节控制对 计算机或网络资源的访问的方法。 RBAC 鉴权机制使用 rbac.authorization.k8s.io API 组来驱动鉴权决定， 允许你通过 Kubernetes API 动态配置策略。 要启用 RBAC，在启动 API 服务器时将 --authorization-mode 参数设置为一个逗号分隔的列表并确保其中包含 RBAC。 kube-apiserver --authorization-mode=Example,RBAC -- -- 编写自定义CRD控制器或部署其他开源组件时，经常需要给组件配置RBAC权限。 理解RBAC权限体系，只需要理解以下三个概念对象即可： 【权限】Role：角色，它其实是一组规则，定义了一组对 Kubernetes API 对象的操作权限。 【用户】Subject：被作用者，既可以是“人”，也可以是“机器”，也可以是你在 Kubernetes 里定义的“用户”。 【授权】RoleBinding：定义了“被作用者”和“角色”的绑定关系。 一句话理解RBAC，就是将定义的权限与定义的用户之间的关系进行绑定，即授权某个用户某些权限。 快速授权脚本可以参考：https://github.com/huweihuang/kubeadm-scripts/tree/main/kubeconfig/token 2. API对象 角色（权限）---角色（权限）绑定---用户（subject） 集群级别范围 命名空间范围 权限 ClusterRole Role 授权 ClusterRoleBinding RoleBinding 用户 ServiceAccout 以下从权限、用户、授权三个概念进行说明。完成一套授权逻辑，主要分为三个步骤 创建权限，即创建Role或ClusterRole对象。 创建用户，即创建ServiceAccount对象。 分配权限，即创建RoleBinding或ClusterRoleBinding对象。 3. 权限 RBAC 的 Role 或 ClusterRole 中包含一组代表相关权限的规则。 这些权限是纯粹累加的（不存在拒绝某操作的规则）。 3.1. 命名空间权限[Role] Role是针对指定namespace的权限，即创建的时候需要指定namespace。 示例： apiVersion: rbac.authorization.k8s.io/v1 kind: Role metadata: namespace: default name: pod-reader rules: - apiGroups: [\"\"] # \"\" 标明 core API 组 resources: [\"pods\"] verbs: [\"get\", \"watch\", \"list\"] 3.2. 集群级别权限[ClusterRole] ClusterRole用于指定集群内的资源： 集群范围资源（比如节点（Node）） 非资源端点（比如 /healthz） 跨名字空间访问的名字空间作用域的资源（如 访问所有namespace下的Pod） 示例： apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: # \"namespace\" 被忽略，因为 ClusterRoles 不受名字空间限制 name: secret-reader rules: - apiGroups: [\"\"] # 在 HTTP 层面，用来访问 Secret 资源的名称为 \"secrets\" resources: [\"secrets\"] verbs: [\"get\", \"watch\", \"list\"] 3.3. 默认权限角色 在 Kubernetes 中已经内置了很多个为系统保留的 ClusterRole，它们的名字都以system:开头。可以通过 kubectl get clusterroles 查看到它们。 超级用户（Super-User）角色（cluster-admin）、 使用 ClusterRoleBinding 在集群范围内完成授权的角色（cluster-status）、 以及使用 RoleBinding 在特定名字空间中授予的角色（admin、edit、view）。 默认 ClusterRole 默认 ClusterRoleBinding 描述 cluster-admin system:masters 组 允许超级用户在平台上的任何资源上执行所有操作。 当在 ClusterRoleBinding 中使用时，可以授权对集群中以及所有名字空间中的全部资源进行完全控制。 当在 RoleBinding 中使用时，可以授权控制角色绑定所在名字空间中的所有资源，包括名字空间本身。 admin 无 允许管理员访问权限，旨在使用 RoleBinding 在名字空间内执行授权。如果在 RoleBinding 中使用，则可授予对名字空间中的大多数资源的读/写权限， 包括创建角色和角色绑定的能力。 此角色不允许对资源配额或者名字空间本身进行写操作。 此角色也不允许对 Kubernetes v1.22+ 创建的 Endpoints 进行写操作。 更多信息参阅 “Endpoints 写权限”小节。 edit 无 允许对名字空间的大多数对象进行读/写操作。此角色不允许查看或者修改角色或者角色绑定。 不过，此角色可以访问 Secret，以名字空间中任何 ServiceAccount 的身份运行 Pod， 所以可以用来了解名字空间内所有服务账户的 API 访问级别。 此角色也不允许对 Kubernetes v1.22+ 创建的 Endpoints 进行写操作。 更多信息参阅 “Endpoints 写操作”小节。 view 无 允许对名字空间的大多数对象有只读权限。 它不允许查看角色或角色绑定。此角色不允许查看 Secrets，因为读取 Secret 的内容意味着可以访问名字空间中 ServiceAccount 的凭据信息，进而允许利用名字空间中任何 ServiceAccount 的身份访问 API（这是一种特权提升）。 4. 用户 4.1. ServiceAccount 创建指定namespace的ServiceAccount对象。ServiceAccount可以在pod中被使用。 apiVersion: v1 kind: ServiceAccount metadata: namespace: name: 4.2. Secret 创建secret，绑定serviceaccount，会自动生成token。 k8s 1.24后的版本不再自动生成secret，绑定后当删除ServiceAccount时会自动删除secret apiVersion: v1 kind: Secret metadata: name: namespace: annotations: kubernetes.io/service-account.name: \"ServiceAccountName\" type: kubernetes.io/service-account-token 5. 授权 5.1. 授权命名空间权限[RoleBinding] RoleBinding角色绑定（Role Binding）是将角色中定义的权限赋予一个或者一组用户。 它包含若干 主体（用户、组或服务账户）的列表和对这些主体所获得的角色的引用。 RoleBinding 在指定的名字空间中执行授权，而 ClusterRoleBinding 在集群范围执行授权。 字段说明： subjects：表示权限所授予的用户，包括ServiceAccount，Group，User。 roleRef：表示权限对应的角色，包括Role，ClusterRole。 示例： kind: RoleBinding apiVersion: rbac.authorization.k8s.io/v1 metadata: name: ${USER}-rolebinding namespace: ${NAMESPACE} subjects: - kind: ServiceAccount name: ${ServiceAccountName} namespace: ${ServiceAccountNS} roleRef: kind: ClusterRole name: ${ROLE} apiGroup: rbac.authorization.k8s.io 可以给指定命名空间下的serviceaccount授权其他命名空间的权限。只需要新增RoleBinding在预授权的命名空间下即可。可以理解为可以给一个用户分配多个命名空间的权限。 #!/bin/bash set -e # 给已存在的用户USER 添加其他NAMESPACE的权限 USER=$1 NAMESPACE=$2 ROLE=$3 ROLE=${ROLE:-edit} ServiceAccountName=\"${USER}-user\" ServiceAccountNS=\"kubernetes-dashboard\" cat 5.2. 授权集群级别权限[ClusterRoleBinding] 要跨整个集群完成访问权限的授予，可以使用一个 ClusterRoleBinding。 示例： apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: ${USER} subjects: - kind: ServiceAccount name: ${USER} namespace: ${NAMESPACE} roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: ${ROLE} 参考： 使用 RBAC 鉴权 | Kubernetes 用户认证 | Kubernetes k8s serviceaccount创建后没有生成对应的secret Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/deployment/_index.html":{"url":"operation/deployment/_index.html","title":"版本发布","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"operation/deployment/canary-deployment.html":{"url":"operation/deployment/canary-deployment.html","title":"金丝雀发布","keywords":"","body":"Deployment配置金丝雀发布1. 设置发版节奏2. 升级版本并暂停3. 查看升级状态4. 恢复继续发版5. 回滚发布5.1. 回滚上一个版本5.2. 查看历史版本5.3. 回滚指定版本Deployment配置金丝雀发布 金丝雀发布是指控制更新过程中的滚动节奏，通过“暂停”（pause）或“继续”（resume）更新发布操作。通过一小部分的版本发布实例来观察新版本是否有异常，如果没有异常则依次发布剩余的实例。 1. 设置发版节奏 主要是两个字段的设置： maxSurge：最大发版实例数，可以创建的超出期望 Pod 个数的 Pod 数量。可以是百分比或者是数字。 maxUnavailable：最大不可用实例数。可以是百分比或数字。 # 例如将maxSurge设置为1，maxUnavailable设置为0 $ kubectl patch deployment myapp-deploy -p '{\"spec\": {\"strategy\": {\"rollingUpdate\": {\"maxSurge\": 1, \"maxUnavailable\": 0}}}}' 2. 升级版本并暂停 kubectl set image deployment myapp-deploy myapp=kubernetes/myapp:v3 && \\ kubectl rollout pause deployments myapp-deploy 3. 查看升级状态 $ kubectl rollout status deployment myapp-deploy Waiting for deployment \"myapp-deploy\" rollout to finish: 1 out of 3 new replicas have been updated... 4. 恢复继续发版 观察灰度的实例的流量是否正常，如果正常则继续发版，如果不正常则回滚之前的升级。 $ kubectl rollout resume deployments myapp-deploy 5. 回滚发布 5.1. 回滚上一个版本 kubectl rollout undo deployments myapp-deploy 5.2. 查看历史版本 $ kubectl rollout history deployment myapp-deploy deployment.apps/myapp-deploy REVISION CHANGE-CAUSE 3 5 6 5.3. 回滚指定版本 kubectl rollout undo deployment myapp-deploy --to-revision 3 参考： https://kubernetes.io/zh-cn/docs/concepts/workloads/controllers/deployment/ https://kubernetes.io/zh-cn/docs/concepts/cluster-administration/manage-deployment/#canary-deployments https://kubernetes.renkeju.com/chapter_5/5.3.4.Canary_release.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"develop/client-go.html":{"url":"develop/client-go.html","title":"client-go的使用及源码分析","keywords":"","body":"1. client-go简介1.1 client-go说明1.2 示例代码1.3 运行结果2. client-go源码分析2.1 kubeconfig2.2 rest.config2.3 clientset2.3.1 NewForConfig2.3.2 clientset的结构体2.3.3 clientset.Interface2.4 CoreV1Client2.4.1 corev1.NewForConfig2.4.2 CoreV1Client结构体2.4.3 CoreV1Interface2.4.4 PodsGetter2.5 RESTClient2.5.1 rest.RESTClientFor2.5.2 NewRESTClient2.5.3 RESTClient结构体2.5.4 RESTClient.Interface2.6 总结3. client-go对k8s资源的调用3.1 deployment3.2 service3.3 ingress3.4 replicaSet3.5 pod3.6 statefulset1. client-go简介 1.1 client-go说明 ​ client-go是一个调用kubernetes集群资源对象API的客户端，即通过client-go实现对kubernetes集群中资源对象（包括deployment、service、ingress、replicaSet、pod、namespace、node等）的增删改查等操作。大部分对kubernetes进行前置API封装的二次开发都通过client-go这个第三方包来实现。 ​ client-go官方文档：https://github.com/kubernetes/client-go 1.2 示例代码 git clone https://github.com/huweihuang/client-go.git cd client-go #保证本地HOME目录有配置kubernetes集群的配置文件 go run client-go.go client-go.go package main import ( \"flag\" \"fmt\" \"os\" \"path/filepath\" \"time\" metav1 \"k8s.io/apimachinery/pkg/apis/meta/v1\" \"k8s.io/client-go/kubernetes\" \"k8s.io/client-go/tools/clientcmd\" ) func main() { var kubeconfig *string if home := homeDir(); home != \"\" { kubeconfig = flag.String(\"kubeconfig\", filepath.Join(home, \".kube\", \"config\"), \"(optional) absolute path to the kubeconfig file\") } else { kubeconfig = flag.String(\"kubeconfig\", \"\", \"absolute path to the kubeconfig file\") } flag.Parse() // uses the current context in kubeconfig config, err := clientcmd.BuildConfigFromFlags(\"\", *kubeconfig) if err != nil { panic(err.Error()) } // creates the clientset clientset, err := kubernetes.NewForConfig(config) if err != nil { panic(err.Error()) } for { pods, err := clientset.CoreV1().Pods(\"\").List(metav1.ListOptions{}) if err != nil { panic(err.Error()) } fmt.Printf(\"There are %d pods in the cluster\\n\", len(pods.Items)) time.Sleep(10 * time.Second) } } func homeDir() string { if h := os.Getenv(\"HOME\"); h != \"\" { return h } return os.Getenv(\"USERPROFILE\") // windows } 1.3 运行结果 ➜ go run client-go.go There are 9 pods in the cluster There are 7 pods in the cluster There are 7 pods in the cluster There are 7 pods in the cluster There are 7 pods in the cluster 2. client-go源码分析 client-go源码：https://github.com/kubernetes/client-go client-go源码目录结构 The kubernetes package contains the clientset to access Kubernetes API. The discovery package is used to discover APIs supported by a Kubernetes API server. The dynamic package contains a dynamic client that can perform generic operations on arbitrary Kubernetes API objects. The transport package is used to set up auth and start a connection. The tools/cache package is useful for writing controllers. 2.1 kubeconfig kubeconfig = flag.String(\"kubeconfig\", filepath.Join(home, \".kube\", \"config\"), \"(optional) absolute path to the kubeconfig file\") 获取kubernetes配置文件kubeconfig的绝对路径。一般路径为$HOME/.kube/config。该文件主要用来配置本地连接的kubernetes集群。 config内容如下： apiVersion: v1 clusters: - cluster: server: http://:8080 name: k8s contexts: - context: cluster: k8s namespace: default user: \"\" name: default current-context: default kind: Config preferences: {} users: [] 2.2 rest.config 通过参数（master的url或者kubeconfig路径）和BuildConfigFromFlags方法来获取rest.Config对象，一般是通过参数kubeconfig的路径。 config, err := clientcmd.BuildConfigFromFlags(\"\", *kubeconfig) BuildConfigFromFlags函数源码 k8s.io/client-go/tools/clientcmd/client_config.go // BuildConfigFromFlags is a helper function that builds configs from a master // url or a kubeconfig filepath. These are passed in as command line flags for cluster // components. Warnings should reflect this usage. If neither masterUrl or kubeconfigPath // are passed in we fallback to inClusterConfig. If inClusterConfig fails, we fallback // to the default config. func BuildConfigFromFlags(masterUrl, kubeconfigPath string) (*restclient.Config, error) { if kubeconfigPath == \"\" && masterUrl == \"\" { glog.Warningf(\"Neither --kubeconfig nor --master was specified. Using the inClusterConfig. This might not work.\") kubeconfig, err := restclient.InClusterConfig() if err == nil { return kubeconfig, nil } glog.Warning(\"error creating inClusterConfig, falling back to default config: \", err) } return NewNonInteractiveDeferredLoadingClientConfig( &ClientConfigLoadingRules{ExplicitPath: kubeconfigPath}, &ConfigOverrides{ClusterInfo: clientcmdapi.Cluster{Server: masterUrl}}).ClientConfig() } 2.3 clientset 通过*rest.Config参数和NewForConfig方法来获取clientset对象，clientset是多个client的集合，每个client可能包含不同版本的方法调用。 clientset, err := kubernetes.NewForConfig(config) 2.3.1 NewForConfig NewForConfig函数就是初始化clientset中的每个client。 k8s.io/client-go/kubernetes/clientset.go // NewForConfig creates a new Clientset for the given config. func NewForConfig(c *rest.Config) (*Clientset, error) { configShallowCopy := *c ... var cs Clientset cs.appsV1beta1, err = appsv1beta1.NewForConfig(&configShallowCopy) ... cs.coreV1, err = corev1.NewForConfig(&configShallowCopy) ... } 2.3.2 clientset的结构体 k8s.io/client-go/kubernetes/clientset.go // Clientset contains the clients for groups. Each group has exactly one // version included in a Clientset. type Clientset struct { *discovery.DiscoveryClient admissionregistrationV1alpha1 *admissionregistrationv1alpha1.AdmissionregistrationV1alpha1Client appsV1beta1 *appsv1beta1.AppsV1beta1Client appsV1beta2 *appsv1beta2.AppsV1beta2Client authenticationV1 *authenticationv1.AuthenticationV1Client authenticationV1beta1 *authenticationv1beta1.AuthenticationV1beta1Client authorizationV1 *authorizationv1.AuthorizationV1Client authorizationV1beta1 *authorizationv1beta1.AuthorizationV1beta1Client autoscalingV1 *autoscalingv1.AutoscalingV1Client autoscalingV2beta1 *autoscalingv2beta1.AutoscalingV2beta1Client batchV1 *batchv1.BatchV1Client batchV1beta1 *batchv1beta1.BatchV1beta1Client batchV2alpha1 *batchv2alpha1.BatchV2alpha1Client certificatesV1beta1 *certificatesv1beta1.CertificatesV1beta1Client coreV1 *corev1.CoreV1Client extensionsV1beta1 *extensionsv1beta1.ExtensionsV1beta1Client networkingV1 *networkingv1.NetworkingV1Client policyV1beta1 *policyv1beta1.PolicyV1beta1Client rbacV1 *rbacv1.RbacV1Client rbacV1beta1 *rbacv1beta1.RbacV1beta1Client rbacV1alpha1 *rbacv1alpha1.RbacV1alpha1Client schedulingV1alpha1 *schedulingv1alpha1.SchedulingV1alpha1Client settingsV1alpha1 *settingsv1alpha1.SettingsV1alpha1Client storageV1beta1 *storagev1beta1.StorageV1beta1Client storageV1 *storagev1.StorageV1Client } 2.3.3 clientset.Interface clientset实现了以下的Interface，因此可以通过调用以下方法获得具体的client。例如： pods, err := clientset.CoreV1().Pods(\"\").List(metav1.ListOptions{}) clientset的方法集接口 k8s.io/client-go/kubernetes/clientset.go type Interface interface { Discovery() discovery.DiscoveryInterface AdmissionregistrationV1alpha1() admissionregistrationv1alpha1.AdmissionregistrationV1alpha1Interface // Deprecated: please explicitly pick a version if possible. Admissionregistration() admissionregistrationv1alpha1.AdmissionregistrationV1alpha1Interface AppsV1beta1() appsv1beta1.AppsV1beta1Interface AppsV1beta2() appsv1beta2.AppsV1beta2Interface // Deprecated: please explicitly pick a version if possible. Apps() appsv1beta2.AppsV1beta2Interface AuthenticationV1() authenticationv1.AuthenticationV1Interface // Deprecated: please explicitly pick a version if possible. Authentication() authenticationv1.AuthenticationV1Interface AuthenticationV1beta1() authenticationv1beta1.AuthenticationV1beta1Interface AuthorizationV1() authorizationv1.AuthorizationV1Interface // Deprecated: please explicitly pick a version if possible. Authorization() authorizationv1.AuthorizationV1Interface AuthorizationV1beta1() authorizationv1beta1.AuthorizationV1beta1Interface AutoscalingV1() autoscalingv1.AutoscalingV1Interface // Deprecated: please explicitly pick a version if possible. Autoscaling() autoscalingv1.AutoscalingV1Interface AutoscalingV2beta1() autoscalingv2beta1.AutoscalingV2beta1Interface BatchV1() batchv1.BatchV1Interface // Deprecated: please explicitly pick a version if possible. Batch() batchv1.BatchV1Interface BatchV1beta1() batchv1beta1.BatchV1beta1Interface BatchV2alpha1() batchv2alpha1.BatchV2alpha1Interface CertificatesV1beta1() certificatesv1beta1.CertificatesV1beta1Interface // Deprecated: please explicitly pick a version if possible. Certificates() certificatesv1beta1.CertificatesV1beta1Interface CoreV1() corev1.CoreV1Interface // Deprecated: please explicitly pick a version if possible. Core() corev1.CoreV1Interface ExtensionsV1beta1() extensionsv1beta1.ExtensionsV1beta1Interface // Deprecated: please explicitly pick a version if possible. Extensions() extensionsv1beta1.ExtensionsV1beta1Interface NetworkingV1() networkingv1.NetworkingV1Interface // Deprecated: please explicitly pick a version if possible. Networking() networkingv1.NetworkingV1Interface PolicyV1beta1() policyv1beta1.PolicyV1beta1Interface // Deprecated: please explicitly pick a version if possible. Policy() policyv1beta1.PolicyV1beta1Interface RbacV1() rbacv1.RbacV1Interface // Deprecated: please explicitly pick a version if possible. Rbac() rbacv1.RbacV1Interface RbacV1beta1() rbacv1beta1.RbacV1beta1Interface RbacV1alpha1() rbacv1alpha1.RbacV1alpha1Interface SchedulingV1alpha1() schedulingv1alpha1.SchedulingV1alpha1Interface // Deprecated: please explicitly pick a version if possible. Scheduling() schedulingv1alpha1.SchedulingV1alpha1Interface SettingsV1alpha1() settingsv1alpha1.SettingsV1alpha1Interface // Deprecated: please explicitly pick a version if possible. Settings() settingsv1alpha1.SettingsV1alpha1Interface StorageV1beta1() storagev1beta1.StorageV1beta1Interface StorageV1() storagev1.StorageV1Interface // Deprecated: please explicitly pick a version if possible. Storage() storagev1.StorageV1Interface } 2.4 CoreV1Client 我们以clientset中的CoreV1Client为例做分析。 通过传入的配置信息rest.Config初始化CoreV1Client对象。 k8s.io/client-go/kubernetes/clientset.go cs.coreV1, err = corev1.NewForConfig(&configShallowCopy) 2.4.1 corev1.NewForConfig k8s.io/client-go/kubernetes/typed/core/v1/core_client.go // NewForConfig creates a new CoreV1Client for the given config. func NewForConfig(c *rest.Config) (*CoreV1Client, error) { config := *c if err := setConfigDefaults(&config); err != nil { return nil, err } client, err := rest.RESTClientFor(&config) if err != nil { return nil, err } return &CoreV1Client{client}, nil } corev1.NewForConfig方法本质是调用了rest.RESTClientFor(&config)方法创建RESTClient对象，即CoreV1Client的本质就是一个RESTClient对象。 2.4.2 CoreV1Client结构体 以下是CoreV1Client结构体的定义： k8s.io/client-go/kubernetes/typed/core/v1/core_client.go // CoreV1Client is used to interact with features provided by the group. type CoreV1Client struct { restClient rest.Interface } CoreV1Client实现了CoreV1Interface的接口，即以下方法，从而对kubernetes的资源对象进行增删改查的操作。 k8s.io/client-go/kubernetes/typed/core/v1/core_client.go //CoreV1Client的方法 func (c *CoreV1Client) ComponentStatuses() ComponentStatusInterface {...} //ConfigMaps func (c *CoreV1Client) ConfigMaps(namespace string) ConfigMapInterface {...} //Endpoints func (c *CoreV1Client) Endpoints(namespace string) EndpointsInterface {...} func (c *CoreV1Client) Events(namespace string) EventInterface {...} func (c *CoreV1Client) LimitRanges(namespace string) LimitRangeInterface {...} //Namespaces func (c *CoreV1Client) Namespaces() NamespaceInterface {...} //Nodes func (c *CoreV1Client) Nodes() NodeInterface {...} func (c *CoreV1Client) PersistentVolumes() PersistentVolumeInterface {...} func (c *CoreV1Client) PersistentVolumeClaims(namespace string) PersistentVolumeClaimInterface {...} //Pods func (c *CoreV1Client) Pods(namespace string) PodInterface {...} func (c *CoreV1Client) PodTemplates(namespace string) PodTemplateInterface {...} //ReplicationControllers func (c *CoreV1Client) ReplicationControllers(namespace string) ReplicationControllerInterface {...} func (c *CoreV1Client) ResourceQuotas(namespace string) ResourceQuotaInterface {...} func (c *CoreV1Client) Secrets(namespace string) SecretInterface {...} //Services func (c *CoreV1Client) Services(namespace string) ServiceInterface {...} func (c *CoreV1Client) ServiceAccounts(namespace string) ServiceAccountInterface {...} 2.4.3 CoreV1Interface k8s.io/client-go/kubernetes/typed/core/v1/core_client.go type CoreV1Interface interface { RESTClient() rest.Interface ComponentStatusesGetter ConfigMapsGetter EndpointsGetter EventsGetter LimitRangesGetter NamespacesGetter NodesGetter PersistentVolumesGetter PersistentVolumeClaimsGetter PodsGetter PodTemplatesGetter ReplicationControllersGetter ResourceQuotasGetter SecretsGetter ServicesGetter ServiceAccountsGetter } CoreV1Interface中包含了各种kubernetes对象的调用接口，例如PodsGetter是对kubernetes中pod对象增删改查操作的接口。ServicesGetter是对service对象的操作的接口。 2.4.4 PodsGetter 以下我们以PodsGetter接口为例分析CoreV1Client对pod对象的增删改查接口调用。 示例中的代码如下： pods, err := clientset.CoreV1().Pods(\"\").List(metav1.ListOptions{}) CoreV1().Pods() k8s.io/client-go/kubernetes/typed/core/v1/core_client.go func (c *CoreV1Client) Pods(namespace string) PodInterface { return newPods(c, namespace) } newPods() k8s.io/client-go/kubernetes/typed/core/v1/pod.go // newPods returns a Pods func newPods(c *CoreV1Client, namespace string) *pods { return &pods{ client: c.RESTClient(), ns: namespace, } } CoreV1().Pods()的方法实际上是调用了newPods()的方法，创建了一个pods对象，pods对象继承了rest.Interface接口，即最终的实现本质是RESTClient的HTTP调用。 k8s.io/client-go/kubernetes/typed/core/v1/pod.go // pods implements PodInterface type pods struct { client rest.Interface ns string } pods对象实现了PodInterface接口。PodInterface定义了pods对象的增删改查等方法。 k8s.io/client-go/kubernetes/typed/core/v1/pod.go // PodInterface has methods to work with Pod resources. type PodInterface interface { Create(*v1.Pod) (*v1.Pod, error) Update(*v1.Pod) (*v1.Pod, error) UpdateStatus(*v1.Pod) (*v1.Pod, error) Delete(name string, options *meta_v1.DeleteOptions) error DeleteCollection(options *meta_v1.DeleteOptions, listOptions meta_v1.ListOptions) error Get(name string, options meta_v1.GetOptions) (*v1.Pod, error) List(opts meta_v1.ListOptions) (*v1.PodList, error) Watch(opts meta_v1.ListOptions) (watch.Interface, error) Patch(name string, pt types.PatchType, data []byte, subresources ...string) (result *v1.Pod, err error) PodExpansion } PodsGetter PodsGetter继承了PodInterface的接口。 k8s.io/client-go/kubernetes/typed/core/v1/pod.go // PodsGetter has a method to return a PodInterface. // A group's client should implement this interface. type PodsGetter interface { Pods(namespace string) PodInterface } Pods().List() pods.List()方法通过RESTClient的HTTP调用来实现对kubernetes的pod资源的获取。 k8s.io/client-go/kubernetes/typed/core/v1/pod.go // List takes label and field selectors, and returns the list of Pods that match those selectors. func (c *pods) List(opts meta_v1.ListOptions) (result *v1.PodList, err error) { result = &v1.PodList{} err = c.client.Get(). Namespace(c.ns). Resource(\"pods\"). VersionedParams(&opts, scheme.ParameterCodec). Do(). Into(result) return } 以上分析了clientset.CoreV1().Pods(\"\").List(metav1.ListOptions{})对pod资源获取的过程，最终是调用RESTClient的方法实现。 2.5 RESTClient 以下分析RESTClient的创建过程及作用。 RESTClient对象的创建同样是依赖传入的config信息。 k8s.io/client-go/kubernetes/typed/core/v1/core_client.go client, err := rest.RESTClientFor(&config) 2.5.1 rest.RESTClientFor k8s.io/client-go/rest/config.go // RESTClientFor returns a RESTClient that satisfies the requested attributes on a client Config // object. Note that a RESTClient may require fields that are optional when initializing a Client. // A RESTClient created by this method is generic - it expects to operate on an API that follows // the Kubernetes conventions, but may not be the Kubernetes API. func RESTClientFor(config *Config) (*RESTClient, error) { ... qps := config.QPS ... burst := config.Burst ... baseURL, versionedAPIPath, err := defaultServerUrlFor(config) ... transport, err := TransportFor(config) ... var httpClient *http.Client if transport != http.DefaultTransport { httpClient = &http.Client{Transport: transport} if config.Timeout > 0 { httpClient.Timeout = config.Timeout } } return NewRESTClient(baseURL, versionedAPIPath, config.ContentConfig, qps, burst, config.RateLimiter, httpClient) } RESTClientFor函数调用了NewRESTClient的初始化函数。 2.5.2 NewRESTClient k8s.io/client-go/rest/client.go // NewRESTClient creates a new RESTClient. This client performs generic REST functions // such as Get, Put, Post, and Delete on specified paths. Codec controls encoding and // decoding of responses from the server. func NewRESTClient(baseURL *url.URL, versionedAPIPath string, config ContentConfig, maxQPS float32, maxBurst int, rateLimiter flowcontrol.RateLimiter, client *http.Client) (*RESTClient, error) { base := *baseURL ... serializers, err := createSerializers(config) ... return &RESTClient{ base: &base, versionedAPIPath: versionedAPIPath, contentConfig: config, serializers: *serializers, createBackoffMgr: readExpBackoffConfig, Throttle: throttle, Client: client, }, nil } 2.5.3 RESTClient结构体 以下介绍RESTClient的结构体定义，RESTClient结构体中包含了http.Client，即本质上RESTClient就是一个http.Client的封装实现。 k8s.io/client-go/rest/client.go // RESTClient imposes common Kubernetes API conventions on a set of resource paths. // The baseURL is expected to point to an HTTP or HTTPS path that is the parent // of one or more resources. The server should return a decodable API resource // object, or an api.Status object which contains information about the reason for // any failure. // // Most consumers should use client.New() to get a Kubernetes API client. type RESTClient struct { // base is the root URL for all invocations of the client base *url.URL // versionedAPIPath is a path segment connecting the base URL to the resource root versionedAPIPath string // contentConfig is the information used to communicate with the server. contentConfig ContentConfig // serializers contain all serializers for underlying content type. serializers Serializers // creates BackoffManager that is passed to requests. createBackoffMgr func() BackoffManager // TODO extract this into a wrapper interface via the RESTClient interface in kubectl. Throttle flowcontrol.RateLimiter // Set specific behavior of the client. If not set http.DefaultClient will be used. Client *http.Client } 2.5.4 RESTClient.Interface RESTClient实现了以下的接口方法： k8s.io/client-go/rest/client.go // Interface captures the set of operations for generically interacting with Kubernetes REST apis. type Interface interface { GetRateLimiter() flowcontrol.RateLimiter Verb(verb string) *Request Post() *Request Put() *Request Patch(pt types.PatchType) *Request Get() *Request Delete() *Request APIVersion() schema.GroupVersion } 在调用HTTP方法（Post()，Put()，Get()，Delete() ）时，实际上调用了Verb(verb string)函数。 k8s.io/client-go/rest/client.go // Verb begins a request with a verb (GET, POST, PUT, DELETE). // // Example usage of RESTClient's request building interface: // c, err := NewRESTClient(...) // if err != nil { ... } // resp, err := c.Verb(\"GET\"). // Path(\"pods\"). // SelectorParam(\"labels\", \"area=staging\"). // Timeout(10*time.Second). // Do() // if err != nil { ... } // list, ok := resp.(*api.PodList) // func (c *RESTClient) Verb(verb string) *Request { backoff := c.createBackoffMgr() if c.Client == nil { return NewRequest(nil, verb, c.base, c.versionedAPIPath, c.contentConfig, c.serializers, backoff, c.Throttle) } return NewRequest(c.Client, verb, c.base, c.versionedAPIPath, c.contentConfig, c.serializers, backoff, c.Throttle) } Verb函数调用了NewRequest方法，最后调用Do()方法实现一个HTTP请求获取Result。 2.6 总结 client-go对kubernetes资源对象的调用，需要先获取kubernetes的配置信息，即$HOME/.kube/config。 整个调用的过程如下： kubeconfig→rest.config→clientset→具体的client(CoreV1Client)→具体的资源对象(pod)→RESTClient→http.Client→HTTP请求的发送及响应 通过clientset中不同的client和client中不同资源对象的方法实现对kubernetes中资源对象的增删改查等操作，常用的client有CoreV1Client、AppsV1beta1Client、ExtensionsV1beta1Client等。 3. client-go对k8s资源的调用 创建clientset //获取kubeconfig kubeconfig = flag.String(\"kubeconfig\", filepath.Join(home, \".kube\", \"config\"), \"(optional) absolute path to the kubeconfig file\") //创建config config, err := clientcmd.BuildConfigFromFlags(\"\", *kubeconfig) //创建clientset clientset, err := kubernetes.NewForConfig(config) //具体的资源调用见以下例子 3.1 deployment //声明deployment对象 var deployment *v1beta1.Deployment //构造deployment对象 //创建deployment deployment, err := clientset.AppsV1beta1().Deployments().Create() //更新deployment deployment, err := clientset.AppsV1beta1().Deployments().Update() //删除deployment err := clientset.AppsV1beta1().Deployments().Delete(, &meta_v1.DeleteOptions{}) //查询deployment deployment, err := clientset.AppsV1beta1().Deployments().Get(, meta_v1.GetOptions{}) //列出deployment deploymentList, err := clientset.AppsV1beta1().Deployments().List(&meta_v1.ListOptions{}) //watch deployment watchInterface, err := clientset.AppsV1beta1().Deployments().Watch(&meta_v1.ListOptions{}) 3.2 service //声明service对象 var service *v1.Service //构造service对象 //创建service service, err := clientset.CoreV1().Services().Create() //更新service service, err := clientset.CoreV1().Services().Update() //删除service err := clientset.CoreV1().Services().Delete(, &meta_v1.DeleteOptions{}) //查询service service, err := clientset.CoreV1().Services().Get(, meta_v1.GetOptions{}) //列出service serviceList, err := clientset.CoreV1().Services().List(&meta_v1.ListOptions{}) //watch service watchInterface, err := clientset.CoreV1().Services().Watch(&meta_v1.ListOptions{}) 3.3 ingress //声明ingress对象 var ingress *v1beta1.Ingress //构造ingress对象 //创建ingress ingress, err := clientset.ExtensionsV1beta1().Ingresses().Create() //更新ingress ingress, err := clientset.ExtensionsV1beta1().Ingresses().Update() //删除ingress err := clientset.ExtensionsV1beta1().Ingresses().Delete(, &meta_v1.DeleteOptions{}) //查询ingress ingress, err := clientset.ExtensionsV1beta1().Ingresses().Get(, meta_v1.GetOptions{}) //列出ingress ingressList, err := clientset.ExtensionsV1beta1().Ingresses().List(&meta_v1.ListOptions{}) //watch ingress watchInterface, err := clientset.ExtensionsV1beta1().Ingresses().Watch(&meta_v1.ListOptions{}) 3.4 replicaSet //声明replicaSet对象 var replicaSet *v1beta1.ReplicaSet //构造replicaSet对象 //创建replicaSet replicaSet, err := clientset.ExtensionsV1beta1().ReplicaSets().Create() //更新replicaSet replicaSet, err := clientset.ExtensionsV1beta1().ReplicaSets().Update() //删除replicaSet err := clientset.ExtensionsV1beta1().ReplicaSets().Delete(, &meta_v1.DeleteOptions{}) //查询replicaSet replicaSet, err := clientset.ExtensionsV1beta1().ReplicaSets().Get(, meta_v1.GetOptions{}) //列出replicaSet replicaSetList, err := clientset.ExtensionsV1beta1().ReplicaSets().List(&meta_v1.ListOptions{}) //watch replicaSet watchInterface, err := clientset.ExtensionsV1beta1().ReplicaSets().Watch(&meta_v1.ListOptions{}) 新版的kubernetes中一般通过deployment来创建replicaSet，再通过replicaSet来控制pod。 3.5 pod //声明pod对象 var pod *v1.Pod //创建pod pod, err := clientset.CoreV1().Pods().Create() //更新pod pod, err := clientset.CoreV1().Pods().Update() //删除pod err := clientset.CoreV1().Pods().Delete(, &meta_v1.DeleteOptions{}) //查询pod pod, err := clientset.CoreV1().Pods().Get(, meta_v1.GetOptions{}) //列出pod podList, err := clientset.CoreV1().Pods().List(&meta_v1.ListOptions{}) //watch pod watchInterface, err := clientset.CoreV1().Pods().Watch(&meta_v1.ListOptions{}) 3.6 statefulset //声明statefulset对象 var statefulset *v1.StatefulSet //创建statefulset statefulset, err := clientset.AppsV1().StatefulSets().Create() //更新statefulset statefulset, err := clientset.AppsV1().StatefulSets().Update() //删除statefulset err := clientset.AppsV1().StatefulSets().Delete(, &meta_v1.DeleteOptions{}) //查询statefulset statefulset, err := clientset.AppsV1().StatefulSets().Get(, meta_v1.GetOptions{}) //列出statefulset statefulsetList, err := clientset.AppsV1().StatefulSets().List(&meta_v1.ListOptions{}) //watch statefulset watchInterface, err := clientset.AppsV1().StatefulSets().Watch(&meta_v1.ListOptions{}) ​ 通过以上对kubernetes的资源对象的操作函数可以看出，每个资源对象都有增删改查等方法，基本调用逻辑类似。一般二次开发只需要创建deployment、service、ingress三个资源对象即可，pod对象由deployment包含的replicaSet来控制创建和删除。函数调用的入参一般只有NAMESPACE和kubernetesObject两个参数，部分操作有Options的参数。在创建前，需要对资源对象构造数据，可以理解为编辑一个资源对象的yaml文件，然后通过kubectl create -f xxx.yaml来创建对象。 参考文档: https://github.com/kubernetes/client-go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"develop/csi/_index.html":{"url":"develop/csi/_index.html","title":"CSI插件开发","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"develop/csi/nfs-client-provisioner.html":{"url":"develop/csi/nfs-client-provisioner.html","title":"nfs-client-provisioner源码分析","keywords":"","body":"1. Dynamic Provisioner1.1. Provisioner Interface1.2. VolumeOptions1.3. ProvisionController1.4. 开发provisioner的步骤2. NFS Client Provisioner2.1. Main函数2.1.1. 读取环境变量2.1.2. 获取clientset对象2.1.3. 构造nfsProvisioner对象2.1.4. 构建并运行ProvisionController2.2. Provision和Delete方法2.2.1. Provision方法2.2.2. Delete方法3. ProvisionController3.1. ProvisionController结构体3.1.1. 入参3.1.2. Controller和Informer3.1.3. workqueue3.1.4. 其他3.2. NewProvisionController方法3.2.1. 初始化默认值3.2.2. 初始化任务队列3.2.3. ListWatch3.2.4. ResourceEventHandlerFuncs3.2.5. 构造Store和Controller3.3. ProvisionController.Run方法3.3.1. prometheus数据收集3.3.2. Controller.Run3.3.3. Worker3.4. Operation3.4.1. provisionClaimOperation3.4.2. deleteVolumeOperation4. 总结 如果要开发一个Dynamic Provisioner，需要使用到the helper library。 1. Dynamic Provisioner 1.1. Provisioner Interface 开发Dynamic Provisioner需要实现Provisioner接口，该接口有两个方法，分别是： Provision：创建存储资源，并且返回一个PV对象。 Delete：移除对应的存储资源，但并没有删除PV对象。 Provisioner 接口源码如下： // Provisioner is an interface that creates templates for PersistentVolumes // and can create the volume as a new resource in the infrastructure provider. // It can also remove the volume it created from the underlying storage // provider. type Provisioner interface { // Provision creates a volume i.e. the storage asset and returns a PV object // for the volume Provision(VolumeOptions) (*v1.PersistentVolume, error) // Delete removes the storage asset that was created by Provision backing the // given PV. Does not delete the PV object itself. // // May return IgnoredError to indicate that the call has been ignored and no // action taken. Delete(*v1.PersistentVolume) error } 1.2. VolumeOptions Provisioner接口的Provision方法的入参是一个VolumeOptions对象。VolumeOptions对象包含了创建PV对象所需要的信息，例如：PV的回收策略，PV的名字，PV所对应的PVC对象以及PVC的StorageClass对象使用的参数等。 VolumeOptions 源码如下： // VolumeOptions contains option information about a volume // https://github.com/kubernetes/kubernetes/blob/release-1.4/pkg/volume/plugins.go type VolumeOptions struct { // Reclamation policy for a persistent volume PersistentVolumeReclaimPolicy v1.PersistentVolumeReclaimPolicy // PV.Name of the appropriate PersistentVolume. Used to generate cloud // volume name. PVName string // PV mount options. Not validated - mount of the PVs will simply fail if one is invalid. MountOptions []string // PVC is reference to the claim that lead to provisioning of a new PV. // Provisioners *must* create a PV that would be matched by this PVC, // i.e. with required capacity, accessMode, labels matching PVC.Selector and // so on. PVC *v1.PersistentVolumeClaim // Volume provisioning parameters from StorageClass Parameters map[string]string // Node selected by the scheduler for the volume. SelectedNode *v1.Node // Topology constraint parameter from StorageClass AllowedTopologies []v1.TopologySelectorTerm } 1.3. ProvisionController ProvisionController是一个给PVC提供PV的控制器，具体执行Provisioner接口的Provision和Delete的方法的所有逻辑。 1.4. 开发provisioner的步骤 写一个provisioner实现Provisioner接口（包含Provision和Delete的方法）。 通过该provisioner构建ProvisionController。 执行ProvisionController的Run方法。 2. NFS Client Provisioner nfs-client-provisioner是一个automatic provisioner，使用NFS作为存储，自动创建PV和对应的PVC，本身不提供NFS存储，需要外部先有一套NFS存储服务。 PV以 ${namespace}-${pvcName}-${pvName}的命名格式提供（在NFS服务器上） PV回收的时候以 archieved-${namespace}-${pvcName}-${pvName} 的命名格式（在NFS服务器上） 以下通过nfs-client-provisioner的源码分析来说明开发自定义provisioner整个过程。nfs-client-provisioner的主要代码都在provisioner.go的文件中。 nfs-client-provisioner源码地址：https://github.com/kubernetes-incubator/external-storage/tree/master/nfs-client 2.1. Main函数 2.1.1. 读取环境变量 源码如下： func main() { flag.Parse() flag.Set(\"logtostderr\", \"true\") server := os.Getenv(\"NFS_SERVER\") if server == \"\" { glog.Fatal(\"NFS_SERVER not set\") } path := os.Getenv(\"NFS_PATH\") if path == \"\" { glog.Fatal(\"NFS_PATH not set\") } provisionerName := os.Getenv(provisionerNameKey) if provisionerName == \"\" { glog.Fatalf(\"environment variable %s is not set! Please set it.\", provisionerNameKey) } ... } main函数先获取NFS_SERVER、NFS_PATH、PROVISIONER_NAME三个环境变量的值，因此在部署nfs-client-provisioner的时候，需要将这三个环境变量的值传入。 NFS_SERVER：NFS服务端的IP地址。 NFS_PATH：NFS服务端设置的共享目录 PROVISIONER_NAME：provisioner的名字，需要和StorageClass对象中的provisioner字段一致。 例如StorageClass对象的yaml文件如下： apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: managed-nfs-storage provisioner: fuseim.pri/ifs # or choose another name, must match deployment's env PROVISIONER_NAME' parameters: archiveOnDelete: \"false\" # When set to \"false\" your PVs will not be archived by the provisioner upon deletion of the PVC. 2.1.2. 获取clientset对象 源码如下： // Create an InClusterConfig and use it to create a client for the controller // to use to communicate with Kubernetes config, err := rest.InClusterConfig() if err != nil { glog.Fatalf(\"Failed to create config: %v\", err) } clientset, err := kubernetes.NewForConfig(config) if err != nil { glog.Fatalf(\"Failed to create client: %v\", err) } 通过读取对应的k8s的配置，创建clientset对象，用来执行k8s对应的API，其中主要包括对PV和PVC等对象的创建删除等操作。 2.1.3. 构造nfsProvisioner对象 源码如下： // The controller needs to know what the server version is because out-of-tree // provisioners aren't officially supported until 1.5 serverVersion, err := clientset.Discovery().ServerVersion() if err != nil { glog.Fatalf(\"Error getting server version: %v\", err) } clientNFSProvisioner := &nfsProvisioner{ client: clientset, server: server, path: path, } 通过clientset、server、path等值构造nfsProvisioner对象，同时还获取了k8s的版本信息，因为provisioners的功能在k8s 1.5及以上版本才支持。 nfsProvisioner类型定义如下： type nfsProvisioner struct { client kubernetes.Interface server string path string } var _ controller.Provisioner = &nfsProvisioner{} nfsProvisioner是一个自定义的provisioner，用来实现Provisioner的接口，其中的属性除了server、path这两个关于NFS相关的参数，还包含了client，主要用来调用k8s的API。 var _ controller.Provisioner = &nfsProvisioner{} 以上用法用来检测nfsProvisioner是否实现了Provisioner的接口。 2.1.4. 构建并运行ProvisionController 源码如下： // Start the provision controller which will dynamically provision efs NFS // PVs pc := controller.NewProvisionController(clientset, provisionerName, clientNFSProvisioner, serverVersion.GitVersion) pc.Run(wait.NeverStop) 通过nfsProvisioner构造ProvisionController对象并执行Run方法，ProvisionController实现了具体的PV和PVC的相关逻辑，Run方法以常驻进程的方式运行。 2.2. Provision和Delete方法 2.2.1. Provision方法 nfsProvisioner的Provision方法具体源码参考：https://github.com/kubernetes-incubator/external-storage/blob/master/nfs-client/cmd/nfs-client-provisioner/provisioner.go#L56 Provision方法用来创建存储资源，并且返回一个PV对象。其中入参是VolumeOptions，用来指定PV对象的相关属性。 1、构建PV和PVC的名称 func (p *nfsProvisioner) Provision(options controller.VolumeOptions) (*v1.PersistentVolume, error) { if options.PVC.Spec.Selector != nil { return nil, fmt.Errorf(\"claim Selector is not supported\") } glog.V(4).Infof(\"nfs provisioner: VolumeOptions %v\", options) pvcNamespace := options.PVC.Namespace pvcName := options.PVC.Name pvName := strings.Join([]string{pvcNamespace, pvcName, options.PVName}, \"-\") fullPath := filepath.Join(mountPath, pvName) glog.V(4).Infof(\"creating path %s\", fullPath) if err := os.MkdirAll(fullPath, 0777); err != nil { return nil, errors.New(\"unable to create directory to provision new pv: \" + err.Error()) } os.Chmod(fullPath, 0777) path := filepath.Join(p.path, pvName) ... } 通过VolumeOptions的入参，构建PV和PVC的名称，以及创建路径path。 2、构造PV对象 pv := &v1.PersistentVolume{ ObjectMeta: metav1.ObjectMeta{ Name: options.PVName, }, Spec: v1.PersistentVolumeSpec{ PersistentVolumeReclaimPolicy: options.PersistentVolumeReclaimPolicy, AccessModes: options.PVC.Spec.AccessModes, MountOptions: options.MountOptions, Capacity: v1.ResourceList{ v1.ResourceName(v1.ResourceStorage): options.PVC.Spec.Resources.Requests[v1.ResourceName(v1.ResourceStorage)], }, PersistentVolumeSource: v1.PersistentVolumeSource{ NFS: &v1.NFSVolumeSource{ Server: p.server, Path: path, ReadOnly: false, }, }, }, } return pv, nil 综上可以看出，Provision方法只是通过VolumeOptions参数来构建PV对象，并没有执行具体PV的创建或删除的操作。 不同类型的Provisioner的，一般是PersistentVolumeSource类型和参数不同，例如nfs-provisioner对应的PersistentVolumeSource为NFS，并且需要传入NFS相关的参数：Server，Path等。 2.2.2. Delete方法 nfsProvisioner的delete方法具体源码参考：https://github.com/kubernetes-incubator/external-storage/blob/master/nfs-client/cmd/nfs-client-provisioner/provisioner.go#L99 1、获取pvName和path等相关参数 func (p *nfsProvisioner) Delete(volume *v1.PersistentVolume) error { path := volume.Spec.PersistentVolumeSource.NFS.Path pvName := filepath.Base(path) oldPath := filepath.Join(mountPath, pvName) if _, err := os.Stat(oldPath); os.IsNotExist(err) { glog.Warningf(\"path %s does not exist, deletion skipped\", oldPath) return nil } ... } 通过path和pvName生成oldPath，其中oldPath是原先NFS服务器上pod对应的数据持久化存储路径。 2、获取archiveOnDelete参数并删除数据 // Get the storage class for this volume. storageClass, err := p.getClassForVolume(volume) if err != nil { return err } // Determine if the \"archiveOnDelete\" parameter exists. // If it exists and has a falsey value, delete the directory. // Otherwise, archive it. archiveOnDelete, exists := storageClass.Parameters[\"archiveOnDelete\"] if exists { archiveBool, err := strconv.ParseBool(archiveOnDelete) if err != nil { return err } if !archiveBool { return os.RemoveAll(oldPath) } } 如果storageClass对象中指定archiveOnDelete参数并且值为false，则会自动删除oldPath下的所有数据，即pod对应的数据持久化存储数据。 archiveOnDelete字面意思为删除时是否存档，false表示不存档，即删除数据，true表示存档，即重命名路径。 3、重命名旧数据路径 archivePath := filepath.Join(mountPath, \"archived-\"+pvName) glog.V(4).Infof(\"archiving path %s to %s\", oldPath, archivePath) return os.Rename(oldPath, archivePath) 如果storageClass对象中没有指定archiveOnDelete参数或者值为true，表明需要删除时存档，即将oldPath重命名，命名格式为oldPath前面增加archived-的前缀。 3. ProvisionController 3.1. ProvisionController结构体 源码具体参考：https://github.com/kubernetes-incubator/external-storage/blob/master/lib/controller/controller.go#L82 ProvisionController是一个给PVC提供PV的控制器，具体执行Provisioner接口的Provision和Delete的方法的所有逻辑。 3.1.1. 入参 // ProvisionController is a controller that provisions PersistentVolumes for // PersistentVolumeClaims. type ProvisionController struct { client kubernetes.Interface // The name of the provisioner for which this controller dynamically // provisions volumes. The value of annDynamicallyProvisioned and // annStorageProvisioner to set & watch for, respectively provisionerName string // The provisioner the controller will use to provision and delete volumes. // Presumably this implementer of Provisioner carries its own // volume-specific options and such that it needs in order to provision // volumes. provisioner Provisioner // Kubernetes cluster server version: // * 1.4: storage classes introduced as beta. Technically out-of-tree dynamic // provisioning is not officially supported, though it works // * 1.5: storage classes stay in beta. Out-of-tree dynamic provisioning is // officially supported // * 1.6: storage classes enter GA kubeVersion *utilversion.Version ... } client、provisionerName、provisioner、kubeVersion等属性作为NewProvisionController的入参。 client：clientset客户端，用来调用k8s的API。 provisionerName：provisioner的名字，需要和StorageClass对象中的provisioner字段一致。 provisioner：具体的provisioner的实现者，本文为nfsProvisioner。 kubeVersion：k8s的版本信息。 3.1.2. Controller和Informer type ProvisionController struct { ... claimInformer cache.SharedInformer claims cache.Store claimController cache.Controller volumeInformer cache.SharedInformer volumes cache.Store volumeController cache.Controller classInformer cache.SharedInformer classes cache.Store classController cache.Controller ... } ProvisionController结构体中包含了PV、PVC、StorageClass三个对象的Controller、Informer和Store，主要用来执行这三个对象的相关操作。 Controller：通用的控制框架 Informer：消息通知器 Store：通用的对象存储接口 3.1.3. workqueue type ProvisionController struct { ... claimQueue workqueue.RateLimitingInterface volumeQueue workqueue.RateLimitingInterface ... } claimQueue和volumeQueue分别是PV和PVC的任务队列。 3.1.4. 其他 // Identity of this controller, generated at creation time and not persisted // across restarts. Useful only for debugging, for seeing the source of // events. controller.provisioner may have its own, different notion of // identity which may/may not persist across restarts id string component string eventRecorder record.EventRecorder resyncPeriod time.Duration exponentialBackOffOnError bool threadiness int createProvisionedPVRetryCount int createProvisionedPVInterval time.Duration failedProvisionThreshold, failedDeleteThreshold int // The port for metrics server to serve on. metricsPort int32 // The IP address for metrics server to serve on. metricsAddress string // The path of metrics endpoint path. metricsPath string // Parameters of leaderelection.LeaderElectionConfig. leaseDuration, renewDeadline, retryPeriod time.Duration hasRun bool hasRunLock *sync.Mutex 3.2. NewProvisionController方法 源码地址：https://github.com/kubernetes-incubator/external-storage/blob/master/lib/controller/controller.go#L418 NewProvisionController方法主要用来构造ProvisionController。 3.2.1. 初始化默认值 // NewProvisionController creates a new provision controller using // the given configuration parameters and with private (non-shared) informers. func NewProvisionController( client kubernetes.Interface, provisionerName string, provisioner Provisioner, kubeVersion string, options ...func(*ProvisionController) error, ) *ProvisionController { ... controller := &ProvisionController{ client: client, provisionerName: provisionerName, provisioner: provisioner, kubeVersion: utilversion.MustParseSemantic(kubeVersion), id: id, component: component, eventRecorder: eventRecorder, resyncPeriod: DefaultResyncPeriod, exponentialBackOffOnError: DefaultExponentialBackOffOnError, threadiness: DefaultThreadiness, createProvisionedPVRetryCount: DefaultCreateProvisionedPVRetryCount, createProvisionedPVInterval: DefaultCreateProvisionedPVInterval, failedProvisionThreshold: DefaultFailedProvisionThreshold, failedDeleteThreshold: DefaultFailedDeleteThreshold, leaseDuration: DefaultLeaseDuration, renewDeadline: DefaultRenewDeadline, retryPeriod: DefaultRetryPeriod, metricsPort: DefaultMetricsPort, metricsAddress: DefaultMetricsAddress, metricsPath: DefaultMetricsPath, hasRun: false, hasRunLock: &sync.Mutex{}, } ... } 3.2.2. 初始化任务队列 ratelimiter := workqueue.NewMaxOfRateLimiter( workqueue.NewItemExponentialFailureRateLimiter(15*time.Second, 1000*time.Second), &workqueue.BucketRateLimiter{Limiter: rate.NewLimiter(rate.Limit(10), 100)}, ) if !controller.exponentialBackOffOnError { ratelimiter = workqueue.NewMaxOfRateLimiter( workqueue.NewItemExponentialFailureRateLimiter(15*time.Second, 15*time.Second), &workqueue.BucketRateLimiter{Limiter: rate.NewLimiter(rate.Limit(10), 100)}, ) } controller.claimQueue = workqueue.NewNamedRateLimitingQueue(ratelimiter, \"claims\") controller.volumeQueue = workqueue.NewNamedRateLimitingQueue(ratelimiter, \"volumes\") 3.2.3. ListWatch // PVC claimSource := &cache.ListWatch{ ListFunc: func(options metav1.ListOptions) (runtime.Object, error) { return client.CoreV1().PersistentVolumeClaims(v1.NamespaceAll).List(options) }, WatchFunc: func(options metav1.ListOptions) (watch.Interface, error) { return client.CoreV1().PersistentVolumeClaims(v1.NamespaceAll).Watch(options) }, } // PV volumeSource := &cache.ListWatch{ ListFunc: func(options metav1.ListOptions) (runtime.Object, error) { return client.CoreV1().PersistentVolumes().List(options) }, WatchFunc: func(options metav1.ListOptions) (watch.Interface, error) { return client.CoreV1().PersistentVolumes().Watch(options) }, } // StorageClass classSource = &cache.ListWatch{ ListFunc: func(options metav1.ListOptions) (runtime.Object, error) { return client.StorageV1().StorageClasses().List(options) }, WatchFunc: func(options metav1.ListOptions) (watch.Interface, error) { return client.StorageV1().StorageClasses().Watch(options) }, } list-watch机制是k8s中用来监听对象变化的核心机制，ListWatch包含ListFunc和WatchFunc两个函数，且不能为空，以上代码分别构造了PV、PVC、StorageClass三个对象的ListWatch结构体。该机制的实现在client-go的cache包中，具体参考：https://godoc.org/k8s.io/client-go/tools/cache。 更多ListWatch代码如下: 具体参考：https://github.com/kubernetes-incubator/external-storage/blob/89b0aaf6413b249b37834b124fc314ef7b8ee949/vendor/k8s.io/client-go/tools/cache/listwatch.go#L34 // ListerWatcher is any object that knows how to perform an initial list and start a watch on a resource. type ListerWatcher interface { // List should return a list type object; the Items field will be extracted, and the // ResourceVersion field will be used to start the watch in the right place. List(options metav1.ListOptions) (runtime.Object, error) // Watch should begin a watch at the specified version. Watch(options metav1.ListOptions) (watch.Interface, error) } // ListFunc knows how to list resources type ListFunc func(options metav1.ListOptions) (runtime.Object, error) // WatchFunc knows how to watch resources type WatchFunc func(options metav1.ListOptions) (watch.Interface, error) // ListWatch knows how to list and watch a set of apiserver resources. It satisfies the ListerWatcher interface. // It is a convenience function for users of NewReflector, etc. // ListFunc and WatchFunc must not be nil type ListWatch struct { ListFunc ListFunc WatchFunc WatchFunc // DisableChunking requests no chunking for this list watcher. DisableChunking bool } 3.2.4. ResourceEventHandlerFuncs // PVC claimHandler := cache.ResourceEventHandlerFuncs{ AddFunc: func(obj interface{}) { controller.enqueueWork(controller.claimQueue, obj) }, UpdateFunc: func(oldObj, newObj interface{}) { controller.enqueueWork(controller.claimQueue, newObj) }, DeleteFunc: func(obj interface{}) { controller.forgetWork(controller.claimQueue, obj) }, } // PV volumeHandler := cache.ResourceEventHandlerFuncs{ AddFunc: func(obj interface{}) { controller.enqueueWork(controller.volumeQueue, obj) }, UpdateFunc: func(oldObj, newObj interface{}) { controller.enqueueWork(controller.volumeQueue, newObj) }, DeleteFunc: func(obj interface{}) { controller.forgetWork(controller.volumeQueue, obj) }, } // StorageClass classHandler := cache.ResourceEventHandlerFuncs{ // We don't need an actual event handler for StorageClasses, // but we must pass a non-nil one to cache.NewInformer() AddFunc: nil, UpdateFunc: nil, DeleteFunc: nil, } ResourceEventHandlerFuncs是资源事件处理函数，主要用来对k8s资源对象增删改变化的事件进行消息通知，该函数实现了ResourceEventHandler的接口。具体代码逻辑在client-go的cache包中。 更多ResourceEventHandlerFuncs代码可参考： // ResourceEventHandler can handle notifications for events that happen to a // resource. The events are informational only, so you can't return an // error. // * OnAdd is called when an object is added. // * OnUpdate is called when an object is modified. Note that oldObj is the // last known state of the object-- it is possible that several changes // were combined together, so you can't use this to see every single // change. OnUpdate is also called when a re-list happens, and it will // get called even if nothing changed. This is useful for periodically // evaluating or syncing something. // * OnDelete will get the final state of the item if it is known, otherwise // it will get an object of type DeletedFinalStateUnknown. This can // happen if the watch is closed and misses the delete event and we don't // notice the deletion until the subsequent re-list. type ResourceEventHandler interface { OnAdd(obj interface{}) OnUpdate(oldObj, newObj interface{}) OnDelete(obj interface{}) } // ResourceEventHandlerFuncs is an adaptor to let you easily specify as many or // as few of the notification functions as you want while still implementing // ResourceEventHandler. type ResourceEventHandlerFuncs struct { AddFunc func(obj interface{}) UpdateFunc func(oldObj, newObj interface{}) DeleteFunc func(obj interface{}) } 3.2.5. 构造Store和Controller 1、PVC if controller.claimInformer != nil { controller.claimInformer.AddEventHandlerWithResyncPeriod(claimHandler, controller.resyncPeriod) controller.claims, controller.claimController = controller.claimInformer.GetStore(), controller.claimInformer.GetController() } else { controller.claims, controller.claimController = cache.NewInformer( claimSource, &v1.PersistentVolumeClaim{}, controller.resyncPeriod, claimHandler, ) } 2、PV if controller.volumeInformer != nil { controller.volumeInformer.AddEventHandlerWithResyncPeriod(volumeHandler, controller.resyncPeriod) controller.volumes, controller.volumeController = controller.volumeInformer.GetStore(), controller.volumeInformer.GetController() } else { controller.volumes, controller.volumeController = cache.NewInformer( volumeSource, &v1.PersistentVolume{}, controller.resyncPeriod, volumeHandler, ) } 3、StorageClass if controller.classInformer != nil { // no resource event handler needed for StorageClasses controller.classes, controller.classController = controller.classInformer.GetStore(), controller.classInformer.GetController() } else { controller.classes, controller.classController = cache.NewInformer( classSource, versionedClassType, controller.resyncPeriod, classHandler, ) } 通过cache.NewInformer的方法构造，入参是ListWatch结构体和ResourceEventHandlerFuncs函数等，返回值是Store和Controller。 通过以上各个部分的构造，最后返回一个具体的ProvisionController对象。 3.3. ProvisionController.Run方法 ProvisionController的Run方法是以常驻进程的方式运行，函数内部再运行其他的controller。 3.3.1. prometheus数据收集 // Run starts all of this controller's control loops func (ctrl *ProvisionController) Run(stopCh 0 { prometheus.MustRegister([]prometheus.Collector{ metrics.PersistentVolumeClaimProvisionTotal, metrics.PersistentVolumeClaimProvisionFailedTotal, metrics.PersistentVolumeClaimProvisionDurationSeconds, metrics.PersistentVolumeDeleteTotal, metrics.PersistentVolumeDeleteFailedTotal, metrics.PersistentVolumeDeleteDurationSeconds, }...) http.Handle(ctrl.metricsPath, promhttp.Handler()) address := net.JoinHostPort(ctrl.metricsAddress, strconv.FormatInt(int64(ctrl.metricsPort), 10)) glog.Infof(\"Starting metrics server at %s\\n\", address) go wait.Forever(func() { err := http.ListenAndServe(address, nil) if err != nil { glog.Errorf(\"Failed to listen on %s: %v\", address, err) } }, 5*time.Second) } ... } 3.3.2. Controller.Run // If a SharedInformer has been passed in, this controller should not // call Run again if ctrl.claimInformer == nil { go ctrl.claimController.Run(stopCh) } if ctrl.volumeInformer == nil { go ctrl.volumeController.Run(stopCh) } if ctrl.classInformer == nil { go ctrl.classController.Run(stopCh) } 运行消息通知器Informer。 3.3.3. Worker for i := 0; i runClaimWorker和runVolumeWorker分别为PVC和PV的worker，这两个的具体执行体分别是processNextClaimWorkItem和processNextVolumeWorkItem。 执行流程如下： PVC的函数调用流程 runClaimWorker→processNextClaimWorkItem→syncClaimHandler→syncClaim→provisionClaimOperation PV的函数调用流程 runVolumeWorker→processNextVolumeWorkItem→syncVolumeHandler→syncVolume→deleteVolumeOperation 可见最后执行的函数分别是provisionClaimOperation和deleteVolumeOperation。 3.4. Operation 3.4.1. provisionClaimOperation 1、provisionClaimOperation入参是PVC，通过PVC获得PV对象，并判断PV对象是否存在，如果存在则退出后续操作。 // provisionClaimOperation attempts to provision a volume for the given claim. // Returns error, which indicates whether provisioning should be retried // (requeue the claim) or not func (ctrl *ProvisionController) provisionClaimOperation(claim *v1.PersistentVolumeClaim) error { // Most code here is identical to that found in controller.go of kube's PV controller... claimClass := helper.GetPersistentVolumeClaimClass(claim) operation := fmt.Sprintf(\"provision %q class %q\", claimToClaimKey(claim), claimClass) glog.Infof(logOperation(operation, \"started\")) // A previous doProvisionClaim may just have finished while we were waiting for // the locks. Check that PV (with deterministic name) hasn't been provisioned // yet. pvName := ctrl.getProvisionedVolumeNameForClaim(claim) volume, err := ctrl.client.CoreV1().PersistentVolumes().Get(pvName, metav1.GetOptions{}) if err == nil && volume != nil { // Volume has been already provisioned, nothing to do. glog.Infof(logOperation(operation, \"persistentvolume %q already exists, skipping\", pvName)) return nil } ... } 2、获取StorageClass对象中的Provisioner和ReclaimPolicy参数，如果provisionerName和StorageClass对象中的provisioner字段不一致则报错并退出执行。 provisioner, parameters, err := ctrl.getStorageClassFields(claimClass) if err != nil { glog.Errorf(logOperation(operation, \"error getting claim's StorageClass's fields: %v\", err)) return nil } if provisioner != ctrl.provisionerName { // class.Provisioner has either changed since shouldProvision() or // annDynamicallyProvisioned contains different provisioner than // class.Provisioner. glog.Errorf(logOperation(operation, \"unknown provisioner %q requested in claim's StorageClass\", provisioner)) return nil } // Check if this provisioner can provision this claim. if err = ctrl.canProvision(claim); err != nil { ctrl.eventRecorder.Event(claim, v1.EventTypeWarning, \"ProvisioningFailed\", err.Error()) glog.Errorf(logOperation(operation, \"failed to provision volume: %v\", err)) return nil } reclaimPolicy := v1.PersistentVolumeReclaimDelete if ctrl.kubeVersion.AtLeast(utilversion.MustParseSemantic(\"v1.8.0\")) { reclaimPolicy, err = ctrl.fetchReclaimPolicy(claimClass) if err != nil { return err } } 3、执行具体的provisioner.Provision方法，构建PV对象，例如本文中的provisioner是nfs-provisioner。 options := VolumeOptions{ PersistentVolumeReclaimPolicy: reclaimPolicy, PVName: pvName, PVC: claim, MountOptions: mountOptions, Parameters: parameters, SelectedNode: selectedNode, AllowedTopologies: allowedTopologies, } ctrl.eventRecorder.Event(claim, v1.EventTypeNormal, \"Provisioning\", fmt.Sprintf(\"External provisioner is provisioning volume for claim %q\", claimToClaimKey(claim))) volume, err = ctrl.provisioner.Provision(options) if err != nil { if ierr, ok := err.(*IgnoredError); ok { // Provision ignored, do nothing and hope another provisioner will provision it. glog.Infof(logOperation(operation, \"volume provision ignored: %v\", ierr)) return nil } err = fmt.Errorf(\"failed to provision volume with StorageClass %q: %v\", claimClass, err) ctrl.eventRecorder.Event(claim, v1.EventTypeWarning, \"ProvisioningFailed\", err.Error()) return err } 4、创建k8s的PV对象。 // Try to create the PV object several times for i := 0; i 5、创建PV失败，清理存储资源。 if err != nil { // Save failed. Now we have a storage asset outside of Kubernetes, // but we don't have appropriate PV object for it. // Emit some event here and try to delete the storage asset several // times. ... for i := 0; i 如果创建成功，则打印成功的日志，并返回nil。 3.4.2. deleteVolumeOperation 1、deleteVolumeOperation入参是PV，先获得PV对象，并判断是否需要删除。 // deleteVolumeOperation attempts to delete the volume backing the given // volume. Returns error, which indicates whether deletion should be retried // (requeue the volume) or not func (ctrl *ProvisionController) deleteVolumeOperation(volume *v1.PersistentVolume) error { ... // This method may have been waiting for a volume lock for some time. // Our check does not have to be as sophisticated as PV controller's, we can // trust that the PV controller has set the PV to Released/Failed and it's // ours to delete newVolume, err := ctrl.client.CoreV1().PersistentVolumes().Get(volume.Name, metav1.GetOptions{}) if err != nil { return nil } if !ctrl.shouldDelete(newVolume) { glog.Infof(logOperation(operation, \"persistentvolume no longer needs deletion, skipping\")) return nil } ... } 2、调用具体的provisioner的Delete方法，例如，如果是nfs-provisioner，则是调用nfs-provisioner的Delete方法。 err = ctrl.provisioner.Delete(volume) if err != nil { if ierr, ok := err.(*IgnoredError); ok { // Delete ignored, do nothing and hope another provisioner will delete it. glog.Infof(logOperation(operation, \"volume deletion ignored: %v\", ierr)) return nil } // Delete failed, emit an event. glog.Errorf(logOperation(operation, \"volume deletion failed: %v\", err)) ctrl.eventRecorder.Event(volume, v1.EventTypeWarning, \"VolumeFailedDelete\", err.Error()) return err } 3、删除k8s中的PV对象。 // Delete the volume if err = ctrl.client.CoreV1().PersistentVolumes().Delete(volume.Name, nil); err != nil { // Oops, could not delete the volume and therefore the controller will // try to delete the volume again on next update. glog.Infof(logOperation(operation, \"failed to delete persistentvolume: %v\", err)) return err } 4. 总结 Provisioner接口包含Provision和Delete两个方法，自定义的provisioner需要实现这两个方法，这两个方法只是处理了跟存储类型相关的事项，并没有针对PV、PVC对象的增删等操作。 Provision方法主要用来构造PV对象，不同类型的Provisioner的，一般是PersistentVolumeSource类型和参数不同，例如nfs-provisioner对应的PersistentVolumeSource为NFS，并且需要传入NFS相关的参数：Server，Path等。 Delete方法主要针对对应的存储类型，做数据存档（备份）或删除的处理。 StorageClass对象需要单独创建，用来指定具体的provisioner来执行相关逻辑。 provisionClaimOperation和deleteVolumeOperation具体执行了k8s中PV对象的创建和删除操作，同时调用了具体provisioner的Provision和Delete两个方法来对存储数据做处理。 参考文章 https://github.com/kubernetes-incubator/external-storage/tree/master/docs/demo/hostpath-provisioner https://github.com/kubernetes-incubator/external-storage/tree/master/nfs-client https://github.com/kubernetes-incubator/external-storage/blob/master/lib/controller/controller.go https://github.com/kubernetes-incubator/external-storage/blob/master/lib/controller/volume.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"develop/csi/csi-provisioner.html":{"url":"develop/csi/csi-provisioner.html","title":"csi-provisioner源码分析","keywords":"","body":"1. Dynamic Provisioner1.1. Provisioner Interface1.2. 开发provisioner的步骤2. CSI Provisioner2.1. Main 函数2.1.1. 读取环境变量2.1.2. 获取clientset对象2.1.3. k8s版本校验2.1.4. 连接 csi socket2.1.5. 构造csi-Provisioner对象2.1.6. 运行ProvisionController2.2. Provision和Delete方法2.2.1. Provision方法2.2.2. Delete方法2.3. 总结3. csi-client3.1. 构造csi-client3.1.1. 构造grpcClient3.1.2. 构造csi-client3.2. csiClient.CreateVolume3.3. csiClient.DeleteVolume4. ProvisionController.Run 本文主要分析csi-provisioner的源码，关于开发一个Dynamic Provisioner，具体可参考nfs-client-provisioner的源码分析 1. Dynamic Provisioner 1.1. Provisioner Interface 开发Dynamic Provisioner需要实现Provisioner接口，该接口有两个方法，分别是： Provision：创建存储资源，并且返回一个PV对象。 Delete：移除对应的存储资源，但并没有删除PV对象。 1.2. 开发provisioner的步骤 写一个provisioner实现Provisioner接口（包含Provision和Delete的方法）。 通过该provisioner构建ProvisionController。 执行ProvisionController的Run方法。 2. CSI Provisioner CSI Provisioner的源码可参考：https://github.com/kubernetes-csi/external-provisioner。 2.1. Main 函数 2.1.1. 读取环境变量 源码如下： var ( provisioner = flag.String(\"provisioner\", \"\", \"Name of the provisioner. The provisioner will only provision volumes for claims that request a StorageClass with a provisioner field set equal to this name.\") master = flag.String(\"master\", \"\", \"Master URL to build a client config from. Either this or kubeconfig needs to be set if the provisioner is being run out of cluster.\") kubeconfig = flag.String(\"kubeconfig\", \"\", \"Absolute path to the kubeconfig file. Either this or master needs to be set if the provisioner is being run out of cluster.\") csiEndpoint = flag.String(\"csi-address\", \"/run/csi/socket\", \"The gRPC endpoint for Target CSI Volume\") connectionTimeout = flag.Duration(\"connection-timeout\", 10*time.Second, \"Timeout for waiting for CSI driver socket.\") volumeNamePrefix = flag.String(\"volume-name-prefix\", \"pvc\", \"Prefix to apply to the name of a created volume\") volumeNameUUIDLength = flag.Int(\"volume-name-uuid-length\", -1, \"Truncates generated UUID of a created volume to this length. Defaults behavior is to NOT truncate.\") showVersion = flag.Bool(\"version\", false, \"Show version.\") provisionController *controller.ProvisionController version = \"unknown\" ) func init() { var config *rest.Config var err error flag.Parse() flag.Set(\"logtostderr\", \"true\") if *showVersion { fmt.Println(os.Args[0], version) os.Exit(0) } glog.Infof(\"Version: %s\", version) ... } 通过init函数解析相关参数，其实provisioner指明为PVC提供PV的provisioner的名字，需要和StorageClass对象中的provisioner字段一致。 2.1.2. 获取clientset对象 源码如下： // get the KUBECONFIG from env if specified (useful for local/debug cluster) kubeconfigEnv := os.Getenv(\"KUBECONFIG\") if kubeconfigEnv != \"\" { glog.Infof(\"Found KUBECONFIG environment variable set, using that..\") kubeconfig = &kubeconfigEnv } if *master != \"\" || *kubeconfig != \"\" { glog.Infof(\"Either master or kubeconfig specified. building kube config from that..\") config, err = clientcmd.BuildConfigFromFlags(*master, *kubeconfig) } else { glog.Infof(\"Building kube configs for running in cluster...\") config, err = rest.InClusterConfig() } if err != nil { glog.Fatalf(\"Failed to create config: %v\", err) } clientset, err := kubernetes.NewForConfig(config) if err != nil { glog.Fatalf(\"Failed to create client: %v\", err) } // snapclientset.NewForConfig creates a new Clientset for VolumesnapshotV1alpha1Client snapClient, err := snapclientset.NewForConfig(config) if err != nil { glog.Fatalf(\"Failed to create snapshot client: %v\", err) } csiAPIClient, err := csiclientset.NewForConfig(config) if err != nil { glog.Fatalf(\"Failed to create CSI API client: %v\", err) } 通过读取对应的k8s的配置，创建clientset对象，用来执行k8s对应的API，其中主要包括对PV和PVC等对象的创建删除等操作。 2.1.3. k8s版本校验 // The controller needs to know what the server version is because out-of-tree // provisioners aren't officially supported until 1.5 serverVersion, err := clientset.Discovery().ServerVersion() if err != nil { glog.Fatalf(\"Error getting server version: %v\", err) } 获取了k8s的版本信息，因为provisioners的功能在k8s 1.5及以上版本才支持。 2.1.4. 连接 csi socket // Generate a unique ID for this provisioner timeStamp := time.Now().UnixNano() / int64(time.Millisecond) identity := strconv.FormatInt(timeStamp, 10) + \"-\" + strconv.Itoa(rand.Intn(10000)) + \"-\" + *provisioner // Provisioner will stay in Init until driver opens csi socket, once it's done // controller will exit this loop and proceed normally. socketDown := true grpcClient := &grpc.ClientConn{} for socketDown { grpcClient, err = ctrl.Connect(*csiEndpoint, *connectionTimeout) if err == nil { socketDown = false continue } time.Sleep(10 * time.Second) } 在Provisioner会停留在初始化状态，直到csi socket连接成功才正常运行。如果连接失败，会暂停10秒后重试，其中涉及以下2个参数： csiEndpoint：CSI Volume的gRPC地址，默认通过为/run/csi/socket。 connectionTimeout：连接CSI driver socket的超时时间，默认为10秒。 2.1.5. 构造csi-Provisioner对象 // Create the provisioner: it implements the Provisioner interface expected by // the controller csiProvisioner := ctrl.NewCSIProvisioner(clientset, csiAPIClient, *csiEndpoint, *connectionTimeout, identity, *volumeNamePrefix, *volumeNameUUIDLength, grpcClient, snapClient) provisionController = controller.NewProvisionController( clientset, *provisioner, csiProvisioner, serverVersion.GitVersion, ) 通过参数clientset,csiAPIClient, csiEndpoint, connectionTimeout, identity, volumeNamePrefix, volumeNameUUIDLength,grpcClient, snapClient构造csi-Provisioner对象。 通过csiProvisioner构造ProvisionController对象。 2.1.6. 运行ProvisionController func main() { provisionController.Run(wait.NeverStop) } ProvisionController实现了具体的PV和PVC的相关逻辑，Run方法以常驻进程的方式运行。 2.2. Provision和Delete方法 2.2.1. Provision方法 csiProvisioner的Provision方法具体源码参考：https://github.com/kubernetes-csi/external-provisioner/blob/master/pkg/controller/controller.go#L336 Provision方法用来创建存储资源，并且返回一个PV对象。其中入参是VolumeOptions，用来指定PV对象的相关属性。 1、构造PV相关属性 pvName, err := makeVolumeName(p.volumeNamePrefix, fmt.Sprintf(\"%s\", options.PVC.ObjectMeta.UID), p.volumeNameUUIDLength) if err != nil { return nil, err } 2、构造CSIPersistentVolumeSource相关属性 driverState, err := checkDriverState(p.grpcClient, p.timeout, needSnapshotSupport) if err != nil { return nil, err } ... // Resolve controller publish, node stage, node publish secret references controllerPublishSecretRef, err := getSecretReference(controllerPublishSecretNameKey, controllerPublishSecretNamespaceKey, options.Parameters, pvName, options.PVC) if err != nil { return nil, err } nodeStageSecretRef, err := getSecretReference(nodeStageSecretNameKey, nodeStageSecretNamespaceKey, options.Parameters, pvName, options.PVC) if err != nil { return nil, err } nodePublishSecretRef, err := getSecretReference(nodePublishSecretNameKey, nodePublishSecretNamespaceKey, options.Parameters, pvName, options.PVC) if err != nil { return nil, err } ... volumeAttributes := map[string]string{provisionerIDKey: p.identity} for k, v := range rep.Volume.Attributes { volumeAttributes[k] = v } ... fsType := \"\" for k, v := range options.Parameters { switch strings.ToLower(k) { case \"fstype\": fsType = v } } if len(fsType) == 0 { fsType = defaultFSType } 3、创建CSI CreateVolumeRequest // Create a CSI CreateVolumeRequest and Response req := csi.CreateVolumeRequest{ Name: pvName, Parameters: options.Parameters, VolumeCapabilities: volumeCaps, CapacityRange: &csi.CapacityRange{ RequiredBytes: int64(volSizeBytes), }, } ... glog.V(5).Infof(\"CreateVolumeRequest %+v\", req) rep := &csi.CreateVolumeResponse{} ... opts := wait.Backoff{Duration: backoffDuration, Factor: backoffFactor, Steps: backoffSteps} err = wait.ExponentialBackoff(opts, func() (bool, error) { ctx, cancel := context.WithTimeout(context.Background(), p.timeout) defer cancel() rep, err = p.csiClient.CreateVolume(ctx, &req) if err == nil { // CreateVolume has finished successfully return true, nil } if status, ok := status.FromError(err); ok { if status.Code() == codes.DeadlineExceeded { // CreateVolume timed out, give it another chance to complete glog.Warningf(\"CreateVolume timeout: %s has expired, operation will be retried\", p.timeout.String()) return false, nil } } // CreateVolume failed , no reason to retry, bailing from ExponentialBackoff return false, err }) if err != nil { return nil, err } if rep.Volume != nil { glog.V(3).Infof(\"create volume rep: %+v\", *rep.Volume) } respCap := rep.GetVolume().GetCapacityBytes() if respCap Provison方法核心功能是调用p.csiClient.CreateVolume(ctx, &req)。 4、构造PV对象 pv := &v1.PersistentVolume{ ObjectMeta: metav1.ObjectMeta{ Name: pvName, }, Spec: v1.PersistentVolumeSpec{ PersistentVolumeReclaimPolicy: options.PersistentVolumeReclaimPolicy, AccessModes: options.PVC.Spec.AccessModes, Capacity: v1.ResourceList{ v1.ResourceName(v1.ResourceStorage): bytesToGiQuantity(respCap), }, // TODO wait for CSI VolumeSource API PersistentVolumeSource: v1.PersistentVolumeSource{ CSI: &v1.CSIPersistentVolumeSource{ Driver: driverState.driverName, VolumeHandle: p.volumeIdToHandle(rep.Volume.Id), FSType: fsType, VolumeAttributes: volumeAttributes, ControllerPublishSecretRef: controllerPublishSecretRef, NodeStageSecretRef: nodeStageSecretRef, NodePublishSecretRef: nodePublishSecretRef, }, }, }, } if driverState.capabilities.Has(PluginCapability_ACCESSIBILITY_CONSTRAINTS) { pv.Spec.NodeAffinity = GenerateVolumeNodeAffinity(rep.Volume.AccessibleTopology) } glog.Infof(\"successfully created PV %+v\", pv.Spec.PersistentVolumeSource) return pv, nil Provision方法只是通过VolumeOptions参数来构建PV对象，并没有执行具体PV的创建或删除的操作。 不同类型的Provisioner的，一般是PersistentVolumeSource类型和参数不同，例如csi-provisioner对应的PersistentVolumeSource为CSI，并且需要传入CSI相关的参数： Driver VolumeHandle FSType VolumeAttributes ControllerPublishSecretRef NodeStageSecretRef NodePublishSecretRef 2.2.2. Delete方法 csiProvisioner的delete方法具体源码参考：https://github.com/kubernetes-csi/external-provisioner/blob/master/pkg/controller/controller.go#L606 func (p *csiProvisioner) Delete(volume *v1.PersistentVolume) error { if volume == nil || volume.Spec.CSI == nil { return fmt.Errorf(\"invalid CSI PV\") } volumeId := p.volumeHandleToId(volume.Spec.CSI.VolumeHandle) _, err := checkDriverState(p.grpcClient, p.timeout, false) if err != nil { return err } req := csi.DeleteVolumeRequest{ VolumeId: volumeId, } // get secrets if StorageClass specifies it storageClassName := volume.Spec.StorageClassName if len(storageClassName) != 0 { if storageClass, err := p.client.StorageV1().StorageClasses().Get(storageClassName, metav1.GetOptions{}); err == nil { // Resolve provision secret credentials. // No PVC is provided when resolving provision/delete secret names, since the PVC may or may not exist at delete time. provisionerSecretRef, err := getSecretReference(provisionerSecretNameKey, provisionerSecretNamespaceKey, storageClass.Parameters, volume.Name, nil) if err != nil { return err } credentials, err := getCredentials(p.client, provisionerSecretRef) if err != nil { return err } req.ControllerDeleteSecrets = credentials } } ctx, cancel := context.WithTimeout(context.Background(), p.timeout) defer cancel() _, err = p.csiClient.DeleteVolume(ctx, &req) return err } Delete方法主要是调用了p.csiClient.DeleteVolume(ctx, &req)方法。 2.3. 总结 csi provisioner实现了Provisioner接口，其中包含Provison和Delete两个方法: Provision：调用csiClient.CreateVolume方法，同时构造并返回PV对象。 Delete：调用csiClient.DeleteVolume方法。 csi provisioner的核心方法都调用了csi-client相关方法。 3. csi-client csi client的相关代码参考：https://github.com/container-storage-interface/spec/blob/master/lib/go/csi/v0/csi.pb.go 3.1. 构造csi-client 3.1.1. 构造grpcClient // Provisioner will stay in Init until driver opens csi socket, once it's done // controller will exit this loop and proceed normally. socketDown := true grpcClient := &grpc.ClientConn{} for socketDown { grpcClient, err = ctrl.Connect(*csiEndpoint, *connectionTimeout) if err == nil { socketDown = false continue } time.Sleep(10 * time.Second) } 通过连接csi socket，连接成功才构造可用的grpcClient。 3.1.2. 构造csi-client 通过grpcClient构造csi-client。 // Create the provisioner: it implements the Provisioner interface expected by // the controller csiProvisioner := ctrl.NewCSIProvisioner(clientset, csiAPIClient, *csiEndpoint, *connectionTimeout, identity, *volumeNamePrefix, *volumeNameUUIDLength, grpcClient, snapClient) NewCSIProvisioner // NewCSIProvisioner creates new CSI provisioner func NewCSIProvisioner(client kubernetes.Interface, csiAPIClient csiclientset.Interface, csiEndpoint string, connectionTimeout time.Duration, identity string, volumeNamePrefix string, volumeNameUUIDLength int, grpcClient *grpc.ClientConn, snapshotClient snapclientset.Interface) controller.Provisioner { csiClient := csi.NewControllerClient(grpcClient) provisioner := &csiProvisioner{ client: client, grpcClient: grpcClient, csiClient: csiClient, csiAPIClient: csiAPIClient, snapshotClient: snapshotClient, timeout: connectionTimeout, identity: identity, volumeNamePrefix: volumeNamePrefix, volumeNameUUIDLength: volumeNameUUIDLength, } return provisioner } NewControllerClient csiClient := csi.NewControllerClient(grpcClient) ... type controllerClient struct { cc *grpc.ClientConn } func NewControllerClient(cc *grpc.ClientConn) ControllerClient { return &controllerClient{cc} } 3.2. csiClient.CreateVolume csi provisoner中调用csiClient.CreateVolume代码如下： opts := wait.Backoff{Duration: backoffDuration, Factor: backoffFactor, Steps: backoffSteps} err = wait.ExponentialBackoff(opts, func() (bool, error) { ctx, cancel := context.WithTimeout(context.Background(), p.timeout) defer cancel() rep, err = p.csiClient.CreateVolume(ctx, &req) if err == nil { // CreateVolume has finished successfully return true, nil } if status, ok := status.FromError(err); ok { if status.Code() == codes.DeadlineExceeded { // CreateVolume timed out, give it another chance to complete glog.Warningf(\"CreateVolume timeout: %s has expired, operation will be retried\", p.timeout.String()) return false, nil } } // CreateVolume failed , no reason to retry, bailing from ExponentialBackoff return false, err }) CreateVolumeRequest的构造： // Create a CSI CreateVolumeRequest and Response req := csi.CreateVolumeRequest{ Name: pvName, Parameters: options.Parameters, VolumeCapabilities: volumeCaps, CapacityRange: &csi.CapacityRange{ RequiredBytes: int64(volSizeBytes), }, } ... req.VolumeContentSource = volumeContentSource ... req.AccessibilityRequirements = requirements ... req.ControllerCreateSecrets = provisionerCredentials 具体的Create实现方法如下： 其中csiClient是个接口类型 具体代码参考controllerClient.CreateVolume func (c *controllerClient) CreateVolume(ctx context.Context, in *CreateVolumeRequest, opts ...grpc.CallOption) (*CreateVolumeResponse, error) { out := new(CreateVolumeResponse) err := grpc.Invoke(ctx, \"/csi.v0.Controller/CreateVolume\", in, out, c.cc, opts...) if err != nil { return nil, err } return out, nil } 3.3. csiClient.DeleteVolume csi provisoner中调用csiClient.DeleteVolume代码如下： func (p *csiProvisioner) Delete(volume *v1.PersistentVolume) error { ... req := csi.DeleteVolumeRequest{ VolumeId: volumeId, } // get secrets if StorageClass specifies it ... ctx, cancel := context.WithTimeout(context.Background(), p.timeout) defer cancel() _, err = p.csiClient.DeleteVolume(ctx, &req) return err } DeleteVolumeRequest的构造： req := csi.DeleteVolumeRequest{ VolumeId: volumeId, } ... req.ControllerDeleteSecrets = credentials 将构造的DeleteVolumeRequest传给DeleteVolume方法。 具体的Delete实现方法如下： 具体代码参考：controllerClient.DeleteVolume func (c *controllerClient) DeleteVolume(ctx context.Context, in *DeleteVolumeRequest, opts ...grpc.CallOption) (*DeleteVolumeResponse, error) { out := new(DeleteVolumeResponse) err := grpc.Invoke(ctx, \"/csi.v0.Controller/DeleteVolume\", in, out, c.cc, opts...) if err != nil { return nil, err } return out, nil } 4. ProvisionController.Run 自定义的provisioner实现了Provisoner接口的Provision和Delete方法，这两个方法主要对后端存储做创建和删除操作，并没有对PV对象进行创建和删除操作。 PV对象的相关操作具体由ProvisionController中的provisionClaimOperation和deleteVolumeOperation具体执行，同时调用了具体provisioner的Provision和Delete两个方法来对存储数据做处理。 func main() { provisionController.Run(wait.NeverStop) } 这块代码逻辑可参考：nfs-client-provisioner 源码分析 参考文章： https://github.com/kubernetes-csi/external-provisioner https://github.com/container-storage-interface/spec https://github.com/kubernetes/community/blob/master/contributors/design-proposals/storage/container-storage-interface.md https://github.com/container-storage-interface/spec/blob/master/spec.md Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"develop/operator/_index.html":{"url":"develop/operator/_index.html","title":"operator开发","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"develop/operator/kubebuilder.html":{"url":"develop/operator/kubebuilder.html","title":"kubebuilder的使用","keywords":"","body":"1. kubebuilder1.1. 安装kubebuilder1.2. kubebuilder命令2. 操作步骤2.1. 初始化2.2. 创建项目2.3. 创建API3. troubleshooting3.1. controller-gen: No such file or directory1. kubebuilder 1.1. 安装kubebuilder # download kubebuilder and install locally. curl -L -o kubebuilder https://go.kubebuilder.io/dl/latest/$(go env GOOS)/$(go env GOARCH) chmod +x kubebuilder && mv kubebuilder /usr/local/bin/ 1.2. kubebuilder命令 Development kit for building Kubernetes extensions and tools. Provides libraries and tools to create new projects, APIs and controllers. Includes tools for packaging artifacts into an installer container. Typical project lifecycle: - initialize a project: kubebuilder init --domain example.com --license apache2 --owner \"The Kubernetes authors\" - create one or more a new resource APIs and add your code to them: kubebuilder create api --group --version --kind Create resource will prompt the user for if it should scaffold the Resource and / or Controller. To only scaffold a Controller for an existing Resource, select \"n\" for Resource. To only define the schema for a Resource without writing a Controller, select \"n\" for Controller. After the scaffold is written, api will run make on the project. Usage: kubebuilder [command] Available Commands: create Scaffold a Kubernetes API or webhook. edit This command will edit the project configuration help Help about any command init Initialize a new project version Print the kubebuilder version Flags: -h, --help help for kubebuilder Use \"kubebuilder [command] --help\" for more information about a command. 2. 操作步骤 2.1. 初始化 mkdir $GOPATH/src/github.com/huweihuang/operator-example cd $GOPATH/src/github.com/huweihuang/operator-example go mod init github.com/huweihuang/operator-example 2.2. 创建项目 # kubebuilder init --domain github.com --license apache2 --owner \"Hu Weihuang\" Writing scaffold for you to edit... Get controller runtime: $ go get sigs.k8s.io/controller-runtime@v0.5.0 Update go.mod: $ go mod tidy Running make: $ make go: creating new go.mod: module tmp go: finding sigs.k8s.io v0.2.5 go: finding sigs.k8s.io/controller-tools/cmd v0.2.5 go: finding sigs.k8s.io/controller-tools/cmd/controller-gen v0.2.5 /Users/weihuanghu/go/bin/controller-gen object:headerFile=\"hack/boilerplate.go.txt\" paths=\"./...\" go fmt ./... go vet ./... go build -o bin/manager main.go Next: define a resource with: $ kubebuilder create api 查看生成文件： ./ ├── Dockerfile ├── Makefile ├── PROJECT ├── bin │ └── manager ├── config │ ├── certmanager │ │ ├── certificate.yaml │ │ ├── kustomization.yaml │ │ └── kustomizeconfig.yaml │ ├── default │ │ ├── kustomization.yaml │ │ ├── manager_auth_proxy_patch.yaml │ │ ├── manager_webhook_patch.yaml │ │ └── webhookcainjection_patch.yaml │ ├── manager │ │ ├── kustomization.yaml │ │ └── manager.yaml │ ├── prometheus │ │ ├── kustomization.yaml │ │ └── monitor.yaml │ ├── rbac │ │ ├── auth_proxy_client_clusterrole.yaml │ │ ├── auth_proxy_role.yaml │ │ ├── auth_proxy_role_binding.yaml │ │ ├── auth_proxy_service.yaml │ │ ├── kustomization.yaml │ │ ├── leader_election_role.yaml │ │ ├── leader_election_role_binding.yaml │ │ └── role_binding.yaml │ └── webhook │ ├── kustomization.yaml │ ├── kustomizeconfig.yaml │ └── service.yaml ├── go.mod ├── go.sum ├── hack │ └── boilerplate.go.txt └── main.go 2.3. 创建API # kubebuilder create api --group webapp --version v1 --kind Guestbook Create Resource [y/n] y Create Controller [y/n] y Writing scaffold for you to edit... api/v1/guestbook_types.go controllers/guestbook_controller.go Running make: $ make go: creating new go.mod: module tmp go: finding sigs.k8s.io/controller-tools/cmd v0.2.5 go: finding sigs.k8s.io/controller-tools/cmd/controller-gen v0.2.5 go: finding sigs.k8s.io v0.2.5 /Users/weihuanghu/go/bin/controller-gen object:headerFile=\"hack/boilerplate.go.txt\" paths=\"./...\" go fmt ./... go vet ./... go build -o bin/manager main.go 查看创建文件 api └── v1 ├── groupversion_info.go ├── guestbook_types.go └── zz_generated.deepcopy.go controllers ├── guestbook_controller.go └── suite_test.go 查看api/v1/guestbook_types.go // GuestbookSpec defines the desired state of Guestbook type GuestbookSpec struct { // INSERT ADDITIONAL SPEC FIELDS - desired state of cluster // Important: Run \"make\" to regenerate code after modifying this file // Quantity of instances // +kubebuilder:validation:Minimum=1 // +kubebuilder:validation:Maximum=10 Size int32 `json:\"size\"` // Name of the ConfigMap for GuestbookSpec's configuration // +kubebuilder:validation:MaxLength=15 // +kubebuilder:validation:MinLength=1 ConfigMapName string `json:\"configMapName\"` // +kubebuilder:validation:Enum=Phone;Address;Name Type string `json:\"alias,omitempty\"` } // GuestbookStatus defines the observed state of Guestbook type GuestbookStatus struct { // INSERT ADDITIONAL STATUS FIELD - define observed state of cluster // Important: Run \"make\" to regenerate code after modifying this file // PodName of the active Guestbook node. Active string `json:\"active\"` // PodNames of the standby Guestbook nodes. Standby []string `json:\"standby\"` } // +kubebuilder:object:root=true // +kubebuilder:subresource:status // +kubebuilder:resource:scope=Cluster // Guestbook is the Schema for the guestbooks API type Guestbook struct { metav1.TypeMeta `json:\",inline\"` metav1.ObjectMeta `json:\"metadata,omitempty\"` Spec GuestbookSpec `json:\"spec,omitempty\"` Status GuestbookStatus `json:\"status,omitempty\"` } 3. troubleshooting 3.1. controller-gen: No such file or directory ➜ operator-example kubebuilder init --domain github.com --license apache2 --owner \"Hu Weihuang\" Writing scaffold for you to edit... Get controller runtime: $ go get sigs.k8s.io/controller-runtime@v0.5.0 Update go.mod: $ go mod tidy Running make: $ make go: creating new go.mod: module tmp go: finding sigs.k8s.io v0.2.5 go: finding sigs.k8s.io/controller-tools/cmd v0.2.5 go: finding sigs.k8s.io/controller-tools/cmd/controller-gen v0.2.5 /Users/weihuanghu/go:/Users/weihuanghu/k8spath/bin/controller-gen object:headerFile=\"hack/boilerplate.go.txt\" paths=\"./...\" /bin/sh: /Users/weihuanghu/go:/Users/weihuanghu/k8spath/bin/controller-gen: No such file or directory make: *** [generate] Error 127 2020/04/13 14:34:47 failed to initialize project: exit status 2 由于本地存在多个GOPATH的目录，而获取了非当前项目下的GOPATH目录，因此将当前项目所在的GOPATH目录export到GOPATH环境变量中，就可以解决。 export GOPATH=\"/path/to/gopath\" 参考： https://kubernetes.io/zh/docs/concepts/extend-kubernetes/operator/ https://github.com/kubernetes-sigs/kubebuilder https://book.kubebuilder.io/quick-start.html https://operatorhub.io/ https://devops.college/developing-kubernetes-operator-is-now-easy-with-operator-framework-d3194a7428ff Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"develop/operator/develop-operator.html":{"url":"develop/operator/develop-operator.html","title":"如何开发一个Operator","keywords":"","body":"1. 准备工具环境及创建项目2. 创建CRD对象3. 实现控制器逻辑4. 注册控制器开发一个k8s operator组件主要会用到以下几个仓库或工具： kubebuilder/operator-sdk：主要用于创建CRD对象。 controller-manager：主要用于实现operator的controller逻辑。 本文以kubebuilder的工具和controller-manager example为例。 1. 准备工具环境及创建项目 安装kubebuilder工具 curl -L -o kubebuilder https://go.kubebuilder.io/dl/latest/$(go env GOOS)/$(go env GOARCH) chmod +x kubebuilder && mv kubebuilder /usr/local/bin/ 创建operator项目目录 $ kubebuilder init --domain example.com --license apache2 --owner \"Your Name\" --repo github.com/example/my-operator 将 example.com 替换为你的域名，Your Name 替换为你的名字，github.com/example/my-operator 替换为你的 GitHub 仓库。 2. 创建CRD对象 $ kubebuilder create api --group mygroup --version v1alpha1 --kind MyResource 这将在 api/v1alpha1 文件夹中创建一个名为 myresource_types.go 的文件，其中定义了 MyResource 资源的规范和状态。 编辑 api/v1alpha1/myresource_types.go 文件，添加自定义资源的规范和状态字段。 3. 实现控制器逻辑 在 controllers/myresource_controller.go 文件中实现控制器逻辑。该部分是不同的CRD实现自定义逻辑的核心部分。 主要包含以下几个部分： 定义ReconcileMyResource结构体。 实现func (r *ReconcileMyResource) Reconcile(ctx context.Context, request reconcile.Request) (reconcile.Result, error)函数接口。 定义finalizer逻辑。 定义reconcile逻辑。 package controllers import ( \"context\" \"github.com/go-logr/logr\" \"sigs.k8s.io/controller-runtime/pkg/controller/controllerutil\" \"sigs.k8s.io/controller-runtime/pkg/controller/inject\" \"sigs.k8s.io/controller-runtime/pkg/reconcile\" myv1alpha1 \"github.com/example/my-operator/api/v1alpha1\" corev1 \"k8s.io/api/core/v1\" \"k8s.io/apimachinery/pkg/api/errors\" \"k8s.io/apimachinery/pkg/runtime\" \"sigs.k8s.io/controller-runtime/pkg/client\" \"sigs.k8s.io/controller-runtime/pkg/controller\" logf \"sigs.k8s.io/controller-runtime/pkg/log\" \"sigs.k8s.io/controller-runtime/pkg/log/zap\" \"sigs.k8s.io/controller-runtime/pkg/manager\" \"sigs.k8s.io/controller-runtime/pkg/reconcile\" \"sigs.k8s.io/controller-runtime/pkg/source\" ) var log = logf.Log.WithName(\"controller_myresource\") // Add creates a new MyResource Controller and adds it to the Manager. The Manager will set fields on the Controller // and Start it when the Manager is Started. func Add(mgr manager.Manager) error { return add(mgr, newReconciler(mgr)) } // newReconciler returns a new reconcile.Reconciler func newReconciler(mgr manager.Manager) reconcile.Reconciler { return &ReconcileMyResource{client: mgr.GetClient(), scheme: mgr.GetScheme()} } // blank assignment to verify that ReconcileMyResource implements reconcile.Reconciler var _ reconcile.Reconciler = &ReconcileMyResource{} // ReconcileMyResource reconciles a MyResource object type ReconcileMyResource struct { client client.Client scheme *runtime.Scheme } // Reconcile reads that state of the cluster for a MyResource object and makes changes based on the state read // and what is in the MyResource.Spec func (r *ReconcileMyResource) Reconcile(ctx context.Context, request reconcile.Request) (reconcile.Result, error) { reqLogger := log.WithValues(\"Namespace\", request.Namespace, \"Name\", request.Name) reqLogger.Info(\"Reconciling MyResource\") // Fetch the MyResource instance myresource := &myv1alpha1.MyResource{} err := r.client.Get(ctx, request.NamespacedName, myresource) if err != nil { if errors.IsNotFound(err) { // Request object not found, could have been deleted after reconcile request. // Owned objects are automatically garbage collected. For additional cleanup logic use finalizers. // Return and don't requeue return reconcile.Result{}, nil } // Error reading the object - requeue the request. return reconcile.Result{}, err } // Add finalizer logic here // Add reconcile logic here return reconcile.Result{}, nil } 4. 注册控制器 在 main.go 文件中注册控制器，主要包含以下几个步骤 ctrl.SetLogger(zap.New())：注册日志工具 ctrl.NewManager：创建一个manager，必要时可设置选主逻辑。 ctrl.NewControllerManagedBy(mgr)：预计mgr创建manager controller并注册Reconciler。 mgr.Start(ctrl.SetupSignalHandler()): 运行mgr。 func main() { // set logger ctrl.SetLogger(zap.New()) // Set up a Manager mgr, err := ctrl.NewManager(ctrl.GetConfigOrDie(), ctrl.Options{ Scheme: myv1alpha1.SchemeBuilder.Scheme, MetricsBindAddress: *metricsHost, Port: 9443, LeaderElection: *enableLeaderElection, LeaderElectionID: \"my-operator-lock\", }) if err != nil { setupLog.Error(err, \"unable to start manager\") os.Exit(1) } // in a real controller, we'd create a new scheme for this err = api.AddToScheme(mgr.GetScheme()) if err != nil { setupLog.Error(err, \"unable to add scheme\") os.Exit(1) } // Create a new Reconciler r := &ReconcileMyResource{ client: m.GetClient(), scheme: m.GetScheme(), } // Create a new Controller and register the Reconciler err = ctrl.NewControllerManagedBy(mgr). For(&myv1alpha1.MyResource{}). // 自定义crd Complete(r) // 注册Reconciler if err != nil { setupLog.Error(err, \"unable to create controller\") os.Exit(1) } err = ctrl.NewWebhookManagedBy(mgr). For(&api.ChaosPod{}). Complete() if err != nil { setupLog.Error(err, \"unable to create webhook\") os.Exit(1) } setupLog.Info(\"starting manager\") if err := mgr.Start(ctrl.SetupSignalHandler()); err != nil { setupLog.Error(err, \"problem running manager\") os.Exit(1) } } 参考： Implementing a controller - The Kubebuilder Book controller-runtime源码分析 | 李乾坤的博客 Kubebuilder - SDK for building Kubernetes APIs using CRDs operator-framework/operator-sdk: SDK for building Kubernetes applications. https://blog.hdls.me/16500403497126.html kubebuilder-cronjob_controller Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"develop/develop-k8s.html":{"url":"develop/develop-k8s.html","title":"k8s社区开发指南","keywords":"","body":"1. 社区说明1.1. Community membership1.2. 社区活动日历1.3. 加入k8s slack1.4. 特别兴趣小组（SIG）2. 编译k8s仓库2.1. 编译二进制2.1.1. 基于docker构建容器编译。2.1.2. 基于构建机环境编译2.2. 编译镜像3. 如何给k8s提交PR1. 社区说明 1.1. Community membership Role Responsibilities Requirements Defined by Member Active contributor in the community Sponsored by 2 reviewers and multiple contributions to the project Kubernetes GitHub org member Reviewer Review contributions from other members History of review and authorship in a subproject OWNERS file reviewer entry Approver Contributions acceptance approval Highly experienced active reviewer and contributor to a subproject OWNERS file approver entry Subproject owner Set direction and priorities for a subproject Demonstrated responsibility and excellent technical judgement for the subproject sigs.yaml subproject OWNERS file owners entry 1.2. 社区活动日历 Community Calendar | Kubernetes Contributors 1.3. 加入k8s slack 点击 https://communityinviter.com/apps/kubernetes/community 1.4. 特别兴趣小组（SIG） 列表： https://github.com/kubernetes/community/blob/master/sig-list.md 2. 编译k8s仓库 参考： Building Kubernetes https://github.com/kubernetes/community/blob/master/contributors/devel/development.md#building-kubernetes 2.1. 编译二进制 2.1.1. 基于docker构建容器编译。 该方式为官方镜像及二进制文件的构建方式。 构建镜像(大小：5.97GB)为： kube-build:build-8faa8d3cb7-5-v1.27.0-go1.20.6-bullseye.0 git clone https://github.com/kubernetes/kubernetes.git cd kubernetes build/run.sh make # 构建全部 #指定模块构建 build/run.sh make kubeadm 输出如下： # build/run.sh make +++ [0804 18:39:11] Verifying Prerequisites.... +++ [0804 18:39:16] Building Docker image kube-build:build-8faa8d3cb7-5-v1.27.0-go1.20.6-bullseye.0 +++ [0804 18:40:49] Creating data container kube-build-data-8faa8d3cb7-5-v1.27.0-go1.20.6-bullseye.0 +++ [0804 18:40:50] Syncing sources to container +++ [0804 18:40:58] Output from this container will be rsynced out upon completion. Set KUBE_RUN_COPY_OUTPUT=n to disable. +++ [0804 18:40:58] Running build command... go: downloading go.uber.org/automaxprocs v1.5.2 +++ [0804 18:41:04] Setting GOMAXPROCS: 8 Go version: go version go1.20.6 linux/amd64 +++ [0804 18:41:04] Building go targets for linux/amd64 k8s.io/kubernetes/cmd/kube-proxy (static) k8s.io/kubernetes/cmd/kube-apiserver (static) k8s.io/kubernetes/cmd/kube-controller-manager (static) k8s.io/kubernetes/cmd/kubelet (non-static) k8s.io/kubernetes/cmd/kubeadm (static) k8s.io/kubernetes/cmd/kube-scheduler (static) k8s.io/component-base/logs/kube-log-runner (static) k8s.io/kube-aggregator (static) k8s.io/apiextensions-apiserver (static) k8s.io/kubernetes/cluster/gce/gci/mounter (non-static) k8s.io/kubernetes/cmd/kubectl (static) k8s.io/kubernetes/cmd/kubectl-convert (static) github.com/onsi/ginkgo/v2/ginkgo (non-static) k8s.io/kubernetes/test/e2e/e2e.test (test) k8s.io/kubernetes/test/conformance/image/go-runner (non-static) k8s.io/kubernetes/cmd/kubemark (static) github.com/onsi/ginkgo/v2/ginkgo (non-static) k8s.io/kubernetes/test/e2e_node/e2e_node.test (test) Env for linux/amd64: GOOS=linux GOARCH=amd64 GOROOT=/usr/local/go CGO_ENABLED= CC= Coverage is disabled. Coverage is disabled. +++ [0804 18:48:17] Placing binaries +++ [0804 18:48:25] Syncing out of container 产物文件在_output目录上。 kubernetes/_output# tree . |-- dockerized | |-- bin | | `-- linux | | `-- amd64 | | |-- apiextensions-apiserver | | |-- e2e_node.test | | |-- e2e.test | | |-- ginkgo | | |-- go-runner | | |-- kubeadm | | |-- kube-aggregator | | |-- kube-apiserver | | |-- kube-controller-manager | | |-- kubectl | | |-- kubectl-convert | | |-- kubelet | | |-- kube-log-runner | | |-- kubemark | | |-- kube-proxy | | |-- kube-scheduler | | |-- mounter | | `-- ncpu | `-- go `-- images `-- kube-build:build-8faa8d3cb7-5-v1.27.0-go1.20.6-bullseye.0 |-- Dockerfile |-- localtime |-- rsyncd.password `-- rsyncd.sh 2.1.2. 基于构建机环境编译 git clone https://github.com/kubernetes/kubernetes.git cd kubernetes # 构建全部二进制 make # 构建指定二进制 make WHAT=cmd/kubeadm 输出如下： # make WHAT=cmd/kubeadm go version go1.20.6 linux/amd64 +++ [0804 19:30:55] Setting GOMAXPROCS: 8 +++ [0804 19:30:56] Building go targets for linux/amd64 k8s.io/kubernetes/cmd/kubeadm (static) 2.2. 编译镜像 git clone https://github.com/kubernetes/kubernetes cd kubernetes make quick-release 3. 如何给k8s提交PR 参考： https://github.com/kubernetes/community/blob/master/contributors/guide/pull-requests.md https://github.com/kubernetes/community/blob/master/contributors/guide/first-contribution.md Here is the bot commands documentation. testing guide 参考： https://github.com/kubernetes/community/ https://github.com/kubernetes/community/tree/master/contributors/guide https://github.com/kubernetes/community/blob/master/contributors/guide/first-contribution.md issues labeled as a good first issue Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"trouble-shooting/node/_index.html":{"url":"trouble-shooting/node/_index.html","title":"节点相关问题","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"trouble-shooting/node/keycreate-permission-denied.html":{"url":"trouble-shooting/node/keycreate-permission-denied.html","title":"keycreate permission denied","keywords":"","body":"问题描述解决办法问题描述 write /proc/self/attr/keycreate: permission denied 具体报错： kuberuntime_manager.go:758] createPodSandbox for pod \"ecc-hostpath-provisioner-8jbhf_kube-system(b8050fd3-4ffe-11eb-a82e-c6090b53405b)\" failed: rpc error: code = Unknown desc = failed to start sandbox container for pod \"ecc-hostpath-provisioner-8jbhf\": Error response from daemon: OCI runtime create failed: container_linux.go:349: starting container process caused \"process_linux.go:449: container init caused \\\"write /proc/self/attr/keycreate: permission denied\\\"\": unknown 解决办法 SELINUX未设置成disabled # 将SELINUX设置成disabled setenforce 0 # 临时生效 # 永久生效，但需重启，配合上述命令可以不用立即重启 sed -i \"s/SELINUX=enforcing/SELINUX=disabled/g\" /etc/selinux/config # 查看SELinux状态 $ /usr/sbin/sestatus -v SELinux status: disabled $ getenforce Disabled Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"trouble-shooting/node/cgroup-pid-error.html":{"url":"trouble-shooting/node/cgroup-pid-error.html","title":"Cgroup不支持pid资源","keywords":"","body":"问题描述原因分析解决方案问题描述 机器内核版本较低，kubelet启动异常，报错如下： Failed to start ContainerManager failed to initialize top level QOS containers: failed to update top level Burstable QOS cgroup : failed to set supported cgroup subsystems for cgroup [kubepods burstable]: Failed to find subsystem mount for required subsystem: pids 原因分析 低版本内核的cgroup不支持pids资源的功能， cat /proc/cgroups #subsys_name hierarchy num_cgroups enabled cpuset 5 6 1 cpu 2 76 1 cpuacct 2 76 1 memory 4 76 1 devices 10 76 1 freezer 7 6 1 net_cls 3 6 1 blkio 8 76 1 perf_event 9 6 1 hugetlb 6 6 1 正常机器的cgroup root@host:~# cat /proc/cgroups #subsys_name hierarchy num_cgroups enabled cpuset 5 17 1 cpu 7 80 1 cpuacct 7 80 1 memory 12 80 1 devices 10 80 1 freezer 2 17 1 net_cls 4 17 1 blkio 8 80 1 perf_event 6 17 1 hugetlb 11 17 1 pids 3 80 1 # 此处支持pids资源 oom 9 1 1 解决方案 1、升级内核版本，使得cgroup支持pids资源。 或者 2、将kubelet的启动参数添加 SupportPodPidsLimit=false,SupportNodePidsLimit=false vi /etc/systemd/system/kubelet.service # 添加 kubelet 启动参数 --feature-gates=... ,SupportPodPidsLimit=false,SupportNodePidsLimit=false \\ systemctl daemon-reload && systemctl restart kubelet.service 文档参考： Kubernetes 1.14 稳定性改进中的进程ID限制 https://blog.csdn.net/qq_38900565/article/details/100707025 https://adoyle.me/Today-I-Learned/k8s/k8s-deployment.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"trouble-shooting/node/cgroup-subsystem-not-mount.html":{"url":"trouble-shooting/node/cgroup-subsystem-not-mount.html","title":"Cgroup子系统无法挂载","keywords":"","body":"问题描述解决问题描述 内核版本： 5.4.56-200.el7.x86_64 docker报错 May 13 16:54:26 8b26d7a8 dockerd[44352]: time=\"2021-05-13T16:54:26.565235530+08:00\" level=warning msg=\"failed to load plugin io.containerd.snapshotter.v1.devmapper\" error=\"devmapper not configured\" May 13 16:54:26 8b26d7a8 dockerd[44352]: time=\"2021-05-13T16:54:26.565525512+08:00\" level=warning msg=\"could not use snapshotter devmapper in metadata plugin\" error=\"devmapper not configured\" May 13 16:54:26 8b26d7a8 dockerd[44352]: time=\"2021-05-13T16:54:26.574734345+08:00\" level=warning msg=\"Your kernel does not support CPU realtime scheduler\" May 13 16:54:26 8b26d7a8 dockerd[44352]: time=\"2021-05-13T16:54:26.574792864+08:00\" level=warning msg=\"Your kernel does not support cgroup blkio weight\" May 13 16:54:26 8b26d7a8 dockerd[44352]: time=\"2021-05-13T16:54:26.574800326+08:00\" level=warning msg=\"Your kernel does not support cgroup blkio weight_device\" kubelet报错 图片 - kubelet 解决 cgroup问题解决： 1、curl https://pi-ops.oss-cn-hangzhou.aliyuncs.com/scripts/cgroupfs-mount.sh | bash 2、重启设备即可解决 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"trouble-shooting/pod-evicted.html":{"url":"trouble-shooting/pod-evicted.html","title":"Pod驱逐","keywords":"","body":"问题描述原因1. 查看节点和该节点pod状态2. 查看对应节点kubelet的日志3. 查看磁盘相关信息4. 查看kubelet配置解决方案问题描述 节点Pod被驱逐 原因 1. 查看节点和该节点pod状态 查看节点状态为Ready，查看该节点的所有pod，发现存在被驱逐的pod和nvidia-device-plugin为pending root@host:~$ kgpoallowide |grep 192.168.1.1 department-56 173e397c-ea35-4aac-85d8-07106e55d7b7 0/1 Evicted 0 52d 192.168.1.1 kube-system nvidia-device-plugin-daemonset-d58d2 0/1 Pending 0 1s 192.168.1.1 2. 查看对应节点kubelet的日志 0905 15:42:13.182280 23506 eviction_manager.go:142] Failed to admit pod rdma-device-plugin-daemonset-8nwb8_kube-system(acc28a85-cfb0-11e9-9729-6c92bf5e2432) - node has conditions: [DiskPressure] I0905 15:42:14.827343 23506 kubelet.go:1836] SyncLoop (ADD, \"api\"): \"nvidia-device-plugin-daemonset-88sm6_kube-system(adbd9227-cfb0-11e9-9729-6c92bf5e2432)\" W0905 15:42:14.827372 23506 eviction_manager.go:142] Failed to admit pod nvidia-device-plugin-daemonset-88sm6_kube-system(adbd9227-cfb0-11e9-9729-6c92bf5e2432) - node has conditions: [DiskPressure] I0905 15:42:15.722378 23506 kubelet_node_status.go:607] Update capacity for nvidia.com/gpu-share to 0 I0905 15:42:16.692488 23506 kubelet.go:1852] SyncLoop (DELETE, \"api\"): \"rdma-device-plugin-daemonset-8nwb8_kube-system(acc28a85-cfb0-11e9-9729-6c92bf5e2432)\" W0905 15:42:16.698445 23506 status_manager.go:489] Failed to delete status for pod \"rdma-device-plugin-daemonset-8nwb8_kube-system(acc28a85-cfb0-11e9-9729-6c92bf5e2432)\": pod \"rdma-device-plugin-daemonset-8nwb8\" not found I0905 15:42:16.698490 23506 kubelet.go:1846] SyncLoop (REMOVE, \"api\"): \"rdma-device-plugin-daemonset-8nwb8_kube-system(acc28a85-cfb0-11e9-9729-6c92bf5e2432)\" I0905 15:42:16.699267 23506 kubelet.go:2040] Failed to delete pod \"rdma-device-plugin-daemonset-8nwb8_kube-system(acc28a85-cfb0-11e9-9729-6c92bf5e2432)\", err: pod not found W0905 15:42:16.777355 23506 eviction_manager.go:332] eviction manager: attempting to reclaim nodefs I0905 15:42:16.777384 23506 eviction_manager.go:346] eviction manager: must evict pod(s) to reclaim nodefs E0905 15:42:16.777390 23506 eviction_manager.go:357] eviction manager: eviction thresholds have been met, but no pods are active to evict 存在关于pod驱逐相关的日志，驱逐的原因为node has conditions: [DiskPressure]。 3. 查看磁盘相关信息 [root@host /]# df -h Filesystem Size Used Avail Use% Mounted on devtmpfs 126G 0 126G 0% /dev tmpfs 126G 0 126G 0% /dev/shm tmpfs 126G 27M 126G 1% /run tmpfs 126G 0 126G 0% /sys/fs/cgroup /dev/sda1 20G 19G 0 100% / # 根目录磁盘满 /dev/nvme1n1 3.0T 191G 2.8T 7% /data2 /dev/nvme0n1 3.0T 1.3T 1.7T 44% /data1 /dev/sda4 182G 95G 87G 53% /data /dev/sda3 20G 3.8G 15G 20% /usr/local tmpfs 26G 0 26G 0% /run/user/0 发现根目录的磁盘盘，接着查看哪些文件占用磁盘。 [root@host ~/kata]# du -sh ./* 1.0M ./log 944K ./netlink 6.6G ./kernel3 /var/log/下存在7G 的日志。清理相关日志和无用文件后，根目录恢复空间。 [root@host /data]# df -h Filesystem Size Used Avail Use% Mounted on devtmpfs 126G 0 126G 0% /dev tmpfs 126G 0 126G 0% /dev/shm tmpfs 126G 27M 126G 1% /run tmpfs 126G 0 126G 0% /sys/fs/cgroup /dev/sda1 20G 5.8G 13G 32% / # 根目录正常 /dev/nvme1n1 3.0T 191G 2.8T 7% /data2 查看节点pod状态，相关plugin的pod恢复正常。 root@host:~$ kgpoallowide |grep 192.168.1.1 kube-system nvidia-device-plugin-daemonset-h4pjc 1/1 Running 0 16m 192.168.1.1 192.168.1.1 kube-system rdma-device-plugin-daemonset-xlkbv 1/1 Running 0 16m 192.168.1.1 192.168.1.1 4. 查看kubelet配置 查看kubelet关于pod驱逐相关的参数配置，可见节点kubelet开启了驱逐机制，正常情况下该配置应该是关闭的。 ExecStart=/usr/local/bin/kubelet \\ ... --eviction-hard=nodefs.available 解决方案 总结以上原因为，kubelet开启了pod驱逐的机制，根目录的磁盘达到100%，pod被驱逐，且无法再正常创建在该节点。 解决方案如下： 1、关闭kubelet的驱逐机制。 2、清除根目录的文件，恢复根目录空间，并后续增加根目录的磁盘监控。 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"trouble-shooting/pod-image-error.html":{"url":"trouble-shooting/pod-image-error.html","title":"镜像拉取失败问题","keywords":"","body":"1. Pod状态为ErrImagePull或ImagePullBackOff2. 查看pod的事件2.1. http: server gave HTTP response to HTTPS client2.2. no basic auth credentials常见镜像拉取问题排查 1. Pod状态为ErrImagePull或ImagePullBackOff docker-hub-75d4dfb984-5hggg 0/1 ImagePullBackOff 0 14m 192.168.1.30 docker-hub-75d4dfb984-9r57b 0/1 ErrImagePull 0 53s 192.168.0.42 ErrImagePull：表示pod已经调度到node节点，kubelet调用docker去拉取镜像失败。 ImagePullBackOff：表示kubelet拉取镜像失败后，不断重试去拉取仍然失败。 2. 查看pod的事件 通过kubectl describe pod 命令查看pod事件，该事件的报错信息在kubelet或docker的日志中也会查看到。 2.1. http: server gave HTTP response to HTTPS client 如果遇到以下报错，尝试将该镜像仓库添加到docker可信任的镜像仓库配置中。 Error getting v2 registry: Get https://docker.com:8080/v2/: http: server gave HTTP response to HTTPS client\" 具体操作是修改/etc/docker/daemon.json的insecure-registries参数 #cat /etc/docker/daemon.json { ... \"insecure-registries\": [ ... \"docker.com:8080\" ], ... } 2.2. no basic auth credentials 如果遇到no basic auth credentials报错，说明kubelet调用docker接口去拉取镜像时，镜像仓库的认证信息失败。 Normal BackOff 18s kubelet, 192.168.1.1 Back-off pulling image \"docker.com:8080/public/2048:latest\" Warning Failed 18s kubelet, 192.168.1.1 Error: ImagePullBackOff Normal Pulling 5s (x2 over 18s) kubelet, 192.168.1.1 Pulling image \"docker.com:8080/public/2048:latest\" Warning Failed 5s (x2 over 18s) kubelet, 192.168.1.1 Failed to pull image \"docker.com:8080/public/2048:latest\": rpc error: code = Unknown desc = Error response from daemon: Get http://docker.com:8080/v2/public/2048/manifests/latest: no basic auth credentials Warning Failed 5s (x2 over 18s) kubelet, 192.168.1.1 Error: ErrImagePull 具体操作，在拉取镜像失败的节点上登录该镜像仓库，认证信息会更新到$HOME/.docker/config.json文件中。将该文件拷贝到/var/lib/kubelet/config.json中。 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"trouble-shooting/pvc-terminating.html":{"url":"trouble-shooting/pvc-terminating.html","title":"PVC Terminating","keywords":"","body":"问题描述解决方法问题描述 pvc terminating pvc在删除时，卡在terminating中。 解决方法 kubectl patch pvc {PVC_NAME} -p '{\"metadata\":{\"finalizers\":null}}' Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"code-analysis/code-analysis-notes.html":{"url":"code-analysis/code-analysis-notes.html","title":"Kubernetes源码分析笔记","keywords":"","body":"详见：Kubernetes源码分析笔记详见：Kubernetes源码分析笔记 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kubelet/_index.html":{"url":"code-analysis/kubelet/_index.html","title":"kubelet","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kubelet/NewKubeletCommand.html":{"url":"code-analysis/kubelet/NewKubeletCommand.html","title":"NewKubeletCommand","keywords":"","body":"kubelet源码分析（一）之 NewKubeletCommand1. Main 函数2. NewKubeletCommand2.1. 参数解析2.1.1. 初始化参数和配置2.1.2. 打印帮助信息和版本信息2.1.3. kubelet config2.1.4. dynamic kubelet config2.2. 初始化kubeletServer和kubeletDeps2.2.1. kubeletServer2.2.2. kubeletDeps2.2.3. docker shim2.3. AddFlags2.4. 运行kubelet3. Run3.1. 构造kubeDeps3.1.1. clientConfig3.1.2. kubeClient3.1.3. dynamicKubeClient3.1.4. eventClient3.1.5. heartbeatClient3.1.6. csiClient3.1.7. CAdvisorInterface3.1.8. ContainerManager3.1.9. oomAdjuster3.2. Health check3.3. RunKubelet4. RunKubelet4.1. CreateAndInitKubelet4.1.1. NewMainKubelet4.1.2. PodConfig4.2. startKubelet4.2.1. k.Run5. 总结kubelet源码分析（一）之 NewKubeletCommand 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析 https://github.com/kubernetes/kubernetes/tree/v1.12.0/cmd/kubelet 部分的代码。 本文主要分析 kubernetes/cmd/kubelet部分，该部分主要涉及kubelet的参数解析，及初始化和构造相关的依赖组件（主要在kubeDeps结构体中），并没有kubelet运行的详细逻辑，该部分位于kubernetes/pkg/kubelet模块，待后续文章分析。 kubelet的cmd代码目录结构如下： kubelet ├── app │ ├── auth.go │ ├── init_others.go │ ├── init_windows.go │ ├── options # 包括kubelet使用到的option │ │ ├── container_runtime.go │ │ ├── globalflags.go │ │ ├── globalflags_linux.go │ │ ├── globalflags_other.go │ │ ├── options.go # 包括KubeletFlags、AddFlags、AddKubeletConfigFlags等 │ │ ├── osflags_others.go │ │ └── osflags_windows.go │ ├── plugins.go │ ├── server.go # 包括NewKubeletCommand、Run、RunKubelet、CreateAndInitKubelet、startKubelet等 │ ├── server_linux.go │ └── server_unsupported.go └── kubelet.go # kubelet的main入口函数 1. Main 函数 kubelet的入口函数Main 函数，具体代码参考：https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kubelet/kubelet.go。 func main() { rand.Seed(time.Now().UTC().UnixNano()) command := app.NewKubeletCommand(server.SetupSignalHandler()) logs.InitLogs() defer logs.FlushLogs() if err := command.Execute(); err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } } kubelet代码主要采用了Cobra命令行框架，核心代码如下： // 初始化命令行 command := app.NewKubeletCommand(server.SetupSignalHandler()) // 执行Execute err := command.Execute() 2. NewKubeletCommand NewKubeletCommand基于参数创建了一个*cobra.Command对象。其中核心部分代码为参数解析部分和Run函数。 // NewKubeletCommand creates a *cobra.Command object with default parameters func NewKubeletCommand(stopCh 2.1. 参数解析 kubelet开启了DisableFlagParsing参数，没有使用Cobra框架中的默认参数解析，而是自定义参数解析。 2.1.1. 初始化参数和配置 初始化参数解析，初始化cleanFlagSet，kubeletFlags，kubeletConfig。 cleanFlagSet := pflag.NewFlagSet(componentKubelet, pflag.ContinueOnError) cleanFlagSet.SetNormalizeFunc(flag.WordSepNormalizeFunc) kubeletFlags := options.NewKubeletFlags() kubeletConfig, err := options.NewKubeletConfiguration() 2.1.2. 打印帮助信息和版本信息 如果输入非法参数则打印使用帮助信息。 // initial flag parse, since we disable cobra's flag parsing if err := cleanFlagSet.Parse(args); err != nil { cmd.Usage() glog.Fatal(err) } // check if there are non-flag arguments in the command line cmds := cleanFlagSet.Args() if len(cmds) > 0 { cmd.Usage() glog.Fatalf(\"unknown command: %s\", cmds[0]) } 遇到help和version参数则打印相关内容并退出。 // short-circuit on help help, err := cleanFlagSet.GetBool(\"help\") if err != nil { glog.Fatal(`\"help\" flag is non-bool, programmer error, please correct`) } if help { cmd.Help() return } // short-circuit on verflag verflag.PrintAndExitIfRequested() utilflag.PrintFlags(cleanFlagSet) 2.1.3. kubelet config 加载并校验kubelet config。其中包括校验初始化的kubeletFlags，并从kubeletFlags的KubeletConfigFile参数获取kubelet config的内容。 // set feature gates from initial flags-based config if err := utilfeature.DefaultFeatureGate.SetFromMap(kubeletConfig.FeatureGates); err != nil { glog.Fatal(err) } // validate the initial KubeletFlags if err := options.ValidateKubeletFlags(kubeletFlags); err != nil { glog.Fatal(err) } if kubeletFlags.ContainerRuntime == \"remote\" && cleanFlagSet.Changed(\"pod-infra-container-image\") { glog.Warning(\"Warning: For remote container runtime, --pod-infra-container-image is ignored in kubelet, which should be set in that remote runtime instead\") } // load kubelet config file, if provided if configFile := kubeletFlags.KubeletConfigFile; len(configFile) > 0 { kubeletConfig, err = loadConfigFile(configFile) if err != nil { glog.Fatal(err) } // We must enforce flag precedence by re-parsing the command line into the new object. // This is necessary to preserve backwards-compatibility across binary upgrades. // See issue #56171 for more details. if err := kubeletConfigFlagPrecedence(kubeletConfig, args); err != nil { glog.Fatal(err) } // update feature gates based on new config if err := utilfeature.DefaultFeatureGate.SetFromMap(kubeletConfig.FeatureGates); err != nil { glog.Fatal(err) } } // We always validate the local configuration (command line + config file). // This is the default \"last-known-good\" config for dynamic config, and must always remain valid. if err := kubeletconfigvalidation.ValidateKubeletConfiguration(kubeletConfig); err != nil { glog.Fatal(err) } 2.1.4. dynamic kubelet config 如果开启使用动态kubelet的配置，则由动态配置文件替换kubelet配置文件。 // use dynamic kubelet config, if enabled var kubeletConfigController *dynamickubeletconfig.Controller if dynamicConfigDir := kubeletFlags.DynamicConfigDir.Value(); len(dynamicConfigDir) > 0 { var dynamicKubeletConfig *kubeletconfiginternal.KubeletConfiguration dynamicKubeletConfig, kubeletConfigController, err = BootstrapKubeletConfigController(dynamicConfigDir, func(kc *kubeletconfiginternal.KubeletConfiguration) error { // Here, we enforce flag precedence inside the controller, prior to the controller's validation sequence, // so that we get a complete validation at the same point where we can decide to reject dynamic config. // This fixes the flag-precedence component of issue #63305. // See issue #56171 for general details on flag precedence. return kubeletConfigFlagPrecedence(kc, args) }) if err != nil { glog.Fatal(err) } // If we should just use our existing, local config, the controller will return a nil config if dynamicKubeletConfig != nil { kubeletConfig = dynamicKubeletConfig // Note: flag precedence was already enforced in the controller, prior to validation, // by our above transform function. Now we simply update feature gates from the new config. if err := utilfeature.DefaultFeatureGate.SetFromMap(kubeletConfig.FeatureGates); err != nil { glog.Fatal(err) } } } 总结：以上通过对各种特定参数的解析，最终生成kubeletFlags和kubeletConfig两个重要的参数对象，用来构造kubeletServer和其他需求。 2.2. 初始化kubeletServer和kubeletDeps 2.2.1. kubeletServer // construct a KubeletServer from kubeletFlags and kubeletConfig kubeletServer := &options.KubeletServer{ KubeletFlags: *kubeletFlags, KubeletConfiguration: *kubeletConfig, } 2.2.2. kubeletDeps // use kubeletServer to construct the default KubeletDeps kubeletDeps, err := UnsecuredDependencies(kubeletServer) if err != nil { glog.Fatal(err) } // add the kubelet config controller to kubeletDeps kubeletDeps.KubeletConfigController = kubeletConfigController 2.2.3. docker shim 如果开启了docker shim参数，则执行RunDockershim。 // start the experimental docker shim, if enabled if kubeletServer.KubeletFlags.ExperimentalDockershim { if err := RunDockershim(&kubeletServer.KubeletFlags, kubeletConfig, stopCh); err != nil { glog.Fatal(err) } return } 2.3. AddFlags // keep cleanFlagSet separate, so Cobra doesn't pollute it with the global flags kubeletFlags.AddFlags(cleanFlagSet) options.AddKubeletConfigFlags(cleanFlagSet, kubeletConfig) options.AddGlobalFlags(cleanFlagSet) cleanFlagSet.BoolP(\"help\", \"h\", false, fmt.Sprintf(\"help for %s\", cmd.Name())) // ugly, but necessary, because Cobra's default UsageFunc and HelpFunc pollute the flagset with global flags const usageFmt = \"Usage:\\n %s\\n\\nFlags:\\n%s\" cmd.SetUsageFunc(func(cmd *cobra.Command) error { fmt.Fprintf(cmd.OutOrStderr(), usageFmt, cmd.UseLine(), cleanFlagSet.FlagUsagesWrapped(2)) return nil }) cmd.SetHelpFunc(func(cmd *cobra.Command, args []string) { fmt.Fprintf(cmd.OutOrStdout(), \"%s\\n\\n\"+usageFmt, cmd.Long, cmd.UseLine(), cleanFlagSet.FlagUsagesWrapped(2)) }) 其中： AddFlags代码可参考：kubernetes/cmd/kubelet/app/options/options.go#L323 AddKubeletConfigFlags代码可参考：kubernetes/cmd/kubelet/app/options/options.go#L424 2.4. 运行kubelet 运行kubelet并且不退出。由Run函数进入后续的操作。 // run the kubelet glog.V(5).Infof(\"KubeletConfiguration: %#v\", kubeletServer.KubeletConfiguration) if err := Run(kubeletServer, kubeletDeps, stopCh); err != nil { glog.Fatal(err) } 3. Run // Run runs the specified KubeletServer with the given Dependencies. This should never exit. // The kubeDeps argument may be nil - if so, it is initialized from the settings on KubeletServer. // Otherwise, the caller is assumed to have set up the Dependencies object and a default one will // not be generated. func Run(s *options.KubeletServer, kubeDeps *kubelet.Dependencies, stopCh 当运行环境是Windows的时候，初始化操作，但是该操作为空，只是预留。具体执行run(s, kubeDeps, stopCh)函数。 3.1. 构造kubeDeps 3.1.1. clientConfig 创建clientConfig，该对象用来创建各种的kubeDeps属性中包含的client。 clientConfig, err := createAPIServerClientConfig(s) if err != nil { return fmt.Errorf(\"invalid kubeconfig: %v\", err) } 3.1.2. kubeClient kubeClient, err = clientset.NewForConfig(clientConfig) if err != nil { glog.Warningf(\"New kubeClient from clientConfig error: %v\", err) } else if kubeClient.CertificatesV1beta1() != nil && clientCertificateManager != nil { glog.V(2).Info(\"Starting client certificate rotation.\") clientCertificateManager.SetCertificateSigningRequestClient(kubeClient.CertificatesV1beta1().CertificateSigningRequests()) clientCertificateManager.Start() } 3.1.3. dynamicKubeClient dynamicKubeClient, err = dynamic.NewForConfig(clientConfig) if err != nil { glog.Warningf(\"Failed to initialize dynamic KubeClient: %v\", err) } 3.1.4. eventClient // make a separate client for events eventClientConfig := *clientConfig eventClientConfig.QPS = float32(s.EventRecordQPS) eventClientConfig.Burst = int(s.EventBurst) eventClient, err = v1core.NewForConfig(&eventClientConfig) if err != nil { glog.Warningf(\"Failed to create API Server client for Events: %v\", err) } 3.1.5. heartbeatClient // make a separate client for heartbeat with throttling disabled and a timeout attached heartbeatClientConfig := *clientConfig heartbeatClientConfig.Timeout = s.KubeletConfiguration.NodeStatusUpdateFrequency.Duration // if the NodeLease feature is enabled, the timeout is the minimum of the lease duration and status update frequency if utilfeature.DefaultFeatureGate.Enabled(features.NodeLease) { leaseTimeout := time.Duration(s.KubeletConfiguration.NodeLeaseDurationSeconds) * time.Second if heartbeatClientConfig.Timeout > leaseTimeout { heartbeatClientConfig.Timeout = leaseTimeout } } heartbeatClientConfig.QPS = float32(-1) heartbeatClient, err = clientset.NewForConfig(&heartbeatClientConfig) if err != nil { glog.Warningf(\"Failed to create API Server client for heartbeat: %v\", err) } 3.1.6. csiClient // csiClient works with CRDs that support json only clientConfig.ContentType = \"application/json\" csiClient, err := csiclientset.NewForConfig(clientConfig) if err != nil { glog.Warningf(\"Failed to create CSI API client: %v\", err) } client赋值 kubeDeps.KubeClient = kubeClient kubeDeps.DynamicKubeClient = dynamicKubeClient if heartbeatClient != nil { kubeDeps.HeartbeatClient = heartbeatClient kubeDeps.OnHeartbeatFailure = closeAllConns } if eventClient != nil { kubeDeps.EventClient = eventClient } kubeDeps.CSIClient = csiClient 3.1.7. CAdvisorInterface if kubeDeps.CAdvisorInterface == nil { imageFsInfoProvider := cadvisor.NewImageFsInfoProvider(s.ContainerRuntime, s.RemoteRuntimeEndpoint) kubeDeps.CAdvisorInterface, err = cadvisor.New(imageFsInfoProvider, s.RootDirectory, cadvisor.UsingLegacyCadvisorStats(s.ContainerRuntime, s.RemoteRuntimeEndpoint)) if err != nil { return err } } 3.1.8. ContainerManager if kubeDeps.ContainerManager == nil { if s.CgroupsPerQOS && s.CgroupRoot == \"\" { glog.Infof(\"--cgroups-per-qos enabled, but --cgroup-root was not specified. defaulting to /\") s.CgroupRoot = \"/\" } kubeReserved, err := parseResourceList(s.KubeReserved) if err != nil { return err } systemReserved, err := parseResourceList(s.SystemReserved) if err != nil { return err } var hardEvictionThresholds []evictionapi.Threshold // If the user requested to ignore eviction thresholds, then do not set valid values for hardEvictionThresholds here. if !s.ExperimentalNodeAllocatableIgnoreEvictionThreshold { hardEvictionThresholds, err = eviction.ParseThresholdConfig([]string{}, s.EvictionHard, nil, nil, nil) if err != nil { return err } } experimentalQOSReserved, err := cm.ParseQOSReserved(s.QOSReserved) if err != nil { return err } devicePluginEnabled := utilfeature.DefaultFeatureGate.Enabled(features.DevicePlugins) kubeDeps.ContainerManager, err = cm.NewContainerManager( kubeDeps.Mounter, kubeDeps.CAdvisorInterface, cm.NodeConfig{ RuntimeCgroupsName: s.RuntimeCgroups, SystemCgroupsName: s.SystemCgroups, KubeletCgroupsName: s.KubeletCgroups, ContainerRuntime: s.ContainerRuntime, CgroupsPerQOS: s.CgroupsPerQOS, CgroupRoot: s.CgroupRoot, CgroupDriver: s.CgroupDriver, KubeletRootDir: s.RootDirectory, ProtectKernelDefaults: s.ProtectKernelDefaults, NodeAllocatableConfig: cm.NodeAllocatableConfig{ KubeReservedCgroupName: s.KubeReservedCgroup, SystemReservedCgroupName: s.SystemReservedCgroup, EnforceNodeAllocatable: sets.NewString(s.EnforceNodeAllocatable...), KubeReserved: kubeReserved, SystemReserved: systemReserved, HardEvictionThresholds: hardEvictionThresholds, }, QOSReserved: *experimentalQOSReserved, ExperimentalCPUManagerPolicy: s.CPUManagerPolicy, ExperimentalCPUManagerReconcilePeriod: s.CPUManagerReconcilePeriod.Duration, ExperimentalPodPidsLimit: s.PodPidsLimit, EnforceCPULimits: s.CPUCFSQuota, CPUCFSQuotaPeriod: s.CPUCFSQuotaPeriod.Duration, }, s.FailSwapOn, devicePluginEnabled, kubeDeps.Recorder) if err != nil { return err } } 3.1.9. oomAdjuster // TODO(vmarmol): Do this through container config. oomAdjuster := kubeDeps.OOMAdjuster if err := oomAdjuster.ApplyOOMScoreAdj(0, int(s.OOMScoreAdj)); err != nil { glog.Warning(err) } 3.2. Health check if s.HealthzPort > 0 { healthz.DefaultHealthz() go wait.Until(func() { err := http.ListenAndServe(net.JoinHostPort(s.HealthzBindAddress, strconv.Itoa(int(s.HealthzPort))), nil) if err != nil { glog.Errorf(\"Starting health server failed: %v\", err) } }, 5*time.Second, wait.NeverStop) } 3.3. RunKubelet 通过各种赋值构造了完整的kubeDeps结构体，最后再执行RunKubelet转入后续的kubelet执行流程。 if err := RunKubelet(s, kubeDeps, s.RunOnce); err != nil { return err } 4. RunKubelet // RunKubelet is responsible for setting up and running a kubelet. It is used in three different applications: // 1 Integration tests // 2 Kubelet binary // 3 Standalone 'kubernetes' binary // Eventually, #2 will be replaced with instances of #3 func RunKubelet(kubeServer *options.KubeletServer, kubeDeps *kubelet.Dependencies, runOnce bool) error { ... k, err := CreateAndInitKubelet(&kubeServer.KubeletConfiguration, ... kubeServer.NodeStatusMaxImages) if err != nil { return fmt.Errorf(\"failed to create kubelet: %v\", err) } // NewMainKubelet should have set up a pod source config if one didn't exist // when the builder was run. This is just a precaution. if kubeDeps.PodConfig == nil { return fmt.Errorf(\"failed to create kubelet, pod source config was nil\") } podCfg := kubeDeps.PodConfig rlimit.RlimitNumFiles(uint64(kubeServer.MaxOpenFiles)) // process pods and exit. if runOnce { if _, err := k.RunOnce(podCfg.Updates()); err != nil { return fmt.Errorf(\"runonce failed: %v\", err) } glog.Infof(\"Started kubelet as runonce\") } else { startKubelet(k, podCfg, &kubeServer.KubeletConfiguration, kubeDeps, kubeServer.EnableServer) glog.Infof(\"Started kubelet\") } return nil } RunKubelet函数核心代码为执行了CreateAndInitKubelet和startKubelet两个函数的操作，以下对这两个函数进行分析。 4.1. CreateAndInitKubelet 通过传入kubeDeps调用CreateAndInitKubelet初始化Kubelet。 k, err := CreateAndInitKubelet(&kubeServer.KubeletConfiguration, kubeDeps, &kubeServer.ContainerRuntimeOptions, kubeServer.ContainerRuntime, kubeServer.RuntimeCgroups, kubeServer.HostnameOverride, kubeServer.NodeIP, kubeServer.ProviderID, kubeServer.CloudProvider, kubeServer.CertDirectory, kubeServer.RootDirectory, kubeServer.RegisterNode, kubeServer.RegisterWithTaints, kubeServer.AllowedUnsafeSysctls, kubeServer.RemoteRuntimeEndpoint, kubeServer.RemoteImageEndpoint, kubeServer.ExperimentalMounterPath, kubeServer.ExperimentalKernelMemcgNotification, kubeServer.ExperimentalCheckNodeCapabilitiesBeforeMount, kubeServer.ExperimentalNodeAllocatableIgnoreEvictionThreshold, kubeServer.MinimumGCAge, kubeServer.MaxPerPodContainerCount, kubeServer.MaxContainerCount, kubeServer.MasterServiceNamespace, kubeServer.RegisterSchedulable, kubeServer.NonMasqueradeCIDR, kubeServer.KeepTerminatedPodVolumes, kubeServer.NodeLabels, kubeServer.SeccompProfileRoot, kubeServer.BootstrapCheckpointPath, kubeServer.NodeStatusMaxImages) if err != nil { return fmt.Errorf(\"failed to create kubelet: %v\", err) } 4.1.1. NewMainKubelet CreateAndInitKubelet方法中执行的核心函数是NewMainKubelet，NewMainKubelet实例化一个kubelet对象，该部分的具体代码在kubernetes/pkg/kubelet中，具体参考：kubernetes/pkg/kubelet/kubelet.go#L325。 func CreateAndInitKubelet(kubeCfg *kubeletconfiginternal.KubeletConfiguration, ... nodeStatusMaxImages int32) (k kubelet.Bootstrap, err error) { // TODO: block until all sources have delivered at least one update to the channel, or break the sync loop // up into \"per source\" synchronizations k, err = kubelet.NewMainKubelet(kubeCfg, kubeDeps, crOptions, containerRuntime, runtimeCgroups, hostnameOverride, nodeIP, providerID, cloudProvider, certDirectory, rootDirectory, registerNode, registerWithTaints, allowedUnsafeSysctls, remoteRuntimeEndpoint, remoteImageEndpoint, experimentalMounterPath, experimentalKernelMemcgNotification, experimentalCheckNodeCapabilitiesBeforeMount, experimentalNodeAllocatableIgnoreEvictionThreshold, minimumGCAge, maxPerPodContainerCount, maxContainerCount, masterServiceNamespace, registerSchedulable, nonMasqueradeCIDR, keepTerminatedPodVolumes, nodeLabels, seccompProfileRoot, bootstrapCheckpointPath, nodeStatusMaxImages) if err != nil { return nil, err } k.BirthCry() k.StartGarbageCollection() return k, nil } 4.1.2. PodConfig if kubeDeps.PodConfig == nil { var err error kubeDeps.PodConfig, err = makePodSourceConfig(kubeCfg, kubeDeps, nodeName, bootstrapCheckpointPath) if err != nil { return nil, err } } NewMainKubelet-->PodConfig-->NewPodConfig-->kubetypes.PodUpdate。会生成一个podUpdate的channel来监听pod的变化，该channel会在k.Run(podCfg.Updates())中作为关键入参。 4.2. startKubelet // process pods and exit. if runOnce { if _, err := k.RunOnce(podCfg.Updates()); err != nil { return fmt.Errorf(\"runonce failed: %v\", err) } glog.Infof(\"Started kubelet as runonce\") } else { startKubelet(k, podCfg, &kubeServer.KubeletConfiguration, kubeDeps, kubeServer.EnableServer) glog.Infof(\"Started kubelet\") } 如果设置了只运行一次的参数，则执行k.RunOnce，否则执行核心函数startKubelet。具体实现如下： func startKubelet(k kubelet.Bootstrap, podCfg *config.PodConfig, kubeCfg *kubeletconfiginternal.KubeletConfiguration, kubeDeps *kubelet.Dependencies, enableServer bool) { // start the kubelet go wait.Until(func() { k.Run(podCfg.Updates()) }, 0, wait.NeverStop) // start the kubelet server if enableServer { go k.ListenAndServe(net.ParseIP(kubeCfg.Address), uint(kubeCfg.Port), kubeDeps.TLSOptions, kubeDeps.Auth, kubeCfg.EnableDebuggingHandlers, kubeCfg.EnableContentionProfiling) } if kubeCfg.ReadOnlyPort > 0 { go k.ListenAndServeReadOnly(net.ParseIP(kubeCfg.Address), uint(kubeCfg.ReadOnlyPort)) } } 4.2.1. k.Run // start the kubelet go wait.Until(func() { k.Run(podCfg.Updates()) }, 0, wait.NeverStop) 通过长驻进程的方式运行k.Run，不退出，将kubelet的运行逻辑引入kubernetes/pkg/kubelet/kubelet.go部分，kubernetes/pkg/kubelet部分的运行逻辑待后续文章分析。 5. 总结 kubelet采用Cobra命令行框架和pflag参数解析框架，和apiserver、scheduler、controller-manager形成统一的代码风格。 kubernetes/cmd/kubelet部分主要对运行参数进行定义和解析，初始化和构造相关的依赖组件（主要在kubeDeps结构体中），并没有kubelet运行的详细逻辑，该部分位于kubernetes/pkg/kubelet模块。 cmd部分调用流程如下：Main-->NewKubeletCommand-->Run(kubeletServer, kubeletDeps, stopCh)-->run(s *options.KubeletServer, kubeDeps ..., stopCh ...)--> RunKubelet(s, kubeDeps, s.RunOnce)-->startKubelet-->k.Run(podCfg.Updates())-->pkg/kubelet。 同时RunKubelet(s, kubeDeps, s.RunOnce)-->CreateAndInitKubelet-->kubelet.NewMainKubelet-->pkg/kubelet。 参考文章： https://github.com/kubernetes/kubernetes/tree/v1.12.0 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kubelet/NewMainKubelet.html":{"url":"code-analysis/kubelet/NewMainKubelet.html","title":"NewMainKubelet","keywords":"","body":"kubelet源码分析（二）之 NewMainKubelet1. NewMainKubelet1.1. PodConfig1.1.1. makePodSourceConfig1.1.2. NewPodConfig1.1.3. NewSourceApiserver1.2. Lister1.2.1. serviceLister1.2.2. nodeLister1.3. 各种Manager1.3.1. containerRefManager1.3.2. oomWatcher1.3.3. dnsConfigurer1.3.4. secretManager & configMapManager1.3.5. livenessManager1.3.6. podManager1.3.7. resourceAnalyzer1.3.8. containerGC1.3.9. imageManager1.3.10. statusManager1.3.11. probeManager1.3.12. tokenManager1.3.13. volumePluginMgr1.3.14. volumeManager1.3.15. evictionManager1.4. containerRuntime1.4.1. NewDockerService1.4.2. NewDockerServer1.4.3. DockerServer.Start1.5. podWorker1.5.1. PodWorkers接口2. 总结kubelet源码分析（二）之 NewMainKubelet 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析 https://github.com/kubernetes/kubernetes/tree/v1.12.0/pkg/kubelet 部分的代码。 本文主要分析kubelet中的NewMainKubelet部分。 1. NewMainKubelet NewMainKubelet主要用来初始化和构造一个kubelet结构体，kubelet结构体定义参考:https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/kubelet/kubelet.go#L888 // NewMainKubelet instantiates a new Kubelet object along with all the required internal modules. // No initialization of Kubelet and its modules should happen here. func NewMainKubelet(kubeCfg *kubeletconfiginternal.KubeletConfiguration, kubeDeps *Dependencies, crOptions *config.ContainerRuntimeOptions, containerRuntime string, runtimeCgroups string, hostnameOverride string, nodeIP string, providerID string, cloudProvider string, certDirectory string, rootDirectory string, registerNode bool, registerWithTaints []api.Taint, allowedUnsafeSysctls []string, remoteRuntimeEndpoint string, remoteImageEndpoint string, experimentalMounterPath string, experimentalKernelMemcgNotification bool, experimentalCheckNodeCapabilitiesBeforeMount bool, experimentalNodeAllocatableIgnoreEvictionThreshold bool, minimumGCAge metav1.Duration, maxPerPodContainerCount int32, maxContainerCount int32, masterServiceNamespace string, registerSchedulable bool, nonMasqueradeCIDR string, keepTerminatedPodVolumes bool, nodeLabels map[string]string, seccompProfileRoot string, bootstrapCheckpointPath string, nodeStatusMaxImages int32) (*Kubelet, error) { ... } 1.1. PodConfig 通过makePodSourceConfig生成Pod config。 if kubeDeps.PodConfig == nil { var err error kubeDeps.PodConfig, err = makePodSourceConfig(kubeCfg, kubeDeps, nodeName, bootstrapCheckpointPath) if err != nil { return nil, err } } 1.1.1. makePodSourceConfig // makePodSourceConfig creates a config.PodConfig from the given // KubeletConfiguration or returns an error. func makePodSourceConfig(kubeCfg *kubeletconfiginternal.KubeletConfiguration, kubeDeps *Dependencies, nodeName types.NodeName, bootstrapCheckpointPath string) (*config.PodConfig, error) { ... // source of all configuration cfg := config.NewPodConfig(config.PodConfigNotificationIncremental, kubeDeps.Recorder) // define file config source if kubeCfg.StaticPodPath != \"\" { glog.Infof(\"Adding pod path: %v\", kubeCfg.StaticPodPath) config.NewSourceFile(kubeCfg.StaticPodPath, nodeName, kubeCfg.FileCheckFrequency.Duration, cfg.Channel(kubetypes.FileSource)) } // define url config source if kubeCfg.StaticPodURL != \"\" { glog.Infof(\"Adding pod url %q with HTTP header %v\", kubeCfg.StaticPodURL, manifestURLHeader) config.NewSourceURL(kubeCfg.StaticPodURL, manifestURLHeader, nodeName, kubeCfg.HTTPCheckFrequency.Duration, cfg.Channel(kubetypes.HTTPSource)) } // Restore from the checkpoint path // NOTE: This MUST happen before creating the apiserver source // below, or the checkpoint would override the source of truth. ... if kubeDeps.KubeClient != nil { glog.Infof(\"Watching apiserver\") if updatechannel == nil { updatechannel = cfg.Channel(kubetypes.ApiserverSource) } config.NewSourceApiserver(kubeDeps.KubeClient, nodeName, updatechannel) } return cfg, nil } 1.1.2. NewPodConfig // NewPodConfig creates an object that can merge many configuration sources into a stream // of normalized updates to a pod configuration. func NewPodConfig(mode PodConfigNotificationMode, recorder record.EventRecorder) *PodConfig { updates := make(chan kubetypes.PodUpdate, 50) storage := newPodStorage(updates, mode, recorder) podConfig := &PodConfig{ pods: storage, mux: config.NewMux(storage), updates: updates, sources: sets.String{}, } return podConfig } 1.1.3. NewSourceApiserver // NewSourceApiserver creates a config source that watches and pulls from the apiserver. func NewSourceApiserver(c clientset.Interface, nodeName types.NodeName, updates chan 1.2. Lister serviceLister和nodeLister分别通过List-Watch机制监听service和node的列表变化。 1.2.1. serviceLister serviceIndexer := cache.NewIndexer(cache.MetaNamespaceKeyFunc, cache.Indexers{cache.NamespaceIndex: cache.MetaNamespaceIndexFunc}) if kubeDeps.KubeClient != nil { serviceLW := cache.NewListWatchFromClient(kubeDeps.KubeClient.CoreV1().RESTClient(), \"services\", metav1.NamespaceAll, fields.Everything()) r := cache.NewReflector(serviceLW, &v1.Service{}, serviceIndexer, 0) go r.Run(wait.NeverStop) } serviceLister := corelisters.NewServiceLister(serviceIndexer) 1.2.2. nodeLister nodeIndexer := cache.NewIndexer(cache.MetaNamespaceKeyFunc, cache.Indexers{}) if kubeDeps.KubeClient != nil { fieldSelector := fields.Set{api.ObjectNameField: string(nodeName)}.AsSelector() nodeLW := cache.NewListWatchFromClient(kubeDeps.KubeClient.CoreV1().RESTClient(), \"nodes\", metav1.NamespaceAll, fieldSelector) r := cache.NewReflector(nodeLW, &v1.Node{}, nodeIndexer, 0) go r.Run(wait.NeverStop) } nodeInfo := &predicates.CachedNodeInfo{NodeLister: corelisters.NewNodeLister(nodeIndexer)} 1.3. 各种Manager 1.3.1. containerRefManager containerRefManager := kubecontainer.NewRefManager() 1.3.2. oomWatcher oomWatcher := NewOOMWatcher(kubeDeps.CAdvisorInterface, kubeDeps.Recorder) 1.3.3. dnsConfigurer clusterDNS := make([]net.IP, 0, len(kubeCfg.ClusterDNS)) for _, ipEntry := range kubeCfg.ClusterDNS { ip := net.ParseIP(ipEntry) if ip == nil { glog.Warningf(\"Invalid clusterDNS ip '%q'\", ipEntry) } else { clusterDNS = append(clusterDNS, ip) } } ... dns.NewConfigurer(kubeDeps.Recorder, nodeRef, parsedNodeIP, clusterDNS, kubeCfg.ClusterDomain, kubeCfg.ResolverConfig), 1.3.4. secretManager & configMapManager var secretManager secret.Manager var configMapManager configmap.Manager switch kubeCfg.ConfigMapAndSecretChangeDetectionStrategy { case kubeletconfiginternal.WatchChangeDetectionStrategy: secretManager = secret.NewWatchingSecretManager(kubeDeps.KubeClient) configMapManager = configmap.NewWatchingConfigMapManager(kubeDeps.KubeClient) case kubeletconfiginternal.TTLCacheChangeDetectionStrategy: secretManager = secret.NewCachingSecretManager( kubeDeps.KubeClient, manager.GetObjectTTLFromNodeFunc(klet.GetNode)) configMapManager = configmap.NewCachingConfigMapManager( kubeDeps.KubeClient, manager.GetObjectTTLFromNodeFunc(klet.GetNode)) case kubeletconfiginternal.GetChangeDetectionStrategy: secretManager = secret.NewSimpleSecretManager(kubeDeps.KubeClient) configMapManager = configmap.NewSimpleConfigMapManager(kubeDeps.KubeClient) default: return nil, fmt.Errorf(\"unknown configmap and secret manager mode: %v\", kubeCfg.ConfigMapAndSecretChangeDetectionStrategy) } klet.secretManager = secretManager klet.configMapManager = configMapManager 1.3.5. livenessManager klet.livenessManager = proberesults.NewManager() 1.3.6. podManager // podManager is also responsible for keeping secretManager and configMapManager contents up-to-date. klet.podManager = kubepod.NewBasicPodManager(kubepod.NewBasicMirrorClient(klet.kubeClient), secretManager, configMapManager, checkpointManager) 1.3.7. resourceAnalyzer klet.resourceAnalyzer = serverstats.NewResourceAnalyzer(klet, kubeCfg.VolumeStatsAggPeriod.Duration) 1.3.8. containerGC // setup containerGC containerGC, err := kubecontainer.NewContainerGC(klet.containerRuntime, containerGCPolicy, klet.sourcesReady) if err != nil { return nil, err } klet.containerGC = containerGC klet.containerDeletor = newPodContainerDeletor(klet.containerRuntime, integer.IntMax(containerGCPolicy.MaxPerPodContainer, minDeadContainerInPod)) 1.3.9. imageManager // setup imageManager imageManager, err := images.NewImageGCManager(klet.containerRuntime, klet.StatsProvider, kubeDeps.Recorder, nodeRef, imageGCPolicy, crOptions.PodSandboxImage) if err != nil { return nil, fmt.Errorf(\"failed to initialize image manager: %v\", err) } klet.imageManager = imageManager 1.3.10. statusManager klet.statusManager = status.NewManager(klet.kubeClient, klet.podManager, klet) 1.3.11. probeManager klet.probeManager = prober.NewManager( klet.statusManager, klet.livenessManager, klet.runner, containerRefManager, kubeDeps.Recorder) 1.3.12. tokenManager tokenManager := token.NewManager(kubeDeps.KubeClient) 1.3.13. volumePluginMgr klet.volumePluginMgr, err = NewInitializedVolumePluginMgr(klet, secretManager, configMapManager, tokenManager, kubeDeps.VolumePlugins, kubeDeps.DynamicPluginProber) if err != nil { return nil, err } if klet.enablePluginsWatcher { klet.pluginWatcher = pluginwatcher.NewWatcher(klet.getPluginsDir()) } 1.3.14. volumeManager // setup volumeManager klet.volumeManager = volumemanager.NewVolumeManager( kubeCfg.EnableControllerAttachDetach, nodeName, klet.podManager, klet.statusManager, klet.kubeClient, klet.volumePluginMgr, klet.containerRuntime, kubeDeps.Mounter, klet.getPodsDir(), kubeDeps.Recorder, experimentalCheckNodeCapabilitiesBeforeMount, keepTerminatedPodVolumes) 1.3.15. evictionManager // setup eviction manager evictionManager, evictionAdmitHandler := eviction.NewManager(klet.resourceAnalyzer, evictionConfig, killPodNow(klet.podWorkers, kubeDeps.Recorder), klet.imageManager, klet.containerGC, kubeDeps.Recorder, nodeRef, klet.clock) klet.evictionManager = evictionManager klet.admitHandlers.AddPodAdmitHandler(evictionAdmitHandler) 1.4. containerRuntime 目前pod所使用的runtime只有docker和remote两种，rkt已经废弃。 if containerRuntime == \"rkt\" { glog.Fatalln(\"rktnetes has been deprecated in favor of rktlet. Please see https://github.com/kubernetes-incubator/rktlet for more information.\") } 当runtime是docker的时候，会执行docker相关操作。 switch containerRuntime { case kubetypes.DockerContainerRuntime: // Create and start the CRI shim running as a grpc server. ... // The unix socket for kubelet dockershim communication. ... // Create dockerLegacyService when the logging driver is not supported. ... case kubetypes.RemoteContainerRuntime: // No-op. break default: return nil, fmt.Errorf(\"unsupported CRI runtime: %q\", containerRuntime) } 1.4.1. NewDockerService // Create and start the CRI shim running as a grpc server. streamingConfig := getStreamingConfig(kubeCfg, kubeDeps, crOptions) ds, err := dockershim.NewDockerService(kubeDeps.DockerClientConfig, crOptions.PodSandboxImage, streamingConfig, &pluginSettings, runtimeCgroups, kubeCfg.CgroupDriver, crOptions.DockershimRootDirectory, !crOptions.RedirectContainerStreaming) if err != nil { return nil, err } if crOptions.RedirectContainerStreaming { klet.criHandler = ds } 1.4.2. NewDockerServer // The unix socket for kubelet dockershim communication. glog.V(5).Infof(\"RemoteRuntimeEndpoint: %q, RemoteImageEndpoint: %q\", remoteRuntimeEndpoint, remoteImageEndpoint) glog.V(2).Infof(\"Starting the GRPC server for the docker CRI shim.\") server := dockerremote.NewDockerServer(remoteRuntimeEndpoint, ds) if err := server.Start(); err != nil { return nil, err } 1.4.3. DockerServer.Start // Start starts the dockershim grpc server. func (s *DockerServer) Start() error { // Start the internal service. if err := s.service.Start(); err != nil { glog.Errorf(\"Unable to start docker service\") return err } glog.V(2).Infof(\"Start dockershim grpc server\") l, err := util.CreateListener(s.endpoint) if err != nil { return fmt.Errorf(\"failed to listen on %q: %v\", s.endpoint, err) } // Create the grpc server and register runtime and image services. s.server = grpc.NewServer( grpc.MaxRecvMsgSize(maxMsgSize), grpc.MaxSendMsgSize(maxMsgSize), ) runtimeapi.RegisterRuntimeServiceServer(s.server, s.service) runtimeapi.RegisterImageServiceServer(s.server, s.service) go func() { if err := s.server.Serve(l); err != nil { glog.Fatalf(\"Failed to serve connections: %v\", err) } }() return nil } 1.5. podWorker 构造podWorkers和workQueue。 klet.workQueue = queue.NewBasicWorkQueue(klet.clock) klet.podWorkers = newPodWorkers(klet.syncPod, kubeDeps.Recorder, klet.workQueue, klet.resyncInterval, backOffPeriod, klet.podCache) 1.5.1. PodWorkers接口 // PodWorkers is an abstract interface for testability. type PodWorkers interface { UpdatePod(options *UpdatePodOptions) ForgetNonExistingPodWorkers(desiredPods map[types.UID]empty) ForgetWorker(uid types.UID) } podWorker主要用来对pod相应事件进行处理和同步，包含以下三个方法：UpdatePod、ForgetNonExistingPodWorkers、ForgetWorker。 2. 总结 NewMainKubelet主要用来构造kubelet结构体，其中kubelet除了包含必要的配置和client（例如：kubeClient、csiClient等）外，最主要的包含各种manager来管理不同的任务。 核心的manager有以下几种： oomWatcher：监控pod内存是否发生OOM。 podManager：管理pod的生命周期，包括对pod的增删改查操作等。 containerGC：对死亡容器进行垃圾回收。 imageManager：对容器镜像进行垃圾回收。 statusManager：与apiserver同步pod状态，同时也作状态缓存。 volumeManager：对pod的volume进行attached/detached/mounted/unmounted操作。 evictionManager：保证节点稳定，必要时对pod进行驱逐（例如资源不足的情况下）。 NewMainKubelet还包含了serviceLister和nodeLister来监听service和node的列表变化。 kubelet使用到的containerRuntime目前主要是docker，其中rkt已废弃。NewMainKubelet启动了dockershim grpc server来执行docker相关操作。 构建了podWorker来对pod相关的更新逻辑进行处理。 参考文章： https://github.com/kubernetes/kubernetes/tree/v1.12.0 https://github.com/kubernetes/kubernetes/tree/v1.12.0/pkg/kubelet Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kubelet/startKubelet.html":{"url":"code-analysis/kubelet/startKubelet.html","title":"startKubelet","keywords":"","body":"kubelet源码分析（三）之 startKubelet1. startKubelet2. Kubelet.Run3. initializeModules3.1. setupDataDirs3.2. manager4. 运行各种manager4.1. volumeManager4.2. syncNodeStatus4.3. updateRuntimeUp4.4. syncNetworkUtil4.5. podKiller4.6. statusManager4.7. probeManager4.8. runtimeClassManager4.9. PodLifecycleEventGenerator4.10. syncLoop5. syncLoop6. 总结6.1. 基本流程6.2. Managerkubelet源码分析（三）之 startKubelet 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析startKubelet，其中主要是kubelet.Run部分，该部分的内容主要是初始化并运行一些manager。对于kubelet所包含的各种manager的执行逻辑和pod的生命周期管理逻辑待后续文章分析。 后续的文章主要会分类分析pkg/kubelet部分的代码实现。 kubelet的pkg代码目录结构： kubelet ├── apis # 定义一些相关接口 ├── cadvisor # cadvisor ├── cm # ContainerManager、cpu manger、cgroup manager ├── config ├── configmap # configmap manager ├── container # Runtime、ImageService ├── dockershim # docker的相关调用 ├── eviction # eviction manager ├── images # image manager ├── kubeletconfig ├── kuberuntime # 核心：kubeGenericRuntimeManager、runtime容器的相关操作 ├── lifecycle ├── mountpod ├── network # pod dns ├── nodelease ├── nodestatus # MachineInfo、节点相关信息 ├── pleg # PodLifecycleEventGenerator ├── pod # 核心：pod manager、mirror pod ├── preemption ├── qos # 资源服务质量，不过暂时内容很少 ├── remote # RemoteRuntimeService ├── server ├── stats # StatsProvider ├── status # status manager ├── types # PodUpdate、PodOperation ├── volumemanager # VolumeManager ├── kubelet.go # 核心: SyncHandler、kubelet的大部分操作 ├── kubelet_getters.go # 各种get操作，例如获取相关目录：getRootDir、getPodsDir、getPluginsDir ├── kubelet_network.go # ├── kubelet_network_linux.go ├── kubelet_node_status.go # registerWithAPIServer、initialNode、syncNodeStatus ├── kubelet_pods.go # 核心：pod的增删改查等相关操作、podKiller、 ├── kubelet_resources.go ├── kubelet_volumes.go # ListVolumesForPod、cleanupOrphanedPodDirs ├── oom_watcher.go # OOMWatcher ├── pod_container_deletor.go ├── pod_workers.go # 核心：PodWorkers、UpdatePodOptions、syncPodOptions、managePodLoop ├── runonce.go # RunOnce ├── runtime.go ... 1. startKubelet startKubelet的函数位于cmd/kubelet/app/server.go，启动并运行一个kubelet，运行kubelet的逻辑代码位于pkg/kubelet/kubelet.go。 主要内容如下： 运行一个kubelet，执行kubelet中各种manager的相关逻辑。 运行kubelet server启动监听服务。 此部分代码位于cmd/kubelet/app/server.go func startKubelet(k kubelet.Bootstrap, podCfg *config.PodConfig, kubeCfg *kubeletconfiginternal.KubeletConfiguration, kubeDeps *kubelet.Dependencies, enableServer bool) { // start the kubelet go wait.Until(func() { k.Run(podCfg.Updates()) }, 0, wait.NeverStop) // start the kubelet server if enableServer { go k.ListenAndServe(net.ParseIP(kubeCfg.Address), uint(kubeCfg.Port), kubeDeps.TLSOptions, kubeDeps.Auth, kubeCfg.EnableDebuggingHandlers, kubeCfg.EnableContentionProfiling) } if kubeCfg.ReadOnlyPort > 0 { go k.ListenAndServeReadOnly(net.ParseIP(kubeCfg.Address), uint(kubeCfg.ReadOnlyPort)) } } 2. Kubelet.Run Kubelet.Run方法主要将NewMainKubelet构造的各种manager运行起来，让各种manager执行相应的功能，大部分manager为常驻进程的方式运行。 Kubelet.Run完整代码如下： 此部分代码位于pkg/kubelet/kubelet.go // Run starts the kubelet reacting to config updates func (kl *Kubelet) Run(updates 以下对Kubelet.Run分段进行分析。 3. initializeModules initializeModules包含了imageManager、serverCertificateManager、oomWatcher和resourceAnalyzer。 主要流程如下： 创建文件系统目录，包括kubelet的root目录、pods的目录、plugins的目录和容器日志目录。 启动imageManager、serverCertificateManager、oomWatcher、resourceAnalyzer。 各种manager的说明如下： imageManager：负责镜像垃圾回收。 serverCertificateManager：负责处理证书。 oomWatcher：监控内存使用，是否发生内存耗尽。 resourceAnalyzer：监控资源使用情况。 完整代码如下： 此部分代码位于pkg/kubelet/kubelet.go // initializeModules will initialize internal modules that do not require the container runtime to be up. // Note that the modules here must not depend on modules that are not initialized here. func (kl *Kubelet) initializeModules() error { // Prometheus metrics. metrics.Register(kl.runtimeCache, collectors.NewVolumeStatsCollector(kl)) // Setup filesystem directories. if err := kl.setupDataDirs(); err != nil { return err } // If the container logs directory does not exist, create it. if _, err := os.Stat(ContainerLogsDir); err != nil { if err := kl.os.MkdirAll(ContainerLogsDir, 0755); err != nil { glog.Errorf(\"Failed to create directory %q: %v\", ContainerLogsDir, err) } } // Start the image manager. kl.imageManager.Start() // Start the certificate manager if it was enabled. if kl.serverCertificateManager != nil { kl.serverCertificateManager.Start() } // Start out of memory watcher. if err := kl.oomWatcher.Start(kl.nodeRef); err != nil { return fmt.Errorf(\"Failed to start OOM watcher %v\", err) } // Start resource analyzer kl.resourceAnalyzer.Start() return nil } 3.1. setupDataDirs initializeModules先创建相关目录。 具体目录如下： ContainerLogsDir：目录为/var/log/containers。 rootDirectory：由参数传入，一般为/var/lib/kubelet。 PodsDir：目录为{rootDirectory}/pods。 PluginsDir：目录为{rootDirectory}/plugins。 initializeModules中setupDataDirs的相关代码如下： // Setup filesystem directories. if err := kl.setupDataDirs(); err != nil { return err } // If the container logs directory does not exist, create it. if _, err := os.Stat(ContainerLogsDir); err != nil { if err := kl.os.MkdirAll(ContainerLogsDir, 0755); err != nil { glog.Errorf(\"Failed to create directory %q: %v\", ContainerLogsDir, err) } } setupDataDirs代码如下 // setupDataDirs creates: // 1. the root directory // 2. the pods directory // 3. the plugins directory func (kl *Kubelet) setupDataDirs() error { kl.rootDirectory = path.Clean(kl.rootDirectory) if err := os.MkdirAll(kl.getRootDir(), 0750); err != nil { return fmt.Errorf(\"error creating root directory: %v\", err) } if err := kl.mounter.MakeRShared(kl.getRootDir()); err != nil { return fmt.Errorf(\"error configuring root directory: %v\", err) } if err := os.MkdirAll(kl.getPodsDir(), 0750); err != nil { return fmt.Errorf(\"error creating pods directory: %v\", err) } if err := os.MkdirAll(kl.getPluginsDir(), 0750); err != nil { return fmt.Errorf(\"error creating plugins directory: %v\", err) } return nil } 3.2. manager initializeModules中的manager如下： // Start the image manager. kl.imageManager.Start() // Start the certificate manager if it was enabled. if kl.serverCertificateManager != nil { kl.serverCertificateManager.Start() } // Start out of memory watcher. if err := kl.oomWatcher.Start(kl.nodeRef); err != nil { return fmt.Errorf(\"Failed to start OOM watcher %v\", err) } // Start resource analyzer kl.resourceAnalyzer.Start() 4. 运行各种manager 4.1. volumeManager volumeManager主要运行一组异步循环，根据在此节点上安排的pod调整哪些volume需要attached/detached/mounted/unmounted。 // Start volume manager go kl.volumeManager.Run(kl.sourcesReady, wait.NeverStop) volumeManager.Run实现代码如下： func (vm *volumeManager) Run(sourcesReady config.SourcesReady, stopCh 4.2. syncNodeStatus syncNodeStatus通过goroutine的方式定期执行，它将节点的状态同步给master，必要的时候注册kubelet。 if kl.kubeClient != nil { // Start syncing node status immediately, this may set up things the runtime needs to run. go wait.Until(kl.syncNodeStatus, kl.nodeStatusUpdateFrequency, wait.NeverStop) go kl.fastStatusUpdateOnce() // start syncing lease if utilfeature.DefaultFeatureGate.Enabled(features.NodeLease) { go kl.nodeLeaseController.Run(wait.NeverStop) } } 4.3. updateRuntimeUp updateRuntimeUp调用容器运行时状态回调，在容器运行时首次启动时初始化运行时相关模块，如果状态检查失败则返回错误。 如果状态检查正常，在kubelet runtimeState中更新容器运行时的正常运行时间。 go wait.Until(kl.updateRuntimeUp, 5*time.Second, wait.NeverStop) 4.4. syncNetworkUtil 通过循环的方式同步iptables的规则，不过当前代码并没有执行任何操作。 // Start loop to sync iptables util rules if kl.makeIPTablesUtilChains { go wait.Until(kl.syncNetworkUtil, 1*time.Minute, wait.NeverStop) } 4.5. podKiller 但pod没有被podworker正确处理的时候，启动一个goroutine负责杀死pod。 // Start a goroutine responsible for killing pods (that are not properly // handled by pod workers). go wait.Until(kl.podKiller, 1*time.Second, wait.NeverStop) podKiller代码如下： 此部分代码位于pkg/kubelet/kubelet_pods.go // podKiller launches a goroutine to kill a pod received from the channel if // another goroutine isn't already in action. func (kl *Kubelet) podKiller() { killing := sets.NewString() // guard for the killing set lock := sync.Mutex{} for podPair := range kl.podKillingCh { runningPod := podPair.RunningPod apiPod := podPair.APIPod lock.Lock() exists := killing.Has(string(runningPod.ID)) if !exists { killing.Insert(string(runningPod.ID)) } lock.Unlock() if !exists { go func(apiPod *v1.Pod, runningPod *kubecontainer.Pod) { glog.V(2).Infof(\"Killing unwanted pod %q\", runningPod.Name) err := kl.killPod(apiPod, runningPod, nil, nil) if err != nil { glog.Errorf(\"Failed killing the pod %q: %v\", runningPod.Name, err) } lock.Lock() killing.Delete(string(runningPod.ID)) lock.Unlock() }(apiPod, runningPod) } } } 4.6. statusManager 使用apiserver同步pods状态; 也用作状态缓存。 // Start component sync loops. kl.statusManager.Start() statusManager.Start的实现代码如下： func (m *manager) Start() { // Don't start the status manager if we don't have a client. This will happen // on the master, where the kubelet is responsible for bootstrapping the pods // of the master components. if m.kubeClient == nil { glog.Infof(\"Kubernetes client is nil, not starting status manager.\") return } glog.Info(\"Starting to sync pod status with apiserver\") syncTicker := time.Tick(syncPeriod) // syncPod and syncBatch share the same go routine to avoid sync races. go wait.Forever(func() { select { case syncRequest := 4.7. probeManager 处理容器探针 kl.probeManager.Start() 4.8. runtimeClassManager // Start syncing RuntimeClasses if enabled. if kl.runtimeClassManager != nil { go kl.runtimeClassManager.Run(wait.NeverStop) } 4.9. PodLifecycleEventGenerator // Start the pod lifecycle event generator. kl.pleg.Start() PodLifecycleEventGenerator是一个pod生命周期时间生成器接口，具体如下： // PodLifecycleEventGenerator contains functions for generating pod life cycle events. type PodLifecycleEventGenerator interface { Start() Watch() chan *PodLifecycleEvent Healthy() (bool, error) } start方法具体实现如下： // Start spawns a goroutine to relist periodically. func (g *GenericPLEG) Start() { go wait.Until(g.relist, g.relistPeriod, wait.NeverStop) } 4.10. syncLoop 最后调用syncLoop来执行同步变化变更的循环。 kl.syncLoop(updates, kl) 5. syncLoop syncLoop是处理变化的循环。 它监听来自三种channel（file，apiserver和http）的更改。 对于看到的任何新更改，将针对所需状态和运行状态运行同步。 如果没有看到配置的变化，将在每个同步频率秒同步最后已知的所需状态。 // syncLoop is the main loop for processing changes. It watches for changes from // three channels (file, apiserver, and http) and creates a union of them. For // any new change seen, will run a sync against desired state and running state. If // no changes are seen to the configuration, will synchronize the last known desired // state every sync-frequency seconds. Never returns. func (kl *Kubelet) syncLoop(updates 其中调用了syncLoopIteration的函数来执行更具体的监控pod变化的循环。syncLoopIteration代码逻辑待后续单独分析。 6. 总结 6.1. 基本流程 Kubelet.Run主要流程如下： 初始化模块，其实就是运行imageManager、serverCertificateManager、oomWatcher、resourceAnalyzer。 运行各种manager，大部分以常驻goroutine的方式运行，其中包括volumeManager、statusManager等。 执行处理变更的循环函数syncLoop，对pod的生命周期进行管理。 syncLoop： syncLoop函数，对pod的生命周期进行管理，其中syncLoop调用了syncLoopIteration函数，该函数根据podUpdate的信息，针对不同的操作，由SyncHandler来执行pod的增删改查等生命周期的管理，其中的syncHandler包括HandlePodSyncs和HandlePodCleanups等。该部分逻辑待后续文章具体分析。 6.2. Manager 以下介绍kubelet运行时涉及到的manager的内容。 manager 说明 imageManager 负责镜像垃圾回收 serverCertificateManager 负责处理证书 oomWatcher 监控内存使用，是否发生内存耗尽即OOM resourceAnalyzer 监控资源使用情况 volumeManager 对pod执行attached/detached/mounted/unmounted操作 statusManager 使用apiserver同步pods状态; 也用作状态缓存 probeManager 处理容器探针 runtimeClassManager 同步RuntimeClasses podKiller 负责杀死pod 参考文章： https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kubelet/app/server.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/kubelet/kubelet.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kubelet/syncLoopIteration.html":{"url":"code-analysis/kubelet/syncLoopIteration.html","title":"syncLoopIteration","keywords":"","body":"kubelet源码分析（四）之 syncLoopIteration1. syncLoop2. syncLoopIteration2.1. configCh2.2. plegCh2.3. syncCh2.4. livenessManager.Update2.5. housekeepingCh3. SyncHandler3.1. HandlePodAdditions3.2. HandlePodUpdates3.3. HandlePodRemoves3.4. HandlePodReconcile3.5. HandlePodSyncs3.6. HandlePodCleanups4. dispatchWork5. PodWorkers.UpdatePod6. managePodLoop7. 总结kubelet源码分析（四）之 syncLoopIteration 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析kubelet中syncLoopIteration部分。syncLoopIteration通过几种channel来对不同类型的事件进行监听并做增删改查的处理。 1. syncLoop syncLoop是处理变更的循环。 它监听来自三种channel（file，apiserver和http）的更改。 对于看到的任何新更改，将针对所需状态和运行状态运行同步。 如果没有看到配置的变化，将在每个同步频率秒同步最后已知的所需状态。 此部分代码位于pkg/kubelet/kubelet.go // syncLoop is the main loop for processing changes. It watches for changes from // three channels (file, apiserver, and http) and creates a union of them. For // any new change seen, will run a sync against desired state and running state. If // no changes are seen to the configuration, will synchronize the last known desired // state every sync-frequency seconds. Never returns. func (kl *Kubelet) syncLoop(updates 其中调用了syncLoopIteration的函数来执行更具体的监控pod变化的循环。 2. syncLoopIteration syncLoopIteration主要通过几种channel来对不同类型的事件进行监听并处理。其中包括：configCh、plegCh、syncCh、houseKeepingCh、livenessManager.Updates()。 syncLoopIteration实际执行了pod的操作，此部分设置了几种不同的channel: configCh：将配置更改的pod分派给事件类型的相应处理程序回调。 plegCh：更新runtime缓存，同步pod。 syncCh：同步所有等待同步的pod。 houseKeepingCh：触发清理pod。 livenessManager.Updates()：对失败的pod或者liveness检查失败的pod进行sync操作。 syncLoopIteration部分代码位于pkg/kubelet/kubelet.go 2.1. configCh configCh将配置更改的pod分派给事件类型的相应处理程序回调，该部分主要通过SyncHandler对pod的不同事件进行增删改查等操作。 func (kl *Kubelet) syncLoopIteration(configCh 可以看出syncLoopIteration根据podUpdate的值来执行不同的pod操作，具体如下： ADD：HandlePodAdditions UPDATE：HandlePodUpdates REMOVE：HandlePodRemoves RECONCILE：HandlePodReconcile DELETE：HandlePodUpdates RESTORE：HandlePodAdditions podsToSync：HandlePodSyncs 其中执行pod的handler操作的是SyncHandler，该类型是一个接口，实现体为kubelet本身，具体见后续分析。 2.2. plegCh plegCh：更新runtime缓存，同步pod。此处调用了HandlePodSyncs的函数。 case e := 2.3. syncCh syncCh：同步所有等待同步的pod。此处调用了HandlePodSyncs的函数。 case 2.4. livenessManager.Update livenessManager.Updates()：对失败的pod或者liveness检查失败的pod进行sync操作。此处调用了HandlePodSyncs的函数。 case update := 2.5. housekeepingCh houseKeepingCh：触发清理pod。此处调用了HandlePodCleanups的函数。 case 3. SyncHandler SyncHandler是一个定义Pod的不同Handler的接口，具体是实现者是kubelet，该接口的方法主要在syncLoopIteration中调用，接口定义如下： // SyncHandler is an interface implemented by Kubelet, for testability type SyncHandler interface { HandlePodAdditions(pods []*v1.Pod) HandlePodUpdates(pods []*v1.Pod) HandlePodRemoves(pods []*v1.Pod) HandlePodReconcile(pods []*v1.Pod) HandlePodSyncs(pods []*v1.Pod) HandlePodCleanups() error } SyncHandler部分代码位于pkg/kubelet/kubelet.go 3.1. HandlePodAdditions HandlePodAdditions先根据pod创建时间对pod进行排序，然后遍历pod列表，来执行pod的相关操作。 // HandlePodAdditions is the callback in SyncHandler for pods being added from // a config source. func (kl *Kubelet) HandlePodAdditions(pods []*v1.Pod) { start := kl.clock.Now() sort.Sort(sliceutils.PodsByCreationTime(pods)) for _, pod := range pods { ... } } 将pod添加到pod manager中。 for _, pod := range pods { // Responsible for checking limits in resolv.conf if kl.dnsConfigurer != nil && kl.dnsConfigurer.ResolverConfig != \"\" { kl.dnsConfigurer.CheckLimitsForResolvConf() } existingPods := kl.podManager.GetPods() // Always add the pod to the pod manager. Kubelet relies on the pod // manager as the source of truth for the desired state. If a pod does // not exist in the pod manager, it means that it has been deleted in // the apiserver and no action (other than cleanup) is required. kl.podManager.AddPod(pod) ... } 如果是mirror pod，则对mirror pod进行处理。 if kubepod.IsMirrorPod(pod) { kl.handleMirrorPod(pod, start) continue } 如果当前pod的状态不是Terminated状态，则判断是否接受该pod，如果不接受则将pod状态改为Failed。 if !kl.podIsTerminated(pod) { // Only go through the admission process if the pod is not // terminated. // We failed pods that we rejected, so activePods include all admitted // pods that are alive. activePods := kl.filterOutTerminatedPods(existingPods) // Check if we can admit the pod; if not, reject it. if ok, reason, message := kl.canAdmitPod(activePods, pod); !ok { kl.rejectPod(pod, reason, message) continue } } 执行dispatchWork函数，该函数是syncHandler中调用到的核心函数，该函数在pod worker中启动一个异步循环，来分派pod的相关操作。该函数的具体操作待后续分析。 mirrorPod, _ := kl.podManager.GetMirrorPodByPod(pod) kl.dispatchWork(pod, kubetypes.SyncPodCreate, mirrorPod, start) 最后加pod添加到probe manager中。 kl.probeManager.AddPod(pod) 3.2. HandlePodUpdates HandlePodUpdates同样遍历pod列表，执行相应的操作。 // HandlePodUpdates is the callback in the SyncHandler interface for pods // being updated from a config source. func (kl *Kubelet) HandlePodUpdates(pods []*v1.Pod) { start := kl.clock.Now() for _, pod := range pods { ... } } 将pod更新到pod manager中。 for _, pod := range pods { // Responsible for checking limits in resolv.conf if kl.dnsConfigurer != nil && kl.dnsConfigurer.ResolverConfig != \"\" { kl.dnsConfigurer.CheckLimitsForResolvConf() } kl.podManager.UpdatePod(pod) ... } 如果是mirror pod，则对mirror pod进行处理。 if kubepod.IsMirrorPod(pod) { kl.handleMirrorPod(pod, start) continue } 执行dispatchWork函数。 // TODO: Evaluate if we need to validate and reject updates. mirrorPod, _ := kl.podManager.GetMirrorPodByPod(pod) kl.dispatchWork(pod, kubetypes.SyncPodUpdate, mirrorPod, start) 3.3. HandlePodRemoves HandlePodRemoves遍历pod列表。 // HandlePodRemoves is the callback in the SyncHandler interface for pods // being removed from a config source. func (kl *Kubelet) HandlePodRemoves(pods []*v1.Pod) { start := kl.clock.Now() for _, pod := range pods { ... } } 从pod manager中删除pod。 for _, pod := range pods { kl.podManager.DeletePod(pod) ... } 如果是mirror pod，则对mirror pod进行处理。 if kubepod.IsMirrorPod(pod) { kl.handleMirrorPod(pod, start) continue } 调用kubelet的deletePod函数来删除pod。 // Deletion is allowed to fail because the periodic cleanup routine // will trigger deletion again. if err := kl.deletePod(pod); err != nil { glog.V(2).Infof(\"Failed to delete pod %q, err: %v\", format.Pod(pod), err) } deletePod 函数将需要删除的pod加入podKillingCh的channel中，有podKiller监听这个channel去执行删除任务，实现如下： // deletePod deletes the pod from the internal state of the kubelet by: // 1. stopping the associated pod worker asynchronously // 2. signaling to kill the pod by sending on the podKillingCh channel // // deletePod returns an error if not all sources are ready or the pod is not // found in the runtime cache. func (kl *Kubelet) deletePod(pod *v1.Pod) error { if pod == nil { return fmt.Errorf(\"deletePod does not allow nil pod\") } if !kl.sourcesReady.AllReady() { // If the sources aren't ready, skip deletion, as we may accidentally delete pods // for sources that haven't reported yet. return fmt.Errorf(\"skipping delete because sources aren't ready yet\") } kl.podWorkers.ForgetWorker(pod.UID) // Runtime cache may not have been updated to with the pod, but it's okay // because the periodic cleanup routine will attempt to delete again later. runningPods, err := kl.runtimeCache.GetPods() if err != nil { return fmt.Errorf(\"error listing containers: %v\", err) } runningPod := kubecontainer.Pods(runningPods).FindPod(\"\", pod.UID) if runningPod.IsEmpty() { return fmt.Errorf(\"pod not found\") } podPair := kubecontainer.PodPair{APIPod: pod, RunningPod: &runningPod} kl.podKillingCh 从probe manager中移除pod。 kl.probeManager.RemovePod(pod) 3.4. HandlePodReconcile 遍历pod列表。 // HandlePodReconcile is the callback in the SyncHandler interface for pods // that should be reconciled. func (kl *Kubelet) HandlePodReconcile(pods []*v1.Pod) { start := kl.clock.Now() for _, pod := range pods { ... } } 将pod更新到pod manager中。 for _, pod := range pods { // Update the pod in pod manager, status manager will do periodically reconcile according // to the pod manager. kl.podManager.UpdatePod(pod) ... } 必要时调整pod的Ready状态，执行dispatchWork函数。 // Reconcile Pod \"Ready\" condition if necessary. Trigger sync pod for reconciliation. if status.NeedToReconcilePodReadiness(pod) { mirrorPod, _ := kl.podManager.GetMirrorPodByPod(pod) kl.dispatchWork(pod, kubetypes.SyncPodSync, mirrorPod, start) } 如果pod被设定为需要被驱逐的，则删除pod中的容器。 // After an evicted pod is synced, all dead containers in the pod can be removed. if eviction.PodIsEvicted(pod.Status) { if podStatus, err := kl.podCache.Get(pod.UID); err == nil { kl.containerDeletor.deleteContainersInPod(\"\", podStatus, true) } } 3.5. HandlePodSyncs HandlePodSyncs是syncHandler接口回调函数，调用dispatchWork，通过pod worker来执行任务。 // HandlePodSyncs is the callback in the syncHandler interface for pods // that should be dispatched to pod workers for sync. func (kl *Kubelet) HandlePodSyncs(pods []*v1.Pod) { start := kl.clock.Now() for _, pod := range pods { mirrorPod, _ := kl.podManager.GetMirrorPodByPod(pod) kl.dispatchWork(pod, kubetypes.SyncPodSync, mirrorPod, start) } } 3.6. HandlePodCleanups HandlePodCleanups主要用来执行pod的清理任务，其中包括terminating的pod，orphaned的pod等。 首先查看pod使用到的cgroup。 // HandlePodCleanups performs a series of cleanup work, including terminating // pod workers, killing unwanted pods, and removing orphaned volumes/pod // directories. // NOTE: This function is executed by the main sync loop, so it // should not contain any blocking calls. func (kl *Kubelet) HandlePodCleanups() error { // The kubelet lacks checkpointing, so we need to introspect the set of pods // in the cgroup tree prior to inspecting the set of pods in our pod manager. // this ensures our view of the cgroup tree does not mistakenly observe pods // that are added after the fact... var ( cgroupPods map[types.UID]cm.CgroupName err error ) if kl.cgroupsPerQOS { pcm := kl.containerManager.NewPodContainerManager() cgroupPods, err = pcm.GetAllPodsFromCgroups() if err != nil { return fmt.Errorf(\"failed to get list of pods that still exist on cgroup mounts: %v\", err) } } ... } 列出所有pod包括mirror pod。 allPods, mirrorPods := kl.podManager.GetPodsAndMirrorPods() // Pod phase progresses monotonically. Once a pod has reached a final state, // it should never leave regardless of the restart policy. The statuses // of such pods should not be changed, and there is no need to sync them. // TODO: the logic here does not handle two cases: // 1. If the containers were removed immediately after they died, kubelet // may fail to generate correct statuses, let alone filtering correctly. // 2. If kubelet restarted before writing the terminated status for a pod // to the apiserver, it could still restart the terminated pod (even // though the pod was not considered terminated by the apiserver). // These two conditions could be alleviated by checkpointing kubelet. activePods := kl.filterOutTerminatedPods(allPods) desiredPods := make(map[types.UID]empty) for _, pod := range activePods { desiredPods[pod.UID] = empty{} } pod worker停止不再存在的pod的任务，并从probe manager中清除pod。 // Stop the workers for no-longer existing pods. // TODO: is here the best place to forget pod workers? kl.podWorkers.ForgetNonExistingPodWorkers(desiredPods) kl.probeManager.CleanupPods(activePods) 将需要杀死的pod加入到podKillingCh的channel中，podKiller的任务会监听该channel并获取需要杀死的pod列表来执行杀死pod的操作。 runningPods, err := kl.runtimeCache.GetPods() if err != nil { glog.Errorf(\"Error listing containers: %#v\", err) return err } for _, pod := range runningPods { if _, found := desiredPods[pod.ID]; !found { kl.podKillingCh 当pod不再被绑定到该节点，移除podStatus，其中removeOrphanedPodStatuses最后调用的函数是statusManager的RemoveOrphanedStatuses方法。 kl.removeOrphanedPodStatuses(allPods, mirrorPods) 移除所有的orphaned volume。 // Remove any orphaned volumes. // Note that we pass all pods (including terminated pods) to the function, // so that we don't remove volumes associated with terminated but not yet // deleted pods. err = kl.cleanupOrphanedPodDirs(allPods, runningPods) if err != nil { // We want all cleanup tasks to be run even if one of them failed. So // we just log an error here and continue other cleanup tasks. // This also applies to the other clean up tasks. glog.Errorf(\"Failed cleaning up orphaned pod directories: %v\", err) } 移除mirror pod。 // Remove any orphaned mirror pods. kl.podManager.DeleteOrphanedMirrorPods() 删除不再运行的pod的cgroup。 // Remove any cgroups in the hierarchy for pods that are no longer running. if kl.cgroupsPerQOS { kl.cleanupOrphanedPodCgroups(cgroupPods, activePods) } 执行垃圾回收（GC）操作。 kl.backOff.GC() 4. dispatchWork dispatchWork通过pod worker启动一个异步的循环。 完整代码如下： // dispatchWork starts the asynchronous sync of the pod in a pod worker. // If the pod is terminated, dispatchWork func (kl *Kubelet) dispatchWork(pod *v1.Pod, syncType kubetypes.SyncPodType, mirrorPod *v1.Pod, start time.Time) { if kl.podIsTerminated(pod) { if pod.DeletionTimestamp != nil { // If the pod is in a terminated state, there is no pod worker to // handle the work item. Check if the DeletionTimestamp has been // set, and force a status update to trigger a pod deletion request // to the apiserver. kl.statusManager.TerminatePod(pod) } return } // Run the sync in an async worker. kl.podWorkers.UpdatePod(&UpdatePodOptions{ Pod: pod, MirrorPod: mirrorPod, UpdateType: syncType, OnCompleteFunc: func(err error) { if err != nil { metrics.PodWorkerLatency.WithLabelValues(syncType.String()).Observe(metrics.SinceInMicroseconds(start)) } }, }) // Note the number of containers for new pods. if syncType == kubetypes.SyncPodCreate { metrics.ContainersPerPodCount.Observe(float64(len(pod.Spec.Containers))) } } 以下分段进行分析： 如果pod的状态是处于Terminated状态，则执行statusManager的TerminatePod操作。 // dispatchWork starts the asynchronous sync of the pod in a pod worker. // If the pod is terminated, dispatchWork func (kl *Kubelet) dispatchWork(pod *v1.Pod, syncType kubetypes.SyncPodType, mirrorPod *v1.Pod, start time.Time) { if kl.podIsTerminated(pod) { if pod.DeletionTimestamp != nil { // If the pod is in a terminated state, there is no pod worker to // handle the work item. Check if the DeletionTimestamp has been // set, and force a status update to trigger a pod deletion request // to the apiserver. kl.statusManager.TerminatePod(pod) } return } ... } 执行pod worker的UpdatePod函数，该函数是pod worker的核心函数，来执行pod相关操作。具体逻辑待下文分析。 // Run the sync in an async worker. kl.podWorkers.UpdatePod(&UpdatePodOptions{ Pod: pod, MirrorPod: mirrorPod, UpdateType: syncType, OnCompleteFunc: func(err error) { if err != nil { metrics.PodWorkerLatency.WithLabelValues(syncType.String()).Observe(metrics.SinceInMicroseconds(start)) } }, }) 当创建类型是SyncPodCreate（即创建pod的时候），统计新pod中容器的数目。 // Note the number of containers for new pods. if syncType == kubetypes.SyncPodCreate { metrics.ContainersPerPodCount.Observe(float64(len(pod.Spec.Containers))) } 5. PodWorkers.UpdatePod PodWorkers是一个接口类型： // PodWorkers is an abstract interface for testability. type PodWorkers interface { UpdatePod(options *UpdatePodOptions) ForgetNonExistingPodWorkers(desiredPods map[types.UID]empty) ForgetWorker(uid types.UID) } 其中UpdatePod是一个核心方法，通过podUpdates的channel来传递需要处理的pod信息，对于新创建的pod每个pod都会由一个goroutine来执行managePodLoop。 此部分代码位于pkg/kubelet/pod_workers.go // Apply the new setting to the specified pod. // If the options provide an OnCompleteFunc, the function is invoked if the update is accepted. // Update requests are ignored if a kill pod request is pending. func (p *podWorkers) UpdatePod(options *UpdatePodOptions) { pod := options.Pod uid := pod.UID var podUpdates chan UpdatePodOptions var exists bool p.podLock.Lock() defer p.podLock.Unlock() if podUpdates, exists = p.podUpdates[uid]; !exists { // We need to have a buffer here, because checkForUpdates() method that // puts an update into channel is called from the same goroutine where // the channel is consumed. However, it is guaranteed that in such case // the channel is empty, so buffer of size 1 is enough. podUpdates = make(chan UpdatePodOptions, 1) p.podUpdates[uid] = podUpdates // Creating a new pod worker either means this is a new pod, or that the // kubelet just restarted. In either case the kubelet is willing to believe // the status of the pod for the first pod worker sync. See corresponding // comment in syncPod. go func() { defer runtime.HandleCrash() p.managePodLoop(podUpdates) }() } if !p.isWorking[pod.UID] { p.isWorking[pod.UID] = true podUpdates 6. managePodLoop managePodLoop通过读取podUpdateschannel的信息，执行syncPodFn函数，而syncPodFn函数在newPodWorkers的时候赋值了，即kubelet.syncPod。kubelet.syncPod具体代码逻辑待后续文章单独分析。 // newPodWorkers传入syncPod函数 klet.podWorkers = newPodWorkers(klet.syncPod, kubeDeps.Recorder, klet.workQueue, klet.resyncInterval, backOffPeriod, klet.podCache) newPodWorkers函数参考： func newPodWorkers(syncPodFn syncPodFnType, recorder record.EventRecorder, workQueue queue.WorkQueue, resyncInterval, backOffPeriod time.Duration, podCache kubecontainer.Cache) *podWorkers { return &podWorkers{ podUpdates: map[types.UID]chan UpdatePodOptions{}, isWorking: map[types.UID]bool{}, lastUndeliveredWorkUpdate: map[types.UID]UpdatePodOptions{}, syncPodFn: syncPodFn, // 构造传入klet.syncPod函数 recorder: recorder, workQueue: workQueue, resyncInterval: resyncInterval, backOffPeriod: backOffPeriod, podCache: podCache, } } managePodLoop函数参考： 此部分代码位于pkg/kubelet/pod_workers.go func (p *podWorkers) managePodLoop(podUpdates 7. 总结 syncLoopIteration基本流程如下： 通过几种channel来对不同类型的事件进行监听并处理。其中channel包括：configCh、plegCh、syncCh、houseKeepingCh、livenessManager.Updates()。 不同的SyncHandler执行不同的增删改查操作。 其中HandlePodAdditions、HandlePodUpdates、HandlePodReconcile、HandlePodSyncs都调用到了dispatchWork来执行pod的相关操作。HandlePodCleanups的pod清理任务，通过channel的方式加需要清理的pod给podKiller来清理。 dispatchWork调用podWorkers.UpdatePod执行异步操作。 podWorkers.UpdatePod中调用managePodLoop来执行pod相关操作循环。 channel类型及作用： configCh：将配置更改的pod分派给事件类型的相应处理程序回调。 plegCh：更新runtime缓存，同步pod。 syncCh：同步所有等待同步的pod。 houseKeepingCh：触发清理pod。 livenessManager.Updates()：对失败的pod或者liveness检查失败的pod进行sync操作。 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/kubelet/kubelet.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/kubelet/pod_workers.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kubelet/syncPod.html":{"url":"code-analysis/kubelet/syncPod.html","title":"syncPod","keywords":"","body":"kubelet源码分析（五）之 syncPod1. managePodLoop2. syncPod2.1. SyncPodKill2.2. SyncPodCreate2.3. Cgroups2.4. Mirror Pod2.5. makePodDataDirs2.6. mount volumes2.7. PullSecretsForPod2.8. containerRuntime.SyncPod3. Runtime.SyncPod3.1. computePodActions3.2. killPodWithSyncResult3.3. killContainer3.4. createPodSandbox3.5. start init container3.6. start containers4. startContainer4.1. pull image4.2. CreateContainer4.3. StartContainer4.4. execute post start hook5. 总结kubelet源码分析（五）之 syncPod 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析kubelet中syncPod的部分。 1. managePodLoop managePodLoop通过读取podUpdateschannel的信息，执行syncPodFn函数，而syncPodFn函数在newPodWorkers的时候赋值了，即kubelet.syncPod。 managePodLoop完整代码如下： 此部分代码位于pkg/kubelet/pod_workers.go func (p *podWorkers) managePodLoop(podUpdates 以下分析syncPod相关逻辑。 2. syncPod syncPod可以理解为是一个单个pod进行同步任务的事务脚本。其中入参是syncPodOptions，syncPodOptions记录了需要同步的pod的相关信息。具体定义如下： // syncPodOptions provides the arguments to a SyncPod operation. type syncPodOptions struct { // the mirror pod for the pod to sync, if it is a static pod mirrorPod *v1.Pod // pod to sync pod *v1.Pod // the type of update (create, update, sync) updateType kubetypes.SyncPodType // the current status podStatus *kubecontainer.PodStatus // if update type is kill, use the specified options to kill the pod. killPodOptions *KillPodOptions } syncPod主要执行以下的工作流： 如果是正在创建的pod，则记录pod worker的启动latency。 调用generateAPIPodStatus为pod提供v1.PodStatus信息。 如果pod是第一次运行，记录pod的启动latency。 更新status manager中的pod状态。 如果pod不应该被运行则杀死pod。 如果pod是一个static pod，并且没有对应的mirror pod，则创建一个mirror pod。 如果没有pod的数据目录则给pod创建对应的数据目录。 等待volume被attach或mount。 获取pod的secret数据。 调用container runtime的SyncPod函数，执行相关pod操作。 更新pod的ingress和egress的traffic limit。 当以上任务流中有任何的error，则return error。在下一次执行syncPod的任务流会被再次执行。对于错误信息会被记录到event中，方便debug。 以下对syncPod的执行过程进行分析。 syncPod的代码位于pkg/kubelet/kubelet.go 2.1. SyncPodKill 首先，获取syncPodOptions的pod信息。 func (kl *Kubelet) syncPod(o syncPodOptions) error { // pull out the required options pod := o.pod mirrorPod := o.mirrorPod podStatus := o.podStatus updateType := o.updateType ... } 如果pod是需要被杀死的，则执行killPod，会在指定的宽限期内杀死pod。 // if we want to kill a pod, do it now! if updateType == kubetypes.SyncPodKill { killPodOptions := o.killPodOptions if killPodOptions == nil || killPodOptions.PodStatusFunc == nil { return fmt.Errorf(\"kill pod options are required if update type is kill\") } apiPodStatus := killPodOptions.PodStatusFunc(pod, podStatus) kl.statusManager.SetPodStatus(pod, apiPodStatus) // we kill the pod with the specified grace period since this is a termination if err := kl.killPod(pod, nil, podStatus, killPodOptions.PodTerminationGracePeriodSecondsOverride); err != nil { kl.recorder.Eventf(pod, v1.EventTypeWarning, events.FailedToKillPod, \"error killing pod: %v\", err) // there was an error killing the pod, so we return that error directly utilruntime.HandleError(err) return err } return nil } 2.2. SyncPodCreate 如果pod是需要被创建的，则记录pod的启动latency，latency与pod在apiserver中第一次被记录相关。 // Latency measurements for the main workflow are relative to the // first time the pod was seen by the API server. var firstSeenTime time.Time if firstSeenTimeStr, ok := pod.Annotations[kubetypes.ConfigFirstSeenAnnotationKey]; ok { firstSeenTime = kubetypes.ConvertToTimestamp(firstSeenTimeStr).Get() } // Record pod worker start latency if being created // TODO: make pod workers record their own latencies if updateType == kubetypes.SyncPodCreate { if !firstSeenTime.IsZero() { // This is the first time we are syncing the pod. Record the latency // since kubelet first saw the pod if firstSeenTime is set. metrics.PodWorkerStartLatency.Observe(metrics.SinceInMicroseconds(firstSeenTime)) } else { glog.V(3).Infof(\"First seen time not recorded for pod %q\", pod.UID) } } 通过pod和pod status生成最终的api pod status并设置pod的IP。 // Generate final API pod status with pod and status manager status apiPodStatus := kl.generateAPIPodStatus(pod, podStatus) // The pod IP may be changed in generateAPIPodStatus if the pod is using host network. (See #24576) // TODO(random-liu): After writing pod spec into container labels, check whether pod is using host network, and // set pod IP to hostIP directly in runtime.GetPodStatus podStatus.IP = apiPodStatus.PodIP 记录pod到running状态的时间。 // Record the time it takes for the pod to become running. existingStatus, ok := kl.statusManager.GetPodStatus(pod.UID) if !ok || existingStatus.Phase == v1.PodPending && apiPodStatus.Phase == v1.PodRunning && !firstSeenTime.IsZero() { metrics.PodStartLatency.Observe(metrics.SinceInMicroseconds(firstSeenTime)) } 如果pod是不可运行的，则更新pod和container的状态和相应的原因。 runnable := kl.canRunPod(pod) if !runnable.Admit { // Pod is not runnable; update the Pod and Container statuses to why. apiPodStatus.Reason = runnable.Reason apiPodStatus.Message = runnable.Message // Waiting containers are not creating. const waitingReason = \"Blocked\" for _, cs := range apiPodStatus.InitContainerStatuses { if cs.State.Waiting != nil { cs.State.Waiting.Reason = waitingReason } } for _, cs := range apiPodStatus.ContainerStatuses { if cs.State.Waiting != nil { cs.State.Waiting.Reason = waitingReason } } } 并更新status manager中的状态信息，杀死不可运行的pod。 // Update status in the status manager kl.statusManager.SetPodStatus(pod, apiPodStatus) // Kill pod if it should not be running if !runnable.Admit || pod.DeletionTimestamp != nil || apiPodStatus.Phase == v1.PodFailed { var syncErr error if err := kl.killPod(pod, nil, podStatus, nil); err != nil { kl.recorder.Eventf(pod, v1.EventTypeWarning, events.FailedToKillPod, \"error killing pod: %v\", err) syncErr = fmt.Errorf(\"error killing pod: %v\", err) utilruntime.HandleError(syncErr) } else { if !runnable.Admit { // There was no error killing the pod, but the pod cannot be run. // Return an error to signal that the sync loop should back off. syncErr = fmt.Errorf(\"pod cannot be run: %s\", runnable.Message) } } return syncErr } 如果网络插件还没到Ready状态，则只有在使用host网络模式的情况下才启动pod。 // If the network plugin is not ready, only start the pod if it uses the host network if rs := kl.runtimeState.networkErrors(); len(rs) != 0 && !kubecontainer.IsHostNetworkPod(pod) { kl.recorder.Eventf(pod, v1.EventTypeWarning, events.NetworkNotReady, \"%s: %v\", NetworkNotReadyErrorMsg, rs) return fmt.Errorf(\"%s: %v\", NetworkNotReadyErrorMsg, rs) } 2.3. Cgroups 给pod创建Cgroups，如果cgroups-per-qos参数开启，则申请相应的资源。对于terminated的pod不需要创建或更新pod的Cgroups。 当重新启动kubelet并且启用cgroups-per-qos时，应该间歇性地终止所有pod的运行容器并在qos cgroup hierarchy下重新启动。 如果pod的cgroup已经存在或者pod第一次运行，不杀死pod中容器。 // Create Cgroups for the pod and apply resource parameters // to them if cgroups-per-qos flag is enabled. pcm := kl.containerManager.NewPodContainerManager() // If pod has already been terminated then we need not create // or update the pod's cgroup if !kl.podIsTerminated(pod) { // When the kubelet is restarted with the cgroups-per-qos // flag enabled, all the pod's running containers // should be killed intermittently and brought back up // under the qos cgroup hierarchy. // Check if this is the pod's first sync firstSync := true for _, containerStatus := range apiPodStatus.ContainerStatuses { if containerStatus.State.Running != nil { firstSync = false break } } // Don't kill containers in pod if pod's cgroups already // exists or the pod is running for the first time podKilled := false if !pcm.Exists(pod) && !firstSync { if err := kl.killPod(pod, nil, podStatus, nil); err == nil { podKilled = true } } ... 如果pod被杀死并且重启策略是Never，则不创建或更新对应的Cgroups，否则创建和更新pod的Cgroups。 // Create and Update pod's Cgroups // Don't create cgroups for run once pod if it was killed above // The current policy is not to restart the run once pods when // the kubelet is restarted with the new flag as run once pods are // expected to run only once and if the kubelet is restarted then // they are not expected to run again. // We don't create and apply updates to cgroup if its a run once pod and was killed above if !(podKilled && pod.Spec.RestartPolicy == v1.RestartPolicyNever) { if !pcm.Exists(pod) { if err := kl.containerManager.UpdateQOSCgroups(); err != nil { glog.V(2).Infof(\"Failed to update QoS cgroups while syncing pod: %v\", err) } if err := pcm.EnsureExists(pod); err != nil { kl.recorder.Eventf(pod, v1.EventTypeWarning, events.FailedToCreatePodContainer, \"unable to ensure pod container exists: %v\", err) return fmt.Errorf(\"failed to ensure that the pod: %v cgroups exist and are correctly applied: %v\", pod.UID, err) } } } 其中创建Cgroups是通过containerManager的UpdateQOSCgroups来执行。 if err := kl.containerManager.UpdateQOSCgroups(); err != nil { glog.V(2).Infof(\"Failed to update QoS cgroups while syncing pod: %v\", err) } 2.4. Mirror Pod 如果pod是一个static pod，没有对应的mirror pod，则创建一个mirror pod；如果存在mirror pod则删除再重建一个mirror pod。 // Create Mirror Pod for Static Pod if it doesn't already exist if kubepod.IsStaticPod(pod) { podFullName := kubecontainer.GetPodFullName(pod) deleted := false if mirrorPod != nil { if mirrorPod.DeletionTimestamp != nil || !kl.podManager.IsMirrorPodOf(mirrorPod, pod) { // The mirror pod is semantically different from the static pod. Remove // it. The mirror pod will get recreated later. glog.Warningf(\"Deleting mirror pod %q because it is outdated\", format.Pod(mirrorPod)) if err := kl.podManager.DeleteMirrorPod(podFullName); err != nil { glog.Errorf(\"Failed deleting mirror pod %q: %v\", format.Pod(mirrorPod), err) } else { deleted = true } } } if mirrorPod == nil || deleted { node, err := kl.GetNode() if err != nil || node.DeletionTimestamp != nil { glog.V(4).Infof(\"No need to create a mirror pod, since node %q has been removed from the cluster\", kl.nodeName) } else { glog.V(4).Infof(\"Creating a mirror pod for static pod %q\", format.Pod(pod)) if err := kl.podManager.CreateMirrorPod(pod); err != nil { glog.Errorf(\"Failed creating a mirror pod for %q: %v\", format.Pod(pod), err) } } } } 2.5. makePodDataDirs 给pod创建数据目录。 // Make data directories for the pod if err := kl.makePodDataDirs(pod); err != nil { kl.recorder.Eventf(pod, v1.EventTypeWarning, events.FailedToMakePodDataDirectories, \"error making pod data directories: %v\", err) glog.Errorf(\"Unable to make pod data directories for pod %q: %v\", format.Pod(pod), err) return err } 其中数据目录包括 PodDir：{kubelet.rootDirectory}/pods/podUID PodVolumesDir：{PodDir}/volumes PodPluginsDir：{PodDir}/plugins // makePodDataDirs creates the dirs for the pod datas. func (kl *Kubelet) makePodDataDirs(pod *v1.Pod) error { uid := pod.UID if err := os.MkdirAll(kl.getPodDir(uid), 0750); err != nil && !os.IsExist(err) { return err } if err := os.MkdirAll(kl.getPodVolumesDir(uid), 0750); err != nil && !os.IsExist(err) { return err } if err := os.MkdirAll(kl.getPodPluginsDir(uid), 0750); err != nil && !os.IsExist(err) { return err } return nil } 2.6. mount volumes 对非terminated状态的pod挂载volume。 // Volume manager will not mount volumes for terminated pods if !kl.podIsTerminated(pod) { // Wait for volumes to attach/mount if err := kl.volumeManager.WaitForAttachAndMount(pod); err != nil { kl.recorder.Eventf(pod, v1.EventTypeWarning, events.FailedMountVolume, \"Unable to mount volumes for pod %q: %v\", format.Pod(pod), err) glog.Errorf(\"Unable to mount volumes for pod %q: %v; skipping pod\", format.Pod(pod), err) return err } } 2.7. PullSecretsForPod 获取pod的secret数据。 // Fetch the pull secrets for the pod pullSecrets := kl.getPullSecretsForPod(pod) getPullSecretsForPod具体实现函数如下： // getPullSecretsForPod inspects the Pod and retrieves the referenced pull // secrets. func (kl *Kubelet) getPullSecretsForPod(pod *v1.Pod) []v1.Secret { pullSecrets := []v1.Secret{} for _, secretRef := range pod.Spec.ImagePullSecrets { secret, err := kl.secretManager.GetSecret(pod.Namespace, secretRef.Name) if err != nil { glog.Warningf(\"Unable to retrieve pull secret %s/%s for %s/%s due to %v. The image pull may not succeed.\", pod.Namespace, secretRef.Name, pod.Namespace, pod.Name, err) continue } pullSecrets = append(pullSecrets, *secret) } return pullSecrets } 2.8. containerRuntime.SyncPod 调用container runtime的SyncPod函数，执行相关pod操作，由此kubelet.syncPod的操作逻辑转入containerRuntime.SyncPod函数中。 // Call the container runtime's SyncPod callback result := kl.containerRuntime.SyncPod(pod, apiPodStatus, podStatus, pullSecrets, kl.backOff) kl.reasonCache.Update(pod.UID, result) if err := result.Error(); err != nil { // Do not return error if the only failures were pods in backoff for _, r := range result.SyncResults { if r.Error != kubecontainer.ErrCrashLoopBackOff && r.Error != images.ErrImagePullBackOff { // Do not record an event here, as we keep all event logging for sync pod failures // local to container runtime so we get better errors return err } } return nil } 3. Runtime.SyncPod SyncPod主要执行sync操作使得运行的pod达到期望状态的pod。主要执行以下操作： 计算sandbox和container的变化。 必要的时候杀死pod。 杀死所有不需要运行的container。 必要时创建sandbox。 创建init container。 创建正常的container。 Runtime.SyncPod部分代码位于pkg/kubelet/kuberuntime/kuberuntime_manager.go 3.1. computePodActions 计算sandbox和container的变化。 // Step 1: Compute sandbox and container changes. podContainerChanges := m.computePodActions(pod, podStatus) glog.V(3).Infof(\"computePodActions got %+v for pod %q\", podContainerChanges, format.Pod(pod)) if podContainerChanges.CreateSandbox { ref, err := ref.GetReference(legacyscheme.Scheme, pod) if err != nil { glog.Errorf(\"Couldn't make a ref to pod %q: '%v'\", format.Pod(pod), err) } if podContainerChanges.SandboxID != \"\" { m.recorder.Eventf(ref, v1.EventTypeNormal, events.SandboxChanged, \"Pod sandbox changed, it will be killed and re-created.\") } else { glog.V(4).Infof(\"SyncPod received new pod %q, will create a sandbox for it\", format.Pod(pod)) } } 3.2. killPodWithSyncResult 必要的时候杀死pod。 // Step 2: Kill the pod if the sandbox has changed. if podContainerChanges.KillPod { if !podContainerChanges.CreateSandbox { glog.V(4).Infof(\"Stopping PodSandbox for %q because all other containers are dead.\", format.Pod(pod)) } else { glog.V(4).Infof(\"Stopping PodSandbox for %q, will start new one\", format.Pod(pod)) } killResult := m.killPodWithSyncResult(pod, kubecontainer.ConvertPodStatusToRunningPod(m.runtimeName, podStatus), nil) result.AddPodSyncResult(killResult) if killResult.Error() != nil { glog.Errorf(\"killPodWithSyncResult failed: %v\", killResult.Error()) return } if podContainerChanges.CreateSandbox { m.purgeInitContainers(pod, podStatus) } } 3.3. killContainer 杀死所有不需要运行的container。 // Step 3: kill any running containers in this pod which are not to keep. for containerID, containerInfo := range podContainerChanges.ContainersToKill { glog.V(3).Infof(\"Killing unwanted container %q(id=%q) for pod %q\", containerInfo.name, containerID, format.Pod(pod)) killContainerResult := kubecontainer.NewSyncResult(kubecontainer.KillContainer, containerInfo.name) result.AddSyncResult(killContainerResult) if err := m.killContainer(pod, containerID, containerInfo.name, containerInfo.message, nil); err != nil { killContainerResult.Fail(kubecontainer.ErrKillContainer, err.Error()) glog.Errorf(\"killContainer %q(id=%q) for pod %q failed: %v\", containerInfo.name, containerID, format.Pod(pod), err) return } } 3.4. createPodSandbox 必要时创建sandbox。 // Step 4: Create a sandbox for the pod if necessary. ... glog.V(4).Infof(\"Creating sandbox for pod %q\", format.Pod(pod)) createSandboxResult := kubecontainer.NewSyncResult(kubecontainer.CreatePodSandbox, format.Pod(pod)) result.AddSyncResult(createSandboxResult) podSandboxID, msg, err = m.createPodSandbox(pod, podContainerChanges.Attempt) if err != nil { createSandboxResult.Fail(kubecontainer.ErrCreatePodSandbox, msg) glog.Errorf(\"createPodSandbox for pod %q failed: %v\", format.Pod(pod), err) ref, referr := ref.GetReference(legacyscheme.Scheme, pod) if referr != nil { glog.Errorf(\"Couldn't make a ref to pod %q: '%v'\", format.Pod(pod), referr) } m.recorder.Eventf(ref, v1.EventTypeWarning, events.FailedCreatePodSandBox, \"Failed create pod sandbox: %v\", err) return } glog.V(4).Infof(\"Created PodSandbox %q for pod %q\", podSandboxID, format.Pod(pod)) 3.5. start init container 创建init container。 // Step 5: start the init container. if container := podContainerChanges.NextInitContainerToStart; container != nil { // Start the next init container. startContainerResult := kubecontainer.NewSyncResult(kubecontainer.StartContainer, container.Name) result.AddSyncResult(startContainerResult) isInBackOff, msg, err := m.doBackOff(pod, container, podStatus, backOff) if isInBackOff { startContainerResult.Fail(err, msg) glog.V(4).Infof(\"Backing Off restarting init container %+v in pod %v\", container, format.Pod(pod)) return } glog.V(4).Infof(\"Creating init container %+v in pod %v\", container, format.Pod(pod)) if msg, err := m.startContainer(podSandboxID, podSandboxConfig, container, pod, podStatus, pullSecrets, podIP, kubecontainer.ContainerTypeInit); err != nil { startContainerResult.Fail(err, msg) utilruntime.HandleError(fmt.Errorf(\"init container start failed: %v: %s\", err, msg)) return } // Successfully started the container; clear the entry in the failure glog.V(4).Infof(\"Completed init container %q for pod %q\", container.Name, format.Pod(pod)) } 3.6. start containers 创建正常的container。 // Step 6: start containers in podContainerChanges.ContainersToStart. for _, idx := range podContainerChanges.ContainersToStart { container := &pod.Spec.Containers[idx] startContainerResult := kubecontainer.NewSyncResult(kubecontainer.StartContainer, container.Name) result.AddSyncResult(startContainerResult) isInBackOff, msg, err := m.doBackOff(pod, container, podStatus, backOff) if isInBackOff { startContainerResult.Fail(err, msg) glog.V(4).Infof(\"Backing Off restarting container %+v in pod %v\", container, format.Pod(pod)) continue } glog.V(4).Infof(\"Creating container %+v in pod %v\", container, format.Pod(pod)) // 通过startContainer来运行容器 if msg, err := m.startContainer(podSandboxID, podSandboxConfig, container, pod, podStatus, pullSecrets, podIP, kubecontainer.ContainerTypeRegular); err != nil { startContainerResult.Fail(err, msg) // known errors that are logged in other places are logged at higher levels here to avoid // repetitive log spam switch { case err == images.ErrImagePullBackOff: glog.V(3).Infof(\"container start failed: %v: %s\", err, msg) default: utilruntime.HandleError(fmt.Errorf(\"container start failed: %v: %s\", err, msg)) } continue } } 4. startContainer startContainer启动一个容器并返回是否成功。 主要包括以下几个步骤： 拉取镜像 创建容器 启动容器 运行post start lifecycle hooks(如果有设置此项) startContainer完整代码如下： startContainer部分代码位于pkg/kubelet/kuberuntime/kuberuntime_container.go // startContainer starts a container and returns a message indicates why it is failed on error. // It starts the container through the following steps: // * pull the image // * create the container // * start the container // * run the post start lifecycle hooks (if applicable) func (m *kubeGenericRuntimeManager) startContainer(podSandboxID string, podSandboxConfig *runtimeapi.PodSandboxConfig, container *v1.Container, pod *v1.Pod, podStatus *kubecontainer.PodStatus, pullSecrets []v1.Secret, podIP string, containerType kubecontainer.ContainerType) (string, error) { // Step 1: pull the image. imageRef, msg, err := m.imagePuller.EnsureImageExists(pod, container, pullSecrets) if err != nil { m.recordContainerEvent(pod, container, \"\", v1.EventTypeWarning, events.FailedToCreateContainer, \"Error: %v\", grpc.ErrorDesc(err)) return msg, err } // Step 2: create the container. ref, err := kubecontainer.GenerateContainerRef(pod, container) if err != nil { glog.Errorf(\"Can't make a ref to pod %q, container %v: %v\", format.Pod(pod), container.Name, err) } glog.V(4).Infof(\"Generating ref for container %s: %#v\", container.Name, ref) // For a new container, the RestartCount should be 0 restartCount := 0 containerStatus := podStatus.FindContainerStatusByName(container.Name) if containerStatus != nil { restartCount = containerStatus.RestartCount + 1 } containerConfig, cleanupAction, err := m.generateContainerConfig(container, pod, restartCount, podIP, imageRef, containerType) if cleanupAction != nil { defer cleanupAction() } if err != nil { m.recordContainerEvent(pod, container, \"\", v1.EventTypeWarning, events.FailedToCreateContainer, \"Error: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), ErrCreateContainerConfig } containerID, err := m.runtimeService.CreateContainer(podSandboxID, containerConfig, podSandboxConfig) if err != nil { m.recordContainerEvent(pod, container, containerID, v1.EventTypeWarning, events.FailedToCreateContainer, \"Error: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), ErrCreateContainer } err = m.internalLifecycle.PreStartContainer(pod, container, containerID) if err != nil { m.recordContainerEvent(pod, container, containerID, v1.EventTypeWarning, events.FailedToStartContainer, \"Internal PreStartContainer hook failed: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), ErrPreStartHook } m.recordContainerEvent(pod, container, containerID, v1.EventTypeNormal, events.CreatedContainer, \"Created container\") if ref != nil { m.containerRefManager.SetRef(kubecontainer.ContainerID{ Type: m.runtimeName, ID: containerID, }, ref) } // Step 3: start the container. err = m.runtimeService.StartContainer(containerID) if err != nil { m.recordContainerEvent(pod, container, containerID, v1.EventTypeWarning, events.FailedToStartContainer, \"Error: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), kubecontainer.ErrRunContainer } m.recordContainerEvent(pod, container, containerID, v1.EventTypeNormal, events.StartedContainer, \"Started container\") // Symlink container logs to the legacy container log location for cluster logging // support. // TODO(random-liu): Remove this after cluster logging supports CRI container log path. containerMeta := containerConfig.GetMetadata() sandboxMeta := podSandboxConfig.GetMetadata() legacySymlink := legacyLogSymlink(containerID, containerMeta.Name, sandboxMeta.Name, sandboxMeta.Namespace) containerLog := filepath.Join(podSandboxConfig.LogDirectory, containerConfig.LogPath) // only create legacy symlink if containerLog path exists (or the error is not IsNotExist). // Because if containerLog path does not exist, only dandling legacySymlink is created. // This dangling legacySymlink is later removed by container gc, so it does not make sense // to create it in the first place. it happens when journald logging driver is used with docker. if _, err := m.osInterface.Stat(containerLog); !os.IsNotExist(err) { if err := m.osInterface.Symlink(containerLog, legacySymlink); err != nil { glog.Errorf(\"Failed to create legacy symbolic link %q to container %q log %q: %v\", legacySymlink, containerID, containerLog, err) } } // Step 4: execute the post start hook. if container.Lifecycle != nil && container.Lifecycle.PostStart != nil { kubeContainerID := kubecontainer.ContainerID{ Type: m.runtimeName, ID: containerID, } msg, handlerErr := m.runner.Run(kubeContainerID, pod, container, container.Lifecycle.PostStart) if handlerErr != nil { m.recordContainerEvent(pod, container, kubeContainerID.ID, v1.EventTypeWarning, events.FailedPostStartHook, msg) if err := m.killContainer(pod, kubeContainerID, container.Name, \"FailedPostStartHook\", nil); err != nil { glog.Errorf(\"Failed to kill container %q(id=%q) in pod %q: %v, %v\", container.Name, kubeContainerID.String(), format.Pod(pod), ErrPostStartHook, err) } return msg, fmt.Errorf(\"%s: %v\", ErrPostStartHook, handlerErr) } } return \"\", nil } 以下对startContainer分段分析： 4.1. pull image 通过EnsureImageExists方法拉取拉取指定pod容器的镜像，并返回镜像信息和错误。 // Step 1: pull the image. imageRef, msg, err := m.imagePuller.EnsureImageExists(pod, container, pullSecrets) if err != nil { m.recordContainerEvent(pod, container, \"\", v1.EventTypeWarning, events.FailedToCreateContainer, \"Error: %v\", grpc.ErrorDesc(err)) return msg, err } 4.2. CreateContainer 首先生成container的*v1.ObjectReference对象，该对象包括container的相关信息。 // Step 2: create the container. ref, err := kubecontainer.GenerateContainerRef(pod, container) if err != nil { glog.Errorf(\"Can't make a ref to pod %q, container %v: %v\", format.Pod(pod), container.Name, err) } glog.V(4).Infof(\"Generating ref for container %s: %#v\", container.Name, ref) 统计container的重启次数，新的容器默认重启次数为0。 // For a new container, the RestartCount should be 0 restartCount := 0 containerStatus := podStatus.FindContainerStatusByName(container.Name) if containerStatus != nil { restartCount = containerStatus.RestartCount + 1 } 生成container的配置。 containerConfig, cleanupAction, err := m.generateContainerConfig(container, pod, restartCount, podIP, imageRef, containerType) if cleanupAction != nil { defer cleanupAction() } if err != nil { m.recordContainerEvent(pod, container, \"\", v1.EventTypeWarning, events.FailedToCreateContainer, \"Error: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), ErrCreateContainerConfig } 调用runtimeService，执行CreateContainer的操作。 containerID, err := m.runtimeService.CreateContainer(podSandboxID, containerConfig, podSandboxConfig) if err != nil { m.recordContainerEvent(pod, container, containerID, v1.EventTypeWarning, events.FailedToCreateContainer, \"Error: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), ErrCreateContainer } err = m.internalLifecycle.PreStartContainer(pod, container, containerID) if err != nil { m.recordContainerEvent(pod, container, containerID, v1.EventTypeWarning, events.FailedToStartContainer, \"Internal PreStartContainer hook failed: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), ErrPreStartHook } m.recordContainerEvent(pod, container, containerID, v1.EventTypeNormal, events.CreatedContainer, \"Created container\") if ref != nil { m.containerRefManager.SetRef(kubecontainer.ContainerID{ Type: m.runtimeName, ID: containerID, }, ref) } 4.3. StartContainer 执行runtimeService的StartContainer方法，来启动容器。 // Step 3: start the container. err = m.runtimeService.StartContainer(containerID) if err != nil { m.recordContainerEvent(pod, container, containerID, v1.EventTypeWarning, events.FailedToStartContainer, \"Error: %v\", grpc.ErrorDesc(err)) return grpc.ErrorDesc(err), kubecontainer.ErrRunContainer } m.recordContainerEvent(pod, container, containerID, v1.EventTypeNormal, events.StartedContainer, \"Started container\") // Symlink container logs to the legacy container log location for cluster logging // support. // TODO(random-liu): Remove this after cluster logging supports CRI container log path. containerMeta := containerConfig.GetMetadata() sandboxMeta := podSandboxConfig.GetMetadata() legacySymlink := legacyLogSymlink(containerID, containerMeta.Name, sandboxMeta.Name, sandboxMeta.Namespace) containerLog := filepath.Join(podSandboxConfig.LogDirectory, containerConfig.LogPath) // only create legacy symlink if containerLog path exists (or the error is not IsNotExist). // Because if containerLog path does not exist, only dandling legacySymlink is created. // This dangling legacySymlink is later removed by container gc, so it does not make sense // to create it in the first place. it happens when journald logging driver is used with docker. if _, err := m.osInterface.Stat(containerLog); !os.IsNotExist(err) { if err := m.osInterface.Symlink(containerLog, legacySymlink); err != nil { glog.Errorf(\"Failed to create legacy symbolic link %q to container %q log %q: %v\", legacySymlink, containerID, containerLog, err) } } 4.4. execute post start hook 如果有指定Lifecycle.PostStart，则执行PostStart操作，PostStart如果执行失败，则容器会根据重启的规则进行重启。 // Step 4: execute the post start hook. if container.Lifecycle != nil && container.Lifecycle.PostStart != nil { kubeContainerID := kubecontainer.ContainerID{ Type: m.runtimeName, ID: containerID, } msg, handlerErr := m.runner.Run(kubeContainerID, pod, container, container.Lifecycle.PostStart) if handlerErr != nil { m.recordContainerEvent(pod, container, kubeContainerID.ID, v1.EventTypeWarning, events.FailedPostStartHook, msg) if err := m.killContainer(pod, kubeContainerID, container.Name, \"FailedPostStartHook\", nil); err != nil { glog.Errorf(\"Failed to kill container %q(id=%q) in pod %q: %v, %v\", container.Name, kubeContainerID.String(), format.Pod(pod), ErrPostStartHook, err) } return msg, fmt.Errorf(\"%s: %v\", ErrPostStartHook, handlerErr) } } 5. 总结 kubelet的工作是管理pod在Node上的生命周期（包括增删改查），kubelet通过各种类型的manager异步工作各自执行各自的任务，其中使用到了多种的channel来控制状态信号变化的传递，例如比较重要的channel有podUpdates ，来传递pod的变化情况。 创建pod的调用逻辑 syncLoopIteration-->kubetypes.ADD-->HandlePodAdditions(u.Pods)-->dispatchWork(pod, kubetypes.SyncPodCreate, mirrorPod, start)-->podWorkers.UpdatePod-->managePodLoop(podUpdates)-->syncPod(o syncPodOptions)-->containerRuntime.SyncPod-->startContainer 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/kubelet/kubelet.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/kubelet/pod_workers.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/kubelet/kuberuntime/kuberuntime_container.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-controller-manager/_index.html":{"url":"code-analysis/kube-controller-manager/_index.html","title":"kube-controller-manager","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-controller-manager/NewControllerManagerCommand.html":{"url":"code-analysis/kube-controller-manager/NewControllerManagerCommand.html","title":"NewControllerManagerCommand","keywords":"","body":"kube-controller-manager源码分析（一）之 NewControllerManagerCommand1. Main函数2. NewControllerManagerCommand2.1. NewKubeControllerManagerOptions2.2. AddFlagSet3. Run3.1. CreateControllerContext3.2. NewControllerInitializers3.3. StartControllers3.4. InformerFactory.Start3.4.1. SharedInformerFactory3.4.2. sharedInformerFactory.Start3.4.3. sharedIndexInformer.Run4. initFn(ctx)4.1. startStatefulSetController4.2. startDeploymentController5. 总结kube-controller-manager源码分析（一）之 NewControllerManagerCommand 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析https://github.com/kubernetes/kubernetes/tree/v1.12.0/cmd/kube-controller-manager 部分的代码。 本文主要分析 kubernetes/cmd/kube-controller-manager部分，该部分主要涉及各种类型的controller的参数解析，及初始化，例如 deployment controller 和statefulset controller。并没有具体controller运行的详细逻辑，该部分位于kubernetes/pkg/controller模块，待后续文章分析。 kube-controller-manager的cmd部分代码目录结构如下： kube-controller-manager ├── app │ ├── apps.go # 包含:startDeploymentController、startReplicaSetController、startStatefulSetController、startDaemonSetController │ ├── autoscaling.go # startHPAController │ ├── batch.go # startJobController、startCronJobController │ ├── bootstrap.go │ ├── certificates.go │ ├── cloudproviders.go │ ├── config │ │ └── config.go # config: controller manager执行的上下文 │ ├── controllermanager.go # 包含:NewControllerManagerCommand、Run、NewControllerInitializers、StartControllers等 │ ├── core.go # startServiceController、startNodeIpamController、startPersistentVolumeBinderController、startNamespaceController等 │ ├── options # 包含不同controller的option参数 │ │ ├── attachdetachcontroller.go │ │ ├── csrsigningcontroller.go │ │ ├── daemonsetcontroller.go # DaemonSetControllerOptions │ │ ├── deploymentcontroller.go # DeploymentControllerOptions │ │ ├── deprecatedcontroller.go │ │ ├── endpointcontroller.go │ │ ├── garbagecollectorcontroller.go │ │ ├── hpacontroller.go │ │ ├── jobcontroller.go │ │ ├── namespacecontroller.go # NamespaceControllerOptions │ │ ├── nodeipamcontroller.go │ │ ├── nodelifecyclecontroller.go │ │ ├── options.go # KubeControllerManagerOptions、NewKubeControllerManagerOptions │ │ ├── persistentvolumebindercontroller.go │ │ ├── podgccontroller.go │ │ ├── replicasetcontroller.go # ReplicaSetControllerOptions │ │ ├── replicationcontroller.go │ │ ├── resourcequotacontroller.go │ │ ├── serviceaccountcontroller.go │ │ └── ttlafterfinishedcontroller.go └── controller-manager.go # main入口函数 1. Main函数 kube-controller-manager的入口函数Main函数，仍然是采用统一的代码风格，使用Cobra命令行框架。 func main() { rand.Seed(time.Now().UTC().UnixNano()) command := app.NewControllerManagerCommand() // TODO: once we switch everything over to Cobra commands, we can go back to calling // utilflag.InitFlags() (by removing its pflag.Parse() call). For now, we have to set the // normalize func and add the go flag set by hand. pflag.CommandLine.SetNormalizeFunc(utilflag.WordSepNormalizeFunc) pflag.CommandLine.AddGoFlagSet(goflag.CommandLine) // utilflag.InitFlags() logs.InitLogs() defer logs.FlushLogs() if err := command.Execute(); err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } } 核心代码： // 初始化命令行结构体 command := app.NewControllerManagerCommand() // 执行Execute err := command.Execute() 2. NewControllerManagerCommand 该部分代码位于：kubernetes/cmd/kube-controller-manager/app/controllermanager.go // NewControllerManagerCommand creates a *cobra.Command object with default parameters func NewControllerManagerCommand() *cobra.Command { ... cmd := &cobra.Command{ Use: \"kube-controller-manager\", Long: `The Kubernetes controller manager is a daemon that embeds the core control loops shipped with Kubernetes. In applications of robotics and automation, a control loop is a non-terminating loop that regulates the state of the system. In Kubernetes, a controller is a control loop that watches the shared state of the cluster through the apiserver and makes changes attempting to move the current state towards the desired state. Examples of controllers that ship with Kubernetes today are the replication controller, endpoints controller, namespace controller, and serviceaccounts controller.`, Run: func(cmd *cobra.Command, args []string) { verflag.PrintAndExitIfRequested() utilflag.PrintFlags(cmd.Flags()) c, err := s.Config(KnownControllers(), ControllersDisabledByDefault.List()) if err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } if err := Run(c.Complete(), wait.NeverStop); err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } }, } ... } 构建一个*cobra.Command对象，然后执行Run函数。 2.1. NewKubeControllerManagerOptions s, err := options.NewKubeControllerManagerOptions() if err != nil { glog.Fatalf(\"unable to initialize command options: %v\", err) } 初始化controllerManager的参数，其中主要包括了各种controller的option，例如DeploymentControllerOptions: // DeploymentControllerOptions holds the DeploymentController options. type DeploymentControllerOptions struct { ConcurrentDeploymentSyncs int32 DeploymentControllerSyncPeriod metav1.Duration } 具体代码如下： // NewKubeControllerManagerOptions creates a new KubeControllerManagerOptions with a default config. func NewKubeControllerManagerOptions() (*KubeControllerManagerOptions, error) { componentConfig, err := NewDefaultComponentConfig(ports.InsecureKubeControllerManagerPort) if err != nil { return nil, err } s := KubeControllerManagerOptions{ Generic: cmoptions.NewGenericControllerManagerConfigurationOptions(componentConfig.Generic), KubeCloudShared: cmoptions.NewKubeCloudSharedOptions(componentConfig.KubeCloudShared), AttachDetachController: &AttachDetachControllerOptions{ ReconcilerSyncLoopPeriod: componentConfig.AttachDetachController.ReconcilerSyncLoopPeriod, }, CSRSigningController: &CSRSigningControllerOptions{ ClusterSigningCertFile: componentConfig.CSRSigningController.ClusterSigningCertFile, ClusterSigningKeyFile: componentConfig.CSRSigningController.ClusterSigningKeyFile, ClusterSigningDuration: componentConfig.CSRSigningController.ClusterSigningDuration, }, DaemonSetController: &DaemonSetControllerOptions{ ConcurrentDaemonSetSyncs: componentConfig.DaemonSetController.ConcurrentDaemonSetSyncs, }, DeploymentController: &DeploymentControllerOptions{ ConcurrentDeploymentSyncs: componentConfig.DeploymentController.ConcurrentDeploymentSyncs, DeploymentControllerSyncPeriod: componentConfig.DeploymentController.DeploymentControllerSyncPeriod, }, DeprecatedFlags: &DeprecatedControllerOptions{ RegisterRetryCount: componentConfig.DeprecatedController.RegisterRetryCount, }, EndpointController: &EndpointControllerOptions{ ConcurrentEndpointSyncs: componentConfig.EndpointController.ConcurrentEndpointSyncs, }, GarbageCollectorController: &GarbageCollectorControllerOptions{ ConcurrentGCSyncs: componentConfig.GarbageCollectorController.ConcurrentGCSyncs, EnableGarbageCollector: componentConfig.GarbageCollectorController.EnableGarbageCollector, }, HPAController: &HPAControllerOptions{ HorizontalPodAutoscalerSyncPeriod: componentConfig.HPAController.HorizontalPodAutoscalerSyncPeriod, HorizontalPodAutoscalerUpscaleForbiddenWindow: componentConfig.HPAController.HorizontalPodAutoscalerUpscaleForbiddenWindow, HorizontalPodAutoscalerDownscaleForbiddenWindow: componentConfig.HPAController.HorizontalPodAutoscalerDownscaleForbiddenWindow, HorizontalPodAutoscalerDownscaleStabilizationWindow: componentConfig.HPAController.HorizontalPodAutoscalerDownscaleStabilizationWindow, HorizontalPodAutoscalerCPUInitializationPeriod: componentConfig.HPAController.HorizontalPodAutoscalerCPUInitializationPeriod, HorizontalPodAutoscalerInitialReadinessDelay: componentConfig.HPAController.HorizontalPodAutoscalerInitialReadinessDelay, HorizontalPodAutoscalerTolerance: componentConfig.HPAController.HorizontalPodAutoscalerTolerance, HorizontalPodAutoscalerUseRESTClients: componentConfig.HPAController.HorizontalPodAutoscalerUseRESTClients, }, JobController: &JobControllerOptions{ ConcurrentJobSyncs: componentConfig.JobController.ConcurrentJobSyncs, }, NamespaceController: &NamespaceControllerOptions{ NamespaceSyncPeriod: componentConfig.NamespaceController.NamespaceSyncPeriod, ConcurrentNamespaceSyncs: componentConfig.NamespaceController.ConcurrentNamespaceSyncs, }, NodeIPAMController: &NodeIPAMControllerOptions{ NodeCIDRMaskSize: componentConfig.NodeIPAMController.NodeCIDRMaskSize, }, NodeLifecycleController: &NodeLifecycleControllerOptions{ EnableTaintManager: componentConfig.NodeLifecycleController.EnableTaintManager, NodeMonitorGracePeriod: componentConfig.NodeLifecycleController.NodeMonitorGracePeriod, NodeStartupGracePeriod: componentConfig.NodeLifecycleController.NodeStartupGracePeriod, PodEvictionTimeout: componentConfig.NodeLifecycleController.PodEvictionTimeout, }, PersistentVolumeBinderController: &PersistentVolumeBinderControllerOptions{ PVClaimBinderSyncPeriod: componentConfig.PersistentVolumeBinderController.PVClaimBinderSyncPeriod, VolumeConfiguration: componentConfig.PersistentVolumeBinderController.VolumeConfiguration, }, PodGCController: &PodGCControllerOptions{ TerminatedPodGCThreshold: componentConfig.PodGCController.TerminatedPodGCThreshold, }, ReplicaSetController: &ReplicaSetControllerOptions{ ConcurrentRSSyncs: componentConfig.ReplicaSetController.ConcurrentRSSyncs, }, ReplicationController: &ReplicationControllerOptions{ ConcurrentRCSyncs: componentConfig.ReplicationController.ConcurrentRCSyncs, }, ResourceQuotaController: &ResourceQuotaControllerOptions{ ResourceQuotaSyncPeriod: componentConfig.ResourceQuotaController.ResourceQuotaSyncPeriod, ConcurrentResourceQuotaSyncs: componentConfig.ResourceQuotaController.ConcurrentResourceQuotaSyncs, }, SAController: &SAControllerOptions{ ConcurrentSATokenSyncs: componentConfig.SAController.ConcurrentSATokenSyncs, }, ServiceController: &cmoptions.ServiceControllerOptions{ ConcurrentServiceSyncs: componentConfig.ServiceController.ConcurrentServiceSyncs, }, TTLAfterFinishedController: &TTLAfterFinishedControllerOptions{ ConcurrentTTLSyncs: componentConfig.TTLAfterFinishedController.ConcurrentTTLSyncs, }, SecureServing: apiserveroptions.NewSecureServingOptions().WithLoopback(), InsecureServing: (&apiserveroptions.DeprecatedInsecureServingOptions{ BindAddress: net.ParseIP(componentConfig.Generic.Address), BindPort: int(componentConfig.Generic.Port), BindNetwork: \"tcp\", }).WithLoopback(), Authentication: apiserveroptions.NewDelegatingAuthenticationOptions(), Authorization: apiserveroptions.NewDelegatingAuthorizationOptions(), } s.Authentication.RemoteKubeConfigFileOptional = true s.Authorization.RemoteKubeConfigFileOptional = true s.Authorization.AlwaysAllowPaths = []string{\"/healthz\"} s.SecureServing.ServerCert.CertDirectory = \"/var/run/kubernetes\" s.SecureServing.ServerCert.PairName = \"kube-controller-manager\" s.SecureServing.BindPort = ports.KubeControllerManagerPort gcIgnoredResources := make([]kubectrlmgrconfig.GroupResource, 0, len(garbagecollector.DefaultIgnoredResources())) for r := range garbagecollector.DefaultIgnoredResources() { gcIgnoredResources = append(gcIgnoredResources, kubectrlmgrconfig.GroupResource{Group: r.Group, Resource: r.Resource}) } s.GarbageCollectorController.GCIgnoredResources = gcIgnoredResources return &s, nil } 2.2. AddFlagSet 添加参数及帮助函数。 fs := cmd.Flags() namedFlagSets := s.Flags(KnownControllers(), ControllersDisabledByDefault.List()) for _, f := range namedFlagSets.FlagSets { fs.AddFlagSet(f) } usageFmt := \"Usage:\\n %s\\n\" cols, _, _ := apiserverflag.TerminalSize(cmd.OutOrStdout()) cmd.SetUsageFunc(func(cmd *cobra.Command) error { fmt.Fprintf(cmd.OutOrStderr(), usageFmt, cmd.UseLine()) apiserverflag.PrintSections(cmd.OutOrStderr(), namedFlagSets, cols) return nil }) cmd.SetHelpFunc(func(cmd *cobra.Command, args []string) { fmt.Fprintf(cmd.OutOrStdout(), \"%s\\n\\n\"+usageFmt, cmd.Long, cmd.UseLine()) apiserverflag.PrintSections(cmd.OutOrStdout(), namedFlagSets, cols) }) 3. Run 此部分的代码位于cmd/kube-controller-manager/app/controllermanager.go 基于KubeControllerManagerOptions运行controllerManager，不退出。 // Run runs the KubeControllerManagerOptions. This should never exit. func Run(c *config.CompletedConfig, stopCh Run函数涉及的核心代码如下： // 创建controller的context controllerContext, err := CreateControllerContext(c, rootClientBuilder, clientBuilder, ctx.Done()) // 启动各种controller err := StartControllers(controllerContext, saTokenControllerInitFunc, NewControllerInitializers(controllerContext.LoopMode), unsecuredMux) 其中StartControllers中的入参NewControllerInitializers初始化了各种controller。 3.1. CreateControllerContext CreateControllerContext构建了各种controller所需的资源的上下文，各种controller在启动时，入参为该context，具体参考initFn(ctx)。 // CreateControllerContext creates a context struct containing references to resources needed by the // controllers such as the cloud provider and clientBuilder. rootClientBuilder is only used for // the shared-informers client and token controller. func CreateControllerContext(s *config.CompletedConfig, rootClientBuilder, clientBuilder controller.ControllerClientBuilder, stop 核心代码为NewSharedInformerFactory。 // 创建SharedInformerFactory sharedInformers := informers.NewSharedInformerFactory(versionedClient, ResyncPeriod(s)()) // 赋值给ControllerContext ctx := ControllerContext{ InformerFactory: sharedInformers, } SharedInformerFactory提供了公共的k8s对象的informers。 // SharedInformerFactory provides shared informers for resources in all known // API group versions. type SharedInformerFactory interface { internalinterfaces.SharedInformerFactory ForResource(resource schema.GroupVersionResource) (GenericInformer, error) WaitForCacheSync(stopCh 3.2. NewControllerInitializers NewControllerInitializers定义了各种controller的类型和其对于的启动函数，例如deployment``、statefulset、replicaset、replicationcontroller、namespace等。 // NewControllerInitializers is a public map of named controller groups (you can start more than one in an init func) // paired to their InitFunc. This allows for structured downstream composition and subdivision. func NewControllerInitializers(loopMode ControllerLoopMode) map[string]InitFunc { controllers := map[string]InitFunc{} controllers[\"endpoint\"] = startEndpointController controllers[\"replicationcontroller\"] = startReplicationController controllers[\"podgc\"] = startPodGCController controllers[\"resourcequota\"] = startResourceQuotaController controllers[\"namespace\"] = startNamespaceController controllers[\"serviceaccount\"] = startServiceAccountController controllers[\"garbagecollector\"] = startGarbageCollectorController controllers[\"daemonset\"] = startDaemonSetController controllers[\"job\"] = startJobController controllers[\"deployment\"] = startDeploymentController controllers[\"replicaset\"] = startReplicaSetController controllers[\"horizontalpodautoscaling\"] = startHPAController controllers[\"disruption\"] = startDisruptionController controllers[\"statefulset\"] = startStatefulSetController controllers[\"cronjob\"] = startCronJobController controllers[\"csrsigning\"] = startCSRSigningController controllers[\"csrapproving\"] = startCSRApprovingController controllers[\"csrcleaner\"] = startCSRCleanerController controllers[\"ttl\"] = startTTLController controllers[\"bootstrapsigner\"] = startBootstrapSignerController controllers[\"tokencleaner\"] = startTokenCleanerController controllers[\"nodeipam\"] = startNodeIpamController if loopMode == IncludeCloudLoops { controllers[\"service\"] = startServiceController controllers[\"route\"] = startRouteController // TODO: volume controller into the IncludeCloudLoops only set. // TODO: Separate cluster in cloud check from node lifecycle controller. } controllers[\"nodelifecycle\"] = startNodeLifecycleController controllers[\"persistentvolume-binder\"] = startPersistentVolumeBinderController controllers[\"attachdetach\"] = startAttachDetachController controllers[\"persistentvolume-expander\"] = startVolumeExpandController controllers[\"clusterrole-aggregation\"] = startClusterRoleAggregrationController controllers[\"pvc-protection\"] = startPVCProtectionController controllers[\"pv-protection\"] = startPVProtectionController controllers[\"ttl-after-finished\"] = startTTLAfterFinishedController return controllers } 3.3. StartControllers func StartControllers(ctx ControllerContext, startSATokenController InitFunc, controllers map[string]InitFunc, unsecuredMux *mux.PathRecorderMux) error { ... for controllerName, initFn := range controllers { if !ctx.IsControllerEnabled(controllerName) { glog.Warningf(\"%q is disabled\", controllerName) continue } time.Sleep(wait.Jitter(ctx.ComponentConfig.Generic.ControllerStartInterval.Duration, ControllerStartJitter)) glog.V(1).Infof(\"Starting %q\", controllerName) debugHandler, started, err := initFn(ctx) if err != nil { glog.Errorf(\"Error starting %q\", controllerName) return err } if !started { glog.Warningf(\"Skipping %q\", controllerName) continue } if debugHandler != nil && unsecuredMux != nil { basePath := \"/debug/controllers/\" + controllerName unsecuredMux.UnlistedHandle(basePath, http.StripPrefix(basePath, debugHandler)) unsecuredMux.UnlistedHandlePrefix(basePath+\"/\", http.StripPrefix(basePath, debugHandler)) } glog.Infof(\"Started %q\", controllerName) } return nil } 核心代码： for controllerName, initFn := range controllers { debugHandler, started, err := initFn(ctx) } 启动各种controller，controller的启动函数在NewControllerInitializers中定义了，例如： // deployment controllers[\"deployment\"] = startDeploymentController // statefulset controllers[\"statefulset\"] = startStatefulSetController 3.4. InformerFactory.Start InformerFactory实际上是SharedInformerFactory，具体的实现逻辑在client-go中的informer的实现机制。 controllerContext.InformerFactory.Start(controllerContext.Stop) close(controllerContext.InformersStarted) 3.4.1. SharedInformerFactory SharedInformerFactory是一个informer工厂的接口定义。 // SharedInformerFactory a small interface to allow for adding an informer without an import cycle type SharedInformerFactory interface { Start(stopCh 3.4.2. sharedInformerFactory.Start Start方法初始化各种类型的informer // Start initializes all requested informers. func (f *sharedInformerFactory) Start(stopCh 3.4.3. sharedIndexInformer.Run sharedIndexInformer.Run具体运行了sharedIndexInformer的实现逻辑，该部分待后续对informer机制做专题分析。 func (s *sharedIndexInformer) Run(stopCh 4. initFn(ctx) initFn实际调用的就是各种类型的controller，代码位于kubernetes/cmd/kube-controller-manager/app/apps.go，本文以startStatefulSetController和startDeploymentController为例，controller中实际调用的函数逻辑位于kubernetes/pkg/controller中，待后续分析。 4.1. startStatefulSetController func startStatefulSetController(ctx ControllerContext) (http.Handler, bool, error) { if !ctx.AvailableResources[schema.GroupVersionResource{Group: \"apps\", Version: \"v1\", Resource: \"statefulsets\"}] { return nil, false, nil } go statefulset.NewStatefulSetController( ctx.InformerFactory.Core().V1().Pods(), ctx.InformerFactory.Apps().V1().StatefulSets(), ctx.InformerFactory.Core().V1().PersistentVolumeClaims(), ctx.InformerFactory.Apps().V1().ControllerRevisions(), ctx.ClientBuilder.ClientOrDie(\"statefulset-controller\"), ).Run(1, ctx.Stop) return nil, true, nil } 其中使用到了InformerFactory，包含了Pods、StatefulSets、PersistentVolumeClaims、ControllerRevisions的informer。 startStatefulSetController主要调用的函数为NewStatefulSetController和对应的Run函数。 4.2. startDeploymentController func startDeploymentController(ctx ControllerContext) (http.Handler, bool, error) { if !ctx.AvailableResources[schema.GroupVersionResource{Group: \"apps\", Version: \"v1\", Resource: \"deployments\"}] { return nil, false, nil } dc, err := deployment.NewDeploymentController( ctx.InformerFactory.Apps().V1().Deployments(), ctx.InformerFactory.Apps().V1().ReplicaSets(), ctx.InformerFactory.Core().V1().Pods(), ctx.ClientBuilder.ClientOrDie(\"deployment-controller\"), ) if err != nil { return nil, true, fmt.Errorf(\"error creating Deployment controller: %v\", err) } go dc.Run(int(ctx.ComponentConfig.DeploymentController.ConcurrentDeploymentSyncs), ctx.Stop) return nil, true, nil } startDeploymentController主要调用的函数为NewDeploymentController和对应的Run函数。该部分逻辑在kubernetes/pkg/controller中。 5. 总结 Kube-controller-manager的代码风格仍然是Cobra命令行框架。通过构造ControllerManagerCommand，然后执行command.Execute()函数。基本的流程就是构造option，添加Flags，执行Run函数。 cmd部分的调用流程如下：Main-->NewControllerManagerCommand--> Run(c.Complete(), wait.NeverStop)-->StartControllers-->initFn(ctx)-->startDeploymentController/startStatefulSetController-->sts.NewStatefulSetController.Run/dc.NewDeploymentController.Run-->pkg/controller。 其中CreateControllerContext函数用来创建各类型controller所需要使用的context，NewControllerInitializers初始化了各种类型的controller，其中就包括DeploymentController和StatefulSetController等。 基本流程如下： 构造controller manager option，并转化为Config对象，执行Run函数。 基于Config对象创建ControllerContext，其中包含InformerFactory。 基于ControllerContext运行各种controller，各种controller的定义在NewControllerInitializers中。 执行InformerFactory.Start。 每种controller都会构造自身的结构体并执行对应的Run函数。 参考： https://github.com/kubernetes/kubernetes/tree/v1.12.0/cmd/kube-controller-manager https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-controller-manager/controller-manager.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-controller-manager/app/controllermanager.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-controller-manager/app/apps.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-controller-manager/deployment-controller.html":{"url":"code-analysis/kube-controller-manager/deployment-controller.html","title":"DeploymentController","keywords":"","body":"kube-controller-manager源码分析（二）之 DeploymentController1. startDeploymentController2. NewDeploymentController2.1. eventBroadcaster2.2. rsControl2.3. Informer().AddEventHandler2.4. Informer.Lister()2.5. Informer().HasSynced2.6. syncHandler3. DeploymentController.Run3.1. WaitForCacheSync3.2. dc.worker4. syncDeployment4.1. Get deployment4.2. getReplicaSetsForDeployment4.3. getPodMapForDeployment4.4. checkPausedConditions4.5. isScalingEvent4.6. rolloutRecreate4.7. rolloutRolling5. 总结kube-controller-manager源码分析（二）之 DeploymentController 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要以deployment controller为例，分析该类controller的运行逻辑。此部分代码主要为位于pkg/controller/deployment。pkg/controller部分的代码包括了各种类型的controller的具体实现。 controller manager的pkg部分代码目录结构如下： controller # 主要包含各种controller的具体实现 ├── apis ├── bootstrap ├── certificates ├── client_builder.go ├── cloud ├── clusterroleaggregation ├── controller_ref_manager.go ├── controller_utils.go # WaitForCacheSync ├── cronjob ├── daemon ├── deployment # deployment controller │ ├── deployment_controller.go # NewDeploymentController、Run、syncDeployment │ ├── progress.go # syncRolloutStatus │ ├── recreate.go # rolloutRecreate │ ├── rollback.go # rollback │ ├── rolling.go # rolloutRolling │ ├── sync.go ├── disruption # disruption controller ├── endpoint ├── garbagecollector ├── history ├── job ├── lookup_cache.go ├── namespace # namespace controller ├── nodeipam ├── nodelifecycle ├── podautoscaler ├── podgc ├── replicaset # replicaset controller ├── replication # replication controller ├── resourcequota ├── route ├── service # service controller ├── serviceaccount ├── statefulset # statefulset controller └── volume # PersistentVolumeController、AttachDetachController、PVCProtectionController 1. startDeploymentController func startDeploymentController(ctx ControllerContext) (http.Handler, bool, error) { if !ctx.AvailableResources[schema.GroupVersionResource{Group: \"apps\", Version: \"v1\", Resource: \"deployments\"}] { return nil, false, nil } dc, err := deployment.NewDeploymentController( ctx.InformerFactory.Apps().V1().Deployments(), ctx.InformerFactory.Apps().V1().ReplicaSets(), ctx.InformerFactory.Core().V1().Pods(), ctx.ClientBuilder.ClientOrDie(\"deployment-controller\"), ) if err != nil { return nil, true, fmt.Errorf(\"error creating Deployment controller: %v\", err) } go dc.Run(int(ctx.ComponentConfig.DeploymentController.ConcurrentDeploymentSyncs), ctx.Stop) return nil, true, nil } startDeploymentController主要调用的函数为NewDeploymentController和对应的Run函数。该部分逻辑在kubernetes/pkg/controller中。 2. NewDeploymentController NewDeploymentController主要构建DeploymentController结构体。 该部分主要处理了以下逻辑： 构建并运行事件处理器eventBroadcaster。 初始化赋值rsControl、clientset、workqueue。 添加dInformer、rsInformer、podInformer的ResourceEventHandlerFuncs，其中主要为AddFunc、UpdateFunc、DeleteFunc三类方法。 构造deployment、rs、pod的Informer的Lister函数和HasSynced函数。 调用syncHandler，来实现syncDeployment。 2.1. eventBroadcaster 调用事件处理器来记录deployment相关的事件。 eventBroadcaster := record.NewBroadcaster() eventBroadcaster.StartLogging(glog.Infof) // TODO: remove the wrapper when every clients have moved to use the clientset. eventBroadcaster.StartRecordingToSink(&v1core.EventSinkImpl{Interface: v1core.New(client.CoreV1().RESTClient()).Events(\"\")}) 2.2. rsControl 构造DeploymentController，包括clientset、workqueue和rsControl。其中rsControl是具体实现rs逻辑的controller。 dc := &DeploymentController{ client: client, eventRecorder: eventBroadcaster.NewRecorder(scheme.Scheme, v1.EventSource{Component: \"deployment-controller\"}), queue: workqueue.NewNamedRateLimitingQueue(workqueue.DefaultControllerRateLimiter(), \"deployment\"), } dc.rsControl = controller.RealRSControl{ KubeClient: client, Recorder: dc.eventRecorder, } 2.3. Informer().AddEventHandler 添加dInformer、rsInformer、podInformer的ResourceEventHandlerFuncs，其中主要为AddFunc、UpdateFunc、DeleteFunc三类方法。 dInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ AddFunc: dc.addDeployment, UpdateFunc: dc.updateDeployment, // This will enter the sync loop and no-op, because the deployment has been deleted from the store. DeleteFunc: dc.deleteDeployment, }) rsInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ AddFunc: dc.addReplicaSet, UpdateFunc: dc.updateReplicaSet, DeleteFunc: dc.deleteReplicaSet, }) podInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ DeleteFunc: dc.deletePod, }) 2.4. Informer.Lister() 调用dInformer、rsInformer和podInformer的Lister()方法。 dc.dLister = dInformer.Lister() dc.rsLister = rsInformer.Lister() dc.podLister = podInformer.Lister() 2.5. Informer().HasSynced 调用Informer().HasSynced，判断是否缓存完成； dc.dListerSynced = dInformer.Informer().HasSynced dc.rsListerSynced = rsInformer.Informer().HasSynced dc.podListerSynced = podInformer.Informer().HasSynced 2.6. syncHandler syncHandler具体为syncDeployment，syncHandler负责deployment的同步实现。 dc.syncHandler = dc.syncDeployment dc.enqueueDeployment = dc.enqueue 完整代码如下： // NewDeploymentController creates a new DeploymentController. func NewDeploymentController(dInformer extensionsinformers.DeploymentInformer, rsInformer extensionsinformers.ReplicaSetInformer, podInformer coreinformers.PodInformer, client clientset.Interface) (*DeploymentController, error) { eventBroadcaster := record.NewBroadcaster() eventBroadcaster.StartLogging(glog.Infof) // TODO: remove the wrapper when every clients have moved to use the clientset. eventBroadcaster.StartRecordingToSink(&v1core.EventSinkImpl{Interface: v1core.New(client.CoreV1().RESTClient()).Events(\"\")}) if client != nil && client.CoreV1().RESTClient().GetRateLimiter() != nil { if err := metrics.RegisterMetricAndTrackRateLimiterUsage(\"deployment_controller\", client.CoreV1().RESTClient().GetRateLimiter()); err != nil { return nil, err } } dc := &DeploymentController{ client: client, eventRecorder: eventBroadcaster.NewRecorder(scheme.Scheme, v1.EventSource{Component: \"deployment-controller\"}), queue: workqueue.NewNamedRateLimitingQueue(workqueue.DefaultControllerRateLimiter(), \"deployment\"), } dc.rsControl = controller.RealRSControl{ KubeClient: client, Recorder: dc.eventRecorder, } dInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ AddFunc: dc.addDeployment, UpdateFunc: dc.updateDeployment, // This will enter the sync loop and no-op, because the deployment has been deleted from the store. DeleteFunc: dc.deleteDeployment, }) rsInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ AddFunc: dc.addReplicaSet, UpdateFunc: dc.updateReplicaSet, DeleteFunc: dc.deleteReplicaSet, }) podInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ DeleteFunc: dc.deletePod, }) dc.syncHandler = dc.syncDeployment dc.enqueueDeployment = dc.enqueue dc.dLister = dInformer.Lister() dc.rsLister = rsInformer.Lister() dc.podLister = podInformer.Lister() dc.dListerSynced = dInformer.Informer().HasSynced dc.rsListerSynced = rsInformer.Informer().HasSynced dc.podListerSynced = podInformer.Informer().HasSynced return dc, nil } 3. DeploymentController.Run Run执行watch和sync的操作。 // Run begins watching and syncing. func (dc *DeploymentController) Run(workers int, stopCh 3.1. WaitForCacheSync WaitForCacheSync主要是用来在List-Watch机制中可以保持当前cache的数据与etcd的数据一致。 // WaitForCacheSync is a wrapper around cache.WaitForCacheSync that generates log messages // indicating that the controller identified by controllerName is waiting for syncs, followed by // either a successful or failed sync. func WaitForCacheSync(controllerName string, stopCh 3.2. dc.worker worker调用了processNextWorkItem，processNextWorkItem最终调用了syncHandler，而syncHandler在NewDeploymentController中赋值的具体函数为syncDeployment。 // worker runs a worker thread that just dequeues items, processes them, and marks them done. // It enforces that the syncHandler is never invoked concurrently with the same key. func (dc *DeploymentController) worker() { for dc.processNextWorkItem() { } } func (dc *DeploymentController) processNextWorkItem() bool { key, quit := dc.queue.Get() if quit { return false } defer dc.queue.Done(key) err := dc.syncHandler(key.(string)) dc.handleErr(err, key) return true } NewDeploymentController中的syncHandler赋值： func NewDeploymentController(dInformer appsinformers.DeploymentInformer, rsInformer appsinformers.ReplicaSetInformer, podInformer coreinformers.PodInformer, client clientset.Interface) (*DeploymentController, error) { ... dc.syncHandler = dc.syncDeployment ... } 4. syncDeployment syncDeployment基于给定的key执行sync deployment的操作。 主要流程如下： 通过SplitMetaNamespaceKey获取namespace和deployment对象的name。 调用Lister的接口获取的deployment的对象。 getReplicaSetsForDeployment获取deployment管理的ReplicaSet对象。 getPodMapForDeployment获取deployment管理的pod，基于ReplicaSet来分组。 checkPausedConditions检查deployment是否是pause状态并添加合适的condition。 isScalingEvent检查deployment的更新是否来自于一个scale的事件，如果是则执行scale的操作。 根据DeploymentStrategyType类型执行rolloutRecreate或rolloutRolling。 完整代码如下： // syncDeployment will sync the deployment with the given key. // This function is not meant to be invoked concurrently with the same key. func (dc *DeploymentController) syncDeployment(key string) error { startTime := time.Now() glog.V(4).Infof(\"Started syncing deployment %q (%v)\", key, startTime) defer func() { glog.V(4).Infof(\"Finished syncing deployment %q (%v)\", key, time.Since(startTime)) }() namespace, name, err := cache.SplitMetaNamespaceKey(key) if err != nil { return err } deployment, err := dc.dLister.Deployments(namespace).Get(name) if errors.IsNotFound(err) { glog.V(2).Infof(\"Deployment %v has been deleted\", key) return nil } if err != nil { return err } // Deep-copy otherwise we are mutating our cache. // TODO: Deep-copy only when needed. d := deployment.DeepCopy() everything := metav1.LabelSelector{} if reflect.DeepEqual(d.Spec.Selector, &everything) { dc.eventRecorder.Eventf(d, v1.EventTypeWarning, \"SelectingAll\", \"This deployment is selecting all pods. A non-empty selector is required.\") if d.Status.ObservedGeneration 4.1. Get deployment // get namespace and deployment name namespace, name, err := cache.SplitMetaNamespaceKey(key) // get deployment by name deployment, err := dc.dLister.Deployments(namespace).Get(name) 4.2. getReplicaSetsForDeployment // List ReplicaSets owned by this Deployment, while reconciling ControllerRef // through adoption/orphaning. rsList, err := dc.getReplicaSetsForDeployment(d) getReplicaSetsForDeployment具体代码: // getReplicaSetsForDeployment uses ControllerRefManager to reconcile // ControllerRef by adopting and orphaning. // It returns the list of ReplicaSets that this Deployment should manage. func (dc *DeploymentController) getReplicaSetsForDeployment(d *apps.Deployment) ([]*apps.ReplicaSet, error) { // List all ReplicaSets to find those we own but that no longer match our // selector. They will be orphaned by ClaimReplicaSets(). rsList, err := dc.rsLister.ReplicaSets(d.Namespace).List(labels.Everything()) if err != nil { return nil, err } deploymentSelector, err := metav1.LabelSelectorAsSelector(d.Spec.Selector) if err != nil { return nil, fmt.Errorf(\"deployment %s/%s has invalid label selector: %v\", d.Namespace, d.Name, err) } // If any adoptions are attempted, we should first recheck for deletion with // an uncached quorum read sometime after listing ReplicaSets (see #42639). canAdoptFunc := controller.RecheckDeletionTimestamp(func() (metav1.Object, error) { fresh, err := dc.client.AppsV1().Deployments(d.Namespace).Get(d.Name, metav1.GetOptions{}) if err != nil { return nil, err } if fresh.UID != d.UID { return nil, fmt.Errorf(\"original Deployment %v/%v is gone: got uid %v, wanted %v\", d.Namespace, d.Name, fresh.UID, d.UID) } return fresh, nil }) cm := controller.NewReplicaSetControllerRefManager(dc.rsControl, d, deploymentSelector, controllerKind, canAdoptFunc) return cm.ClaimReplicaSets(rsList) } 4.3. getPodMapForDeployment // List all Pods owned by this Deployment, grouped by their ReplicaSet. // Current uses of the podMap are: // // * check if a Pod is labeled correctly with the pod-template-hash label. // * check that no old Pods are running in the middle of Recreate Deployments. podMap, err := dc.getPodMapForDeployment(d, rsList) getPodMapForDeployment具体代码： // getPodMapForDeployment returns the Pods managed by a Deployment. // // It returns a map from ReplicaSet UID to a list of Pods controlled by that RS, // according to the Pod's ControllerRef. func (dc *DeploymentController) getPodMapForDeployment(d *apps.Deployment, rsList []*apps.ReplicaSet) (map[types.UID]*v1.PodList, error) { // Get all Pods that potentially belong to this Deployment. selector, err := metav1.LabelSelectorAsSelector(d.Spec.Selector) if err != nil { return nil, err } pods, err := dc.podLister.Pods(d.Namespace).List(selector) if err != nil { return nil, err } // Group Pods by their controller (if it's in rsList). podMap := make(map[types.UID]*v1.PodList, len(rsList)) for _, rs := range rsList { podMap[rs.UID] = &v1.PodList{} } for _, pod := range pods { // Do not ignore inactive Pods because Recreate Deployments need to verify that no // Pods from older versions are running before spinning up new Pods. controllerRef := metav1.GetControllerOf(pod) if controllerRef == nil { continue } // Only append if we care about this UID. if podList, ok := podMap[controllerRef.UID]; ok { podList.Items = append(podList.Items, *pod) } } return podMap, nil } 4.4. checkPausedConditions // Update deployment conditions with an Unknown condition when pausing/resuming // a deployment. In this way, we can be sure that we won't timeout when a user // resumes a Deployment with a set progressDeadlineSeconds. if err = dc.checkPausedConditions(d); err != nil { return err } if d.Spec.Paused { return dc.sync(d, rsList) } checkPausedConditions具体代码: // checkPausedConditions checks if the given deployment is paused or not and adds an appropriate condition. // These conditions are needed so that we won't accidentally report lack of progress for resumed deployments // that were paused for longer than progressDeadlineSeconds. func (dc *DeploymentController) checkPausedConditions(d *apps.Deployment) error { if !deploymentutil.HasProgressDeadline(d) { return nil } cond := deploymentutil.GetDeploymentCondition(d.Status, apps.DeploymentProgressing) if cond != nil && cond.Reason == deploymentutil.TimedOutReason { // If we have reported lack of progress, do not overwrite it with a paused condition. return nil } pausedCondExists := cond != nil && cond.Reason == deploymentutil.PausedDeployReason needsUpdate := false if d.Spec.Paused && !pausedCondExists { condition := deploymentutil.NewDeploymentCondition(apps.DeploymentProgressing, v1.ConditionUnknown, deploymentutil.PausedDeployReason, \"Deployment is paused\") deploymentutil.SetDeploymentCondition(&d.Status, *condition) needsUpdate = true } else if !d.Spec.Paused && pausedCondExists { condition := deploymentutil.NewDeploymentCondition(apps.DeploymentProgressing, v1.ConditionUnknown, deploymentutil.ResumedDeployReason, \"Deployment is resumed\") deploymentutil.SetDeploymentCondition(&d.Status, *condition) needsUpdate = true } if !needsUpdate { return nil } var err error d, err = dc.client.AppsV1().Deployments(d.Namespace).UpdateStatus(d) return err } 4.5. isScalingEvent scalingEvent, err := dc.isScalingEvent(d, rsList) if err != nil { return err } if scalingEvent { return dc.sync(d, rsList) } isScalingEvent具体代码: // isScalingEvent checks whether the provided deployment has been updated with a scaling event // by looking at the desired-replicas annotation in the active replica sets of the deployment. // // rsList should come from getReplicaSetsForDeployment(d). // podMap should come from getPodMapForDeployment(d, rsList). func (dc *DeploymentController) isScalingEvent(d *apps.Deployment, rsList []*apps.ReplicaSet) (bool, error) { newRS, oldRSs, err := dc.getAllReplicaSetsAndSyncRevision(d, rsList, false) if err != nil { return false, err } allRSs := append(oldRSs, newRS) for _, rs := range controller.FilterActiveReplicaSets(allRSs) { desired, ok := deploymentutil.GetDesiredReplicasAnnotation(rs) if !ok { continue } if desired != *(d.Spec.Replicas) { return true, nil } } return false, nil } 4.6. rolloutRecreate switch d.Spec.Strategy.Type { case apps.RecreateDeploymentStrategyType: return dc.rolloutRecreate(d, rsList, podMap) rolloutRecreate具体代码: // rolloutRecreate implements the logic for recreating a replica set. func (dc *DeploymentController) rolloutRecreate(d *apps.Deployment, rsList []*apps.ReplicaSet, podMap map[types.UID]*v1.PodList) error { // Don't create a new RS if not already existed, so that we avoid scaling up before scaling down. newRS, oldRSs, err := dc.getAllReplicaSetsAndSyncRevision(d, rsList, false) if err != nil { return err } allRSs := append(oldRSs, newRS) activeOldRSs := controller.FilterActiveReplicaSets(oldRSs) // scale down old replica sets. scaledDown, err := dc.scaleDownOldReplicaSetsForRecreate(activeOldRSs, d) if err != nil { return err } if scaledDown { // Update DeploymentStatus. return dc.syncRolloutStatus(allRSs, newRS, d) } // Do not process a deployment when it has old pods running. if oldPodsRunning(newRS, oldRSs, podMap) { return dc.syncRolloutStatus(allRSs, newRS, d) } // If we need to create a new RS, create it now. if newRS == nil { newRS, oldRSs, err = dc.getAllReplicaSetsAndSyncRevision(d, rsList, true) if err != nil { return err } allRSs = append(oldRSs, newRS) } // scale up new replica set. if _, err := dc.scaleUpNewReplicaSetForRecreate(newRS, d); err != nil { return err } if util.DeploymentComplete(d, &d.Status) { if err := dc.cleanupDeployment(oldRSs, d); err != nil { return err } } // Sync deployment status. return dc.syncRolloutStatus(allRSs, newRS, d) } 4.7. rolloutRolling switch d.Spec.Strategy.Type { case apps.RecreateDeploymentStrategyType: return dc.rolloutRecreate(d, rsList, podMap) case apps.RollingUpdateDeploymentStrategyType: return dc.rolloutRolling(d, rsList) } rolloutRolling具体代码: // rolloutRolling implements the logic for rolling a new replica set. func (dc *DeploymentController) rolloutRolling(d *apps.Deployment, rsList []*apps.ReplicaSet) error { newRS, oldRSs, err := dc.getAllReplicaSetsAndSyncRevision(d, rsList, true) if err != nil { return err } allRSs := append(oldRSs, newRS) // Scale up, if we can. scaledUp, err := dc.reconcileNewReplicaSet(allRSs, newRS, d) if err != nil { return err } if scaledUp { // Update DeploymentStatus return dc.syncRolloutStatus(allRSs, newRS, d) } // Scale down, if we can. scaledDown, err := dc.reconcileOldReplicaSets(allRSs, controller.FilterActiveReplicaSets(oldRSs), newRS, d) if err != nil { return err } if scaledDown { // Update DeploymentStatus return dc.syncRolloutStatus(allRSs, newRS, d) } if deploymentutil.DeploymentComplete(d, &d.Status) { if err := dc.cleanupDeployment(oldRSs, d); err != nil { return err } } // Sync deployment status return dc.syncRolloutStatus(allRSs, newRS, d) } 5. 总结 startDeploymentController主要包括NewDeploymentController和DeploymentController.Run两部分。 NewDeploymentController主要构建DeploymentController结构体。 该部分主要处理了以下逻辑： 构建并运行事件处理器eventBroadcaster。 初始化赋值rsControl、clientset、workqueue。 添加dInformer、rsInformer、podInformer的ResourceEventHandlerFuncs，其中主要为AddFunc、UpdateFunc、DeleteFunc三类方法。 构造deployment、rs、pod的Informer的Lister函数和HasSynced函数。 赋值syncHandler，来实现syncDeployment。 DeploymentController.Run主要包含WaitForCacheSync和syncDeployment两部分。 syncDeployment基于给定的key执行sync deployment的操作。 主要流程如下： 通过SplitMetaNamespaceKey获取namespace和deployment对象的name。 调用Lister的接口获取的deployment的对象。 getReplicaSetsForDeployment获取deployment管理的ReplicaSet对象。 getPodMapForDeployment获取deployment管理的pod，基于ReplicaSet来分组。 checkPausedConditions检查deployment是否是pause状态并添加合适的condition。 isScalingEvent检查deployment的更新是否来自于一个scale的事件，如果是则执行scale的操作。 根据DeploymentStrategyType类型执行rolloutRecreate或rolloutRolling。 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/controller/deployment/deployment_controller.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/controller/deployment/rolling.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-controller-manager/app/apps.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-controller-manager/sharedIndexInformer.html":{"url":"code-analysis/kube-controller-manager/sharedIndexInformer.html","title":"Informer机制","keywords":"","body":"kube-controller-manager源码分析（三）之 Informer机制0. 原理示意图0.1. client-go组件0.2. 自定义controller组件1. sharedInformerFactory.Start2. sharedIndexInformer.Run2.1. NewDeltaFIFO2.2. Config2.3. controller2.4. cacheMutationDetector.Run2.5. processor.run2.6. controller.Run3. Reflector3.1. Reflector3.2. NewReflector3.3. Reflector.Run3.4. ListAndWatch3.4.1. List3.4.2. store.Resync3.4.3. Watch3.4.4. watchHandler4. DeltaFIFO4.1. NewDeltaFIFO4.2. DeltaFIFO4.3. Queue & Store5. store6.1. cache6.2. ThreadSafeStore6. processLoop5.1. DeltaFIFO.Pop5.2. HandleDeltas5.3. sharedProcessor.distribute7. processor7.1. sharedProcessor.Run7.1.1. listener.pop7.1.2. listener.run7.2. ResourceEventHandler7.2.1. addDeployment8. 总结8.1. Reflector8.2. ListAndWatch8.3. DeltaFIFO8.4. store8.5. processor8.6. 主要步骤kube-controller-manager源码分析（三）之 Informer机制 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析k8s中各个核心组件经常使用到的Informer机制(即List-Watch)。该部分的代码主要位于client-go这个第三方包中。 此部分的逻辑主要位于/vendor/k8s.io/client-go/tools/cache包中，代码目录结构如下： cache ├── controller.go # 包含：Config、Run、processLoop、NewInformer、NewIndexerInformer ├── delta_fifo.go # 包含：NewDeltaFIFO、DeltaFIFO、AddIfNotPresent ├── expiration_cache.go ├── expiration_cache_fakes.go ├── fake_custom_store.go ├── fifo.go # 包含：Queue、FIFO、NewFIFO ├── heap.go ├── index.go # 包含：Indexer、MetaNamespaceIndexFunc ├── listers.go ├── listwatch.go # 包含：ListerWatcher、ListWatch、List、Watch ├── mutation_cache.go ├── mutation_detector.go ├── reflector.go # 包含：Reflector、NewReflector、Run、ListAndWatch ├── reflector_metrics.go ├── shared_informer.go # 包含：NewSharedInformer、WaitForCacheSync、Run、HasSynced ├── store.go # 包含：Store、MetaNamespaceKeyFunc、SplitMetaNamespaceKey ├── testing │ ├── fake_controller_source.go ├── thread_safe_store.go # 包含：ThreadSafeStore、threadSafeMap ├── undelta_store.go 0. 原理示意图 示意图1： 示意图2： 0.1. client-go组件 Reflector：reflector用来watch特定的k8s API资源。具体的实现是通过ListAndWatch的方法，watch可以是k8s内建的资源或者是自定义的资源。当reflector通过watch API接收到有关新资源实例存在的通知时，它使用相应的列表API获取新创建的对象，并将其放入watchHandler函数内的Delta Fifo队列中。 Informer：informer从Delta Fifo队列中弹出对象。执行此操作的功能是processLoop。base controller的作用是保存对象以供以后检索，并调用我们的控制器将对象传递给它。 Indexer：索引器提供对象的索引功能。典型的索引用例是基于对象标签创建索引。 Indexer可以根据多个索引函数维护索引。Indexer使用线程安全的数据存储来存储对象及其键。 在Store中定义了一个名为MetaNamespaceKeyFunc的默认函数，该函数生成对象的键作为该对象的 / 组合。 0.2. 自定义controller组件 Informer reference：指的是Informer实例的引用，定义如何使用自定义资源对象。 自定义控制器代码需要创建对应的Informer。 Indexer reference: 自定义控制器对Indexer实例的引用。自定义控制器需要创建对应的Indexser。 client-go中提供NewIndexerInformer函数可以创建Informer 和 Indexer。 Resource Event Handlers：资源事件回调函数，当它想要将对象传递给控制器时，它将被调用。 编写这些函数的典型模式是获取调度对象的key，并将该key排入工作队列以进行进一步处理。 Work queue：任务队列。 编写资源事件处理程序函数以提取传递的对象的key并将其添加到任务队列。 Process Item：处理任务队列中对象的函数， 这些函数通常使用Indexer引用或Listing包装器来重试与该key对应的对象。 1. sharedInformerFactory.Start 在controller-manager的Run函数部分调用了InformerFactory.Start的方法。 此部分代码位于/cmd/kube-controller-manager/app/controllermanager.go // Run runs the KubeControllerManagerOptions. This should never exit. func Run(c *config.CompletedConfig, stopCh InformerFactory是一个SharedInformerFactory的接口，接口定义如下： 此部分代码位于vendor/k8s.io/client-go/informers/internalinterfaces/factory_interfaces.go // SharedInformerFactory a small interface to allow for adding an informer without an import cycle type SharedInformerFactory interface { Start(stopCh Start方法初始化各种类型的informer，并且每个类型起了个informer.Run的goroutine。 此部分代码位于vendor/k8s.io/client-go/informers/factory.go // Start initializes all requested informers. func (f *sharedInformerFactory) Start(stopCh 2. sharedIndexInformer.Run 此部分的代码位于/vendor/k8s.io/client-go/tools/cache/shared_informer.go func (s *sharedIndexInformer) Run(stopCh 2.1. NewDeltaFIFO DeltaFIFO是一个对象变化的存储队列，依据先进先出的原则，process的函数接收该队列的Pop方法的输出对象来处理相关功能。 fifo := NewDeltaFIFO(MetaNamespaceKeyFunc, nil, s.indexer) 2.2. Config 构造controller的配置文件，构造process，即HandleDeltas，该函数为后面使用到的process函数。 cfg := &Config{ Queue: fifo, ListerWatcher: s.listerWatcher, ObjectType: s.objectType, FullResyncPeriod: s.resyncCheckPeriod, RetryOnError: false, ShouldResync: s.processor.shouldResync, Process: s.HandleDeltas, } 2.3. controller 调用New(cfg)，构建sharedIndexInformer的controller。 func() { s.startedLock.Lock() defer s.startedLock.Unlock() s.controller = New(cfg) s.controller.(*controller).clock = s.clock s.started = true }() 2.4. cacheMutationDetector.Run 调用s.cacheMutationDetector.Run，检查缓存对象是否变化。 wg.StartWithChannel(processorStopCh, s.cacheMutationDetector.Run) defaultCacheMutationDetector.Run func (d *defaultCacheMutationDetector) Run(stopCh CompareObjects func (d *defaultCacheMutationDetector) CompareObjects() { d.lock.Lock() defer d.lock.Unlock() altered := false for i, obj := range d.cachedObjs { if !reflect.DeepEqual(obj.cached, obj.copied) { fmt.Printf(\"CACHE %s[%d] ALTERED!\\n%v\\n\", d.name, i, diff.ObjectDiff(obj.cached, obj.copied)) altered = true } } if altered { msg := fmt.Sprintf(\"cache %s modified\", d.name) if d.failureFunc != nil { d.failureFunc(msg) return } panic(msg) } } 2.5. processor.run 调用s.processor.run，将调用sharedProcessor.run，会调用Listener.run和Listener.pop,执行处理queue的函数。 wg.StartWithChannel(processorStopCh, s.processor.run) sharedProcessor.Run func (p *sharedProcessor) run(stopCh 该部分逻辑待后面分析。 2.6. controller.Run 调用s.controller.Run，构建Reflector，进行对etcd的缓存 defer func() { s.startedLock.Lock() defer s.startedLock.Unlock() s.stopped = true // Don't want any new listeners }() s.controller.Run(stopCh) controller.Run 此部分代码位于/vendor/k8s.io/client-go/tools/cache/controller.go // Run begins processing items, and will continue until a value is sent down stopCh. // It's an error to call Run more than once. // Run blocks; call via go. func (c *controller) Run(stopCh 核心代码： // 构建Reflector r := NewReflector( c.config.ListerWatcher, c.config.ObjectType, c.config.Queue, c.config.FullResyncPeriod, ) // 运行Reflector wg.StartWithChannel(stopCh, r.Run) // 执行processLoop wait.Until(c.processLoop, time.Second, stopCh) 3. Reflector 3.1. Reflector Reflector的主要作用是watch指定的k8s资源，并将变化同步到本地是store中。Reflector只会放置指定的expectedType类型的资源到store中，除非expectedType为nil。如果resyncPeriod不为零，那么Reflector为以resyncPeriod为周期定期执行list的操作，这样就可以使用Reflector来定期处理所有的对象，也可以逐步处理变化的对象。 常用属性说明： expectedType：期望放入缓存store的资源类型。 store：watch的资源对应的本地缓存。 listerWatcher：list和watch的接口。 period：watch的周期，默认为1秒。 resyncPeriod：resync的周期，当非零的时候，会按该周期执行list。 lastSyncResourceVersion：最新一次看到的资源的版本号，主要在watch时候使用。 // Reflector watches a specified resource and causes all changes to be reflected in the given store. type Reflector struct { // name identifies this reflector. By default it will be a file:line if possible. name string // metrics tracks basic metric information about the reflector metrics *reflectorMetrics // The type of object we expect to place in the store. expectedType reflect.Type // The destination to sync up with the watch source store Store // listerWatcher is used to perform lists and watches. listerWatcher ListerWatcher // period controls timing between one watch ending and // the beginning of the next one. period time.Duration resyncPeriod time.Duration ShouldResync func() bool // clock allows tests to manipulate time clock clock.Clock // lastSyncResourceVersion is the resource version token last // observed when doing a sync with the underlying store // it is thread safe, but not synchronized with the underlying store lastSyncResourceVersion string // lastSyncResourceVersionMutex guards read/write access to lastSyncResourceVersion lastSyncResourceVersionMutex sync.RWMutex } 3.2. NewReflector NewReflector主要用来构建Reflector的结构体。 此部分的代码位于/vendor/k8s.io/client-go/tools/cache/reflector.go // NewReflector creates a new Reflector object which will keep the given store up to // date with the server's contents for the given resource. Reflector promises to // only put things in the store that have the type of expectedType, unless expectedType // is nil. If resyncPeriod is non-zero, then lists will be executed after every // resyncPeriod, so that you can use reflectors to periodically process everything as // well as incrementally processing the things that change. func NewReflector(lw ListerWatcher, expectedType interface{}, store Store, resyncPeriod time.Duration) *Reflector { return NewNamedReflector(getDefaultReflectorName(internalPackages...), lw, expectedType, store, resyncPeriod) } // reflectorDisambiguator is used to disambiguate started reflectors. // initialized to an unstable value to ensure meaning isn't attributed to the suffix. var reflectorDisambiguator = int64(time.Now().UnixNano() % 12345) // NewNamedReflector same as NewReflector, but with a specified name for logging func NewNamedReflector(name string, lw ListerWatcher, expectedType interface{}, store Store, resyncPeriod time.Duration) *Reflector { reflectorSuffix := atomic.AddInt64(&reflectorDisambiguator, 1) r := &Reflector{ name: name, // we need this to be unique per process (some names are still the same)but obvious who it belongs to metrics: newReflectorMetrics(makeValidPromethusMetricLabel(fmt.Sprintf(\"reflector_\"+name+\"_%d\", reflectorSuffix))), listerWatcher: lw, store: store, expectedType: reflect.TypeOf(expectedType), period: time.Second, resyncPeriod: resyncPeriod, clock: &clock.RealClock{}, } return r } 3.3. Reflector.Run Reflector.Run主要执行了ListAndWatch的方法。 // Run starts a watch and handles watch events. Will restart the watch if it is closed. // Run will exit when stopCh is closed. func (r *Reflector) Run(stopCh 3.4. ListAndWatch ListAndWatch第一次会列出所有的对象，并获取资源对象的版本号，然后watch资源对象的版本号来查看是否有被变更。首先会将资源版本号设置为0，list()可能会导致本地的缓存相对于etcd里面的内容存在延迟，Reflector会通过watch的方法将延迟的部分补充上，使得本地的缓存数据与etcd的数据保持一致。 3.4.1. List // ListAndWatch first lists all items and get the resource version at the moment of call, // and then use the resource version to watch. // It returns error if ListAndWatch didn't even try to initialize watch. func (r *Reflector) ListAndWatch(stopCh 首先将资源的版本号设置为0，然后调用listerWatcher.List(options)，列出所有list的内容。 // 版本号设置为0 options := metav1.ListOptions{ResourceVersion: \"0\"} // list接口 list, err := r.listerWatcher.List(options) 获取资源版本号，并将list的内容提取成对象列表。 // 获取版本号 resourceVersion = listMetaInterface.GetResourceVersion() // 将list的内容提取成对象列表 items, err := meta.ExtractList(list) 将list中对象列表的内容和版本号存储到本地的缓存store中，并全量替换已有的store的内容。 err := r.syncWith(items, resourceVersion) syncWith调用了store的Replace的方法来替换原来store中的数据。 // syncWith replaces the store's items with the given list. func (r *Reflector) syncWith(items []runtime.Object, resourceVersion string) error { found := make([]interface{}, 0, len(items)) for _, item := range items { found = append(found, item) } return r.store.Replace(found, resourceVersion) } Store.Replace方法定义如下： type Store interface { ... // Replace will delete the contents of the store, using instead the // given list. Store takes ownership of the list, you should not reference // it after calling this function. Replace([]interface{}, string) error ... } 最后设置最新的资源版本号。 r.setLastSyncResourceVersion(resourceVersion) setLastSyncResourceVersion: func (r *Reflector) setLastSyncResourceVersion(v string) { r.lastSyncResourceVersionMutex.Lock() defer r.lastSyncResourceVersionMutex.Unlock() r.lastSyncResourceVersion = v rv, err := strconv.Atoi(v) if err == nil { r.metrics.lastResourceVersion.Set(float64(rv)) } } 3.4.2. store.Resync resyncerrc := make(chan error, 1) cancelCh := make(chan struct{}) defer close(cancelCh) go func() { resyncCh, cleanup := r.resyncChan() defer func() { cleanup() // Call the last one written into cleanup }() for { select { case 核心代码： err := r.store.Resync() store的具体对象为DeltaFIFO，即调用DeltaFIFO.Resync // Resync will send a sync event for each item func (f *DeltaFIFO) Resync() error { f.lock.Lock() defer f.lock.Unlock() if f.knownObjects == nil { return nil } keys := f.knownObjects.ListKeys() for _, k := range keys { if err := f.syncKeyLocked(k); err != nil { return err } } return nil } 3.4.3. Watch for { // give the stopCh a chance to stop the loop, even in case of continue statements further down on errors select { case 设置watch的超时时间，默认为5分钟。 timemoutseconds := int64(minWatchTimeout.Seconds() * (rand.Float64() + 1.0)) options = metav1.ListOptions{ ResourceVersion: resourceVersion, // We want to avoid situations of hanging watchers. Stop any wachers that do not // receive any events within the timeout window. TimeoutSeconds: &timemoutseconds, } 执行listerWatcher.Watch(options)。 w, err := r.listerWatcher.Watch(options) 执行watchHandler。 err := r.watchHandler(w, &resourceVersion, resyncerrc, stopCh) 3.4.4. watchHandler watchHandler主要是通过watch的方式保证当前的资源版本是最新的。 // watchHandler watches w and keeps *resourceVersion up to date. func (r *Reflector) watchHandler(w watch.Interface, resourceVersion *string, errc chan error, stopCh 获取watch接口中的事件的channel，来获取事件的内容。 for { select { ... case event, ok := 当获得添加、更新、删除的事件时，将对应的对象更新到本地缓存store中。 switch event.Type { case watch.Added: err := r.store.Add(event.Object) if err != nil { utilruntime.HandleError(fmt.Errorf(\"%s: unable to add watch event object (%#v) to store: %v\", r.name, event.Object, err)) } case watch.Modified: err := r.store.Update(event.Object) if err != nil { utilruntime.HandleError(fmt.Errorf(\"%s: unable to update watch event object (%#v) to store: %v\", r.name, event.Object, err)) } case watch.Deleted: // TODO: Will any consumers need access to the \"last known // state\", which is passed in event.Object? If so, may need // to change this. err := r.store.Delete(event.Object) if err != nil { utilruntime.HandleError(fmt.Errorf(\"%s: unable to delete watch event object (%#v) from store: %v\", r.name, event.Object, err)) } default: utilruntime.HandleError(fmt.Errorf(\"%s: unable to understand watch event %#v\", r.name, event)) } 更新当前的最新版本号。 newResourceVersion := meta.GetResourceVersion() *resourceVersion = newResourceVersion r.setLastSyncResourceVersion(newResourceVersion) 通过对Reflector模块的分析，可以看到多次使用到本地缓存store模块，而store的数据由DeltaFIFO赋值而来，以下针对DeltaFIFO和store做分析。 4. DeltaFIFO DeltaFIFO由NewDeltaFIFO初始化，并赋值给config.Queue。 func (s *sharedIndexInformer) Run(stopCh 4.1. NewDeltaFIFO // NewDeltaFIFO returns a Store which can be used process changes to items. // // keyFunc is used to figure out what key an object should have. (It's // exposed in the returned DeltaFIFO's KeyOf() method, with bonus features.) // // 'compressor' may compress as many or as few items as it wants // (including returning an empty slice), but it should do what it // does quickly since it is called while the queue is locked. // 'compressor' may be nil if you don't want any delta compression. // // 'keyLister' is expected to return a list of keys that the consumer of // this queue \"knows about\". It is used to decide which items are missing // when Replace() is called; 'Deleted' deltas are produced for these items. // It may be nil if you don't need to detect all deletions. // TODO: consider merging keyLister with this object, tracking a list of // \"known\" keys when Pop() is called. Have to think about how that // affects error retrying. // TODO(lavalamp): I believe there is a possible race only when using an // external known object source that the above TODO would // fix. // // Also see the comment on DeltaFIFO. func NewDeltaFIFO(keyFunc KeyFunc, compressor DeltaCompressor, knownObjects KeyListerGetter) *DeltaFIFO { f := &DeltaFIFO{ items: map[string]Deltas{}, queue: []string{}, keyFunc: keyFunc, deltaCompressor: compressor, knownObjects: knownObjects, } f.cond.L = &f.lock return f } controller.Run的部分调用了NewReflector。 func (c *controller) Run(stopCh NewReflector构造函数，将c.config.Queue赋值给Reflector.store的属性。 func NewReflector(lw ListerWatcher, expectedType interface{}, store Store, resyncPeriod time.Duration) *Reflector { return NewNamedReflector(getDefaultReflectorName(internalPackages...), lw, expectedType, store, resyncPeriod) } // NewNamedReflector same as NewReflector, but with a specified name for logging func NewNamedReflector(name string, lw ListerWatcher, expectedType interface{}, store Store, resyncPeriod time.Duration) *Reflector { reflectorSuffix := atomic.AddInt64(&reflectorDisambiguator, 1) r := &Reflector{ name: name, // we need this to be unique per process (some names are still the same)but obvious who it belongs to metrics: newReflectorMetrics(makeValidPromethusMetricLabel(fmt.Sprintf(\"reflector_\"+name+\"_%d\", reflectorSuffix))), listerWatcher: lw, store: store, expectedType: reflect.TypeOf(expectedType), period: time.Second, resyncPeriod: resyncPeriod, clock: &clock.RealClock{}, } return r } 4.2. DeltaFIFO DeltaFIFO是一个生产者与消费者的队列，其中Reflector是生产者，消费者调用Pop()的方法。 DeltaFIFO主要用在以下场景： 希望对象变更最多处理一次 处理对象时，希望查看自上次处理对象以来发生的所有事情 要处理对象的删除 希望定期重新处理对象 // DeltaFIFO is like FIFO, but allows you to process deletes. // // DeltaFIFO is a producer-consumer queue, where a Reflector is // intended to be the producer, and the consumer is whatever calls // the Pop() method. // // DeltaFIFO solves this use case: // * You want to process every object change (delta) at most once. // * When you process an object, you want to see everything // that's happened to it since you last processed it. // * You want to process the deletion of objects. // * You might want to periodically reprocess objects. // // DeltaFIFO's Pop(), Get(), and GetByKey() methods return // interface{} to satisfy the Store/Queue interfaces, but it // will always return an object of type Deltas. // // A note on threading: If you call Pop() in parallel from multiple // threads, you could end up with multiple threads processing slightly // different versions of the same object. // // A note on the KeyLister used by the DeltaFIFO: It's main purpose is // to list keys that are \"known\", for the purpose of figuring out which // items have been deleted when Replace() or Delete() are called. The deleted // object will be included in the DeleteFinalStateUnknown markers. These objects // could be stale. // // You may provide a function to compress deltas (e.g., represent a // series of Updates as a single Update). type DeltaFIFO struct { // lock/cond protects access to 'items' and 'queue'. lock sync.RWMutex cond sync.Cond // We depend on the property that items in the set are in // the queue and vice versa, and that all Deltas in this // map have at least one Delta. items map[string]Deltas queue []string // populated is true if the first batch of items inserted by Replace() has been populated // or Delete/Add/Update was called first. populated bool // initialPopulationCount is the number of items inserted by the first call of Replace() initialPopulationCount int // keyFunc is used to make the key used for queued item // insertion and retrieval, and should be deterministic. keyFunc KeyFunc // deltaCompressor tells us how to combine two or more // deltas. It may be nil. deltaCompressor DeltaCompressor // knownObjects list keys that are \"known\", for the // purpose of figuring out which items have been deleted // when Replace() or Delete() is called. knownObjects KeyListerGetter // Indication the queue is closed. // Used to indicate a queue is closed so a control loop can exit when a queue is empty. // Currently, not used to gate any of CRED operations. closed bool closedLock sync.Mutex } 4.3. Queue & Store DeltaFIFO的类型是Queue接口，Reflector.store是Store接口，Queue接口是一个存储队列，Process的方法执行Queue.Pop出来的数据对象， // Queue is exactly like a Store, but has a Pop() method too. type Queue interface { Store // Pop blocks until it has something to process. // It returns the object that was process and the result of processing. // The PopProcessFunc may return an ErrRequeue{...} to indicate the item // should be requeued before releasing the lock on the queue. Pop(PopProcessFunc) (interface{}, error) // AddIfNotPresent adds a value previously // returned by Pop back into the queue as long // as nothing else (presumably more recent) // has since been added. AddIfNotPresent(interface{}) error // Return true if the first batch of items has been popped HasSynced() bool // Close queue Close() } 5. store Store是一个通用的存储接口，Reflector通过watch server的方式更新数据到store中，store给Reflector提供本地的缓存，让Reflector可以像消息队列一样的工作。 Store实现的是一种可以准确的写入对象和获取对象的机制。 // Store is a generic object storage interface. Reflector knows how to watch a server // and update a store. A generic store is provided, which allows Reflector to be used // as a local caching system, and an LRU store, which allows Reflector to work like a // queue of items yet to be processed. // // Store makes no assumptions about stored object identity; it is the responsibility // of a Store implementation to provide a mechanism to correctly key objects and to // define the contract for obtaining objects by some arbitrary key type. type Store interface { Add(obj interface{}) error Update(obj interface{}) error Delete(obj interface{}) error List() []interface{} ListKeys() []string Get(obj interface{}) (item interface{}, exists bool, err error) GetByKey(key string) (item interface{}, exists bool, err error) // Replace will delete the contents of the store, using instead the // given list. Store takes ownership of the list, you should not reference // it after calling this function. Replace([]interface{}, string) error Resync() error } 其中Replace方法会删除原来store中的内容，并将新增的list的内容存入store中，即完全替换数据。 6.1. cache cache实现了store的接口，而cache的具体实现又是调用ThreadSafeStore接口来实现功能的。 cache的功能主要有以下两点： 通过keyFunc计算对象的key 调用ThreadSafeStorage接口的方法 // cache responsibilities are limited to: // 1. Computing keys for objects via keyFunc // 2. Invoking methods of a ThreadSafeStorage interface type cache struct { // cacheStorage bears the burden of thread safety for the cache cacheStorage ThreadSafeStore // keyFunc is used to make the key for objects stored in and retrieved from items, and // should be deterministic. keyFunc KeyFunc } 其中ListAndWatch主要用到以下的方法： cache.Replace // Replace will delete the contents of 'c', using instead the given list. // 'c' takes ownership of the list, you should not reference the list again // after calling this function. func (c *cache) Replace(list []interface{}, resourceVersion string) error { items := map[string]interface{}{} for _, item := range list { key, err := c.keyFunc(item) if err != nil { return KeyError{item, err} } items[key] = item } c.cacheStorage.Replace(items, resourceVersion) return nil } cache.Add // Add inserts an item into the cache. func (c *cache) Add(obj interface{}) error { key, err := c.keyFunc(obj) if err != nil { return KeyError{obj, err} } c.cacheStorage.Add(key, obj) return nil } cache.Update // Update sets an item in the cache to its updated state. func (c *cache) Update(obj interface{}) error { key, err := c.keyFunc(obj) if err != nil { return KeyError{obj, err} } c.cacheStorage.Update(key, obj) return nil } cache.Delete // Delete removes an item from the cache. func (c *cache) Delete(obj interface{}) error { key, err := c.keyFunc(obj) if err != nil { return KeyError{obj, err} } c.cacheStorage.Delete(key) return nil } 6.2. ThreadSafeStore cache的具体是调用ThreadSafeStore来实现的。 // ThreadSafeStore is an interface that allows concurrent access to a storage backend. // TL;DR caveats: you must not modify anything returned by Get or List as it will break // the indexing feature in addition to not being thread safe. // // The guarantees of thread safety provided by List/Get are only valid if the caller // treats returned items as read-only. For example, a pointer inserted in the store // through `Add` will be returned as is by `Get`. Multiple clients might invoke `Get` // on the same key and modify the pointer in a non-thread-safe way. Also note that // modifying objects stored by the indexers (if any) will *not* automatically lead // to a re-index. So it's not a good idea to directly modify the objects returned by // Get/List, in general. type ThreadSafeStore interface { Add(key string, obj interface{}) Update(key string, obj interface{}) Delete(key string) Get(key string) (item interface{}, exists bool) List() []interface{} ListKeys() []string Replace(map[string]interface{}, string) Index(indexName string, obj interface{}) ([]interface{}, error) IndexKeys(indexName, indexKey string) ([]string, error) ListIndexFuncValues(name string) []string ByIndex(indexName, indexKey string) ([]interface{}, error) GetIndexers() Indexers // AddIndexers adds more indexers to this store. If you call this after you already have data // in the store, the results are undefined. AddIndexers(newIndexers Indexers) error Resync() error } threadSafeMap // threadSafeMap implements ThreadSafeStore type threadSafeMap struct { lock sync.RWMutex items map[string]interface{} // indexers maps a name to an IndexFunc indexers Indexers // indices maps a name to an Index indices Indices } 6. processLoop func (c *controller) Run(stopCh 在controller.Run方法中会调用processLoop，以下分析processLoop的处理逻辑。 // processLoop drains the work queue. // TODO: Consider doing the processing in parallel. This will require a little thought // to make sure that we don't end up processing the same object multiple times // concurrently. // // TODO: Plumb through the stopCh here (and down to the queue) so that this can // actually exit when the controller is stopped. Or just give up on this stuff // ever being stoppable. Converting this whole package to use Context would // also be helpful. func (c *controller) processLoop() { for { obj, err := c.config.Queue.Pop(PopProcessFunc(c.config.Process)) if err != nil { if err == FIFOClosedError { return } if c.config.RetryOnError { // This is the safe way to re-enqueue. c.config.Queue.AddIfNotPresent(obj) } } } } processLoop主要处理任务队列中的任务，其中处理逻辑是调用具体的ProcessFunc函数来实现，核心代码为： obj, err := c.config.Queue.Pop(PopProcessFunc(c.config.Process)) 5.1. DeltaFIFO.Pop Pop会阻塞住直到队列里面添加了新的对象，如果有多个对象，按照先进先出的原则处理，如果某个对象没有处理成功会重新被加入该队列中。 Pop中会调用具体的process函数来处理对象。 // Pop blocks until an item is added to the queue, and then returns it. If // multiple items are ready, they are returned in the order in which they were // added/updated. The item is removed from the queue (and the store) before it // is returned, so if you don't successfully process it, you need to add it back // with AddIfNotPresent(). // process function is called under lock, so it is safe update data structures // in it that need to be in sync with the queue (e.g. knownKeys). The PopProcessFunc // may return an instance of ErrRequeue with a nested error to indicate the current // item should be requeued (equivalent to calling AddIfNotPresent under the lock). // // Pop returns a 'Deltas', which has a complete list of all the things // that happened to the object (deltas) while it was sitting in the queue. func (f *DeltaFIFO) Pop(process PopProcessFunc) (interface{}, error) { f.lock.Lock() defer f.lock.Unlock() for { for len(f.queue) == 0 { // When the queue is empty, invocation of Pop() is blocked until new item is enqueued. // When Close() is called, the f.closed is set and the condition is broadcasted. // Which causes this loop to continue and return from the Pop(). if f.IsClosed() { return nil, FIFOClosedError } f.cond.Wait() } id := f.queue[0] f.queue = f.queue[1:] item, ok := f.items[id] if f.initialPopulationCount > 0 { f.initialPopulationCount-- } if !ok { // Item may have been deleted subsequently. continue } delete(f.items, id) err := process(item) if e, ok := err.(ErrRequeue); ok { f.addIfNotPresent(id, item) err = e.Err } // Don't need to copyDeltas here, because we're transferring // ownership to the caller. return item, err } } 核心代码： for { ... item, ok := f.items[id] ... err := process(item) if e, ok := err.(ErrRequeue); ok { f.addIfNotPresent(id, item) err = e.Err } // Don't need to copyDeltas here, because we're transferring // ownership to the caller. return item, err } 5.2. HandleDeltas cfg := &Config{ Queue: fifo, ListerWatcher: s.listerWatcher, ObjectType: s.objectType, FullResyncPeriod: s.resyncCheckPeriod, RetryOnError: false, ShouldResync: s.processor.shouldResync, Process: s.HandleDeltas, } 其中process函数就是在sharedIndexInformer.Run方法中，给config.Process赋值的HandleDeltas函数。 func (s *sharedIndexInformer) HandleDeltas(obj interface{}) error { s.blockDeltas.Lock() defer s.blockDeltas.Unlock() // from oldest to newest for _, d := range obj.(Deltas) { switch d.Type { case Sync, Added, Updated: isSync := d.Type == Sync s.cacheMutationDetector.AddObject(d.Object) if old, exists, err := s.indexer.Get(d.Object); err == nil && exists { if err := s.indexer.Update(d.Object); err != nil { return err } s.processor.distribute(updateNotification{oldObj: old, newObj: d.Object}, isSync) } else { if err := s.indexer.Add(d.Object); err != nil { return err } s.processor.distribute(addNotification{newObj: d.Object}, isSync) } case Deleted: if err := s.indexer.Delete(d.Object); err != nil { return err } s.processor.distribute(deleteNotification{oldObj: d.Object}, false) } } return nil } 核心代码： switch d.Type { case Sync, Added, Updated: ... if old, exists, err := s.indexer.Get(d.Object); err == nil && exists { ... s.processor.distribute(updateNotification{oldObj: old, newObj: d.Object}, isSync) } else { ... s.processor.distribute(addNotification{newObj: d.Object}, isSync) } case Deleted: ... s.processor.distribute(deleteNotification{oldObj: d.Object}, false) } 根据不同的类型，调用processor.distribute方法，该方法将对象加入processorListener的channel中。 5.3. sharedProcessor.distribute func (p *sharedProcessor) distribute(obj interface{}, sync bool) { p.listenersLock.RLock() defer p.listenersLock.RUnlock() if sync { for _, listener := range p.syncingListeners { listener.add(obj) } } else { for _, listener := range p.listeners { listener.add(obj) } } } processorListener.add: func (p *processorListener) add(notification interface{}) { p.addCh 综合以上的分析，可以看出processLoop通过调用HandleDeltas，再调用distribute，processorListener.add最终将不同更新类型的对象加入processorListener的channel中，供processorListener.Run使用。以下分析processorListener.Run的部分。 7. processor processor的主要功能就是记录了所有的回调函数实例(即 ResourceEventHandler 实例)，并负责触发这些函数。在sharedIndexInformer.Run部分会调用processor.run。 流程： listenser的add函数负责将notify装进pendingNotifications。 pop函数取出pendingNotifications的第一个nofify,输出到nextCh channel。 run函数则负责取出notify，然后根据notify的类型(增加、删除、更新)触发相应的处理函数，这些函数是在不同的NewXxxcontroller实现中注册的。 func (s *sharedIndexInformer) Run(stopCh 7.1. sharedProcessor.Run func (p *sharedProcessor) run(stopCh 7.1.1. listener.pop pop函数取出pendingNotifications的第一个nofify,输出到nextCh channel。 func (p *processorListener) pop() { defer utilruntime.HandleCrash() defer close(p.nextCh) // Tell .run() to stop var nextCh chan 7.1.2. listener.run listener.run部分根据不同的更新类型调用不同的处理函数。 func (p *processorListener) run() { defer utilruntime.HandleCrash() for next := range p.nextCh { switch notification := next.(type) { case updateNotification: p.handler.OnUpdate(notification.oldObj, notification.newObj) case addNotification: p.handler.OnAdd(notification.newObj) case deleteNotification: p.handler.OnDelete(notification.oldObj) default: utilruntime.HandleError(fmt.Errorf(\"unrecognized notification: %#v\", next)) } } } 其中具体的实现函数handler是在NewDeploymentController（其他不同类型的controller类似）中赋值的，而该handler是一个接口，具体如下： // ResourceEventHandler can handle notifications for events that happen to a // resource. The events are informational only, so you can't return an // error. // * OnAdd is called when an object is added. // * OnUpdate is called when an object is modified. Note that oldObj is the // last known state of the object-- it is possible that several changes // were combined together, so you can't use this to see every single // change. OnUpdate is also called when a re-list happens, and it will // get called even if nothing changed. This is useful for periodically // evaluating or syncing something. // * OnDelete will get the final state of the item if it is known, otherwise // it will get an object of type DeletedFinalStateUnknown. This can // happen if the watch is closed and misses the delete event and we don't // notice the deletion until the subsequent re-list. type ResourceEventHandler interface { OnAdd(obj interface{}) OnUpdate(oldObj, newObj interface{}) OnDelete(obj interface{}) } 7.2. ResourceEventHandler 以下以DeploymentController的处理逻辑为例。 在NewDeploymentController部分会注册deployment的事件函数，以下注册了三种类型的事件函数，其中包括：dInformer、rsInformer和podInformer。 // NewDeploymentController creates a new DeploymentController. func NewDeploymentController(dInformer extensionsinformers.DeploymentInformer, rsInformer extensionsinformers.ReplicaSetInformer, podInformer coreinformers.PodInformer, client clientset.Interface) (*DeploymentController, error) { ... dInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ AddFunc: dc.addDeployment, UpdateFunc: dc.updateDeployment, // This will enter the sync loop and no-op, because the deployment has been deleted from the store. DeleteFunc: dc.deleteDeployment, }) rsInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ AddFunc: dc.addReplicaSet, UpdateFunc: dc.updateReplicaSet, DeleteFunc: dc.deleteReplicaSet, }) podInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{ DeleteFunc: dc.deletePod, }) ... } 7.2.1. addDeployment 以下以addDeployment为例，addDeployment主要是将对象加入到enqueueDeployment的队列中。 func (dc *DeploymentController) addDeployment(obj interface{}) { d := obj.(*extensions.Deployment) glog.V(4).Infof(\"Adding deployment %s\", d.Name) dc.enqueueDeployment(d) } enqueueDeployment的定义 type DeploymentController struct { ... enqueueDeployment func(deployment *extensions.Deployment) ... } 将dc.enqueue赋值给dc.enqueueDeployment dc.enqueueDeployment = dc.enqueue dc.enqueue调用了dc.queue.Add(key) func (dc *DeploymentController) enqueue(deployment *extensions.Deployment) { key, err := controller.KeyFunc(deployment) if err != nil { utilruntime.HandleError(fmt.Errorf(\"Couldn't get key for object %#v: %v\", deployment, err)) return } dc.queue.Add(key) } dc.queue主要记录了需要被同步的deployment的对象，供syncDeployment使用。 dc := &DeploymentController{ ... queue: workqueue.NewNamedRateLimitingQueue(workqueue.DefaultControllerRateLimiter(), \"deployment\"), } NewNamedRateLimitingQueue func NewNamedRateLimitingQueue(rateLimiter RateLimiter, name string) RateLimitingInterface { return &rateLimitingType{ DelayingInterface: NewNamedDelayingQueue(name), rateLimiter: rateLimiter, } } 通过以上分析，可以看出processor记录了不同类似的事件函数，其中事件函数在NewXxxController构造函数部分注册，具体事件函数的处理，一般是将需要处理的对象加入对应的controller的任务队列中，然后由类似syncDeployment的同步函数来维持期望状态的同步逻辑。 8. 总结 本文分析的部分主要是k8s的informer机制，即List-Watch机制。 8.1. Reflector Reflector的主要作用是watch指定的k8s资源，并将变化同步到本地是store中。Reflector只会放置指定的expectedType类型的资源到store中，除非expectedType为nil。如果resyncPeriod不为零，那么Reflector为以resyncPeriod为周期定期执行list的操作，这样就可以使用Reflector来定期处理所有的对象，也可以逐步处理变化的对象。 8.2. ListAndWatch ListAndWatch第一次会列出所有的对象，并获取资源对象的版本号，然后watch资源对象的版本号来查看是否有被变更。首先会将资源版本号设置为0，list()可能会导致本地的缓存相对于etcd里面的内容存在延迟，Reflector会通过watch的方法将延迟的部分补充上，使得本地的缓存数据与etcd的数据保持一致。 8.3. DeltaFIFO DeltaFIFO是一个生产者与消费者的队列，其中Reflector是生产者，消费者调用Pop()的方法。 DeltaFIFO主要用在以下场景： 希望对象变更最多处理一次 处理对象时，希望查看自上次处理对象以来发生的所有事情 要处理对象的删除 希望定期重新处理对象 8.4. store Store是一个通用的存储接口，Reflector通过watch server的方式更新数据到store中，store给Reflector提供本地的缓存，让Reflector可以像消息队列一样的工作。 Store实现的是一种可以准确的写入对象和获取对象的机制。 8.5. processor processor的主要功能就是记录了所有的回调函数实例(即 ResourceEventHandler 实例)，并负责触发这些函数。在sharedIndexInformer.Run部分会调用processor.run。 流程： listenser的add函数负责将notify装进pendingNotifications。 pop函数取出pendingNotifications的第一个nofify,输出到nextCh channel。 run函数则负责取出notify，然后根据notify的类型(增加、删除、更新)触发相应的处理函数，这些函数是在不同的NewXxxcontroller实现中注册的。 processor记录了不同类似的事件函数，其中事件函数在NewXxxController构造函数部分注册，具体事件函数的处理，一般是将需要处理的对象加入对应的controller的任务队列中，然后由类似syncDeployment的同步函数来维持期望状态的同步逻辑。 8.6. 主要步骤 在controller-manager的Run函数部分调用了InformerFactory.Start的方法，Start方法初始化各种类型的informer，并且每个类型起了个informer.Run的goroutine。 informer.Run的部分先生成一个DeltaFIFO的队列来存储对象变化的数据。然后调用processor.Run和controller.Run函数。 controller.Run函数会生成一个Reflector，Reflector的主要作用是watch指定的k8s资源，并将变化同步到本地是store中。Reflector以resyncPeriod为周期定期执行list的操作，这样就可以使用Reflector来定期处理所有的对象，也可以逐步处理变化的对象。 Reflector接着执行ListAndWatch函数，ListAndWatch第一次会列出所有的对象，并获取资源对象的版本号，然后watch资源对象的版本号来查看是否有被变更。首先会将资源版本号设置为0，list()可能会导致本地的缓存相对于etcd里面的内容存在延迟，Reflector会通过watch的方法将延迟的部分补充上，使得本地的缓存数据与etcd的数据保持一致。 controller.Run函数还会调用processLoop函数，processLoop通过调用HandleDeltas，再调用distribute，processorListener.add最终将不同更新类型的对象加入processorListener的channel中，供processorListener.Run使用。 processor的主要功能就是记录了所有的回调函数实例(即 ResourceEventHandler 实例)，并负责触发这些函数。processor记录了不同类型的事件函数，其中事件函数在NewXxxController构造函数部分注册，具体事件函数的处理，一般是将需要处理的对象加入对应的controller的任务队列中，然后由类似syncDeployment的同步函数来维持期望状态的同步逻辑。 参考文章： https://github.com/kubernetes/client-go/tree/master/tools/cache https://github.com/kubernetes/sample-controller/blob/master/docs/controller-client-go.md https://github.com/kubernetes/client-go/blob/master/examples/workqueue/main.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-scheduler/_index.html":{"url":"code-analysis/kube-scheduler/_index.html","title":"kube-scheduler","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-scheduler/NewSchedulerCommand.html":{"url":"code-analysis/kube-scheduler/NewSchedulerCommand.html","title":"NewSchedulerCommand","keywords":"","body":"kube-scheduler源码分析（一）之 NewSchedulerCommand1. Main函数2. NewSchedulerCommand2.1. NewOptions2.2. Options.Config2.3. AddFlags3. Run3.1. NewSchedulerConfig3.2. InformerFactory.Start3.3. WaitForCacheSync3.3.1. InformerFactory.WaitForCacheSync3.3.2. controller.WaitForCacheSync3.4. LeaderElection3.5. Scheduler.Runkube-scheduler源码分析（一）之 NewSchedulerCommand 以下代码分析基于 kubernetes v1.12.0 版本。 scheduler的cmd代码目录结构如下： kube-scheduler ├── BUILD ├── OWNERS ├── app # app的目录下主要为运行scheduler相关的对象 │ ├── BUILD │ ├── config │ │ ├── BUILD │ │ └── config.go # Scheduler的配置对象config │ ├── options # options主要记录 Scheduler 使用到的参数 │ │ ├── BUILD │ │ ├── configfile.go │ │ ├── deprecated.go │ │ ├── deprecated_test.go │ │ ├── insecure_serving.go │ │ ├── insecure_serving_test.go │ │ ├── options.go # 主要包括Options、NewOptions、AddFlags、Config等函数 │ │ └── options_test.go │ └── server.go # 主要包括 NewSchedulerCommand、NewSchedulerConfig、Run等函数 └── scheduler.go # main入口函数 1. Main函数 此部分的代码为/cmd/kube-scheduler/scheduler.go kube-scheduler的入口函数Main函数，仍然是采用统一的代码风格，使用Cobra命令行框架。 func main() { rand.Seed(time.Now().UTC().UnixNano()) command := app.NewSchedulerCommand() // TODO: once we switch everything over to Cobra commands, we can go back to calling // utilflag.InitFlags() (by removing its pflag.Parse() call). For now, we have to set the // normalize func and add the go flag set by hand. pflag.CommandLine.SetNormalizeFunc(utilflag.WordSepNormalizeFunc) pflag.CommandLine.AddGoFlagSet(goflag.CommandLine) // utilflag.InitFlags() logs.InitLogs() defer logs.FlushLogs() if err := command.Execute(); err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } } 核心代码： // 初始化scheduler命令结构体 command := app.NewSchedulerCommand() // 执行Execute err := command.Execute() 2. NewSchedulerCommand 此部分的代码为/cmd/kube-scheduler/app/server.go NewSchedulerCommand主要用来构造和初始化SchedulerCommand结构体， // NewSchedulerCommand creates a *cobra.Command object with default parameters func NewSchedulerCommand() *cobra.Command { opts, err := options.NewOptions() if err != nil { glog.Fatalf(\"unable to initialize command options: %v\", err) } cmd := &cobra.Command{ Use: \"kube-scheduler\", Long: `The Kubernetes scheduler is a policy-rich, topology-aware, workload-specific function that significantly impacts availability, performance, and capacity. The scheduler needs to take into account individual and collective resource requirements, quality of service requirements, hardware/software/policy constraints, affinity and anti-affinity specifications, data locality, inter-workload interference, deadlines, and so on. Workload-specific requirements will be exposed through the API as necessary.`, Run: func(cmd *cobra.Command, args []string) { verflag.PrintAndExitIfRequested() utilflag.PrintFlags(cmd.Flags()) if len(args) != 0 { fmt.Fprint(os.Stderr, \"arguments are not supported\\n\") } if errs := opts.Validate(); len(errs) > 0 { fmt.Fprintf(os.Stderr, \"%v\\n\", utilerrors.NewAggregate(errs)) os.Exit(1) } if len(opts.WriteConfigTo) > 0 { if err := options.WriteConfigFile(opts.WriteConfigTo, &opts.ComponentConfig); err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } glog.Infof(\"Wrote configuration to: %s\\n\", opts.WriteConfigTo) return } c, err := opts.Config() if err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } stopCh := make(chan struct{}) if err := Run(c.Complete(), stopCh); err != nil { fmt.Fprintf(os.Stderr, \"%v\\n\", err) os.Exit(1) } }, } opts.AddFlags(cmd.Flags()) cmd.MarkFlagFilename(\"config\", \"yaml\", \"yml\", \"json\") return cmd } 核心代码： // 构造option opts, err := options.NewOptions() // 初始化config对象 c, err := opts.Config() // 执行run函数 err := Run(c.Complete(), stopCh) // 添加参数 opts.AddFlags(cmd.Flags()) 2.1. NewOptions NewOptions主要用来构造SchedulerServer使用的参数和上下文，其中核心参数是KubeSchedulerConfiguration。 opts, err := options.NewOptions() NewOptions: // NewOptions returns default scheduler app options. func NewOptions() (*Options, error) { cfg, err := newDefaultComponentConfig() if err != nil { return nil, err } hhost, hport, err := splitHostIntPort(cfg.HealthzBindAddress) if err != nil { return nil, err } o := &Options{ ComponentConfig: *cfg, SecureServing: nil, // TODO: enable with apiserveroptions.NewSecureServingOptions() CombinedInsecureServing: &CombinedInsecureServingOptions{ Healthz: &apiserveroptions.DeprecatedInsecureServingOptions{ BindNetwork: \"tcp\", }, Metrics: &apiserveroptions.DeprecatedInsecureServingOptions{ BindNetwork: \"tcp\", }, BindPort: hport, BindAddress: hhost, }, Authentication: nil, // TODO: enable with apiserveroptions.NewDelegatingAuthenticationOptions() Authorization: nil, // TODO: enable with apiserveroptions.NewDelegatingAuthorizationOptions() Deprecated: &DeprecatedOptions{ UseLegacyPolicyConfig: false, PolicyConfigMapNamespace: metav1.NamespaceSystem, }, } return o, nil } 2.2. Options.Config Config初始化调度器的配置对象。 c, err := opts.Config() Config函数主要执行以下操作： 构建scheduler client、leaderElectionClient、eventClient。 创建event recorder 设置leader选举 创建informer对象，主要函数有NewSharedInformerFactory和NewPodInformer。 Config具体代码如下： // Config return a scheduler config object func (o *Options) Config() (*schedulerappconfig.Config, error) { c := &schedulerappconfig.Config{} if err := o.ApplyTo(c); err != nil { return nil, err } // prepare kube clients. client, leaderElectionClient, eventClient, err := createClients(c.ComponentConfig.ClientConnection, o.Master, c.ComponentConfig.LeaderElection.RenewDeadline.Duration) if err != nil { return nil, err } // Prepare event clients. eventBroadcaster := record.NewBroadcaster() recorder := eventBroadcaster.NewRecorder(legacyscheme.Scheme, corev1.EventSource{Component: c.ComponentConfig.SchedulerName}) // Set up leader election if enabled. var leaderElectionConfig *leaderelection.LeaderElectionConfig if c.ComponentConfig.LeaderElection.LeaderElect { leaderElectionConfig, err = makeLeaderElectionConfig(c.ComponentConfig.LeaderElection, leaderElectionClient, recorder) if err != nil { return nil, err } } c.Client = client c.InformerFactory = informers.NewSharedInformerFactory(client, 0) c.PodInformer = factory.NewPodInformer(client, 0) c.EventClient = eventClient c.Recorder = recorder c.Broadcaster = eventBroadcaster c.LeaderElection = leaderElectionConfig return c, nil } 2.3. AddFlags AddFlags为SchedulerServer添加指定的参数。 opts.AddFlags(cmd.Flags()) AddFlags函数的具体代码如下： // AddFlags adds flags for the scheduler options. func (o *Options) AddFlags(fs *pflag.FlagSet) { fs.StringVar(&o.ConfigFile, \"config\", o.ConfigFile, \"The path to the configuration file. Flags override values in this file.\") fs.StringVar(&o.WriteConfigTo, \"write-config-to\", o.WriteConfigTo, \"If set, write the configuration values to this file and exit.\") fs.StringVar(&o.Master, \"master\", o.Master, \"The address of the Kubernetes API server (overrides any value in kubeconfig)\") o.SecureServing.AddFlags(fs) o.CombinedInsecureServing.AddFlags(fs) o.Authentication.AddFlags(fs) o.Authorization.AddFlags(fs) o.Deprecated.AddFlags(fs, &o.ComponentConfig) leaderelectionconfig.BindFlags(&o.ComponentConfig.LeaderElection.LeaderElectionConfiguration, fs) utilfeature.DefaultFeatureGate.AddFlag(fs) } 3. Run 此部分的代码为/cmd/kube-scheduler/app/server.go err := Run(c.Complete(), stopCh) Run运行一个不退出的常驻进程，来执行scheduler的相关操作。 Run函数的主要内容如下： 通过scheduler config来创建scheduler的结构体。 运行event broadcaster、healthz server、metrics server。 运行所有的informer并在调度前等待cache的同步（重点）。 执行sched.Run()来运行scheduler的调度逻辑。 如果多个scheduler并开启了LeaderElect，则执行leader选举。 以下对重点代码分开分析： 3.1. NewSchedulerConfig NewSchedulerConfig初始化SchedulerConfig（此部分具体逻辑待后续专门分析），最后初始化生成scheduler结构体。 // Build a scheduler config from the provided algorithm source. schedulerConfig, err := NewSchedulerConfig(c) if err != nil { return err } // Create the scheduler. sched := scheduler.NewFromConfig(schedulerConfig) 3.2. InformerFactory.Start 运行PodInformer，并运行InformerFactory。此部分的逻辑为client-go的informer机制，在Informer机制中有详细分析。 // Start all informers. go c.PodInformer.Informer().Run(stopCh) c.InformerFactory.Start(stopCh) 3.3. WaitForCacheSync 在调度前等待cache同步。 // Wait for all caches to sync before scheduling. c.InformerFactory.WaitForCacheSync(stopCh) controller.WaitForCacheSync(\"scheduler\", stopCh, c.PodInformer.Informer().HasSynced) 3.3.1. InformerFactory.WaitForCacheSync InformerFactory.WaitForCacheSync等待所有启动的informer的cache进行同步，保持本地的store信息与etcd的信息是最新一致的。 // WaitForCacheSync waits for all started informers' cache were synced. func (f *sharedInformerFactory) WaitForCacheSync(stopCh 接着调用cache.WaitForCacheSync。 // WaitForCacheSync waits for caches to populate. It returns true if it was successful, false // if the controller should shutdown func WaitForCacheSync(stopCh 3.3.2. controller.WaitForCacheSync controller.WaitForCacheSync是对cache.WaitForCacheSync的一层封装，通过不同的controller的名字来记录不同controller等待cache同步。 controller.WaitForCacheSync(\"scheduler\", stop, s.PodInformer.Informer().HasSynced) controller.WaitForCacheSync具体代码如下： // WaitForCacheSync is a wrapper around cache.WaitForCacheSync that generates log messages // indicating that the controller identified by controllerName is waiting for syncs, followed by // either a successful or failed sync. func WaitForCacheSync(controllerName string, stopCh 3.4. LeaderElection 如果有多个scheduler，并开启leader选举，则运行LeaderElector直到选举结束或退出。 // If leader election is enabled, run via LeaderElector until done and exit. if c.LeaderElection != nil { c.LeaderElection.Callbacks = leaderelection.LeaderCallbacks{ OnStartedLeading: run, OnStoppedLeading: func() { utilruntime.HandleError(fmt.Errorf(\"lost master\")) }, } leaderElector, err := leaderelection.NewLeaderElector(*c.LeaderElection) if err != nil { return fmt.Errorf(\"couldn't create leader elector: %v\", err) } leaderElector.Run(ctx) return fmt.Errorf(\"lost lease\") } 3.5. Scheduler.Run // Prepare a reusable run function. run := func(ctx context.Context) { sched.Run() Scheduler.Run先等待cache同步，然后开启调度逻辑的goroutine。 Scheduler.Run的具体代码如下： // Run begins watching and scheduling. It waits for cache to be synced, then starts a goroutine and returns immediately. func (sched *Scheduler) Run() { if !sched.config.WaitForCacheSync() { return } go wait.Until(sched.scheduleOne, 0, sched.config.StopEverything) } 以上是对/cmd/kube-scheduler/scheduler.go部分代码的分析，Scheduler.Run后续的具体代码位于pkg/scheduler/scheduler.go待后续文章分析。 参考： https://github.com/kubernetes/kubernetes/tree/v1.12.0/cmd/kube-scheduler https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-scheduler/scheduler.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-scheduler/app/server.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-scheduler/registerAlgorithmProvider.html":{"url":"code-analysis/kube-scheduler/registerAlgorithmProvider.html","title":"registerAlgorithmProvider","keywords":"","body":"kube-scheduler源码分析（二）之 registerAlgorithmProvider1. ApplyFeatureGates2. init2.1. registerAlgorithmProvider2.2. RegisterFitPredicate2.3. RegisterPriorityFunction23. defaultPredicates4. defaultPrioritieskube-scheduler源码分析（二）之 registerAlgorithmProvider 以下代码分析基于 kubernetes v1.12.0 版本。 此部分主要介绍调度中使用的各种调度算法，包括调度算法的注册部分。注册部分的代码主要在/pkg/scheduler/algorithmprovider中，具体的预选策略和优选策略的算法实现在/pkg/scheduler/algorithm中。 1. ApplyFeatureGates 注册调度算法的调用入口在SchedulerCommand的Run函数中。 此部分代码位于/cmd/kube-scheduler/app/server.go // Run runs the Scheduler. func Run(c schedulerserverconfig.CompletedConfig, stopCh ApplyFeatureGates的具体实现在pkg/scheduler/algorithmprovider的包中。 此部分代码位于/pkg/scheduler/algorithmprovider/plugins.go // ApplyFeatureGates applies algorithm by feature gates. func ApplyFeatureGates() { defaults.ApplyFeatureGates() } ApplyFeatureGates具体实现如下： 此部分代码位于/pkg/scheduler/algorithmprovider/defaults/defaults.go 根据feature移除部分调度策略。 // ApplyFeatureGates applies algorithm by feature gates. func ApplyFeatureGates() { if utilfeature.DefaultFeatureGate.Enabled(features.TaintNodesByCondition) { // Remove \"CheckNodeCondition\", \"CheckNodeMemoryPressure\", \"CheckNodePIDPressurePred\" // and \"CheckNodeDiskPressure\" predicates factory.RemoveFitPredicate(predicates.CheckNodeConditionPred) factory.RemoveFitPredicate(predicates.CheckNodeMemoryPressurePred) factory.RemoveFitPredicate(predicates.CheckNodeDiskPressurePred) factory.RemoveFitPredicate(predicates.CheckNodePIDPressurePred) // Remove key \"CheckNodeCondition\", \"CheckNodeMemoryPressure\" and \"CheckNodeDiskPressure\" // from ALL algorithm provider // The key will be removed from all providers which in algorithmProviderMap[] // if you just want remove specific provider, call func RemovePredicateKeyFromAlgoProvider() factory.RemovePredicateKeyFromAlgorithmProviderMap(predicates.CheckNodeConditionPred) factory.RemovePredicateKeyFromAlgorithmProviderMap(predicates.CheckNodeMemoryPressurePred) factory.RemovePredicateKeyFromAlgorithmProviderMap(predicates.CheckNodeDiskPressurePred) factory.RemovePredicateKeyFromAlgorithmProviderMap(predicates.CheckNodePIDPressurePred) // Fit is determined based on whether a pod can tolerate all of the node's taints factory.RegisterMandatoryFitPredicate(predicates.PodToleratesNodeTaintsPred, predicates.PodToleratesNodeTaints) // Fit is determined based on whether a pod can tolerate unschedulable of node factory.RegisterMandatoryFitPredicate(predicates.CheckNodeUnschedulablePred, predicates.CheckNodeUnschedulablePredicate) // Insert Key \"PodToleratesNodeTaints\" and \"CheckNodeUnschedulable\" To All Algorithm Provider // The key will insert to all providers which in algorithmProviderMap[] // if you just want insert to specific provider, call func InsertPredicateKeyToAlgoProvider() factory.InsertPredicateKeyToAlgorithmProviderMap(predicates.PodToleratesNodeTaintsPred) factory.InsertPredicateKeyToAlgorithmProviderMap(predicates.CheckNodeUnschedulablePred) glog.Warningf(\"TaintNodesByCondition is enabled, PodToleratesNodeTaints predicate is mandatory\") } // Prioritizes nodes that satisfy pod's resource limits if utilfeature.DefaultFeatureGate.Enabled(features.ResourceLimitsPriorityFunction) { factory.RegisterPriorityFunction2(\"ResourceLimitsPriority\", priorities.ResourceLimitsPriorityMap, nil, 1) } } 2. init 当函数逻辑调用到algorithmprovider包时，就会自动调用init的初始化函数，此部分主要包括对预选算法和优选算法的注册。 此部分代码位于/pkg/scheduler/algorithmprovider/defaults/defaults.go func init() { // Register functions that extract metadata used by predicates and priorities computations. factory.RegisterPredicateMetadataProducerFactory( func(args factory.PluginFactoryArgs) algorithm.PredicateMetadataProducer { return predicates.NewPredicateMetadataFactory(args.PodLister) }) factory.RegisterPriorityMetadataProducerFactory( func(args factory.PluginFactoryArgs) algorithm.PriorityMetadataProducer { return priorities.NewPriorityMetadataFactory(args.ServiceLister, args.ControllerLister, args.ReplicaSetLister, args.StatefulSetLister) }) registerAlgorithmProvider(defaultPredicates(), defaultPriorities()) // IMPORTANT NOTES for predicate developers: // We are using cached predicate result for pods belonging to the same equivalence class. // So when implementing a new predicate, you are expected to check whether the result // of your predicate function can be affected by related API object change (ADD/DELETE/UPDATE). // If yes, you are expected to invalidate the cached predicate result for related API object change. // For example: // https://github.com/kubernetes/kubernetes/blob/36a218e/plugin/pkg/scheduler/factory/factory.go#L422 // Registers predicates and priorities that are not enabled by default, but user can pick when creating their // own set of priorities/predicates. // PodFitsPorts has been replaced by PodFitsHostPorts for better user understanding. // For backwards compatibility with 1.0, PodFitsPorts is registered as well. factory.RegisterFitPredicate(\"PodFitsPorts\", predicates.PodFitsHostPorts) // Fit is defined based on the absence of port conflicts. // This predicate is actually a default predicate, because it is invoked from // predicates.GeneralPredicates() factory.RegisterFitPredicate(predicates.PodFitsHostPortsPred, predicates.PodFitsHostPorts) // Fit is determined by resource availability. // This predicate is actually a default predicate, because it is invoked from // predicates.GeneralPredicates() factory.RegisterFitPredicate(predicates.PodFitsResourcesPred, predicates.PodFitsResources) // Fit is determined by the presence of the Host parameter and a string match // This predicate is actually a default predicate, because it is invoked from // predicates.GeneralPredicates() factory.RegisterFitPredicate(predicates.HostNamePred, predicates.PodFitsHost) // Fit is determined by node selector query. factory.RegisterFitPredicate(predicates.MatchNodeSelectorPred, predicates.PodMatchNodeSelector) // ServiceSpreadingPriority is a priority config factory that spreads pods by minimizing // the number of pods (belonging to the same service) on the same node. // Register the factory so that it's available, but do not include it as part of the default priorities // Largely replaced by \"SelectorSpreadPriority\", but registered for backward compatibility with 1.0 factory.RegisterPriorityConfigFactory( \"ServiceSpreadingPriority\", factory.PriorityConfigFactory{ MapReduceFunction: func(args factory.PluginFactoryArgs) (algorithm.PriorityMapFunction, algorithm.PriorityReduceFunction) { return priorities.NewSelectorSpreadPriority(args.ServiceLister, algorithm.EmptyControllerLister{}, algorithm.EmptyReplicaSetLister{}, algorithm.EmptyStatefulSetLister{}) }, Weight: 1, }, ) // EqualPriority is a prioritizer function that gives an equal weight of one to all nodes // Register the priority function so that its available // but do not include it as part of the default priorities factory.RegisterPriorityFunction2(\"EqualPriority\", core.EqualPriorityMap, nil, 1) // Optional, cluster-autoscaler friendly priority function - give used nodes higher priority. factory.RegisterPriorityFunction2(\"MostRequestedPriority\", priorities.MostRequestedPriorityMap, nil, 1) factory.RegisterPriorityFunction2( \"RequestedToCapacityRatioPriority\", priorities.RequestedToCapacityRatioResourceAllocationPriorityDefault().PriorityMap, nil, 1) } 以下对init中的注册进行拆分介绍。 2.1. registerAlgorithmProvider 此部分主要注册默认的预选和优选策略。 // Register functions that extract metadata used by predicates and priorities computations. factory.RegisterPredicateMetadataProducerFactory( func(args factory.PluginFactoryArgs) algorithm.PredicateMetadataProducer { return predicates.NewPredicateMetadataFactory(args.PodLister) }) factory.RegisterPriorityMetadataProducerFactory( func(args factory.PluginFactoryArgs) algorithm.PriorityMetadataProducer { return priorities.NewPriorityMetadataFactory(args.ServiceLister, args.ControllerLister, args.ReplicaSetLister, args.StatefulSetLister) }) registerAlgorithmProvider(defaultPredicates(), defaultPriorities()) registerAlgorithmProvider 注册AlgorithmProvider，其中包括DefaultProvider和ClusterAutoscalerProvider。 func registerAlgorithmProvider(predSet, priSet sets.String) { // Registers algorithm providers. By default we use 'DefaultProvider', but user can specify one to be used // by specifying flag. factory.RegisterAlgorithmProvider(factory.DefaultProvider, predSet, priSet) // Cluster autoscaler friendly scheduling algorithm. factory.RegisterAlgorithmProvider(ClusterAutoscalerProvider, predSet, copyAndReplace(priSet, \"LeastRequestedPriority\", \"MostRequestedPriority\")) } 2.2. RegisterFitPredicate 在init部分注册预选策略函数。 预选策略如下： 调度策略 函数 描述 PodFitsPorts PodFitsHostPorts PodFitsPorts已经被PodFitsHostPorts代替，此处主要是为了兼容性。 PodFitsHostPortsPred PodFitsHostPorts 判断是否与宿主机的端口冲突。 PodFitsResourcesPred PodFitsResources 判断node资源是否充足。 HostNamePred PodFitsHost 判断pod所指定调度的节点是否是当前的节点。 MatchNodeSelectorPred PodMatchNodeSelector 判断pod指定的node selector是否匹配当前的node。 具体代码如下： // PodFitsPorts has been replaced by PodFitsHostPorts for better user understanding. // For backwards compatibility with 1.0, PodFitsPorts is registered as well. factory.RegisterFitPredicate(\"PodFitsPorts\", predicates.PodFitsHostPorts) // Fit is defined based on the absence of port conflicts. // This predicate is actually a default predicate, because it is invoked from // predicates.GeneralPredicates() factory.RegisterFitPredicate(predicates.PodFitsHostPortsPred, predicates.PodFitsHostPorts) // Fit is determined by resource availability. // This predicate is actually a default predicate, because it is invoked from // predicates.GeneralPredicates() factory.RegisterFitPredicate(predicates.PodFitsResourcesPred, predicates.PodFitsResources) // Fit is determined by the presence of the Host parameter and a string match // This predicate is actually a default predicate, because it is invoked from // predicates.GeneralPredicates() factory.RegisterFitPredicate(predicates.HostNamePred, predicates.PodFitsHost) // Fit is determined by node selector query. factory.RegisterFitPredicate(predicates.MatchNodeSelectorPred, predicates.PodMatchNodeSelector) 2.3. RegisterPriorityFunction2 在init部分注册优选策略函数。 // EqualPriority is a prioritizer function that gives an equal weight of one to all nodes // Register the priority function so that its available // but do not include it as part of the default priorities factory.RegisterPriorityFunction2(\"EqualPriority\", core.EqualPriorityMap, nil, 1) // Optional, cluster-autoscaler friendly priority function - give used nodes higher priority. factory.RegisterPriorityFunction2(\"MostRequestedPriority\", priorities.MostRequestedPriorityMap, nil, 1) factory.RegisterPriorityFunction2( \"RequestedToCapacityRatioPriority\", priorities.RequestedToCapacityRatioResourceAllocationPriorityDefault().PriorityMap, nil, 1) 3. defaultPredicates 此部分为默认预选策略的注册函数。 默认的预选策略如下： 预选策略 函数 描述 NoVolumeZoneConflictPred NewVolumeZonePredicate 判断pod使用到的volume是否有节点的要求。目前只支持pvc。 MaxEBSVolumeCountPred NewMaxPDVolumeCountPredicate 判断pod使用EBSVolume在该节点上是否已经达到上限了。 MaxGCEPDVolumeCountPred NewMaxPDVolumeCountPredicate 判断pod使用GCEPDVolume在该节点上是否已经达到上限了。 MaxAzureDiskVolumeCountPred NewMaxPDVolumeCountPredicate 判断pod使用AzureDiskVolume在该节点上是否已经达到上限了。 MaxCSIVolumeCountPred NewCSIMaxVolumeLimitPredicate 判断CSIVolume是否达到上限了。 MatchInterPodAffinityPred NewPodAffinityPredicate 匹配pod的亲缘性。 NoDiskConflictPred NoDiskConflict 判断是否有disk volumes的冲突。 GeneralPred GeneralPredicates 通用的预选策略 CheckNodeMemoryPressurePred CheckNodeMemoryPressurePredicate 判断节点内存是否充足。 CheckNodeDiskPressurePred CheckNodeDiskPressurePredicate 判断节点是否有磁盘压力。 CheckNodePIDPressurePred CheckNodePIDPressurePredicate 判断节点上的PID CheckNodeConditionPred CheckNodeConditionPredicate 判断node是否ready。 PodToleratesNodeTaintsPred PodToleratesNodeTaints 判断pod是否可以容忍节点的taints。 CheckVolumeBindingPred NewVolumeBindingPredicate 判断是否有volume拓扑的要求。 具体代码如下： func defaultPredicates() sets.String { return sets.NewString( // Fit is determined by volume zone requirements. factory.RegisterFitPredicateFactory( predicates.NoVolumeZoneConflictPred, func(args factory.PluginFactoryArgs) algorithm.FitPredicate { return predicates.NewVolumeZonePredicate(args.PVInfo, args.PVCInfo, args.StorageClassInfo) }, ), // Fit is determined by whether or not there would be too many AWS EBS volumes attached to the node factory.RegisterFitPredicateFactory( predicates.MaxEBSVolumeCountPred, func(args factory.PluginFactoryArgs) algorithm.FitPredicate { return predicates.NewMaxPDVolumeCountPredicate(predicates.EBSVolumeFilterType, args.PVInfo, args.PVCInfo) }, ), // Fit is determined by whether or not there would be too many GCE PD volumes attached to the node factory.RegisterFitPredicateFactory( predicates.MaxGCEPDVolumeCountPred, func(args factory.PluginFactoryArgs) algorithm.FitPredicate { return predicates.NewMaxPDVolumeCountPredicate(predicates.GCEPDVolumeFilterType, args.PVInfo, args.PVCInfo) }, ), // Fit is determined by whether or not there would be too many Azure Disk volumes attached to the node factory.RegisterFitPredicateFactory( predicates.MaxAzureDiskVolumeCountPred, func(args factory.PluginFactoryArgs) algorithm.FitPredicate { return predicates.NewMaxPDVolumeCountPredicate(predicates.AzureDiskVolumeFilterType, args.PVInfo, args.PVCInfo) }, ), factory.RegisterFitPredicateFactory( predicates.MaxCSIVolumeCountPred, func(args factory.PluginFactoryArgs) algorithm.FitPredicate { return predicates.NewCSIMaxVolumeLimitPredicate(args.PVInfo, args.PVCInfo) }, ), // Fit is determined by inter-pod affinity. factory.RegisterFitPredicateFactory( predicates.MatchInterPodAffinityPred, func(args factory.PluginFactoryArgs) algorithm.FitPredicate { return predicates.NewPodAffinityPredicate(args.NodeInfo, args.PodLister) }, ), // Fit is determined by non-conflicting disk volumes. factory.RegisterFitPredicate(predicates.NoDiskConflictPred, predicates.NoDiskConflict), // GeneralPredicates are the predicates that are enforced by all Kubernetes components // (e.g. kubelet and all schedulers) factory.RegisterFitPredicate(predicates.GeneralPred, predicates.GeneralPredicates), // Fit is determined by node memory pressure condition. factory.RegisterFitPredicate(predicates.CheckNodeMemoryPressurePred, predicates.CheckNodeMemoryPressurePredicate), // Fit is determined by node disk pressure condition. factory.RegisterFitPredicate(predicates.CheckNodeDiskPressurePred, predicates.CheckNodeDiskPressurePredicate), // Fit is determined by node pid pressure condition. factory.RegisterFitPredicate(predicates.CheckNodePIDPressurePred, predicates.CheckNodePIDPressurePredicate), // Fit is determined by node conditions: not ready, network unavailable or out of disk. factory.RegisterMandatoryFitPredicate(predicates.CheckNodeConditionPred, predicates.CheckNodeConditionPredicate), // Fit is determined based on whether a pod can tolerate all of the node's taints factory.RegisterFitPredicate(predicates.PodToleratesNodeTaintsPred, predicates.PodToleratesNodeTaints), // Fit is determined by volume topology requirements. factory.RegisterFitPredicateFactory( predicates.CheckVolumeBindingPred, func(args factory.PluginFactoryArgs) algorithm.FitPredicate { return predicates.NewVolumeBindingPredicate(args.VolumeBinder) }, ), ) } 4. defaultPriorities 此部分主要为默认优选策略的注册函数。 默认优选策略如下： 优选策略 函数 描述 SelectorSpreadPriority NewSelectorSpreadPriority 属于相同service和rs下的pod尽量分布在不同的node上。 InterPodAffinityPriority NewInterPodAffinityPriority 根据pod的亲缘性，将相同拓扑域中的pod放在同一个节点 LeastRequestedPriority LeastRequestedPriorityMap 按最少请求的利用率对节点进行优先级排序。 BalancedResourceAllocation BalancedResourceAllocationMap 实现资源的平衡使用。 NodePreferAvoidPodsPriority CalculateNodePreferAvoidPodsPriorityMap 将此权重设置为足以覆盖所有其他优先级函数。 NodeAffinityPriority CalculateNodeAffinityPriorityMap pod指定label节点调度，来匹配node亲缘性。 TaintTolerationPriority ComputeTaintTolerationPriorityMap pod有设置tolerate属性来容忍node的taint。 ImageLocalityPriority ImageLocalityPriorityMap 根据节点上是否有该pod使用到的镜像打分。 具体代码实现如下： func defaultPriorities() sets.String { return sets.NewString( // spreads pods by minimizing the number of pods (belonging to the same service or replication controller) on the same node. factory.RegisterPriorityConfigFactory( \"SelectorSpreadPriority\", factory.PriorityConfigFactory{ MapReduceFunction: func(args factory.PluginFactoryArgs) (algorithm.PriorityMapFunction, algorithm.PriorityReduceFunction) { return priorities.NewSelectorSpreadPriority(args.ServiceLister, args.ControllerLister, args.ReplicaSetLister, args.StatefulSetLister) }, Weight: 1, }, ), // pods should be placed in the same topological domain (e.g. same node, same rack, same zone, same power domain, etc.) // as some other pods, or, conversely, should not be placed in the same topological domain as some other pods. factory.RegisterPriorityConfigFactory( \"InterPodAffinityPriority\", factory.PriorityConfigFactory{ Function: func(args factory.PluginFactoryArgs) algorithm.PriorityFunction { return priorities.NewInterPodAffinityPriority(args.NodeInfo, args.NodeLister, args.PodLister, args.HardPodAffinitySymmetricWeight) }, Weight: 1, }, ), // Prioritize nodes by least requested utilization. factory.RegisterPriorityFunction2(\"LeastRequestedPriority\", priorities.LeastRequestedPriorityMap, nil, 1), // Prioritizes nodes to help achieve balanced resource usage factory.RegisterPriorityFunction2(\"BalancedResourceAllocation\", priorities.BalancedResourceAllocationMap, nil, 1), // Set this weight large enough to override all other priority functions. // TODO: Figure out a better way to do this, maybe at same time as fixing #24720. factory.RegisterPriorityFunction2(\"NodePreferAvoidPodsPriority\", priorities.CalculateNodePreferAvoidPodsPriorityMap, nil, 10000), // Prioritizes nodes that have labels matching NodeAffinity factory.RegisterPriorityFunction2(\"NodeAffinityPriority\", priorities.CalculateNodeAffinityPriorityMap, priorities.CalculateNodeAffinityPriorityReduce, 1), // Prioritizes nodes that marked with taint which pod can tolerate. factory.RegisterPriorityFunction2(\"TaintTolerationPriority\", priorities.ComputeTaintTolerationPriorityMap, priorities.ComputeTaintTolerationPriorityReduce, 1), // ImageLocalityPriority prioritizes nodes that have images requested by the pod present. factory.RegisterPriorityFunction2(\"ImageLocalityPriority\", priorities.ImageLocalityPriorityMap, nil, 1), ) } 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/algorithmprovider/defaults/defaults.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-scheduler/scheduleOne.html":{"url":"code-analysis/kube-scheduler/scheduleOne.html","title":"scheduleOne","keywords":"","body":"kube-scheduler源码分析（三）之 scheduleOne1. Scheduler.Run2. Scheduler.scheduleOne3. config.NextPod3.1. getNextPod4. Scheduler.schedule4.1. ScheduleAlgorithm5. genericScheduler.Schedule5.1. podPassesBasicChecks5.2. findNodesThatFit5.3. PrioritizeNodes5.4. selectHost5.4.1. findMaxScores6. Scheduler.preempt7. Scheduler.assume8. Scheduler.bind9. 总结kube-scheduler源码分析（三）之 scheduleOne 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析/pkg/scheduler/中调度的基本流程。具体的预选调度逻辑、优选调度逻辑、节点抢占逻辑待后续再独立分析。 scheduler的pkg代码目录结构如下： scheduler ├── algorithm # 主要包含调度的算法 │ ├── predicates # 预选的策略 │ ├── priorities # 优选的策略 │ ├── scheduler_interface.go # ScheduleAlgorithm、SchedulerExtender接口定义 │ ├── types.go # 使用到的type的定义 ├── algorithmprovider │ ├── defaults │ │ ├── defaults.go # 默认算法的初始化操作，包括预选和优选策略 ├── cache # scheduler调度使用到的cache │ ├── cache.go # schedulerCache │ ├── interface.go │ ├── node_info.go │ ├── node_tree.go ├── core # 调度逻辑的核心代码 │ ├── equivalence │ │ ├── eqivalence.go # 存储相同pod的调度结果缓存，主要给预选策略使用 │ ├── extender.go │ ├── generic_scheduler.go # genericScheduler,主要包含默认调度器的调度逻辑 │ ├── scheduling_queue.go # 调度使用到的队列，主要用来存储需要被调度的pod ├── factory │ ├── factory.go # 主要包括NewConfigFactory、NewPodInformer，监听pod事件来更新调度队列 ├── metrics │ └── metrics.go # 主要给prometheus使用 ├── scheduler.go # pkg部分的Run入口(核心代码)，主要包含Run、scheduleOne、schedule、preempt等函数 └── volumebinder └── volume_binder.go # volume bind 1. Scheduler.Run 此部分代码位于pkg/scheduler/scheduler.go 此处为具体调度逻辑的入口。 // Run begins watching and scheduling. It waits for cache to be synced, then starts a goroutine and returns immediately. func (sched *Scheduler) Run() { if !sched.config.WaitForCacheSync() { return } go wait.Until(sched.scheduleOne, 0, sched.config.StopEverything) } 2. Scheduler.scheduleOne 此部分代码位于pkg/scheduler/scheduler.go scheduleOne主要为单个pod选择一个适合的节点，为调度逻辑的核心函数。 对单个pod进行调度的基本流程如下： 通过podQueue的待调度队列中弹出需要调度的pod。 通过具体的调度算法为该pod选出合适的节点，其中调度算法就包括预选和优选两步策略。 如果上述调度失败，则会尝试抢占机制，将优先级低的pod剔除，让优先级高的pod调度成功。 将该pod和选定的节点进行假性绑定，存入scheduler cache中，方便具体绑定操作可以异步进行。 实际执行绑定操作，将node的名字添加到pod的节点相关属性中。 完整代码如下： // scheduleOne does the entire scheduling workflow for a single pod. It is serialized on the scheduling algorithm's host fitting. func (sched *Scheduler) scheduleOne() { pod := sched.config.NextPod() if pod.DeletionTimestamp != nil { sched.config.Recorder.Eventf(pod, v1.EventTypeWarning, \"FailedScheduling\", \"skip schedule deleting pod: %v/%v\", pod.Namespace, pod.Name) glog.V(3).Infof(\"Skip schedule deleting pod: %v/%v\", pod.Namespace, pod.Name) return } glog.V(3).Infof(\"Attempting to schedule pod: %v/%v\", pod.Namespace, pod.Name) // Synchronously attempt to find a fit for the pod. start := time.Now() suggestedHost, err := sched.schedule(pod) if err != nil { // schedule() may have failed because the pod would not fit on any host, so we try to // preempt, with the expectation that the next time the pod is tried for scheduling it // will fit due to the preemption. It is also possible that a different pod will schedule // into the resources that were preempted, but this is harmless. if fitError, ok := err.(*core.FitError); ok { preemptionStartTime := time.Now() sched.preempt(pod, fitError) metrics.PreemptionAttempts.Inc() metrics.SchedulingAlgorithmPremptionEvaluationDuration.Observe(metrics.SinceInMicroseconds(preemptionStartTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PreemptionEvaluation).Observe(metrics.SinceInSeconds(preemptionStartTime)) } return } metrics.SchedulingAlgorithmLatency.Observe(metrics.SinceInMicroseconds(start)) // Tell the cache to assume that a pod now is running on a given node, even though it hasn't been bound yet. // This allows us to keep scheduling without waiting on binding to occur. assumedPod := pod.DeepCopy() // Assume volumes first before assuming the pod. // // If all volumes are completely bound, then allBound is true and binding will be skipped. // // Otherwise, binding of volumes is started after the pod is assumed, but before pod binding. // // This function modifies 'assumedPod' if volume binding is required. allBound, err := sched.assumeVolumes(assumedPod, suggestedHost) if err != nil { return } // assume modifies `assumedPod` by setting NodeName=suggestedHost err = sched.assume(assumedPod, suggestedHost) if err != nil { return } // bind the pod to its host asynchronously (we can do this b/c of the assumption step above). go func() { // Bind volumes first before Pod if !allBound { err = sched.bindVolumes(assumedPod) if err != nil { return } } err := sched.bind(assumedPod, &v1.Binding{ ObjectMeta: metav1.ObjectMeta{Namespace: assumedPod.Namespace, Name: assumedPod.Name, UID: assumedPod.UID}, Target: v1.ObjectReference{ Kind: \"Node\", Name: suggestedHost, }, }) metrics.E2eSchedulingLatency.Observe(metrics.SinceInMicroseconds(start)) if err != nil { glog.Errorf(\"Internal error binding pod: (%v)\", err) } }() } 以下对重要代码分别进行分析。 3. config.NextPod 通过podQueue的方式存储待调度的pod队列，NextPod拿出下一个需要被调度的pod。 pod := sched.config.NextPod() if pod.DeletionTimestamp != nil { sched.config.Recorder.Eventf(pod, v1.EventTypeWarning, \"FailedScheduling\", \"skip schedule deleting pod: %v/%v\", pod.Namespace, pod.Name) glog.V(3).Infof(\"Skip schedule deleting pod: %v/%v\", pod.Namespace, pod.Name) return } glog.V(3).Infof(\"Attempting to schedule pod: %v/%v\", pod.Namespace, pod.Name) NextPod的具体函数在factory.go的CreateFromKey函数中定义，如下： func (c *configFactory) CreateFromKeys(predicateKeys, priorityKeys sets.String, extenders []algorithm.SchedulerExtender) (*scheduler.Config, error) { ... return &scheduler.Config{ ... NextPod: func() *v1.Pod { return c.getNextPod() } ... } 3.1. getNextPod 通过一个podQueue来存储需要调度的pod的队列，通过队列Pop的方式弹出需要被调度的pod。 func (c *configFactory) getNextPod() *v1.Pod { pod, err := c.podQueue.Pop() if err == nil { glog.V(4).Infof(\"About to try and schedule pod %v/%v\", pod.Namespace, pod.Name) return pod } glog.Errorf(\"Error while retrieving next pod from scheduling queue: %v\", err) return nil } 4. Scheduler.schedule 此部分代码位于pkg/scheduler/scheduler.go 此部分为调度逻辑的核心，通过不同的算法为具体的pod选择一个最合适的节点。 // Synchronously attempt to find a fit for the pod. start := time.Now() suggestedHost, err := sched.schedule(pod) if err != nil { // schedule() may have failed because the pod would not fit on any host, so we try to // preempt, with the expectation that the next time the pod is tried for scheduling it // will fit due to the preemption. It is also possible that a different pod will schedule // into the resources that were preempted, but this is harmless. if fitError, ok := err.(*core.FitError); ok { preemptionStartTime := time.Now() sched.preempt(pod, fitError) metrics.PreemptionAttempts.Inc() metrics.SchedulingAlgorithmPremptionEvaluationDuration.Observe(metrics.SinceInMicroseconds(preemptionStartTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PreemptionEvaluation).Observe(metrics.SinceInSeconds(preemptionStartTime)) } return } schedule通过调度算法返回一个最优的节点。 // schedule implements the scheduling algorithm and returns the suggested host. func (sched *Scheduler) schedule(pod *v1.Pod) (string, error) { host, err := sched.config.Algorithm.Schedule(pod, sched.config.NodeLister) if err != nil { pod = pod.DeepCopy() sched.config.Error(pod, err) sched.config.Recorder.Eventf(pod, v1.EventTypeWarning, \"FailedScheduling\", \"%v\", err) sched.config.PodConditionUpdater.Update(pod, &v1.PodCondition{ Type: v1.PodScheduled, Status: v1.ConditionFalse, Reason: v1.PodReasonUnschedulable, Message: err.Error(), }) return \"\", err } return host, err } 4.1. ScheduleAlgorithm ScheduleAlgorithm是一个调度算法的接口，主要的实现体是genericScheduler，后续分析genericScheduler.Schedule。 ScheduleAlgorithm接口定义如下： // ScheduleAlgorithm is an interface implemented by things that know how to schedule pods // onto machines. type ScheduleAlgorithm interface { Schedule(*v1.Pod, NodeLister) (selectedMachine string, err error) // Preempt receives scheduling errors for a pod and tries to create room for // the pod by preempting lower priority pods if possible. // It returns the node where preemption happened, a list of preempted pods, a // list of pods whose nominated node name should be removed, and error if any. Preempt(*v1.Pod, NodeLister, error) (selectedNode *v1.Node, preemptedPods []*v1.Pod, cleanupNominatedPods []*v1.Pod, err error) // Predicates() returns a pointer to a map of predicate functions. This is // exposed for testing. Predicates() map[string]FitPredicate // Prioritizers returns a slice of priority config. This is exposed for // testing. Prioritizers() []PriorityConfig } 5. genericScheduler.Schedule 此部分代码位于/pkg/scheduler/core/generic_scheduler.go genericScheduler.Schedule实现了基本的调度逻辑，基于给定需要调度的pod和node列表，如果执行成功返回调度的节点的名字，如果执行失败，则返回错误和原因。主要通过预选和优选两步操作完成调度的逻辑。 基本流程如下： 对pod做基本性检查，目前主要是对pvc的检查。 通过findNodesThatFit预选策略选出满足调度条件的node列表。 通过PrioritizeNodes优选策略给预选的node列表中的node进行打分。 在打分的node列表中选择一个分数最高的node作为调度的节点。 完整代码如下： // Schedule tries to schedule the given pod to one of the nodes in the node list. // If it succeeds, it will return the name of the node. // If it fails, it will return a FitError error with reasons. func (g *genericScheduler) Schedule(pod *v1.Pod, nodeLister algorithm.NodeLister) (string, error) { trace := utiltrace.New(fmt.Sprintf(\"Scheduling %s/%s\", pod.Namespace, pod.Name)) defer trace.LogIfLong(100 * time.Millisecond) if err := podPassesBasicChecks(pod, g.pvcLister); err != nil { return \"\", err } nodes, err := nodeLister.List() if err != nil { return \"\", err } if len(nodes) == 0 { return \"\", ErrNoNodesAvailable } // Used for all fit and priority funcs. err = g.cache.UpdateNodeNameToInfoMap(g.cachedNodeInfoMap) if err != nil { return \"\", err } trace.Step(\"Computing predicates\") startPredicateEvalTime := time.Now() filteredNodes, failedPredicateMap, err := g.findNodesThatFit(pod, nodes) if err != nil { return \"\", err } if len(filteredNodes) == 0 { return \"\", &FitError{ Pod: pod, NumAllNodes: len(nodes), FailedPredicates: failedPredicateMap, } } metrics.SchedulingAlgorithmPredicateEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPredicateEvalTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PredicateEvaluation).Observe(metrics.SinceInSeconds(startPredicateEvalTime)) trace.Step(\"Prioritizing\") startPriorityEvalTime := time.Now() // When only one node after predicate, just use it. if len(filteredNodes) == 1 { metrics.SchedulingAlgorithmPriorityEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPriorityEvalTime)) return filteredNodes[0].Name, nil } metaPrioritiesInterface := g.priorityMetaProducer(pod, g.cachedNodeInfoMap) priorityList, err := PrioritizeNodes(pod, g.cachedNodeInfoMap, metaPrioritiesInterface, g.prioritizers, filteredNodes, g.extenders) if err != nil { return \"\", err } metrics.SchedulingAlgorithmPriorityEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPriorityEvalTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PriorityEvaluation).Observe(metrics.SinceInSeconds(startPriorityEvalTime)) trace.Step(\"Selecting host\") return g.selectHost(priorityList) } 5.1. podPassesBasicChecks podPassesBasicChecks主要做一下基本性检查，目前主要是对pvc的检查。 if err := podPassesBasicChecks(pod, g.pvcLister); err != nil { return \"\", err } podPassesBasicChecks具体实现如下： // podPassesBasicChecks makes sanity checks on the pod if it can be scheduled. func podPassesBasicChecks(pod *v1.Pod, pvcLister corelisters.PersistentVolumeClaimLister) error { // Check PVCs used by the pod namespace := pod.Namespace manifest := &(pod.Spec) for i := range manifest.Volumes { volume := &manifest.Volumes[i] if volume.PersistentVolumeClaim == nil { // Volume is not a PVC, ignore continue } pvcName := volume.PersistentVolumeClaim.ClaimName pvc, err := pvcLister.PersistentVolumeClaims(namespace).Get(pvcName) if err != nil { // The error has already enough context (\"persistentvolumeclaim \"myclaim\" not found\") return err } if pvc.DeletionTimestamp != nil { return fmt.Errorf(\"persistentvolumeclaim %q is being deleted\", pvc.Name) } } return nil } 5.2. findNodesThatFit 预选，通过预选函数来判断每个节点是否适合被该Pod调度。 具体的findNodesThatFit代码实现细节待后续文章独立分析。 genericScheduler.Schedule中对findNodesThatFit的调用过程如下： trace.Step(\"Computing predicates\") startPredicateEvalTime := time.Now() filteredNodes, failedPredicateMap, err := g.findNodesThatFit(pod, nodes) if err != nil { return \"\", err } if len(filteredNodes) == 0 { return \"\", &FitError{ Pod: pod, NumAllNodes: len(nodes), FailedPredicates: failedPredicateMap, } } metrics.SchedulingAlgorithmPredicateEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPredicateEvalTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PredicateEvaluation).Observe(metrics.SinceInSeconds(startPredicateEvalTime)) 5.3. PrioritizeNodes 优选，从满足的节点中选择出最优的节点。 具体操作如下： PrioritizeNodes通过并行运行各个优先级函数来对节点进行优先级排序。 每个优先级函数会给节点打分，打分范围为0-10分。 0 表示优先级最低的节点，10表示优先级最高的节点。 每个优先级函数也有各自的权重。 优先级函数返回的节点分数乘以权重以获得加权分数。 最后组合（添加）所有分数以获得所有节点的总加权分数。 具体PrioritizeNodes的实现逻辑待后续文章独立分析。 genericScheduler.Schedule中对PrioritizeNodes的调用过程如下： trace.Step(\"Prioritizing\") startPriorityEvalTime := time.Now() // When only one node after predicate, just use it. if len(filteredNodes) == 1 { metrics.SchedulingAlgorithmPriorityEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPriorityEvalTime)) return filteredNodes[0].Name, nil } metaPrioritiesInterface := g.priorityMetaProducer(pod, g.cachedNodeInfoMap) priorityList, err := PrioritizeNodes(pod, g.cachedNodeInfoMap, metaPrioritiesInterface, g.prioritizers, filteredNodes, g.extenders) if err != nil { return \"\", err } metrics.SchedulingAlgorithmPriorityEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPriorityEvalTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PriorityEvaluation).Observe(metrics.SinceInSeconds(startPriorityEvalTime)) 5.4. selectHost scheduler在最后会从priorityList中选择分数最高的一个节点。 trace.Step(\"Selecting host\") return g.selectHost(priorityList) selectHost获取优先级的节点列表，然后从分数最高的节点以循环方式选择一个节点。 具体代码如下： // selectHost takes a prioritized list of nodes and then picks one // in a round-robin manner from the nodes that had the highest score. func (g *genericScheduler) selectHost(priorityList schedulerapi.HostPriorityList) (string, error) { if len(priorityList) == 0 { return \"\", fmt.Errorf(\"empty priorityList\") } maxScores := findMaxScores(priorityList) ix := int(g.lastNodeIndex % uint64(len(maxScores))) g.lastNodeIndex++ return priorityList[maxScores[ix]].Host, nil } 5.4.1. findMaxScores findMaxScores返回priorityList中具有最高Score的节点的索引。 // findMaxScores returns the indexes of nodes in the \"priorityList\" that has the highest \"Score\". func findMaxScores(priorityList schedulerapi.HostPriorityList) []int { maxScoreIndexes := make([]int, 0, len(priorityList)/2) maxScore := priorityList[0].Score for i, hp := range priorityList { if hp.Score > maxScore { maxScore = hp.Score maxScoreIndexes = maxScoreIndexes[:0] maxScoreIndexes = append(maxScoreIndexes, i) } else if hp.Score == maxScore { maxScoreIndexes = append(maxScoreIndexes, i) } } return maxScoreIndexes } 6. Scheduler.preempt 如果pod在预选和优选调度中失败，则执行抢占操作。抢占主要是将低优先级的pod的资源空间腾出给待调度的高优先级的pod。 具体Scheduler.preempt的实现逻辑待后续文章独立分析。 suggestedHost, err := sched.schedule(pod) if err != nil { // schedule() may have failed because the pod would not fit on any host, so we try to // preempt, with the expectation that the next time the pod is tried for scheduling it // will fit due to the preemption. It is also possible that a different pod will schedule // into the resources that were preempted, but this is harmless. if fitError, ok := err.(*core.FitError); ok { preemptionStartTime := time.Now() sched.preempt(pod, fitError) metrics.PreemptionAttempts.Inc() metrics.SchedulingAlgorithmPremptionEvaluationDuration.Observe(metrics.SinceInMicroseconds(preemptionStartTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PreemptionEvaluation).Observe(metrics.SinceInSeconds(preemptionStartTime)) } return } 7. Scheduler.assume 将该pod和选定的节点进行假性绑定，存入scheduler cache中，方便可以继续执行调度逻辑，而不需要等待绑定操作的发生，具体绑定操作可以异步进行。 // Tell the cache to assume that a pod now is running on a given node, even though it hasn't been bound yet. // This allows us to keep scheduling without waiting on binding to occur. assumedPod := pod.DeepCopy() // Assume volumes first before assuming the pod. // // If all volumes are completely bound, then allBound is true and binding will be skipped. // // Otherwise, binding of volumes is started after the pod is assumed, but before pod binding. // // This function modifies 'assumedPod' if volume binding is required. allBound, err := sched.assumeVolumes(assumedPod, suggestedHost) if err != nil { return } // assume modifies `assumedPod` by setting NodeName=suggestedHost err = sched.assume(assumedPod, suggestedHost) if err != nil { return } 如果假性绑定成功则发送请求给apiserver，如果失败则scheduler会立即释放已分配给假性绑定的pod的资源。 assume方法的具体实现： // assume signals to the cache that a pod is already in the cache, so that binding can be asynchronous. // assume modifies `assumed`. func (sched *Scheduler) assume(assumed *v1.Pod, host string) error { // Optimistically assume that the binding will succeed and send it to apiserver // in the background. // If the binding fails, scheduler will release resources allocated to assumed pod // immediately. assumed.Spec.NodeName = host // NOTE: Because the scheduler uses snapshots of SchedulerCache and the live // version of Ecache, updates must be written to SchedulerCache before // invalidating Ecache. if err := sched.config.SchedulerCache.AssumePod(assumed); err != nil { glog.Errorf(\"scheduler cache AssumePod failed: %v\", err) // This is most probably result of a BUG in retrying logic. // We report an error here so that pod scheduling can be retried. // This relies on the fact that Error will check if the pod has been bound // to a node and if so will not add it back to the unscheduled pods queue // (otherwise this would cause an infinite loop). sched.config.Error(assumed, err) sched.config.Recorder.Eventf(assumed, v1.EventTypeWarning, \"FailedScheduling\", \"AssumePod failed: %v\", err) sched.config.PodConditionUpdater.Update(assumed, &v1.PodCondition{ Type: v1.PodScheduled, Status: v1.ConditionFalse, Reason: \"SchedulerError\", Message: err.Error(), }) return err } // Optimistically assume that the binding will succeed, so we need to invalidate affected // predicates in equivalence cache. // If the binding fails, these invalidated item will not break anything. if sched.config.Ecache != nil { sched.config.Ecache.InvalidateCachedPredicateItemForPodAdd(assumed, host) } return nil } 8. Scheduler.bind 异步的方式给pod绑定到具体的调度节点上。 // bind the pod to its host asynchronously (we can do this b/c of the assumption step above). go func() { // Bind volumes first before Pod if !allBound { err = sched.bindVolumes(assumedPod) if err != nil { return } } err := sched.bind(assumedPod, &v1.Binding{ ObjectMeta: metav1.ObjectMeta{Namespace: assumedPod.Namespace, Name: assumedPod.Name, UID: assumedPod.UID}, Target: v1.ObjectReference{ Kind: \"Node\", Name: suggestedHost, }, }) metrics.E2eSchedulingLatency.Observe(metrics.SinceInMicroseconds(start)) if err != nil { glog.Errorf(\"Internal error binding pod: (%v)\", err) } }() bind具体实现如下： // bind binds a pod to a given node defined in a binding object. We expect this to run asynchronously, so we // handle binding metrics internally. func (sched *Scheduler) bind(assumed *v1.Pod, b *v1.Binding) error { bindingStart := time.Now() // If binding succeeded then PodScheduled condition will be updated in apiserver so that // it's atomic with setting host. err := sched.config.GetBinder(assumed).Bind(b) if err := sched.config.SchedulerCache.FinishBinding(assumed); err != nil { glog.Errorf(\"scheduler cache FinishBinding failed: %v\", err) } if err != nil { glog.V(1).Infof(\"Failed to bind pod: %v/%v\", assumed.Namespace, assumed.Name) if err := sched.config.SchedulerCache.ForgetPod(assumed); err != nil { glog.Errorf(\"scheduler cache ForgetPod failed: %v\", err) } sched.config.Error(assumed, err) sched.config.Recorder.Eventf(assumed, v1.EventTypeWarning, \"FailedScheduling\", \"Binding rejected: %v\", err) sched.config.PodConditionUpdater.Update(assumed, &v1.PodCondition{ Type: v1.PodScheduled, Status: v1.ConditionFalse, Reason: \"BindingRejected\", }) return err } metrics.BindingLatency.Observe(metrics.SinceInMicroseconds(bindingStart)) metrics.SchedulingLatency.WithLabelValues(metrics.Binding).Observe(metrics.SinceInSeconds(bindingStart)) sched.config.Recorder.Eventf(assumed, v1.EventTypeNormal, \"Scheduled\", \"Successfully assigned %v/%v to %v\", assumed.Namespace, assumed.Name, b.Target.Name) return nil } 9. 总结 本文主要分析了单个pod的调度过程。具体流程如下： 通过podQueue的待调度队列中弹出需要调度的pod。 通过具体的调度算法为该pod选出合适的节点，其中调度算法就包括预选和优选两步策略。 如果上述调度失败，则会尝试抢占机制，将优先级低的pod剔除，让优先级高的pod调度成功。 将该pod和选定的节点进行假性绑定，存入scheduler cache中，方便具体绑定操作可以异步进行。 实际执行绑定操作，将node的名字添加到pod的节点相关属性中。 其中核心的部分为通过具体的调度算法选出调度节点的过程，即genericScheduler.Schedule的实现部分。该部分包括预选和优选两个部分。 genericScheduler.Schedule调度的基本流程如下： 对pod做基本性检查，目前主要是对pvc的检查。 通过findNodesThatFit预选策略选出满足调度条件的node列表。 通过PrioritizeNodes优选策略给预选的node列表中的node进行打分。 在打分的node列表中选择一个分数最高的node作为调度的节点。 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/scheduler.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/core/generic_scheduler.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-scheduler/findNodesThatFit.html":{"url":"code-analysis/kube-scheduler/findNodesThatFit.html","title":"findNodesThatFit","keywords":"","body":"kube-scheduler源码分析（四）之 findNodesThatFit1. 调用入口2. findNodesThatFit3. numFeasibleNodesToFind4. checkNode5. podFitsOnNode5.1. predicateFuncs6. PodFitsResources6.1. NodeInfo6.2. Resource6.3. allowedPodNumber6.4. podRequest6.5. AllocatableResource6.6. ScalarResources7. 总结7.1. checkNode7.2. podFitsOnNode7.3. PodFitsResourceskube-scheduler源码分析（四）之 findNodesThatFit 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析调度逻辑中的预选策略，即第一步筛选出符合pod调度条件的节点。 1. 调用入口 预选，通过预选函数来判断每个节点是否适合被该Pod调度。 genericScheduler.Schedule中对findNodesThatFit的调用过程如下： 此部分代码位于pkg/scheduler/core/generic_scheduler.go func (g *genericScheduler) Schedule(pod *v1.Pod, nodeLister algorithm.NodeLister) (string, error) { ... // 列出所有的节点 nodes, err := nodeLister.List() if err != nil { return \"\", err } if len(nodes) == 0 { return \"\", ErrNoNodesAvailable } // Used for all fit and priority funcs. err = g.cache.UpdateNodeNameToInfoMap(g.cachedNodeInfoMap) if err != nil { return \"\", err } trace.Step(\"Computing predicates\") startPredicateEvalTime := time.Now() // 调用findNodesThatFit过滤出预选节点 filteredNodes, failedPredicateMap, err := g.findNodesThatFit(pod, nodes) if err != nil { return \"\", err } if len(filteredNodes) == 0 { return \"\", &FitError{ Pod: pod, NumAllNodes: len(nodes), FailedPredicates: failedPredicateMap, } } // metrics metrics.SchedulingAlgorithmPredicateEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPredicateEvalTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PredicateEvaluation).Observe(metrics.SinceInSeconds(startPredicateEvalTime)) ... } 核心代码： // 调用findNodesThatFit过滤出预选节点 filteredNodes, failedPredicateMap, err := g.findNodesThatFit(pod, nodes) 2. findNodesThatFit findNodesThatFit基于给定的预选函数过滤node，每个node传入到预选函数中来确实该节点是否符合要求。 findNodesThatFit的入参是被调度的pod和当前的节点列表，返回预选节点列表和错误。 findNodesThatFit基本流程如下： 设置可行节点的总数，作为预选节点数组的容量，避免总节点过多需要筛选的节点过多。 通过NodeTree不断获取下一个节点来判断该节点是否满足pod的调度条件。 通过之前注册的各种预选函数来判断当前节点是否符合pod的调度条件。 最后返回满足调度条件的node列表，供下一步的优选操作。 findNodesThatFit完整代码如下： 此部分代码位于pkg/scheduler/core/generic_scheduler.go // Filters the nodes to find the ones that fit based on the given predicate functions // Each node is passed through the predicate functions to determine if it is a fit func (g *genericScheduler) findNodesThatFit(pod *v1.Pod, nodes []*v1.Node) ([]*v1.Node, FailedPredicateMap, error) { var filtered []*v1.Node failedPredicateMap := FailedPredicateMap{} if len(g.predicates) == 0 { filtered = nodes } else { allNodes := int32(g.cache.NodeTree().NumNodes) numNodesToFind := g.numFeasibleNodesToFind(allNodes) // Create filtered list with enough space to avoid growing it // and allow assigning. filtered = make([]*v1.Node, numNodesToFind) errs := errors.MessageCountMap{} var ( predicateResultLock sync.Mutex filteredLen int32 equivClass *equivalence.Class ) ctx, cancel := context.WithCancel(context.Background()) // We can use the same metadata producer for all nodes. meta := g.predicateMetaProducer(pod, g.cachedNodeInfoMap) if g.equivalenceCache != nil { // getEquivalenceClassInfo will return immediately if no equivalence pod found equivClass = equivalence.NewClass(pod) } checkNode := func(i int) { var nodeCache *equivalence.NodeCache nodeName := g.cache.NodeTree().Next() if g.equivalenceCache != nil { nodeCache, _ = g.equivalenceCache.GetNodeCache(nodeName) } fits, failedPredicates, err := podFitsOnNode( pod, meta, g.cachedNodeInfoMap[nodeName], g.predicates, g.cache, nodeCache, g.schedulingQueue, g.alwaysCheckAllPredicates, equivClass, ) if err != nil { predicateResultLock.Lock() errs[err.Error()]++ predicateResultLock.Unlock() return } if fits { length := atomic.AddInt32(&filteredLen, 1) if length > numNodesToFind { cancel() atomic.AddInt32(&filteredLen, -1) } else { filtered[length-1] = g.cachedNodeInfoMap[nodeName].Node() } } else { predicateResultLock.Lock() failedPredicateMap[nodeName] = failedPredicates predicateResultLock.Unlock() } } // Stops searching for more nodes once the configured number of feasible nodes // are found. workqueue.ParallelizeUntil(ctx, 16, int(allNodes), checkNode) filtered = filtered[:filteredLen] if len(errs) > 0 { return []*v1.Node{}, FailedPredicateMap{}, errors.CreateAggregateFromMessageCountMap(errs) } } if len(filtered) > 0 && len(g.extenders) != 0 { for _, extender := range g.extenders { if !extender.IsInterested(pod) { continue } filteredList, failedMap, err := extender.Filter(pod, filtered, g.cachedNodeInfoMap) if err != nil { if extender.IsIgnorable() { glog.Warningf(\"Skipping extender %v as it returned error %v and has ignorable flag set\", extender, err) continue } else { return []*v1.Node{}, FailedPredicateMap{}, err } } for failedNodeName, failedMsg := range failedMap { if _, found := failedPredicateMap[failedNodeName]; !found { failedPredicateMap[failedNodeName] = []algorithm.PredicateFailureReason{} } failedPredicateMap[failedNodeName] = append(failedPredicateMap[failedNodeName], predicates.NewFailureReason(failedMsg)) } filtered = filteredList if len(filtered) == 0 { break } } } return filtered, failedPredicateMap, nil } 以下对findNodesThatFit分段分析。 3. numFeasibleNodesToFind findNodesThatFit先基于所有的节点找出可行的节点是总数。numFeasibleNodesToFind的作用主要是避免当节点过多（超过100）影响调度的效率。 allNodes := int32(g.cache.NodeTree().NumNodes) numNodesToFind := g.numFeasibleNodesToFind(allNodes) // Create filtered list with enough space to avoid growing it // and allow assigning. filtered = make([]*v1.Node, numNodesToFind) numFeasibleNodesToFind基本流程如下： 如果所有的node节点小于minFeasibleNodesToFind(当前默认为100)则返回节点数。 如果节点数超100，则取指定计分的百分比的节点数，当该百分比后的数目仍小于minFeasibleNodesToFind，则返回minFeasibleNodesToFind。 如果百分比后的数目大于minFeasibleNodesToFind，则返回该百分比。 // numFeasibleNodesToFind returns the number of feasible nodes that once found, the scheduler stops // its search for more feasible nodes. func (g *genericScheduler) numFeasibleNodesToFind(numAllNodes int32) int32 { if numAllNodes = 100 { return numAllNodes } numNodes := numAllNodes * g.percentageOfNodesToScore / 100 if numNodes 4. checkNode checkNode是一个校验node是否符合要求的函数，其中实际调用到的核心函数是podFitsOnNode。再通过workqueue并发执行checkNode操作。 checkNode主要流程如下： 通过cache中的nodeTree不断获取下一个node。 将当前node和pod传入podFitsOnNode判断当前node是否符合要求。 如果当前node符合要求就将当前node加入预选节点的数组中filtered。 如果当前node不满足要求，则加入到失败的数组中，并记录原因。 通过workqueue.ParallelizeUntil并发执行checkNode函数，一旦找到配置的可行节点数，就停止搜索更多节点。 checkNode := func(i int) { var nodeCache *equivalence.NodeCache nodeName := g.cache.NodeTree().Next() if g.equivalenceCache != nil { nodeCache, _ = g.equivalenceCache.GetNodeCache(nodeName) } fits, failedPredicates, err := podFitsOnNode( pod, meta, g.cachedNodeInfoMap[nodeName], g.predicates, g.cache, nodeCache, g.schedulingQueue, g.alwaysCheckAllPredicates, equivClass, ) if err != nil { predicateResultLock.Lock() errs[err.Error()]++ predicateResultLock.Unlock() return } if fits { length := atomic.AddInt32(&filteredLen, 1) if length > numNodesToFind { cancel() atomic.AddInt32(&filteredLen, -1) } else { filtered[length-1] = g.cachedNodeInfoMap[nodeName].Node() } } else { predicateResultLock.Lock() failedPredicateMap[nodeName] = failedPredicates predicateResultLock.Unlock() } } workqueue的并发操作： // Stops searching for more nodes once the configured number of feasible nodes // are found. workqueue.ParallelizeUntil(ctx, 16, int(allNodes), checkNode) ParallelizeUntil具体代码如下： // ParallelizeUntil is a framework that allows for parallelizing N // independent pieces of work until done or the context is canceled. func ParallelizeUntil(ctx context.Context, workers, pieces int, doWorkPiece DoWorkPieceFunc) { var stop 5. podFitsOnNode podFitsOnNode主要内容如下： podFitsOnNode会检查给定的某个Node是否满足预选的函数。 对于给定的pod，podFitsOnNode会检查是否有相同的pod存在，尽量复用缓存过的预选结果。 podFitsOnNode主要在Schedule（调度）和Preempt（抢占）的时候被调用。 当在Schedule中被调用的时候，主要判断是否可以被调度到当前节点，依据为当前节点上所有已存在的pod及被提名要运行到该节点的具有相等或更高优先级的pod。 当在Preempt中被调用的时候，即发生抢占的时候，通过SelectVictimsOnNode函数选出需要被移除的pod，移除后然后将预调度的pod调度到该节点上。 podFitsOnNode基本流程如下： 遍历之前注册好的预选策略predicates.Ordering，并获取预选策略的执行函数。 遍历执行每个预选函数，并返回是否合适，预选失败的原因和错误。 如果预选函数执行的结果不合适，则加入预选失败的数组中。 最后返回预选失败的个数是否为0，和预选失败的原因。 入参： pod PredicateMetadata NodeInfo predicateFuncs schedulercache.Cache nodeCache SchedulingQueue alwaysCheckAllPredicates equivClass 出参： fit PredicateFailureReason 完整代码如下： 此部分代码位于pkg/scheduler/core/generic_scheduler.go // podFitsOnNode checks whether a node given by NodeInfo satisfies the given predicate functions. // For given pod, podFitsOnNode will check if any equivalent pod exists and try to reuse its cached // predicate results as possible. // This function is called from two different places: Schedule and Preempt. // When it is called from Schedule, we want to test whether the pod is schedulable // on the node with all the existing pods on the node plus higher and equal priority // pods nominated to run on the node. // When it is called from Preempt, we should remove the victims of preemption and // add the nominated pods. Removal of the victims is done by SelectVictimsOnNode(). // It removes victims from meta and NodeInfo before calling this function. func podFitsOnNode( pod *v1.Pod, meta algorithm.PredicateMetadata, info *schedulercache.NodeInfo, predicateFuncs map[string]algorithm.FitPredicate, cache schedulercache.Cache, nodeCache *equivalence.NodeCache, queue SchedulingQueue, alwaysCheckAllPredicates bool, equivClass *equivalence.Class, ) (bool, []algorithm.PredicateFailureReason, error) { var ( eCacheAvailable bool failedPredicates []algorithm.PredicateFailureReason ) podsAdded := false // We run predicates twice in some cases. If the node has greater or equal priority // nominated pods, we run them when those pods are added to meta and nodeInfo. // If all predicates succeed in this pass, we run them again when these // nominated pods are not added. This second pass is necessary because some // predicates such as inter-pod affinity may not pass without the nominated pods. // If there are no nominated pods for the node or if the first run of the // predicates fail, we don't run the second pass. // We consider only equal or higher priority pods in the first pass, because // those are the current \"pod\" must yield to them and not take a space opened // for running them. It is ok if the current \"pod\" take resources freed for // lower priority pods. // Requiring that the new pod is schedulable in both circumstances ensures that // we are making a conservative decision: predicates like resources and inter-pod // anti-affinity are more likely to fail when the nominated pods are treated // as running, while predicates like pod affinity are more likely to fail when // the nominated pods are treated as not running. We can't just assume the // nominated pods are running because they are not running right now and in fact, // they may end up getting scheduled to a different node. for i := 0; i 5.1. predicateFuncs 根据之前初注册好的预选策略函数来执行预选，判断节点是否符合调度。 for _, predicateKey := range predicates.Ordering() { if predicate, exist := predicateFuncs[predicateKey]; exist { if eCacheAvailable { fit, reasons, err = nodeCache.RunPredicate(predicate, predicateKey, pod, metaToUse, nodeInfoToUse, equivClass, cache) } else { fit, reasons, err = predicate(pod, metaToUse, nodeInfoToUse) } 预选策略如下： var ( predicatesOrdering = []string{CheckNodeConditionPred, CheckNodeUnschedulablePred, GeneralPred, HostNamePred, PodFitsHostPortsPred, MatchNodeSelectorPred, PodFitsResourcesPred, NoDiskConflictPred, PodToleratesNodeTaintsPred, PodToleratesNodeNoExecuteTaintsPred, CheckNodeLabelPresencePred, CheckServiceAffinityPred, MaxEBSVolumeCountPred, MaxGCEPDVolumeCountPred, MaxCSIVolumeCountPred, MaxAzureDiskVolumeCountPred, CheckVolumeBindingPred, NoVolumeZoneConflictPred, CheckNodeMemoryPressurePred, CheckNodePIDPressurePred, CheckNodeDiskPressurePred, MatchInterPodAffinityPred} ) 6. PodFitsResources 以下以PodFitsResources这个预选函数为例做分析，其他重要的预选函数待后续单独分析。 PodFitsResources用来检查一个节点是否有足够的资源来运行当前的pod，包括CPU、内存、GPU等。 PodFitsResources基本流程如下： 判断当前节点上pod总数加上预调度pod个数是否大于node的可分配pod总数，若是则不允许调度。 判断pod的request值是否都为0，若是则允许调度。 判断pod的request值加上当前node上所有pod的request值总和是否大于node的可分配资源，若是则不允许调度。 判断pod的拓展资源request值加上当前node上所有pod对应的request值总和是否大于node对应的可分配资源，若是则不允许调度。 PodFitsResources的注册代码如下： factory.RegisterFitPredicate(predicates.PodFitsResourcesPred, predicates.PodFitsResources) PodFitsResources入参： pod nodeInfo PredicateMetadata PodFitsResources出参： fit PredicateFailureReason PodFitsResources完整代码： 此部分的代码位于pkg/scheduler/algorithm/predicates/predicates.go // PodFitsResources checks if a node has sufficient resources, such as cpu, memory, gpu, opaque int resources etc to run a pod. // First return value indicates whether a node has sufficient resources to run a pod while the second return value indicates the // predicate failure reasons if the node has insufficient resources to run the pod. func PodFitsResources(pod *v1.Pod, meta algorithm.PredicateMetadata, nodeInfo *schedulercache.NodeInfo) (bool, []algorithm.PredicateFailureReason, error) { node := nodeInfo.Node() if node == nil { return false, nil, fmt.Errorf(\"node not found\") } var predicateFails []algorithm.PredicateFailureReason allowedPodNumber := nodeInfo.AllowedPodNumber() if len(nodeInfo.Pods())+1 > allowedPodNumber { predicateFails = append(predicateFails, NewInsufficientResourceError(v1.ResourcePods, 1, int64(len(nodeInfo.Pods())), int64(allowedPodNumber))) } // No extended resources should be ignored by default. ignoredExtendedResources := sets.NewString() var podRequest *schedulercache.Resource if predicateMeta, ok := meta.(*predicateMetadata); ok { podRequest = predicateMeta.podRequest if predicateMeta.ignoredExtendedResources != nil { ignoredExtendedResources = predicateMeta.ignoredExtendedResources } } else { // We couldn't parse metadata - fallback to computing it. podRequest = GetResourceRequest(pod) } if podRequest.MilliCPU == 0 && podRequest.Memory == 0 && podRequest.EphemeralStorage == 0 && len(podRequest.ScalarResources) == 0 { return len(predicateFails) == 0, predicateFails, nil } allocatable := nodeInfo.AllocatableResource() if allocatable.MilliCPU 6.1. NodeInfo NodeInfo是node的聚合信息，主要包括： node：k8s node的结构体 pods：当前node上pod的数量 requestedResource：当前node上所有pod的request总和 allocatableResource：node的实际所有的可分配资源(对应于Node.Status.Allocatable.*)，可理解为node的资源总量。 此部分代码位于pkg/scheduler/cache/node_info.go // NodeInfo is node level aggregated information. type NodeInfo struct { // Overall node information. node *v1.Node pods []*v1.Pod podsWithAffinity []*v1.Pod usedPorts util.HostPortInfo // Total requested resource of all pods on this node. // It includes assumed pods which scheduler sends binding to apiserver but // didn't get it as scheduled yet. requestedResource *Resource nonzeroRequest *Resource // We store allocatedResources (which is Node.Status.Allocatable.*) explicitly // as int64, to avoid conversions and accessing map. allocatableResource *Resource // Cached taints of the node for faster lookup. taints []v1.Taint taintsErr error // imageStates holds the entry of an image if and only if this image is on the node. The entry can be used for // checking an image's existence and advanced usage (e.g., image locality scheduling policy) based on the image // state information. imageStates map[string]*ImageStateSummary // TransientInfo holds the information pertaining to a scheduling cycle. This will be destructed at the end of // scheduling cycle. // TODO: @ravig. Remove this once we have a clear approach for message passing across predicates and priorities. TransientInfo *transientSchedulerInfo // Cached conditions of node for faster lookup. memoryPressureCondition v1.ConditionStatus diskPressureCondition v1.ConditionStatus pidPressureCondition v1.ConditionStatus // Whenever NodeInfo changes, generation is bumped. // This is used to avoid cloning it if the object didn't change. generation int64 } 6.2. Resource Resource是可计算资源的集合体。主要包括： MilliCPU Memory EphemeralStorage AllowedPodNumber：允许的pod总数(对应于Node.Status.Allocatable.Pods().Value())，一般为110。 ScalarResources // Resource is a collection of compute resource. type Resource struct { MilliCPU int64 Memory int64 EphemeralStorage int64 // We store allowedPodNumber (which is Node.Status.Allocatable.Pods().Value()) // explicitly as int, to avoid conversions and improve performance. AllowedPodNumber int // ScalarResources ScalarResources map[v1.ResourceName]int64 } 以下分析podFitsOnNode的具体流程。 6.3. allowedPodNumber 首先获取节点的信息，先判断如果该节点当前所有的pod的个数加上当前预调度的pod是否会大于该节点允许的pod的总数，一般为110个。如果超过，则predicateFails数组增加1，即当前节点不适合该pod。 node := nodeInfo.Node() if node == nil { return false, nil, fmt.Errorf(\"node not found\") } var predicateFails []algorithm.PredicateFailureReason allowedPodNumber := nodeInfo.AllowedPodNumber() if len(nodeInfo.Pods())+1 > allowedPodNumber { predicateFails = append(predicateFails, NewInsufficientResourceError(v1.ResourcePods, 1, int64(len(nodeInfo.Pods())), int64(allowedPodNumber))) } 6.4. podRequest 如果podRequest都为0，则允许调度到该节点，直接返回结果。 if podRequest.MilliCPU == 0 && podRequest.Memory == 0 && podRequest.EphemeralStorage == 0 && len(podRequest.ScalarResources) == 0 { return len(predicateFails) == 0, predicateFails, nil } 6.5. AllocatableResource 如果当前预调度的pod的request资源加上当前node上所有pod的request总和大于该node的可分配资源总量，则不允许调度到该节点，直接返回结果。其中request资源包括CPU、内存、storage。 allocatable := nodeInfo.AllocatableResource() if allocatable.MilliCPU 6.6. ScalarResources 判断其他拓展的标量资源，是否该pod的request值加上当前node上所有pod的对应资源的request总和大于该node上对应资源的可分配总量，如果是，则不允许调度到该节点。 for rName, rQuant := range podRequest.ScalarResources { if v1helper.IsExtendedResourceName(rName) { // If this resource is one of the extended resources that should be // ignored, we will skip checking it. if ignoredExtendedResources.Has(string(rName)) { continue } } if allocatable.ScalarResources[rName] 7. 总结 findNodesThatFit基于给定的预选函数过滤node，每个node传入到预选函数中来确实该节点是否符合要求。 findNodesThatFit的入参是被调度的pod和当前的节点列表，返回预选节点列表和错误。 findNodesThatFit基本流程如下： 设置可行节点的总数，作为预选节点数组的容量，避免总节点过多导致需要筛选的节点过多，效率低。 通过NodeTree不断获取下一个节点来判断该节点是否满足pod的调度条件。 通过之前注册的各种预选函数来判断当前节点是否符合pod的调度条件。 最后返回满足调度条件的node列表，供下一步的优选操作。 7.1. checkNode checkNode是一个校验node是否符合要求的函数，其中实际调用到的核心函数是podFitsOnNode。再通过workqueue并发执行checkNode操作。 checkNode主要流程如下： 通过cache中的nodeTree不断获取下一个node。 将当前node和pod传入podFitsOnNode判断当前node是否符合要求。 如果当前node符合要求就将当前node加入预选节点的数组中filtered。 如果当前node不满足要求，则加入到失败的数组中，并记录原因。 通过workqueue.ParallelizeUntil并发执行checkNode函数，一旦找到配置的可行节点数，就停止搜索更多节点。 7.2. podFitsOnNode 其中会调用到核心函数podFitsOnNode。 podFitsOnNode主要内容如下： podFitsOnNode会检查给定的某个Node是否满足预选的函数。 对于给定的pod，podFitsOnNode会检查是否有相同的pod存在，尽量复用缓存过的预选结果。 podFitsOnNode主要在Schedule（调度）和Preempt（抢占）的时候被调用。 当在Schedule中被调用的时候，主要判断是否可以被调度到当前节点，依据为当前节点上所有已存在的pod及被提名要运行到该节点的具有相等或更高优先级的pod。 当在Preempt中被调用的时候，即发生抢占的时候，通过SelectVictimsOnNode函数选出需要被移除的pod，移除后然后将预调度的pod调度到该节点上。 podFitsOnNode基本流程如下： 遍历之前注册好的预选策略predicates.Ordering，并获取预选策略的执行函数。 遍历执行每个预选函数，并返回是否合适，预选失败的原因和错误。 如果预选函数执行的结果不合适，则加入预选失败的数组中。 最后返回预选失败的个数是否为0，和预选失败的原因。 7.3. PodFitsResources 本文只示例分析了其中一个重要的预选函数：PodFitsResources PodFitsResources用来检查一个节点是否有足够的资源来运行当前的pod，包括CPU、内存、GPU等。 PodFitsResources基本流程如下： 判断当前节点上pod总数加上预调度pod个数是否大于node的可分配pod总数，若是则不允许调度。 判断pod的request值是否都为0，若是则允许调度。 判断pod的request值加上当前node上所有pod的request值总和是否大于node的可分配资源，若是则不允许调度。 判断pod的拓展资源request值加上当前node上所有pod对应的request值总和是否大于node对应的可分配资源，若是则不允许调度。 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/core/generic_scheduler.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/algorithm/predicates/predicates.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-scheduler/PrioritizeNodes.html":{"url":"code-analysis/kube-scheduler/PrioritizeNodes.html","title":"PrioritizeNodes","keywords":"","body":"kube-scheduler源码分析（五）之 PrioritizeNodes1. 调用入口2. PrioritizeNodes3. EqualPriorityMap4. processNode5. PriorityMapFunction6. PriorityReduceFunction7. Summarize all scores8. NewSelectorSpreadPriority8.1. CalculateSpreadPriorityMap8.2. CalculateSpreadPriorityReduce9. 总结9.1. PrioritizeNodes9.2. NewSelectorSpreadPriority9.2.1. CalculateSpreadPriorityMap9.2.2. CalculateSpreadPriorityReducekube-scheduler源码分析（五）之 PrioritizeNodes 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析优选策略逻辑，即从预选的节点中选择出最优的节点。优选策略的具体实现函数为PrioritizeNodes。PrioritizeNodes最终返回是一个记录了各个节点分数的列表。 1. 调用入口 genericScheduler.Schedule中对PrioritizeNodes的调用过程如下： 此部分代码位于pkg/scheduler/core/generic_scheduler.go func (g *genericScheduler) Schedule(pod *v1.Pod, nodeLister algorithm.NodeLister) (string, error) { ... trace.Step(\"Prioritizing\") startPriorityEvalTime := time.Now() // When only one node after predicate, just use it. if len(filteredNodes) == 1 { metrics.SchedulingAlgorithmPriorityEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPriorityEvalTime)) return filteredNodes[0].Name, nil } metaPrioritiesInterface := g.priorityMetaProducer(pod, g.cachedNodeInfoMap) // 执行优选逻辑的操作，返回记录各个节点分数的列表 priorityList, err := PrioritizeNodes(pod, g.cachedNodeInfoMap, metaPrioritiesInterface, g.prioritizers, filteredNodes, g.extenders) if err != nil { return \"\", err } metrics.SchedulingAlgorithmPriorityEvaluationDuration.Observe(metrics.SinceInMicroseconds(startPriorityEvalTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PriorityEvaluation).Observe(metrics.SinceInSeconds(startPriorityEvalTime)) ... } 核心代码： // 基于预选节点filteredNodes进一步筛选优选的节点，返回记录各个节点分数的列表 priorityList, err := PrioritizeNodes(pod, g.cachedNodeInfoMap, metaPrioritiesInterface, g.prioritizers, filteredNodes, g.extenders) 2. PrioritizeNodes 优选，从满足的节点中选择出最优的节点。PrioritizeNodes最终返回是一个记录了各个节点分数的列表。 具体操作如下： PrioritizeNodes通过并行运行各个优先级函数来对节点进行优先级排序。 每个优先级函数会给节点打分，打分范围为0-10分。 0 表示优先级最低的节点，10表示优先级最高的节点。 每个优先级函数也有各自的权重。 优先级函数返回的节点分数乘以权重以获得加权分数。 最后组合（添加）所有分数以获得所有节点的总加权分数。 PrioritizeNodes主要流程如下： 如果没有设置优选函数和拓展函数，则全部节点设置相同的分数，直接返回。 依次给node执行map函数进行打分。 再对上述map函数的执行结果执行reduce函数计算最终得分。 最后根据不同优先级函数的权重对得分取加权平均数。 入参： pod nodeNameToInfo meta interface{}, priorityConfigs nodes extenders 出参： HostPriorityList：记录节点分数的列表。 HostPriority定义如下: // HostPriority represents the priority of scheduling to a particular host, higher priority is better. type HostPriority struct { // Name of the host Host string // Score associated with the host Score int } PrioritizeNodes完整代码如下： 此部分代码位于pkg/scheduler/core/generic_scheduler.go // PrioritizeNodes prioritizes the nodes by running the individual priority functions in parallel. // Each priority function is expected to set a score of 0-10 // 0 is the lowest priority score (least preferred node) and 10 is the highest // Each priority function can also have its own weight // The node scores returned by the priority function are multiplied by the weights to get weighted scores // All scores are finally combined (added) to get the total weighted scores of all nodes func PrioritizeNodes( pod *v1.Pod, nodeNameToInfo map[string]*schedulercache.NodeInfo, meta interface{}, priorityConfigs []algorithm.PriorityConfig, nodes []*v1.Node, extenders []algorithm.SchedulerExtender, ) (schedulerapi.HostPriorityList, error) { // If no priority configs are provided, then the EqualPriority function is applied // This is required to generate the priority list in the required format if len(priorityConfigs) == 0 && len(extenders) == 0 { result := make(schedulerapi.HostPriorityList, 0, len(nodes)) for i := range nodes { hostPriority, err := EqualPriorityMap(pod, meta, nodeNameToInfo[nodes[i].Name]) if err != nil { return nil, err } result = append(result, hostPriority) } return result, nil } var ( mu = sync.Mutex{} wg = sync.WaitGroup{} errs []error ) appendError := func(err error) { mu.Lock() defer mu.Unlock() errs = append(errs, err) } results := make([]schedulerapi.HostPriorityList, len(priorityConfigs), len(priorityConfigs)) for i, priorityConfig := range priorityConfigs { if priorityConfig.Function != nil { // DEPRECATED wg.Add(1) go func(index int, config algorithm.PriorityConfig) { defer wg.Done() var err error results[index], err = config.Function(pod, nodeNameToInfo, nodes) if err != nil { appendError(err) } }(i, priorityConfig) } else { results[i] = make(schedulerapi.HostPriorityList, len(nodes)) } } processNode := func(index int) { nodeInfo := nodeNameToInfo[nodes[index].Name] var err error for i := range priorityConfigs { if priorityConfigs[i].Function != nil { continue } results[i][index], err = priorityConfigs[i].Map(pod, meta, nodeInfo) if err != nil { appendError(err) return } } } workqueue.Parallelize(16, len(nodes), processNode) for i, priorityConfig := range priorityConfigs { if priorityConfig.Reduce == nil { continue } wg.Add(1) go func(index int, config algorithm.PriorityConfig) { defer wg.Done() if err := config.Reduce(pod, meta, nodeNameToInfo, results[index]); err != nil { appendError(err) } if glog.V(10) { for _, hostPriority := range results[index] { glog.Infof(\"%v -> %v: %v, Score: (%d)\", pod.Name, hostPriority.Host, config.Name, hostPriority.Score) } } }(i, priorityConfig) } // Wait for all computations to be finished. wg.Wait() if len(errs) != 0 { return schedulerapi.HostPriorityList{}, errors.NewAggregate(errs) } // Summarize all scores. result := make(schedulerapi.HostPriorityList, 0, len(nodes)) for i := range nodes { result = append(result, schedulerapi.HostPriority{Host: nodes[i].Name, Score: 0}) for j := range priorityConfigs { result[i].Score += results[j][i].Score * priorityConfigs[j].Weight } } if len(extenders) != 0 && nodes != nil { combinedScores := make(map[string]int, len(nodeNameToInfo)) for _, extender := range extenders { if !extender.IsInterested(pod) { continue } wg.Add(1) go func(ext algorithm.SchedulerExtender) { defer wg.Done() prioritizedList, weight, err := ext.Prioritize(pod, nodes) if err != nil { // Prioritization errors from extender can be ignored, let k8s/other extenders determine the priorities return } mu.Lock() for i := range *prioritizedList { host, score := (*prioritizedList)[i].Host, (*prioritizedList)[i].Score combinedScores[host] += score * weight } mu.Unlock() }(extender) } // wait for all go routines to finish wg.Wait() for i := range result { result[i].Score += combinedScores[result[i].Host] } } if glog.V(10) { for i := range result { glog.V(10).Infof(\"Host %s => Score %d\", result[i].Host, result[i].Score) } } return result, nil } 以下对PrioritizeNodes分段进行分析。 3. EqualPriorityMap 如果没有提供优选函数和拓展函数，则将所有的节点设置为相同的优先级，即节点的score都为1，然后直接返回结果。(但一般情况下优选函数列表都不为空) // If no priority configs are provided, then the EqualPriority function is applied // This is required to generate the priority list in the required format if len(priorityConfigs) == 0 && len(extenders) == 0 { result := make(schedulerapi.HostPriorityList, 0, len(nodes)) for i := range nodes { hostPriority, err := EqualPriorityMap(pod, meta, nodeNameToInfo[nodes[i].Name]) if err != nil { return nil, err } result = append(result, hostPriority) } return result, nil } EqualPriorityMap具体实现如下： // EqualPriorityMap is a prioritizer function that gives an equal weight of one to all nodes func EqualPriorityMap(_ *v1.Pod, _ interface{}, nodeInfo *schedulercache.NodeInfo) (schedulerapi.HostPriority, error) { node := nodeInfo.Node() if node == nil { return schedulerapi.HostPriority{}, fmt.Errorf(\"node not found\") } return schedulerapi.HostPriority{ Host: node.Name, Score: 1, }, nil } 4. processNode processNode就是基于index拿出node的信息，调用之前注册的各种优选函数（此处是mapFunction），通过优选函数对node和pod进行处理，最后返回一个记录node分数的列表result。processNode同样也使用workqueue.Parallelize来进行并行处理。(processNode类似于预选逻辑findNodesThatFit中使用到的checkNode的作用) 其中优选函数是通过priorityConfigs来记录，每类优选函数包括PriorityMapFunction和PriorityReduceFunction两种函数。优选函数的注册部分可参考registerAlgorithmProvider。 processNode := func(index int) { nodeInfo := nodeNameToInfo[nodes[index].Name] var err error for i := range priorityConfigs { if priorityConfigs[i].Function != nil { continue } results[i][index], err = priorityConfigs[i].Map(pod, meta, nodeInfo) if err != nil { appendError(err) return } } } // 并行执行processNode workqueue.Parallelize(16, len(nodes), processNode) priorityConfigs定义如下： 核心属性： Map ：PriorityMapFunction Reduce：PriorityReduceFunction // PriorityConfig is a config used for a priority function. type PriorityConfig struct { Name string Map PriorityMapFunction Reduce PriorityReduceFunction // TODO: Remove it after migrating all functions to // Map-Reduce pattern. Function PriorityFunction Weight int } 具体的优选函数处理逻辑待下文分析，本文会以NewSelectorSpreadPriority函数为例。 5. PriorityMapFunction PriorityMapFunction是一个计算给定节点的每个节点结果的函数。 PriorityMapFunction定义如下： // PriorityMapFunction is a function that computes per-node results for a given node. // TODO: Figure out the exact API of this method. // TODO: Change interface{} to a specific type. type PriorityMapFunction func(pod *v1.Pod, meta interface{}, nodeInfo *schedulercache.NodeInfo) (schedulerapi.HostPriority, error) PriorityMapFunction是在processNode中调用的，代码如下： results[i][index], err = priorityConfigs[i].Map(pod, meta, nodeInfo) 下文会分析NewSelectorSpreadPriority在的map函数CalculateSpreadPriorityMap。 6. PriorityReduceFunction PriorityReduceFunction是一个聚合每个节点结果并计算所有节点的最终得分的函数。 PriorityReduceFunction定义如下： // PriorityReduceFunction is a function that aggregated per-node results and computes // final scores for all nodes. // TODO: Figure out the exact API of this method. // TODO: Change interface{} to a specific type. type PriorityReduceFunction func(pod *v1.Pod, meta interface{}, nodeNameToInfo map[string]*schedulercache.NodeInfo, result schedulerapi.HostPriorityList) error PrioritizeNodes中对reduce函数调用部分如下： for i, priorityConfig := range priorityConfigs { if priorityConfig.Reduce == nil { continue } wg.Add(1) go func(index int, config algorithm.PriorityConfig) { defer wg.Done() if err := config.Reduce(pod, meta, nodeNameToInfo, results[index]); err != nil { appendError(err) } if glog.V(10) { for _, hostPriority := range results[index] { glog.Infof(\"%v -> %v: %v, Score: (%d)\", pod.Name, hostPriority.Host, config.Name, hostPriority.Score) } } }(i, priorityConfig) } 下文会分析NewSelectorSpreadPriority在的reduce函数CalculateSpreadPriorityReduce。 7. Summarize all scores 先等待计算完成再计算加权平均数。 // Wait for all computations to be finished. wg.Wait() if len(errs) != 0 { return schedulerapi.HostPriorityList{}, errors.NewAggregate(errs) } 计算所有节点的加权平均数。 // Summarize all scores. result := make(schedulerapi.HostPriorityList, 0, len(nodes)) for i := range nodes { result = append(result, schedulerapi.HostPriority{Host: nodes[i].Name, Score: 0}) for j := range priorityConfigs { result[i].Score += results[j][i].Score * priorityConfigs[j].Weight } } 当设置了拓展的计算方式，则增加拓展计算方式的加权平均数。 if len(extenders) != 0 && nodes != nil { combinedScores := make(map[string]int, len(nodeNameToInfo)) for _, extender := range extenders { if !extender.IsInterested(pod) { continue } wg.Add(1) go func(ext algorithm.SchedulerExtender) { defer wg.Done() prioritizedList, weight, err := ext.Prioritize(pod, nodes) if err != nil { // Prioritization errors from extender can be ignored, let k8s/other extenders determine the priorities return } mu.Lock() for i := range *prioritizedList { host, score := (*prioritizedList)[i].Host, (*prioritizedList)[i].Score combinedScores[host] += score * weight } mu.Unlock() }(extender) } // wait for all go routines to finish wg.Wait() for i := range result { result[i].Score += combinedScores[result[i].Host] } } 8. NewSelectorSpreadPriority 以下以NewSelectorSpreadPriority这个优选函数来做分析，其他重要的优选函数待后续专门分析。 NewSelectorSpreadPriority主要的功能是将属于相同service和rs下的pod尽量分布在不同的node上。 该函数的注册代码如下： 此部分代码位于pkg/scheduler/algorithmprovider/defaults/defaults.go // ServiceSpreadingPriority is a priority config factory that spreads pods by minimizing // the number of pods (belonging to the same service) on the same node. // Register the factory so that it's available, but do not include it as part of the default priorities // Largely replaced by \"SelectorSpreadPriority\", but registered for backward compatibility with 1.0 factory.RegisterPriorityConfigFactory( \"ServiceSpreadingPriority\", factory.PriorityConfigFactory{ MapReduceFunction: func(args factory.PluginFactoryArgs) (algorithm.PriorityMapFunction, algorithm.PriorityReduceFunction) { return priorities.NewSelectorSpreadPriority(args.ServiceLister, algorithm.EmptyControllerLister{}, algorithm.EmptyReplicaSetLister{}, algorithm.EmptyStatefulSetLister{}) }, Weight: 1, }, ) NewSelectorSpreadPriority的具体实现如下： 此部分代码位于pkg/scheduler/algorithm/priorities/selector_spreading.go // NewSelectorSpreadPriority creates a SelectorSpread. func NewSelectorSpreadPriority( serviceLister algorithm.ServiceLister, controllerLister algorithm.ControllerLister, replicaSetLister algorithm.ReplicaSetLister, statefulSetLister algorithm.StatefulSetLister) (algorithm.PriorityMapFunction, algorithm.PriorityReduceFunction) { selectorSpread := &SelectorSpread{ serviceLister: serviceLister, controllerLister: controllerLister, replicaSetLister: replicaSetLister, statefulSetLister: statefulSetLister, } return selectorSpread.CalculateSpreadPriorityMap, selectorSpread.CalculateSpreadPriorityReduce } NewSelectorSpreadPriority主要包括map和reduce两种函数，分别对应CalculateSpreadPriorityMap，CalculateSpreadPriorityReduce。 8.1. CalculateSpreadPriorityMap CalculateSpreadPriorityMap的主要作用是将相同service、RC、RS或statefulset的pod分布在不同的节点上。当调度一个pod的时候，先寻找与该pod匹配的service、RS、RC或statefulset，然后寻找与其selector匹配的已存在的pod，寻找存在这类pod最少的节点。 基本流程如下： 寻找与该pod对应的service、RS、RC、statefulset匹配的selector。 遍历当前节点的所有pod，将该节点上已存在的selector匹配到的pod的个数作为该节点的分数（此时，分数大的表示匹配到的pod越多，越不符合被调度的条件，该分数在reduce阶段会被按10分制处理成分数大的越符合被调度的条件）。 此部分代码位于pkg/scheduler/algorithm/priorities/selector_spreading.go // CalculateSpreadPriorityMap spreads pods across hosts, considering pods // belonging to the same service,RC,RS or StatefulSet. // When a pod is scheduled, it looks for services, RCs,RSs and StatefulSets that match the pod, // then finds existing pods that match those selectors. // It favors nodes that have fewer existing matching pods. // i.e. it pushes the scheduler towards a node where there's the smallest number of // pods which match the same service, RC,RSs or StatefulSets selectors as the pod being scheduled. func (s *SelectorSpread) CalculateSpreadPriorityMap(pod *v1.Pod, meta interface{}, nodeInfo *schedulercache.NodeInfo) (schedulerapi.HostPriority, error) { var selectors []labels.Selector node := nodeInfo.Node() if node == nil { return schedulerapi.HostPriority{}, fmt.Errorf(\"node not found\") } priorityMeta, ok := meta.(*priorityMetadata) if ok { selectors = priorityMeta.podSelectors } else { selectors = getSelectors(pod, s.serviceLister, s.controllerLister, s.replicaSetLister, s.statefulSetLister) } if len(selectors) == 0 { return schedulerapi.HostPriority{ Host: node.Name, Score: int(0), }, nil } count := int(0) for _, nodePod := range nodeInfo.Pods() { if pod.Namespace != nodePod.Namespace { continue } // When we are replacing a failed pod, we often see the previous // deleted version while scheduling the replacement. // Ignore the previous deleted version for spreading purposes // (it can still be considered for resource restrictions etc.) if nodePod.DeletionTimestamp != nil { glog.V(4).Infof(\"skipping pending-deleted pod: %s/%s\", nodePod.Namespace, nodePod.Name) continue } for _, selector := range selectors { if selector.Matches(labels.Set(nodePod.ObjectMeta.Labels)) { count++ break } } } return schedulerapi.HostPriority{ Host: node.Name, Score: int(count), }, nil } 以下分段分析： 先获得selector。 selectors = getSelectors(pod, s.serviceLister, s.controllerLister, s.replicaSetLister, s.statefulSetLister) 计算节点上匹配selector的pod的个数，作为该节点分数，该分数并不是最终节点的分数，只是中间过渡的记录状态。 count := int(0) for _, nodePod := range nodeInfo.Pods() { ... for _, selector := range selectors { if selector.Matches(labels.Set(nodePod.ObjectMeta.Labels)) { count++ break } } } 8.2. CalculateSpreadPriorityReduce CalculateSpreadPriorityReduce根据节点上现有匹配pod的数量计算每个节点的十分制的分数，具有较少现有匹配pod的节点的分数越高，表示节点越可能被调度到。 基本流程如下： 记录所有节点中匹配到pod个数最多的节点的分数（即匹配到的pod最多的个数）。 遍历所有的节点，按比例取十分制的得分，计算方式为：(节点中最多匹配pod的个数-当前节点pod的个数)/节点中最多匹配pod的个数。此时，分数越高表示该节点上匹配到的pod的个数越少，越可能被调度到，即满足把相同selector的pod分散到不同节点的需求。 此部分代码位于pkg/scheduler/algorithm/priorities/selector_spreading.go // CalculateSpreadPriorityReduce calculates the source of each node // based on the number of existing matching pods on the node // where zone information is included on the nodes, it favors nodes // in zones with fewer existing matching pods. func (s *SelectorSpread) CalculateSpreadPriorityReduce(pod *v1.Pod, meta interface{}, nodeNameToInfo map[string]*schedulercache.NodeInfo, result schedulerapi.HostPriorityList) error { countsByZone := make(map[string]int, 10) maxCountByZone := int(0) maxCountByNodeName := int(0) for i := range result { if result[i].Score > maxCountByNodeName { maxCountByNodeName = result[i].Score } zoneID := utilnode.GetZoneKey(nodeNameToInfo[result[i].Host].Node()) if zoneID == \"\" { continue } countsByZone[zoneID] += result[i].Score } for zoneID := range countsByZone { if countsByZone[zoneID] > maxCountByZone { maxCountByZone = countsByZone[zoneID] } } haveZones := len(countsByZone) != 0 maxCountByNodeNameFloat64 := float64(maxCountByNodeName) maxCountByZoneFloat64 := float64(maxCountByZone) MaxPriorityFloat64 := float64(schedulerapi.MaxPriority) for i := range result { // initializing to the default/max node score of maxPriority fScore := MaxPriorityFloat64 if maxCountByNodeName > 0 { fScore = MaxPriorityFloat64 * (float64(maxCountByNodeName-result[i].Score) / maxCountByNodeNameFloat64) } // If there is zone information present, incorporate it if haveZones { zoneID := utilnode.GetZoneKey(nodeNameToInfo[result[i].Host].Node()) if zoneID != \"\" { zoneScore := MaxPriorityFloat64 if maxCountByZone > 0 { zoneScore = MaxPriorityFloat64 * (float64(maxCountByZone-countsByZone[zoneID]) / maxCountByZoneFloat64) } fScore = (fScore * (1.0 - zoneWeighting)) + (zoneWeighting * zoneScore) } } result[i].Score = int(fScore) if glog.V(10) { glog.Infof( \"%v -> %v: SelectorSpreadPriority, Score: (%d)\", pod.Name, result[i].Host, int(fScore), ) } } return nil } 以下分段分析： 先获取所有节点中匹配到的pod最多的个数。 for i := range result { if result[i].Score > maxCountByNodeName { maxCountByNodeName = result[i].Score } zoneID := utilnode.GetZoneKey(nodeNameToInfo[result[i].Host].Node()) if zoneID == \"\" { continue } countsByZone[zoneID] += result[i].Score } 遍历所有的节点，按比例取十分制的得分。 for i := range result { // initializing to the default/max node score of maxPriority fScore := MaxPriorityFloat64 if maxCountByNodeName > 0 { fScore = MaxPriorityFloat64 * (float64(maxCountByNodeName-result[i].Score) / maxCountByNodeNameFloat64) } ... } 9. 总结 优选，从满足的节点中选择出最优的节点。PrioritizeNodes最终返回是一个记录了各个节点分数的列表。 9.1. PrioritizeNodes 主要流程如下： 如果没有设置优选函数和拓展函数，则全部节点设置相同的分数，直接返回。 依次给node执行map函数进行打分。 再对上述map函数的执行结果执行reduce函数计算最终得分。 最后根据不同优先级函数的权重对得分取加权平均数。 其中每类优选函数会包含map函数和reduce函数两种。 9.2. NewSelectorSpreadPriority 其中以NewSelectorSpreadPriority这个优选函数为例作分析，该函数的功能是将相同service、RS、RC或statefulset下pod尽量分散到不同的节点上。包括map函数和reduce函数两部分，具体如下。 9.2.1. CalculateSpreadPriorityMap 基本流程如下： 寻找与该pod对应的service、RS、RC、statefulset匹配的selector。 遍历当前节点的所有pod，将该节点上已存在的selector匹配到的pod的个数作为该节点的分数（此时，分数大的表示匹配到的pod越多，越不符合被调度的条件，该分数在reduce阶段会被按10分制处理成分数大的越符合被调度的条件）。 9.2.2. CalculateSpreadPriorityReduce 基本流程如下： 记录所有节点中匹配到pod个数最多的节点的分数（即匹配到的pod最多的个数）。 遍历所有的节点，按比例取十分制的得分，计算方式为：(节点中最多匹配pod的个数-当前节点pod的个数)/节点中最多匹配pod的个数。此时，分数越高表示该节点上匹配到的pod的个数越少，越可能被调度到，即满足把相同selector的pod分散到不同节点的需求。 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/core/generic_scheduler.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/algorithm/priorities/selector_spreading.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-scheduler/preempt.html":{"url":"code-analysis/kube-scheduler/preempt.html","title":"preempt","keywords":"","body":"kube-scheduler源码分析（六）之 preempt1. 调用入口2. Scheduler.preempt2.1. preempt2.2. NominatedNodeName3. genericScheduler.Preempt3.1. selectNodesForPreemption3.1.1. selectVictimsOnNode3.2. processPreemptionWithExtenders3.3. pickOneNodeForPreemption3.4. getLowerPriorityNominatedPods4. 总结4.1. Scheduler.preempt4.2. genericScheduler.Preemptkube-scheduler源码分析（六）之 preempt 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析调度中的抢占逻辑，当pod不适合任何节点的时候，可能pod会调度失败，这时候可能会发生抢占。抢占逻辑的具体实现函数为Scheduler.preempt。 1. 调用入口 当pod不适合任何节点的时候，可能pod会调度失败。这时候可能会发生抢占。 scheduleOne函数中关于抢占调用的逻辑如下： 此部分的代码位于/pkg/scheduler/scheduler.go // scheduleOne does the entire scheduling workflow for a single pod. It is serialized on the scheduling algorithm's host fitting. func (sched *Scheduler) scheduleOne() { ... suggestedHost, err := sched.schedule(pod) if err != nil { // schedule() may have failed because the pod would not fit on any host, so we try to // preempt, with the expectation that the next time the pod is tried for scheduling it // will fit due to the preemption. It is also possible that a different pod will schedule // into the resources that were preempted, but this is harmless. if fitError, ok := err.(*core.FitError); ok { preemptionStartTime := time.Now() // 执行抢占逻辑 sched.preempt(pod, fitError) metrics.PreemptionAttempts.Inc() metrics.SchedulingAlgorithmPremptionEvaluationDuration.Observe(metrics.SinceInMicroseconds(preemptionStartTime)) metrics.SchedulingLatency.WithLabelValues(metrics.PreemptionEvaluation).Observe(metrics.SinceInSeconds(preemptionStartTime)) } return } ... } 其中核心代码为： // 基于sched.schedule(pod)返回的err和当前待调度的pod执行抢占策略 sched.preempt(pod, fitError) 2. Scheduler.preempt 当pod调度失败的时候，会抢占低优先级pod的空间来给高优先级的pod。其中入参为调度失败的pod对象和调度失败的err。 抢占的基本流程如下： 判断是否有关闭抢占机制，如果关闭抢占机制则直接返回。 获取调度失败pod的最新对象数据。 执行抢占算法Algorithm.Preempt，返回预调度节点和需要被剔除的pod列表。 将抢占算法返回的node添加到pod的Status.NominatedNodeName中，并删除需要被剔除的pod。 当抢占算法返回的node是nil的时候，清除pod的Status.NominatedNodeName信息。 整个抢占流程的最终结果实际上是更新Pod.Status.NominatedNodeName属性的信息。如果抢占算法返回的节点不为空，则将该node更新到Pod.Status.NominatedNodeName中，否则就将Pod.Status.NominatedNodeName设置为空。 2.1. preempt preempt的具体实现函数： 此部分的代码位于/pkg/scheduler/scheduler.go // preempt tries to create room for a pod that has failed to schedule, by preempting lower priority pods if possible. // If it succeeds, it adds the name of the node where preemption has happened to the pod annotations. // It returns the node name and an error if any. func (sched *Scheduler) preempt(preemptor *v1.Pod, scheduleErr error) (string, error) { if !util.PodPriorityEnabled() || sched.config.DisablePreemption { glog.V(3).Infof(\"Pod priority feature is not enabled or preemption is disabled by scheduler configuration.\" + \" No preemption is performed.\") return \"\", nil } preemptor, err := sched.config.PodPreemptor.GetUpdatedPod(preemptor) if err != nil { glog.Errorf(\"Error getting the updated preemptor pod object: %v\", err) return \"\", err } node, victims, nominatedPodsToClear, err := sched.config.Algorithm.Preempt(preemptor, sched.config.NodeLister, scheduleErr) metrics.PreemptionVictims.Set(float64(len(victims))) if err != nil { glog.Errorf(\"Error preempting victims to make room for %v/%v.\", preemptor.Namespace, preemptor.Name) return \"\", err } var nodeName = \"\" if node != nil { nodeName = node.Name err = sched.config.PodPreemptor.SetNominatedNodeName(preemptor, nodeName) if err != nil { glog.Errorf(\"Error in preemption process. Cannot update pod %v/%v annotations: %v\", preemptor.Namespace, preemptor.Name, err) return \"\", err } for _, victim := range victims { if err := sched.config.PodPreemptor.DeletePod(victim); err != nil { glog.Errorf(\"Error preempting pod %v/%v: %v\", victim.Namespace, victim.Name, err) return \"\", err } sched.config.Recorder.Eventf(victim, v1.EventTypeNormal, \"Preempted\", \"by %v/%v on node %v\", preemptor.Namespace, preemptor.Name, nodeName) } } // Clearing nominated pods should happen outside of \"if node != nil\". Node could // be nil when a pod with nominated node name is eligible to preempt again, // but preemption logic does not find any node for it. In that case Preempt() // function of generic_scheduler.go returns the pod itself for removal of the annotation. for _, p := range nominatedPodsToClear { rErr := sched.config.PodPreemptor.RemoveNominatedNodeName(p) if rErr != nil { glog.Errorf(\"Cannot remove nominated node annotation of pod: %v\", rErr) // We do not return as this error is not critical. } } return nodeName, err } 以下对preempt的实现分段分析。 如果设置关闭抢占机制，则直接返回。 if !util.PodPriorityEnabled() || sched.config.DisablePreemption { glog.V(3).Infof(\"Pod priority feature is not enabled or preemption is disabled by scheduler configuration.\" + \" No preemption is performed.\") return \"\", nil } 获取当前pod的最新状态。 preemptor, err := sched.config.PodPreemptor.GetUpdatedPod(preemptor) if err != nil { glog.Errorf(\"Error getting the updated preemptor pod object: %v\", err) return \"\", err } GetUpdatedPod的实现就是去拿pod的对象。 func (p *podPreemptor) GetUpdatedPod(pod *v1.Pod) (*v1.Pod, error) { return p.Client.CoreV1().Pods(pod.Namespace).Get(pod.Name, metav1.GetOptions{}) } 接着执行抢占的算法。抢占的算法返回预调度节点的信息和因抢占被剔除的pod的信息。具体的抢占算法逻辑下文分析。 node, victims, nominatedPodsToClear, err := sched.config.Algorithm.Preempt(preemptor, sched.config.NodeLister, scheduleErr) 将预调度节点的信息更新到pod的Status.NominatedNodeName属性中。 err = sched.config.PodPreemptor.SetNominatedNodeName(preemptor, nodeName) SetNominatedNodeName的具体实现为： func (p *podPreemptor) SetNominatedNodeName(pod *v1.Pod, nominatedNodeName string) error { podCopy := pod.DeepCopy() podCopy.Status.NominatedNodeName = nominatedNodeName _, err := p.Client.CoreV1().Pods(pod.Namespace).UpdateStatus(podCopy) return err } 接着删除因抢占而需要被剔除的pod。 err := sched.config.PodPreemptor.DeletePod(victim) PodPreemptor.DeletePod的具体实现就是删除具体的pod。 func (p *podPreemptor) DeletePod(pod *v1.Pod) error { return p.Client.CoreV1().Pods(pod.Namespace).Delete(pod.Name, &metav1.DeleteOptions{}) } 如果抢占算法得出的node对象为nil，则将pod的Status.NominatedNodeName属性设置为空。 // Clearing nominated pods should happen outside of \"if node != nil\". Node could // be nil when a pod with nominated node name is eligible to preempt again, // but preemption logic does not find any node for it. In that case Preempt() // function of generic_scheduler.go returns the pod itself for removal of the annotation. for _, p := range nominatedPodsToClear { rErr := sched.config.PodPreemptor.RemoveNominatedNodeName(p) if rErr != nil { glog.Errorf(\"Cannot remove nominated node annotation of pod: %v\", rErr) // We do not return as this error is not critical. } } RemoveNominatedNodeName的具体实现如下： func (p *podPreemptor) RemoveNominatedNodeName(pod *v1.Pod) error { if len(pod.Status.NominatedNodeName) == 0 { return nil } return p.SetNominatedNodeName(pod, \"\") } 2.2. NominatedNodeName Pod.Status.NominatedNodeName的说明： nominatedNodeName是调度失败的pod抢占别的pod的时候，被抢占pod的运行节点。但在剔除被抢占pod之前该调度失败的pod不会被调度。同时也不保证最终该pod一定会调度到nominatedNodeName的机器上，也可能因为之后资源充足等原因调度到其他节点上。最终该pod会被加到调度的队列中。 其中加入到调度队列的具体过程如下： func NewConfigFactory(args *ConfigFactoryArgs) scheduler.Configurator { ... // unscheduled pod queue args.PodInformer.Informer().AddEventHandler( ... Handler: cache.ResourceEventHandlerFuncs{ AddFunc: c.addPodToSchedulingQueue, UpdateFunc: c.updatePodInSchedulingQueue, DeleteFunc: c.deletePodFromSchedulingQueue, }, }, ) ... } addPodToSchedulingQueue: func (c *configFactory) addPodToSchedulingQueue(obj interface{}) { if err := c.podQueue.Add(obj.(*v1.Pod)); err != nil { runtime.HandleError(fmt.Errorf(\"unable to queue %T: %v\", obj, err)) } } PriorityQueue.Add: // Add adds a pod to the active queue. It should be called only when a new pod // is added so there is no chance the pod is already in either queue. func (p *PriorityQueue) Add(pod *v1.Pod) error { p.lock.Lock() defer p.lock.Unlock() err := p.activeQ.Add(pod) if err != nil { glog.Errorf(\"Error adding pod %v/%v to the scheduling queue: %v\", pod.Namespace, pod.Name, err) } else { if p.unschedulableQ.get(pod) != nil { glog.Errorf(\"Error: pod %v/%v is already in the unschedulable queue.\", pod.Namespace, pod.Name) p.deleteNominatedPodIfExists(pod) p.unschedulableQ.delete(pod) } p.addNominatedPodIfNeeded(pod) p.cond.Broadcast() } return err } addNominatedPodIfNeeded: // addNominatedPodIfNeeded adds a pod to nominatedPods if it has a NominatedNodeName and it does not // already exist in the map. Adding an existing pod is not going to update the pod. func (p *PriorityQueue) addNominatedPodIfNeeded(pod *v1.Pod) { nnn := NominatedNodeName(pod) if len(nnn) > 0 { for _, np := range p.nominatedPods[nnn] { if np.UID == pod.UID { glog.Errorf(\"Pod %v/%v already exists in the nominated map!\", pod.Namespace, pod.Name) return } } p.nominatedPods[nnn] = append(p.nominatedPods[nnn], pod) } } NominatedNodeName: // NominatedNodeName returns nominated node name of a Pod. func NominatedNodeName(pod *v1.Pod) string { return pod.Status.NominatedNodeName } 3. genericScheduler.Preempt 抢占算法依然是在ScheduleAlgorithm接口中定义。 // ScheduleAlgorithm is an interface implemented by things that know how to schedule pods // onto machines. type ScheduleAlgorithm interface { Schedule(*v1.Pod, NodeLister) (selectedMachine string, err error) // Preempt receives scheduling errors for a pod and tries to create room for // the pod by preempting lower priority pods if possible. // It returns the node where preemption happened, a list of preempted pods, a // list of pods whose nominated node name should be removed, and error if any. Preempt(*v1.Pod, NodeLister, error) (selectedNode *v1.Node, preemptedPods []*v1.Pod, cleanupNominatedPods []*v1.Pod, err error) // Predicates() returns a pointer to a map of predicate functions. This is // exposed for testing. Predicates() map[string]FitPredicate // Prioritizers returns a slice of priority config. This is exposed for // testing. Prioritizers() []PriorityConfig } Preempt的具体实现为genericScheduler结构体。 Preempt的主要实现是找到可以调度的节点和上面因抢占而需要被剔除的pod。 基本流程如下： 根据调度失败的原因对所有节点先进行一批筛选，筛选出潜在的被调度节点列表。 通过selectNodesForPreemption筛选出需要牺牲的pod和其节点。 基于拓展抢占逻辑再次对上述筛选出来的牺牲者做过滤。 基于上述的过滤结果，选择一个最终可能因抢占被调度的节点。 基于上述的候选节点，找出该节点上优先级低于当前被调度pod的牺牲者pod列表。 完整代码如下： 此部分代码位于pkg/scheduler/core/generic_scheduler.go // preempt finds nodes with pods that can be preempted to make room for \"pod\" to // schedule. It chooses one of the nodes and preempts the pods on the node and // returns 1) the node, 2) the list of preempted pods if such a node is found, // 3) A list of pods whose nominated node name should be cleared, and 4) any // possible error. func (g *genericScheduler) Preempt(pod *v1.Pod, nodeLister algorithm.NodeLister, scheduleErr error) (*v1.Node, []*v1.Pod, []*v1.Pod, error) { // Scheduler may return various types of errors. Consider preemption only if // the error is of type FitError. fitError, ok := scheduleErr.(*FitError) if !ok || fitError == nil { return nil, nil, nil, nil } err := g.cache.UpdateNodeNameToInfoMap(g.cachedNodeInfoMap) if err != nil { return nil, nil, nil, err } if !podEligibleToPreemptOthers(pod, g.cachedNodeInfoMap) { glog.V(5).Infof(\"Pod %v/%v is not eligible for more preemption.\", pod.Namespace, pod.Name) return nil, nil, nil, nil } allNodes, err := nodeLister.List() if err != nil { return nil, nil, nil, err } if len(allNodes) == 0 { return nil, nil, nil, ErrNoNodesAvailable } potentialNodes := nodesWherePreemptionMightHelp(allNodes, fitError.FailedPredicates) if len(potentialNodes) == 0 { glog.V(3).Infof(\"Preemption will not help schedule pod %v/%v on any node.\", pod.Namespace, pod.Name) // In this case, we should clean-up any existing nominated node name of the pod. return nil, nil, []*v1.Pod{pod}, nil } pdbs, err := g.cache.ListPDBs(labels.Everything()) if err != nil { return nil, nil, nil, err } // 找出可能被抢占的节点 nodeToVictims, err := selectNodesForPreemption(pod, g.cachedNodeInfoMap, potentialNodes, g.predicates, g.predicateMetaProducer, g.schedulingQueue, pdbs) if err != nil { return nil, nil, nil, err } // We will only check nodeToVictims with extenders that support preemption. // Extenders which do not support preemption may later prevent preemptor from being scheduled on the nominated // node. In that case, scheduler will find a different host for the preemptor in subsequent scheduling cycles. nodeToVictims, err = g.processPreemptionWithExtenders(pod, nodeToVictims) if err != nil { return nil, nil, nil, err } // 选出最终被抢占的节点 candidateNode := pickOneNodeForPreemption(nodeToVictims) if candidateNode == nil { return nil, nil, nil, err } // Lower priority pods nominated to run on this node, may no longer fit on // this node. So, we should remove their nomination. Removing their // nomination updates these pods and moves them to the active queue. It // lets scheduler find another place for them. // 找出被强占节点上牺牲者pod列表 nominatedPods := g.getLowerPriorityNominatedPods(pod, candidateNode.Name) if nodeInfo, ok := g.cachedNodeInfoMap[candidateNode.Name]; ok { return nodeInfo.Node(), nodeToVictims[candidateNode].Pods, nominatedPods, err } return nil, nil, nil, fmt.Errorf( \"preemption failed: the target node %s has been deleted from scheduler cache\", candidateNode.Name) } 以下对genericScheduler.Preempt分段进行分析。 3.1. selectNodesForPreemption selectNodesForPreemption并行地所有节点中找可能被抢占的节点。 nodeToVictims, err := selectNodesForPreemption(pod, g.cachedNodeInfoMap, potentialNodes, g.predicates,g.predicateMetaProducer, g.schedulingQueue, pdbs) selectNodesForPreemption主要基于selectVictimsOnNode构造一个checkNode的函数，然后并发执行该函数。 selectNodesForPreemption具体实现如下： // selectNodesForPreemption finds all the nodes with possible victims for // preemption in parallel. func selectNodesForPreemption(pod *v1.Pod, nodeNameToInfo map[string]*schedulercache.NodeInfo, potentialNodes []*v1.Node, predicates map[string]algorithm.FitPredicate, metadataProducer algorithm.PredicateMetadataProducer, queue SchedulingQueue, pdbs []*policy.PodDisruptionBudget, ) (map[*v1.Node]*schedulerapi.Victims, error) { nodeToVictims := map[*v1.Node]*schedulerapi.Victims{} var resultLock sync.Mutex // We can use the same metadata producer for all nodes. meta := metadataProducer(pod, nodeNameToInfo) checkNode := func(i int) { nodeName := potentialNodes[i].Name var metaCopy algorithm.PredicateMetadata if meta != nil { metaCopy = meta.ShallowCopy() } pods, numPDBViolations, fits := selectVictimsOnNode(pod, metaCopy, nodeNameToInfo[nodeName], predicates, queue, pdbs) if fits { resultLock.Lock() victims := schedulerapi.Victims{ Pods: pods, NumPDBViolations: numPDBViolations, } nodeToVictims[potentialNodes[i]] = &victims resultLock.Unlock() } } workqueue.Parallelize(16, len(potentialNodes), checkNode) return nodeToVictims, nil } 3.1.1. selectVictimsOnNode selectVictimsOnNode找到应该被抢占的给定节点上的最小pod集合，以便给调度失败的pod安排足够的空间。该函数最终返回的是一个pod的数组。当有更低优先级的pod可能被选择的时候，较高优先级的pod不会被选入该待剔除的pod集合。 基本流程如下： 先检查当该节点上所有低于预被调度pod优先级的pod移除后，该pod能否被调度到当前节点上。 如果上述检查可以，则将该节点的所有低优先级pod按照优先级来排序。 // selectVictimsOnNode finds minimum set of pods on the given node that should // be preempted in order to make enough room for \"pod\" to be scheduled. The // minimum set selected is subject to the constraint that a higher-priority pod // is never preempted when a lower-priority pod could be (higher/lower relative // to one another, not relative to the preemptor \"pod\"). // The algorithm first checks if the pod can be scheduled on the node when all the // lower priority pods are gone. If so, it sorts all the lower priority pods by // their priority and then puts them into two groups of those whose PodDisruptionBudget // will be violated if preempted and other non-violating pods. Both groups are // sorted by priority. It first tries to reprieve as many PDB violating pods as // possible and then does them same for non-PDB-violating pods while checking // that the \"pod\" can still fit on the node. // NOTE: This function assumes that it is never called if \"pod\" cannot be scheduled // due to pod affinity, node affinity, or node anti-affinity reasons. None of // these predicates can be satisfied by removing more pods from the node. func selectVictimsOnNode( pod *v1.Pod, meta algorithm.PredicateMetadata, nodeInfo *schedulercache.NodeInfo, fitPredicates map[string]algorithm.FitPredicate, queue SchedulingQueue, pdbs []*policy.PodDisruptionBudget, ) ([]*v1.Pod, int, bool) { potentialVictims := util.SortableList{CompFunc: util.HigherPriorityPod} nodeInfoCopy := nodeInfo.Clone() removePod := func(rp *v1.Pod) { nodeInfoCopy.RemovePod(rp) if meta != nil { meta.RemovePod(rp) } } addPod := func(ap *v1.Pod) { nodeInfoCopy.AddPod(ap) if meta != nil { meta.AddPod(ap, nodeInfoCopy) } } // As the first step, remove all the lower priority pods from the node and // check if the given pod can be scheduled. podPriority := util.GetPodPriority(pod) for _, p := range nodeInfoCopy.Pods() { if util.GetPodPriority(p) 3.2. processPreemptionWithExtenders processPreemptionWithExtenders基于selectNodesForPreemption选出的牺牲者进行扩展的抢占逻辑继续筛选牺牲者。 // We will only check nodeToVictims with extenders that support preemption. // Extenders which do not support preemption may later prevent preemptor from being scheduled on the nominated // node. In that case, scheduler will find a different host for the preemptor in subsequent scheduling cycles. nodeToVictims, err = g.processPreemptionWithExtenders(pod, nodeToVictims) if err != nil { return nil, nil, nil, err } processPreemptionWithExtenders完整代码如下： // processPreemptionWithExtenders processes preemption with extenders func (g *genericScheduler) processPreemptionWithExtenders( pod *v1.Pod, nodeToVictims map[*v1.Node]*schedulerapi.Victims, ) (map[*v1.Node]*schedulerapi.Victims, error) { if len(nodeToVictims) > 0 { for _, extender := range g.extenders { if extender.SupportsPreemption() && extender.IsInterested(pod) { newNodeToVictims, err := extender.ProcessPreemption( pod, nodeToVictims, g.cachedNodeInfoMap, ) if err != nil { if extender.IsIgnorable() { glog.Warningf(\"Skipping extender %v as it returned error %v and has ignorable flag set\", extender, err) continue } return nil, err } // Replace nodeToVictims with new result after preemption. So the // rest of extenders can continue use it as parameter. nodeToVictims = newNodeToVictims // If node list becomes empty, no preemption can happen regardless of other extenders. if len(nodeToVictims) == 0 { break } } } } return nodeToVictims, nil } 3.3. pickOneNodeForPreemption pickOneNodeForPreemption从筛选出的node中再挑选一个节点作为最终调度节点。 candidateNode := pickOneNodeForPreemption(nodeToVictims) if candidateNode == nil { return nil, nil, nil, err } pickOneNodeForPreemption完整代码如下： // pickOneNodeForPreemption chooses one node among the given nodes. It assumes // pods in each map entry are ordered by decreasing priority. // It picks a node based on the following criteria: // 1. A node with minimum number of PDB violations. // 2. A node with minimum highest priority victim is picked. // 3. Ties are broken by sum of priorities of all victims. // 4. If there are still ties, node with the minimum number of victims is picked. // 5. If there are still ties, the first such node is picked (sort of randomly). // The 'minNodes1' and 'minNodes2' are being reused here to save the memory // allocation and garbage collection time. func pickOneNodeForPreemption(nodesToVictims map[*v1.Node]*schedulerapi.Victims) *v1.Node { if len(nodesToVictims) == 0 { return nil } minNumPDBViolatingPods := math.MaxInt32 var minNodes1 []*v1.Node lenNodes1 := 0 for node, victims := range nodesToVictims { if len(victims.Pods) == 0 { // We found a node that doesn't need any preemption. Return it! // This should happen rarely when one or more pods are terminated between // the time that scheduler tries to schedule the pod and the time that // preemption logic tries to find nodes for preemption. return node } numPDBViolatingPods := victims.NumPDBViolations if numPDBViolatingPods = 0. This is // needed so that a node with a few pods with negative priority is not // picked over a node with a smaller number of pods with the same negative // priority (and similar scenarios). sumPriorities += int64(util.GetPodPriority(pod)) + int64(math.MaxInt32+1) } if sumPriorities 0 { return minNodes2[0] } glog.Errorf(\"Error in logic of node scoring for preemption. We should never reach here!\") return nil } 3.4. getLowerPriorityNominatedPods getLowerPriorityNominatedPods的基本流程如下： 获取候选节点上的pod列表。 获取待调度pod的优先级值。 遍历该节点的pod列表，如果低于待调度pod的优先级则放入低优先级pod列表中。 genericScheduler.Preempt中相关代码如下： // Lower priority pods nominated to run on this node, may no longer fit on // this node. So, we should remove their nomination. Removing their // nomination updates these pods and moves them to the active queue. It // lets scheduler find another place for them. nominatedPods := g.getLowerPriorityNominatedPods(pod, candidateNode.Name) if nodeInfo, ok := g.cachedNodeInfoMap[candidateNode.Name]; ok { return nodeInfo.Node(), nodeToVictims[candidateNode].Pods, nominatedPods, err } getLowerPriorityNominatedPods代码如下： 此部分代码位于pkg/scheduler/core/generic_scheduler.go // getLowerPriorityNominatedPods returns pods whose priority is smaller than the // priority of the given \"pod\" and are nominated to run on the given node. // Note: We could possibly check if the nominated lower priority pods still fit // and return those that no longer fit, but that would require lots of // manipulation of NodeInfo and PredicateMeta per nominated pod. It may not be // worth the complexity, especially because we generally expect to have a very // small number of nominated pods per node. func (g *genericScheduler) getLowerPriorityNominatedPods(pod *v1.Pod, nodeName string) []*v1.Pod { pods := g.schedulingQueue.WaitingPodsForNode(nodeName) if len(pods) == 0 { return nil } var lowerPriorityPods []*v1.Pod podPriority := util.GetPodPriority(pod) for _, p := range pods { if util.GetPodPriority(p) 4. 总结 4.1. Scheduler.preempt 当pod调度失败的时候，会抢占低优先级pod的空间来给高优先级的pod。其中入参为调度失败的pod对象和调度失败的err。 抢占的基本流程如下： 判断是否有关闭抢占机制，如果关闭抢占机制则直接返回。 获取调度失败pod的最新对象数据。 执行抢占算法Algorithm.Preempt，返回预调度节点和需要被剔除的pod列表。 将抢占算法返回的node添加到pod的Status.NominatedNodeName中，并删除需要被剔除的pod。 当抢占算法返回的node是nil的时候，清除pod的Status.NominatedNodeName信息。 整个抢占流程的最终结果实际上是更新Pod.Status.NominatedNodeName属性的信息。如果抢占算法返回的节点不为空，则将该node更新到Pod.Status.NominatedNodeName中，否则就将Pod.Status.NominatedNodeName设置为空。 4.2. genericScheduler.Preempt Preempt的主要实现是找到可以调度的节点和上面因抢占而需要被剔除的pod。 基本流程如下： 根据调度失败的原因对所有节点先进行一批筛选，筛选出潜在的被调度节点列表。 通过selectNodesForPreemption筛选出需要牺牲的pod和其节点。 基于拓展抢占逻辑再次对上述筛选出来的牺牲者做过滤。 基于上述的过滤结果，选择一个最终可能因抢占被调度的节点。 基于上述的候选节点，找出该节点上优先级低于当前被调度pod的牺牲者pod列表。 参考： https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/scheduler.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/pkg/scheduler/core/generic_scheduler.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-apiserver/_index.html":{"url":"code-analysis/kube-apiserver/_index.html","title":"kube-apiserver","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"code-analysis/kube-apiserver/NewAPIServerCommand.html":{"url":"code-analysis/kube-apiserver/NewAPIServerCommand.html","title":"NewAPIServerCommand","keywords":"","body":"kube-apiserver源码分析（一）之 NewAPIServerCommand1. Main2. NewAPIServerCommand3. NewServerRunOptions3.1. ServerRunOptions3.2. NewServerRunOptions3.3. Complete3. AddFlagSet3.1. Flags4. Run4.1. CreateServerChain4.2. PrepareRun4.3. preparedGenericAPIServer.Run5. 总结kube-apiserver源码分析（一）之 NewAPIServerCommand 以下代码分析基于 kubernetes v1.12.0 版本。 本文主要分析kube-apiserver中cmd部分的代码，即NewAPIServerCommand相关的代码。更多具体的逻辑待后续文章分析。 kube-apiserver的cmd部分目录代码结构如下： kube-apiserver ├── apiserver.go # kube-apiserver的main入口 └── app ├── aggregator.go ├── apiextensions.go ├── options # 初始化kube-apiserver使用到的option │ ├── options.go # 包括：NewServerRunOptions、Flags等 │ ├── options_test.go │ └── validation.go ├── server.go # 包括：NewAPIServerCommand、Run、CreateServerChain、Complete等 1. Main 此部分代码位于cmd/kube-apiserver/apiserver.go func main() { rand.Seed(time.Now().UTC().UnixNano()) command := app.NewAPIServerCommand(server.SetupSignalHandler()) // TODO: once we switch everything over to Cobra commands, we can go back to calling // utilflag.InitFlags() (by removing its pflag.Parse() call). For now, we have to set the // normalize func and add the go flag set by hand. pflag.CommandLine.SetNormalizeFunc(utilflag.WordSepNormalizeFunc) pflag.CommandLine.AddGoFlagSet(goflag.CommandLine) // utilflag.InitFlags() logs.InitLogs() defer logs.FlushLogs() if err := command.Execute(); err != nil { fmt.Fprintf(os.Stderr, \"error: %v\\n\", err) os.Exit(1) } } 核心代码： // 初始化APIServerCommand command := app.NewAPIServerCommand(server.SetupSignalHandler()) // 执行Execute err := command.Execute() 2. NewAPIServerCommand 此部分的代码位于/cmd/kube-apiserver/app/server.go NewAPIServerCommand即Cobra命令行框架的构造函数，主要包括三部分： 构造option 添加Flags 执行Run函数 完整代码如下： 此部分代码位于cmd/kube-apiserver/app/server.go // NewAPIServerCommand creates a *cobra.Command object with default parameters func NewAPIServerCommand(stopCh 核心代码： // 构造option s := options.NewServerRunOptions() // 添加flags fs := cmd.Flags() namedFlagSets := s.Flags() for _, f := range namedFlagSets.FlagSets { fs.AddFlagSet(f) } // set default options completedOptions, err := Complete(s) // Run Run(completedOptions, stopCh) 3. NewServerRunOptions NewServerRunOptions基于默认的参数构造ServerRunOptions结构体。ServerRunOptions是apiserver运行的配置信息。具体结构体定义如下。 3.1. ServerRunOptions 其中主要的配置如下： GenericServerRunOptions Etcd SecureServing KubeletConfig ... // ServerRunOptions runs a kubernetes api server. type ServerRunOptions struct { GenericServerRunOptions *genericoptions.ServerRunOptions Etcd *genericoptions.EtcdOptions SecureServing *genericoptions.SecureServingOptionsWithLoopback InsecureServing *genericoptions.DeprecatedInsecureServingOptionsWithLoopback Audit *genericoptions.AuditOptions Features *genericoptions.FeatureOptions Admission *kubeoptions.AdmissionOptions Authentication *kubeoptions.BuiltInAuthenticationOptions Authorization *kubeoptions.BuiltInAuthorizationOptions CloudProvider *kubeoptions.CloudProviderOptions StorageSerialization *kubeoptions.StorageSerializationOptions APIEnablement *genericoptions.APIEnablementOptions AllowPrivileged bool EnableLogsHandler bool EventTTL time.Duration KubeletConfig kubeletclient.KubeletClientConfig KubernetesServiceNodePort int MaxConnectionBytesPerSec int64 ServiceClusterIPRange net.IPNet // TODO: make this a list ServiceNodePortRange utilnet.PortRange SSHKeyfile string SSHUser string ProxyClientCertFile string ProxyClientKeyFile string EnableAggregatorRouting bool MasterCount int EndpointReconcilerType string ServiceAccountSigningKeyFile string } 3.2. NewServerRunOptions NewServerRunOptions初始化配置结构体。 // NewServerRunOptions creates a new ServerRunOptions object with default parameters func NewServerRunOptions() *ServerRunOptions { s := ServerRunOptions{ GenericServerRunOptions: genericoptions.NewServerRunOptions(), Etcd: genericoptions.NewEtcdOptions(storagebackend.NewDefaultConfig(kubeoptions.DefaultEtcdPathPrefix, nil)), SecureServing: kubeoptions.NewSecureServingOptions(), InsecureServing: kubeoptions.NewInsecureServingOptions(), Audit: genericoptions.NewAuditOptions(), Features: genericoptions.NewFeatureOptions(), Admission: kubeoptions.NewAdmissionOptions(), Authentication: kubeoptions.NewBuiltInAuthenticationOptions().WithAll(), Authorization: kubeoptions.NewBuiltInAuthorizationOptions(), CloudProvider: kubeoptions.NewCloudProviderOptions(), StorageSerialization: kubeoptions.NewStorageSerializationOptions(), APIEnablement: genericoptions.NewAPIEnablementOptions(), EnableLogsHandler: true, EventTTL: 1 * time.Hour, MasterCount: 1, EndpointReconcilerType: string(reconcilers.LeaseEndpointReconcilerType), KubeletConfig: kubeletclient.KubeletClientConfig{ Port: ports.KubeletPort, ReadOnlyPort: ports.KubeletReadOnlyPort, PreferredAddressTypes: []string{ // --override-hostname string(api.NodeHostName), // internal, preferring DNS if reported string(api.NodeInternalDNS), string(api.NodeInternalIP), // external, preferring DNS if reported string(api.NodeExternalDNS), string(api.NodeExternalIP), }, EnableHttps: true, HTTPTimeout: time.Duration(5) * time.Second, }, ServiceNodePortRange: kubeoptions.DefaultServiceNodePortRange, } s.ServiceClusterIPRange = kubeoptions.DefaultServiceIPCIDR // Overwrite the default for storage data format. s.Etcd.DefaultStorageMediaType = \"application/vnd.kubernetes.protobuf\" return &s } 3.3. Complete 当kube-apiserver的flags被解析后，调用Complete完成默认配置。 此部分代码位于cmd/kube-apiserver/app/server.go // Should be called after kube-apiserver flags parsed. func Complete(s *options.ServerRunOptions) (completedServerRunOptions, error) { var options completedServerRunOptions // set defaults if err := s.GenericServerRunOptions.DefaultAdvertiseAddress(s.SecureServing.SecureServingOptions); err != nil { return options, err } if err := kubeoptions.DefaultAdvertiseAddress(s.GenericServerRunOptions, s.InsecureServing.DeprecatedInsecureServingOptions); err != nil { return options, err } serviceIPRange, apiServerServiceIP, err := master.DefaultServiceIPRange(s.ServiceClusterIPRange) if err != nil { return options, fmt.Errorf(\"error determining service IP ranges: %v\", err) } s.ServiceClusterIPRange = serviceIPRange if err := s.SecureServing.MaybeDefaultWithSelfSignedCerts(s.GenericServerRunOptions.AdvertiseAddress.String(), []string{\"kubernetes.default.svc\", \"kubernetes.default\", \"kubernetes\"}, []net.IP{apiServerServiceIP}); err != nil { return options, fmt.Errorf(\"error creating self-signed certificates: %v\", err) } if len(s.GenericServerRunOptions.ExternalHost) == 0 { if len(s.GenericServerRunOptions.AdvertiseAddress) > 0 { s.GenericServerRunOptions.ExternalHost = s.GenericServerRunOptions.AdvertiseAddress.String() } else { if hostname, err := os.Hostname(); err == nil { s.GenericServerRunOptions.ExternalHost = hostname } else { return options, fmt.Errorf(\"error finding host name: %v\", err) } } glog.Infof(\"external host was not specified, using %v\", s.GenericServerRunOptions.ExternalHost) } s.Authentication.ApplyAuthorization(s.Authorization) // Use (ServiceAccountSigningKeyFile != \"\") as a proxy to the user enabling // TokenRequest functionality. This defaulting was convenient, but messed up // a lot of people when they rotated their serving cert with no idea it was // connected to their service account keys. We are taking this oppurtunity to // remove this problematic defaulting. if s.ServiceAccountSigningKeyFile == \"\" { // Default to the private server key for service account token signing if len(s.Authentication.ServiceAccounts.KeyFiles) == 0 && s.SecureServing.ServerCert.CertKey.KeyFile != \"\" { if kubeauthenticator.IsValidServiceAccountKeyFile(s.SecureServing.ServerCert.CertKey.KeyFile) { s.Authentication.ServiceAccounts.KeyFiles = []string{s.SecureServing.ServerCert.CertKey.KeyFile} } else { glog.Warning(\"No TLS key provided, service account token authentication disabled\") } } } if s.Etcd.StorageConfig.DeserializationCacheSize == 0 { // When size of cache is not explicitly set, estimate its size based on // target memory usage. glog.V(2).Infof(\"Initializing deserialization cache size based on %dMB limit\", s.GenericServerRunOptions.TargetRAMMB) // This is the heuristics that from memory capacity is trying to infer // the maximum number of nodes in the cluster and set cache sizes based // on that value. // From our documentation, we officially recommend 120GB machines for // 2000 nodes, and we scale from that point. Thus we assume ~60MB of // capacity per node. // TODO: We may consider deciding that some percentage of memory will // be used for the deserialization cache and divide it by the max object // size to compute its size. We may even go further and measure // collective sizes of the objects in the cache. clusterSize := s.GenericServerRunOptions.TargetRAMMB / 60 s.Etcd.StorageConfig.DeserializationCacheSize = 25 * clusterSize if s.Etcd.StorageConfig.DeserializationCacheSize 3. AddFlagSet AddFlagSet主要的作用是通过外部传入的flag的具体值，解析的时候传递给option的结构体，最终给apiserver使用。 其中NewAPIServerCommand关于AddFlagSet的相关代码如下： fs := cmd.Flags() namedFlagSets := s.Flags() for _, f := range namedFlagSets.FlagSets { fs.AddFlagSet(f) } 3.1. Flags Flags完整代码如下： 此部分代码位于cmd/kube-apiserver/app/options/options.go // Flags returns flags for a specific APIServer by section name func (s *ServerRunOptions) Flags() (fss apiserverflag.NamedFlagSets) { // Add the generic flags. s.GenericServerRunOptions.AddUniversalFlags(fss.FlagSet(\"generic\")) s.Etcd.AddFlags(fss.FlagSet(\"etcd\")) s.SecureServing.AddFlags(fss.FlagSet(\"secure serving\")) s.InsecureServing.AddFlags(fss.FlagSet(\"insecure serving\")) s.InsecureServing.AddUnqualifiedFlags(fss.FlagSet(\"insecure serving\")) // TODO: remove it until kops stops using `--address` s.Audit.AddFlags(fss.FlagSet(\"auditing\")) s.Features.AddFlags(fss.FlagSet(\"features\")) s.Authentication.AddFlags(fss.FlagSet(\"authentication\")) s.Authorization.AddFlags(fss.FlagSet(\"authorization\")) s.CloudProvider.AddFlags(fss.FlagSet(\"cloud provider\")) s.StorageSerialization.AddFlags(fss.FlagSet(\"storage\")) s.APIEnablement.AddFlags(fss.FlagSet(\"api enablement\")) s.Admission.AddFlags(fss.FlagSet(\"admission\")) // Note: the weird \"\"+ in below lines seems to be the only way to get gofmt to // arrange these text blocks sensibly. Grrr. fs := fss.FlagSet(\"misc\") fs.DurationVar(&s.EventTTL, \"event-ttl\", s.EventTTL, \"Amount of time to retain events.\") fs.BoolVar(&s.AllowPrivileged, \"allow-privileged\", s.AllowPrivileged, \"If true, allow privileged containers. [default=false]\") fs.BoolVar(&s.EnableLogsHandler, \"enable-logs-handler\", s.EnableLogsHandler, \"If true, install a /logs handler for the apiserver logs.\") // Deprecated in release 1.9 fs.StringVar(&s.SSHUser, \"ssh-user\", s.SSHUser, \"If non-empty, use secure SSH proxy to the nodes, using this user name\") fs.MarkDeprecated(\"ssh-user\", \"This flag will be removed in a future version.\") // Deprecated in release 1.9 fs.StringVar(&s.SSHKeyfile, \"ssh-keyfile\", s.SSHKeyfile, \"If non-empty, use secure SSH proxy to the nodes, using this user keyfile\") fs.MarkDeprecated(\"ssh-keyfile\", \"This flag will be removed in a future version.\") fs.Int64Var(&s.MaxConnectionBytesPerSec, \"max-connection-bytes-per-sec\", s.MaxConnectionBytesPerSec, \"\"+ \"If non-zero, throttle each user connection to this number of bytes/sec. \"+ \"Currently only applies to long-running requests.\") fs.IntVar(&s.MasterCount, \"apiserver-count\", s.MasterCount, \"The number of apiservers running in the cluster, must be a positive number. (In use when --endpoint-reconciler-type=master-count is enabled.)\") fs.StringVar(&s.EndpointReconcilerType, \"endpoint-reconciler-type\", string(s.EndpointReconcilerType), \"Use an endpoint reconciler (\"+strings.Join(reconcilers.AllTypes.Names(), \", \")+\")\") // See #14282 for details on how to test/try this option out. // TODO: remove this comment once this option is tested in CI. fs.IntVar(&s.KubernetesServiceNodePort, \"kubernetes-service-node-port\", s.KubernetesServiceNodePort, \"\"+ \"If non-zero, the Kubernetes master service (which apiserver creates/maintains) will be \"+ \"of type NodePort, using this as the value of the port. If zero, the Kubernetes master \"+ \"service will be of type ClusterIP.\") fs.IPNetVar(&s.ServiceClusterIPRange, \"service-cluster-ip-range\", s.ServiceClusterIPRange, \"\"+ \"A CIDR notation IP range from which to assign service cluster IPs. This must not \"+ \"overlap with any IP ranges assigned to nodes for pods.\") fs.Var(&s.ServiceNodePortRange, \"service-node-port-range\", \"\"+ \"A port range to reserve for services with NodePort visibility. \"+ \"Example: '30000-32767'. Inclusive at both ends of the range.\") // Kubelet related flags: fs.BoolVar(&s.KubeletConfig.EnableHttps, \"kubelet-https\", s.KubeletConfig.EnableHttps, \"Use https for kubelet connections.\") fs.StringSliceVar(&s.KubeletConfig.PreferredAddressTypes, \"kubelet-preferred-address-types\", s.KubeletConfig.PreferredAddressTypes, \"List of the preferred NodeAddressTypes to use for kubelet connections.\") fs.UintVar(&s.KubeletConfig.Port, \"kubelet-port\", s.KubeletConfig.Port, \"DEPRECATED: kubelet port.\") fs.MarkDeprecated(\"kubelet-port\", \"kubelet-port is deprecated and will be removed.\") fs.UintVar(&s.KubeletConfig.ReadOnlyPort, \"kubelet-read-only-port\", s.KubeletConfig.ReadOnlyPort, \"DEPRECATED: kubelet port.\") fs.DurationVar(&s.KubeletConfig.HTTPTimeout, \"kubelet-timeout\", s.KubeletConfig.HTTPTimeout, \"Timeout for kubelet operations.\") fs.StringVar(&s.KubeletConfig.CertFile, \"kubelet-client-certificate\", s.KubeletConfig.CertFile, \"Path to a client cert file for TLS.\") fs.StringVar(&s.KubeletConfig.KeyFile, \"kubelet-client-key\", s.KubeletConfig.KeyFile, \"Path to a client key file for TLS.\") fs.StringVar(&s.KubeletConfig.CAFile, \"kubelet-certificate-authority\", s.KubeletConfig.CAFile, \"Path to a cert file for the certificate authority.\") // TODO: delete this flag in 1.13 repair := false fs.BoolVar(&repair, \"repair-malformed-updates\", false, \"deprecated\") fs.MarkDeprecated(\"repair-malformed-updates\", \"This flag will be removed in a future version\") fs.StringVar(&s.ProxyClientCertFile, \"proxy-client-cert-file\", s.ProxyClientCertFile, \"\"+ \"Client certificate used to prove the identity of the aggregator or kube-apiserver \"+ \"when it must call out during a request. This includes proxying requests to a user \"+ \"api-server and calling out to webhook admission plugins. It is expected that this \"+ \"cert includes a signature from the CA in the --requestheader-client-ca-file flag. \"+ \"That CA is published in the 'extension-apiserver-authentication' configmap in \"+ \"the kube-system namespace. Components receiving calls from kube-aggregator should \"+ \"use that CA to perform their half of the mutual TLS verification.\") fs.StringVar(&s.ProxyClientKeyFile, \"proxy-client-key-file\", s.ProxyClientKeyFile, \"\"+ \"Private key for the client certificate used to prove the identity of the aggregator or kube-apiserver \"+ \"when it must call out during a request. This includes proxying requests to a user \"+ \"api-server and calling out to webhook admission plugins.\") fs.BoolVar(&s.EnableAggregatorRouting, \"enable-aggregator-routing\", s.EnableAggregatorRouting, \"Turns on aggregator routing requests to endpoints IP rather than cluster IP.\") fs.StringVar(&s.ServiceAccountSigningKeyFile, \"service-account-signing-key-file\", s.ServiceAccountSigningKeyFile, \"\"+ \"Path to the file that contains the current private key of the service account token issuer. The issuer will sign issued ID tokens with this private key. (Requires the 'TokenRequest' feature gate.)\") return fss } 4. Run Run以常驻的方式运行apiserver。 主要内容如下： 构造一个聚合的server结构体。 执行PrepareRun。 最终执行Run。 此部分代码位于cmd/kube-apiserver/app/server.go // Run runs the specified APIServer. This should never exit. func Run(completeOptions completedServerRunOptions, stopCh 4.1. CreateServerChain 构造聚合的Server。 基本流程如下： 首先生成config对象，包括kubeAPIServerConfig、apiExtensionsConfig。 再通过config生成server对象，包括apiExtensionsServer、kubeAPIServer。 执行apiExtensionsServer、kubeAPIServer的PrepareRun部分。 生成聚合的config对象aggregatorConfig。 基于aggregatorConfig、kubeAPIServer、apiExtensionsServer生成聚合的serveraggregatorServer。 此部分代码位于cmd/kube-apiserver/app/server.go // CreateServerChain creates the apiservers connected via delegation. func CreateServerChain(completedOptions completedServerRunOptions, stopCh 4.2. PrepareRun PrepareRun主要执行一些API安装操作。 此部分的代码位于vendor/k8s.io/apiserver/pkg/server/genericapiserver.go // PrepareRun does post API installation setup steps. func (s *GenericAPIServer) PrepareRun() preparedGenericAPIServer { if s.swaggerConfig != nil { routes.Swagger{Config: s.swaggerConfig}.Install(s.Handler.GoRestfulContainer) } if s.openAPIConfig != nil { routes.OpenAPI{ Config: s.openAPIConfig, }.Install(s.Handler.GoRestfulContainer, s.Handler.NonGoRestfulMux) } s.installHealthz() // Register audit backend preShutdownHook. if s.AuditBackend != nil { s.AddPreShutdownHook(\"audit-backend\", func() error { s.AuditBackend.Shutdown() return nil }) } return preparedGenericAPIServer{s} } 4.3. preparedGenericAPIServer.Run preparedGenericAPIServer.Run运行一个安全的http server。具体的实现逻辑待后续文章分析。 此部分代码位于vendor/k8s.io/apiserver/pkg/server/genericapiserver.go // Run spawns the secure http server. It only returns if stopCh is closed // or the secure port cannot be listened on initially. func (s preparedGenericAPIServer) Run(stopCh 核心函数： err := s.NonBlockingRun(stopCh) preparedGenericAPIServer.Run主要是调用NonBlockingRun函数，最终运行一个http server。该部分逻辑待后续文章分析。 5. 总结 NewAPIServerCommand采用了Cobra命令行框架，该框架使用主要包含以下部分： 构造option参数，提供给执行主体(例如 本文的server)作为配置参数使用。 添加Flags，主要用来通过传入的flags参数最终解析成option中使用的结构体属性。 执行Run函数，执行主体的运行逻辑部分（核心部分）。 其中Run函数的主要内容如下： 构造一个聚合的server结构体。 执行PrepareRun。 最终执行preparedGenericAPIServer.Run。 preparedGenericAPIServer.Run主要是调用NonBlockingRun函数，最终运行一个http server。NonBlockingRun的具体逻辑待后续文章再单独分析。 参考： https://github.com/kubernetes/kubernetes/tree/v1.12.0/cmd/kube-apiserver https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-apiserver/app/server.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-apiserver/app/aggregator.go https://github.com/kubernetes/kubernetes/blob/v1.12.0/cmd/kube-apiserver/app/options/options.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2022-12-29 21:24:00 "},"runtime/_index.html":{"url":"runtime/_index.html","title":"Runtime","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"runtime/runtime.html":{"url":"runtime/runtime.html","title":"Runc和Containerd概述","keywords":"","body":"1. 概述2. OCI（Open Container Initiative）3. RunC4. Containerd4.1. containerd的架构图4.2. docker与containerd、runc的关系图5. CRI（Container Runtime Interface ）5.1. docker与k8s调用containerd的关系图5.2. cri-api5.2.1. runtime service5.2.2. image service5.3. cri-containerd5.3.1. CRI Plugin调用流程5.3.2. k8s对runtime调用的演进5.4. Dockershim5.5. CRI-O 本文主要分析OCI，CRI，runc，containerd，cri-containerd，dockershim等组件说明及调用关系。 1. 概述 各个组件调用关系图如下： 图片 - 关系图 图片来源：https://www.jianshu.com/p/62e71584d1cb 2. OCI（Open Container Initiative） OCI（Open Container Initiative）即开放的容器运行时规范，目的在于定义一个容器运行时及镜像的相关标准和规范，其中包括 runtime-spec：容器的生命周期管理，具体参考runtime-spec。 image-spec：镜像的生命周期管理，具体参考image-spec。 实现OCI标准的容器运行时有runc，kata等。 3. RunC runc(run container)是一个基于OCI标准实现的一个轻量级容器运行工具，用来创建和运行容器。而Containerd是用来维持通过runc创建的容器的运行状态。即runc用来创建和运行容器，containerd作为常驻进程用来管理容器。 runc包含libcontainer，包括对namespace和cgroup的调用操作。 命令参数： To start a new instance of a container: # runc run [ -b bundle ] USAGE: runc [global options] command [command options] [arguments...] COMMANDS: checkpoint checkpoint a running container create create a container delete delete any resources held by the container often used with detached container events display container events such as OOM notifications, cpu, memory, and IO usage statistics exec execute new process inside the container init initialize the namespaces and launch the process (do not call it outside of runc) kill kill sends the specified signal (default: SIGTERM) to the container's init process list lists containers started by runc with the given root pause pause suspends all processes inside the container ps ps displays the processes running inside a container restore restore a container from a previous checkpoint resume resumes all processes that have been previously paused run create and run a container spec create a new specification file start executes the user defined process in a created container state output the state of a container update update container resource constraints help, h Shows a list of commands or help for one command 4. Containerd containerd（container daemon）是一个daemon进程用来管理和运行容器，可以用来拉取/推送镜像和管理容器的存储和网络。其中可以调用runc来创建和运行容器。 4.1. containerd的架构图 4.2. docker与containerd、runc的关系图 更具体的调用逻辑： 5. CRI（Container Runtime Interface ） CRI即容器运行时接口，主要用来定义k8s与容器运行时的API调用，kubelet通过CRI来调用容器运行时，只要实现了CRI接口的容器运行时就可以对接到k8s的kubelet组件。 5.1. docker与k8s调用containerd的关系图 5.2. cri-api 5.2.1. runtime service // Runtime service defines the public APIs for remote container runtimes service RuntimeService { // Version returns the runtime name, runtime version, and runtime API version. rpc Version(VersionRequest) returns (VersionResponse) {} // RunPodSandbox creates and starts a pod-level sandbox. Runtimes must ensure // the sandbox is in the ready state on success. rpc RunPodSandbox(RunPodSandboxRequest) returns (RunPodSandboxResponse) {} // StopPodSandbox stops any running process that is part of the sandbox and // reclaims network resources (e.g., IP addresses) allocated to the sandbox. // If there are any running containers in the sandbox, they must be forcibly // terminated. // This call is idempotent, and must not return an error if all relevant // resources have already been reclaimed. kubelet will call StopPodSandbox // at least once before calling RemovePodSandbox. It will also attempt to // reclaim resources eagerly, as soon as a sandbox is not needed. Hence, // multiple StopPodSandbox calls are expected. rpc StopPodSandbox(StopPodSandboxRequest) returns (StopPodSandboxResponse) {} // RemovePodSandbox removes the sandbox. If there are any running containers // in the sandbox, they must be forcibly terminated and removed. // This call is idempotent, and must not return an error if the sandbox has // already been removed. rpc RemovePodSandbox(RemovePodSandboxRequest) returns (RemovePodSandboxResponse) {} // PodSandboxStatus returns the status of the PodSandbox. If the PodSandbox is not // present, returns an error. rpc PodSandboxStatus(PodSandboxStatusRequest) returns (PodSandboxStatusResponse) {} // ListPodSandbox returns a list of PodSandboxes. rpc ListPodSandbox(ListPodSandboxRequest) returns (ListPodSandboxResponse) {} // CreateContainer creates a new container in specified PodSandbox rpc CreateContainer(CreateContainerRequest) returns (CreateContainerResponse) {} // StartContainer starts the container. rpc StartContainer(StartContainerRequest) returns (StartContainerResponse) {} // StopContainer stops a running container with a grace period (i.e., timeout). // This call is idempotent, and must not return an error if the container has // already been stopped. // The runtime must forcibly kill the container after the grace period is // reached. rpc StopContainer(StopContainerRequest) returns (StopContainerResponse) {} // RemoveContainer removes the container. If the container is running, the // container must be forcibly removed. // This call is idempotent, and must not return an error if the container has // already been removed. rpc RemoveContainer(RemoveContainerRequest) returns (RemoveContainerResponse) {} // ListContainers lists all containers by filters. rpc ListContainers(ListContainersRequest) returns (ListContainersResponse) {} // ContainerStatus returns status of the container. If the container is not // present, returns an error. rpc ContainerStatus(ContainerStatusRequest) returns (ContainerStatusResponse) {} // UpdateContainerResources updates ContainerConfig of the container. rpc UpdateContainerResources(UpdateContainerResourcesRequest) returns (UpdateContainerResourcesResponse) {} // ReopenContainerLog asks runtime to reopen the stdout/stderr log file // for the container. This is often called after the log file has been // rotated. If the container is not running, container runtime can choose // to either create a new log file and return nil, or return an error. // Once it returns error, new container log file MUST NOT be created. rpc ReopenContainerLog(ReopenContainerLogRequest) returns (ReopenContainerLogResponse) {} // ExecSync runs a command in a container synchronously. rpc ExecSync(ExecSyncRequest) returns (ExecSyncResponse) {} // Exec prepares a streaming endpoint to execute a command in the container. rpc Exec(ExecRequest) returns (ExecResponse) {} // Attach prepares a streaming endpoint to attach to a running container. rpc Attach(AttachRequest) returns (AttachResponse) {} // PortForward prepares a streaming endpoint to forward ports from a PodSandbox. rpc PortForward(PortForwardRequest) returns (PortForwardResponse) {} // ContainerStats returns stats of the container. If the container does not // exist, the call returns an error. rpc ContainerStats(ContainerStatsRequest) returns (ContainerStatsResponse) {} // ListContainerStats returns stats of all running containers. rpc ListContainerStats(ListContainerStatsRequest) returns (ListContainerStatsResponse) {} // UpdateRuntimeConfig updates the runtime configuration based on the given request. rpc UpdateRuntimeConfig(UpdateRuntimeConfigRequest) returns (UpdateRuntimeConfigResponse) {} // Status returns the status of the runtime. rpc Status(StatusRequest) returns (StatusResponse) {} } 5.2.2. image service // ImageService defines the public APIs for managing images. service ImageService { // ListImages lists existing images. rpc ListImages(ListImagesRequest) returns (ListImagesResponse) {} // ImageStatus returns the status of the image. If the image is not // present, returns a response with ImageStatusResponse.Image set to // nil. rpc ImageStatus(ImageStatusRequest) returns (ImageStatusResponse) {} // PullImage pulls an image with authentication config. rpc PullImage(PullImageRequest) returns (PullImageResponse) {} // RemoveImage removes the image. // This call is idempotent, and must not return an error if the image has // already been removed. rpc RemoveImage(RemoveImageRequest) returns (RemoveImageResponse) {} // ImageFSInfo returns information of the filesystem that is used to store images. rpc ImageFsInfo(ImageFsInfoRequest) returns (ImageFsInfoResponse) {} } 5.3. cri-containerd 5.3.1. CRI Plugin调用流程 kubelet调用CRI插件，通过CRI Runtime Service接口创建pod cri通过CNI接口创建和配置pod的network namespace cri调用containerd创建sandbox container（pause container ）并将容器放入pod的cgroup和namespace中 kubelet调用CRI插件，通过image service接口拉取镜像，接着通过containerd来拉取镜像 kubelet调用CRI插件，通过runtime service接口运行拉取下来的镜像服务，最后通过containerd来运行业务容器，并将容器放入pod的cgroup和namespace中。 具体参考：https://github.com/containerd/cri/blob/release/1.4/docs/architecture.md 5.3.2. k8s对runtime调用的演进 由原来通过dockershim调用docker再调用containerd，直接变成通过cri-containerd调用containerd，从而减少了一层docker调用逻辑。 具体参考：https://github.com/containerd/cri/blob/release/1.4/docs/proposal.md 5.4. Dockershim 在旧版本的k8s中，由于docker没有实现CRI接口，因此增加一个Dockershim来实现k8s对docker的调用。（shim：垫片，一般用来表示对第三方组件API调用的适配插件，例如k8s使用Dockershim来实现对docker接口的适配调用） 5.5. CRI-O cri-o与containerd类似，用来实现容器的管理，可替换containerd的使用。 参考： https://opencontainers.org/about/overview/ https://github.com/opencontainers/runtime-spec https://github.com/kubernetes/kubernetes/blob/242a97307b34076d5d8f5bbeb154fa4d97c9ef1d/docs/devel/container-runtime-interface.md https://github.com/containerd/containerd/blob/main/docs/cri/architecture.md https://www.tutorialworks.com/difference-docker-containerd-runc-crio-oci/ https://kubernetes.io/zh/docs/setup/production-environment/container-runtimes/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"runtime/containerd/_index.html":{"url":"runtime/containerd/_index.html","title":"Containerd","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"runtime/containerd/install-containerd.html":{"url":"runtime/containerd/install-containerd.html","title":"安装Containerd","keywords":"","body":"1. Ubuntu安装containerd1. 卸载旧版本2. 准备包环境3. 安装containerd4. 修改配置2. 离线二进制安装containerd3. Containerd配置代理1. Ubuntu安装containerd 以下以Ubuntu为例 说明：安装containerd与安装docker流程基本一致，差别在于不需要安装docker-ce containerd: apt-get install -y containerd.io docker: apt-get install docker-ce docker-ce-cli containerd.io 1. 卸载旧版本 sudo apt-get remove docker docker-engine docker.io containerd runc 如果需要删除镜像及容器数据则执行以下命令 sudo rm -rf /var/lib/docker sudo rm -rf /var/lib/containerd 2. 准备包环境 1、更新apt，允许使用https。 sudo apt-get update sudo apt-get install \\ ca-certificates \\ curl \\ gnupg \\ lsb-release 2、添加docker官方GPG key。 sudo mkdir -p /etc/apt/keyrings curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg 3、设置软件仓库源 echo \\ \"deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \\ $(lsb_release -cs) stable\" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null 3. 安装containerd # 安装containerd sudo apt-get update sudo apt-get install -y containerd.io # 如果是安装docker则执行： sudo apt-get install docker-ce docker-ce-cli containerd.io # 查看运行状态 systemctl enable containerd systemctl status containerd 安装指定版本 # 查看版本 apt-cache madison containerd # sudo apt-get install containerd= 4. 修改配置 在 Linux 上，containerd 的默认 CRI 套接字是 /run/containerd/containerd.sock。 1、生成默认配置 containerd config default > /etc/containerd/config.toml 2、修改CgroupDriver为systemd k8s官方推荐使用systemd类型的CgroupDriver。 [plugins.\"io.containerd.grpc.v1.cri\".containerd.runtimes.runc] ... [plugins.\"io.containerd.grpc.v1.cri\".containerd.runtimes.runc.options] SystemdCgroup = true 3、重启containerd systemctl restart containerd 2. 离线二进制安装containerd 把containerd、runc、cni-plugins、nerdctl二进制下载到本地，再上传到对应服务器，解压文件到对应目录，修改containerd配置文件，启动containerd。 #!/bin/bash set -e ContainerdVersion=$1 ContainerdVersion=${ContainerdVersion:-1.6.6} RuncVersion=$2 RuncVersion=${RuncVersion:-1.1.3} CniVersion=$3 CniVersion=${CniVersion:-1.1.1} NerdctlVersion=$4 NerdctlVersion=${NerdctlVersion:-0.21.0} CrictlVersion=$5 CrictlVersion=${CrictlVersion:-1.24.2} echo \"--------------install containerd--------------\" wget https://github.com/containerd/containerd/releases/download/v${ContainerdVersion}/containerd-${ContainerdVersion}-linux-amd64.tar.gz tar Cxzvf /usr/local containerd-${ContainerdVersion}-linux-amd64.tar.gz echo \"--------------install containerd service--------------\" wget https://raw.githubusercontent.com/containerd/containerd/681aaf68b7dcbe08a51c3372cbb8f813fb4466e0/containerd.service mv containerd.service /lib/systemd/system/ mkdir -p /etc/containerd/ containerd config default > /etc/containerd/config.toml sed -i 's/SystemdCgroup = false/SystemdCgroup = true/' /etc/containerd/config.toml echo \"--------------install runc--------------\" wget https://github.com/opencontainers/runc/releases/download/v${RuncVersion}/runc.amd64 chmod +x runc.amd64 mv runc.amd64 /usr/local/bin/runc echo \"--------------install cni plugins--------------\" wget https://github.com/containernetworking/plugins/releases/download/v${CniVersion}/cni-plugins-linux-amd64-v${CniVersion}.tgz rm -fr /opt/cni/bin mkdir -p /opt/cni/bin tar Cxzvf /opt/cni/bin cni-plugins-linux-amd64-v${CniVersion}.tgz echo \"--------------install nerdctl--------------\" wget https://github.com/containerd/nerdctl/releases/download/v${NerdctlVersion}/nerdctl-${NerdctlVersion}-linux-amd64.tar.gz tar Cxzvf /usr/local/bin nerdctl-${NerdctlVersion}-linux-amd64.tar.gz echo \"--------------install crictl--------------\" wget https://github.com/kubernetes-sigs/cri-tools/releases/download/v${CrictlVersion}/crictl-v${CrictlVersion}-linux-amd64.tar.gz tar Cxzvf /usr/local/bin crictl-v${CrictlVersion}-linux-amd64.tar.gz cat > /etc/crictl.yaml 3. Containerd配置代理 由于节点到k8s官方仓库网络不通，或者设备处于内网，可以通过配置http_proxy代理的方式来拉取镜像。 vi /lib/systemd/system/containerd.service # 添加代理环境变量 [Unit] Description=containerd container runtime Documentation=https://containerd.io After=network.target local-fs.target [Service] Environment=\"HTTP_PROXY=http://squid:3128/\" # 添加环境变量代理 Environment=\"HTTPS_PROXY=http://squid:3128/\" # 添加环境变量代理 ExecStartPre=-/sbin/modprobe overlay ExecStart=/usr/local/bin/containerd Type=notify Delegate=yes KillMode=process Restart=always RestartSec=5 # Having non-zero Limit*s causes performance problems due to accounting overhead # in the kernel. We recommend using cgroups to do container-local accounting. LimitNPROC=infinity LimitCORE=infinity LimitNOFILE=infinity # Comment TasksMax if your systemd version does not supports it. # Only systemd 226 and above support this version. TasksMax=infinity OOMScoreAdjust=-999 [Install] WantedBy=multi-user.target # 重启服务 systemctl daemon-reload systemctl restart containerd 参考： https://github.com/containerd/containerd https://github.com/containerd/containerd/blob/main/docs/getting-started.md https://docs.docker.com/engine/install/ubuntu/ https://kubernetes.io/docs/setup/production-environment/container-runtimes/#containerd containerd/containerd.service at main · containerd/containerd · GitHub GitHub - containerd/nerdctl: containerd ctl GitHub - kubernetes-sigs/cri-tools: CLI and validation tools for Kubelet Container Runtime Interface (CRI) . Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"runtime/docker/_index.html":{"url":"runtime/docker/_index.html","title":"Docker","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"runtime/docker/docker-notes.html":{"url":"runtime/docker/docker-notes.html","title":"Docker学习笔记","keywords":"","body":"Docker学习笔记Docker学习笔记 详见：Docker学习笔记 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"runtime/kata/_index.html":{"url":"runtime/kata/_index.html","title":"Kata Container","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"runtime/kata/kata-container.html":{"url":"runtime/kata/kata-container.html","title":"kata容器简介","keywords":"","body":"Kata-container简介1. kata-container架构2. kata-runtime3. shim4. kata-agent5. kata-proxy6. Hypervisor7. QEMU/KVMKata-container简介 kata-container通过轻量型虚拟机技术构建一个安全的容器运行时，表现像容器一样，但通硬件虚拟化技术提供强隔离，作为第二层的安全防护。 特点： 安全：独立的内核，提供网络、I/O、内存的隔离。 兼容性：支持OCI容器标准，k8s的CRI接口。 性能：兼容虚拟机的安全和容器的轻量特点。 简单：使用标准的接口。 1. kata-container架构 kata-container与传统container的比较 2. kata-runtime Kata Containers runtime (kata-runtime)通过QEMU*/KVM技术创建了一种轻量型的虚拟机，兼容 OCI runtime specification 标准，支持Kubernetes* Container Runtime Interface (CRI)接口，可替换CRI shim runtime (runc) 通过k8s来创建pod或容器。 3. shim shim类似Docker的 containerd-shim 或CRI-O的 conmon，主要用来监控和回收容器的进程，kata-shim需要处理所有的容器的IO流(stdout, stdin and stderr)和转发相关信号。 containerd-shim-kata-v2实现了Containerd Runtime V2 (Shim API)，k8s可以通过containerd-shim-kata-v2（替代2N+1个shims[由一个containerd-shim和kata-shim组成]）来创建pod。 4. kata-agent 在虚拟机内kata-agent作为一个daemon进程运行，并拉起容器的进程。kata-agent使用VIRTIO或VSOCK接口（QEMU在主机上暴露的socket文件）在guest虚拟机中运行gRPC服务器。kata-runtime通过grpc协议与kata-agent通信，向kata-agent发送管理容器的命令。该协议还用于容器和管理引擎（例如Docker Engine）之间传送I / O流（stdout，stderr，stdin）。 容器内所有的执行命令和相关的IO流都需要通过QEMU在宿主机暴露的virtio-serial或vsock接口，当使用VIRTIO的情况下，每个虚拟机会创建一个Kata Containers proxy (kata-proxy) 来处理命令和IO流。 kata-agent使用libcontainer 来管理容器的生命周期，复用了runc的部分代码。 5. kata-proxy kata-proxy提供了 kata-shim 和 kata-runtime 与VM中的kata-agent通信的方式，其中通信方式是使用virtio-serial或vsock，默认是使用virtio-serial。 6. Hypervisor kata-container通过QEMU/KVM来创建虚拟机给容器运行，可以支持多种hypervisors。 7. QEMU/KVM 待补充 参考文档： https://katacontainers.io/ https://github.com/kata-containers/documentation/blob/master/design/architecture.md Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"runtime/kata/kata-container-conf.html":{"url":"runtime/kata/kata-container-conf.html","title":"kata配置","keywords":"","body":"1. 配置文件路径2. kata-env3. configuration.toml1. 配置文件路径 默认的配置文件位于/usr/share/defaults/kata-containers/configuration.toml，如果/etc/kata-containers/configuration.toml的配置文件存在，则会替代默认的配置文件。 查看配置文件的路径命令如下： # kata-runtime --kata-show-default-config-paths /etc/kata-containers/configuration.toml /usr/share/defaults/kata-containers/configuration.toml 指定自定义配置文件运行 kata-runtime --kata-config=/some/where/configuration.toml ... 2. kata-env 查看runtime使用到的环境参数， kata-runtime kata-env 输出内容如下： [Meta] Version = \"1.0.23\" [Runtime] Debug = false Trace = false DisableGuestSeccomp = true DisableNewNetNs = false Path = \"/usr/bin/kata-runtime\" [Runtime.Version] Semver = \"1.7.2\" Commit = \"9b9282693cfbcf70d442916bea56771cc6fc3afe\" OCI = \"1.0.1-dev\" [Runtime.Config] Path = \"/usr/share/defaults/kata-containers/configuration.toml\" [Hypervisor] MachineType = \"pc\" Version = \"QEMU emulator version 2.11.0\\nCopyright (c) 2003-2017 Fabrice Bellard and the QEMU Project developers\" Path = \"/usr/bin/qemu-lite-system-x86_64\" BlockDeviceDriver = \"virtio-scsi\" EntropySource = \"/dev/urandom\" Msize9p = 8192 MemorySlots = 10 Debug = false UseVSock = false SharedFS = \"virtio-9p\" [Image] Path = \"/usr/share/kata-containers/kata-containers-image_centos_1.7.2_agent_20190702.img\" [Kernel] Path = \"/usr/share/kata-containers/vmlinuz-4.19.28.42-6.1.container\" Parameters = \"init=/usr/lib/systemd/systemd systemd.unit=kata-containers.target systemd.mask=systemd-networkd.service systemd.mask=systemd-networkd.socket systemd.mask=systemd-journald.service systemd.mask=systemd-journald.socket systemd.mask=systemd-journal-flush.service systemd.mask=systemd-journald-dev-log.socket systemd.mask=systemd-udevd.service systemd.mask=systemd-udevd.socket systemd.mask=systemd-udev-trigger.service systemd.mask=systemd-udevd-kernel.socket systemd.mask=systemd-udevd-control.socket systemd.mask=systemd-timesyncd.service systemd.mask=systemd-update-utmp.service systemd.mask=systemd-tmpfiles-setup.service systemd.mask=systemd-tmpfiles-cleanup.service systemd.mask=systemd-tmpfiles-cleanup.timer systemd.mask=tmp.mount systemd.mask=systemd-random-seed.service systemd.mask=systemd-coredump@.service\" [Initrd] Path = \"\" [Proxy] Type = \"kataProxy\" Version = \"kata-proxy version 1.7.2-a56df7c\" Path = \"/usr/libexec/kata-containers/kata-proxy\" Debug = false [Shim] Type = \"kataShim\" Version = \"kata-shim version 1.7.2-2ea178c\" Path = \"/usr/libexec/kata-containers/kata-shim\" Debug = false [Agent] Type = \"kata\" Debug = false Trace = false TraceMode = \"\" TraceType = \"\" [Host] Kernel = \"4.14.105-1-tlinux3-0008\" Architecture = \"amd64\" VMContainerCapable = true SupportVSocks = true [Host.Distro] Name = \"Tencent tlinux\" Version = \"2.2\" [Host.CPU] Vendor = \"GenuineIntel\" Model = \"Intel(R) Xeon(R) CPU X3440 @ 2.53GHz\" [Netmon] Version = \"kata-netmon version 1.7.2\" Path = \"/usr/libexec/kata-containers/kata-netmon\" Debug = false Enable = false 3. configuration.toml # Copyright (c) 2017-2019 Intel Corporation # # SPDX-License-Identifier: Apache-2.0 # # XXX: WARNING: this file is auto-generated. # XXX: # XXX: Source file: \"cli/config/configuration-qemu.toml.in\" # XXX: Project: # XXX: Name: Kata Containers # XXX: Type: kata [hypervisor.qemu] path = \"/usr/bin/qemu-lite-system-x86_64\" kernel = \"/usr/share/kata-containers/vmlinuz.container\" image = \"/usr/share/kata-containers/kata-containers.img\" machine_type = \"pc\" # Optional space-separated list of options to pass to the guest kernel. # For example, use `kernel_params = \"vsyscall=emulate\"` if you are having # trouble running pre-2.15 glibc. # # WARNING: - any parameter specified here will take priority over the default # parameter value of the same name used to start the virtual machine. # Do not set values here unless you understand the impact of doing so as you # may stop the virtual machine from booting. # To see the list of default parameters, enable hypervisor debug, create a # container and look for 'default-kernel-parameters' log entries. kernel_params = \"\" # Path to the firmware. # If you want that qemu uses the default firmware leave this option empty firmware = \"\" # Machine accelerators # comma-separated list of machine accelerators to pass to the hypervisor. # For example, `machine_accelerators = \"nosmm,nosmbus,nosata,nopit,static-prt,nofw\"` machine_accelerators=\"\" # Default number of vCPUs per SB/VM: # unspecified or 0 --> will be set to 1 # will be set to the actual number of physical cores # > 0 will be set to the specified number # > number of physical cores --> will be set to the actual number of physical cores default_vcpus = 1 # Default maximum number of vCPUs per SB/VM: # unspecified or == 0 --> will be set to the actual number of physical cores or to the maximum number # of vCPUs supported by KVM if that number is exceeded # > 0 will be set to the specified number # > number of physical cores --> will be set to the actual number of physical cores or to the maximum number # of vCPUs supported by KVM if that number is exceeded # WARNING: Depending of the architecture, the maximum number of vCPUs supported by KVM is used when # the actual number of physical cores is greater than it. # WARNING: Be aware that this value impacts the virtual machine's memory footprint and CPU # the hotplug functionality. For example, `default_maxvcpus = 240` specifies that until 240 vCPUs # can be added to a SB/VM, but the memory footprint will be big. Another example, with # `default_maxvcpus = 8` the memory footprint will be small, but 8 will be the maximum number of # vCPUs supported by the SB/VM. In general, we recommend that you do not edit this variable, # unless you know what are you doing. default_maxvcpus = 0 # Bridges can be used to hot plug devices. # Limitations: # * Currently only pci bridges are supported # * Until 30 devices per bridge can be hot plugged. # * Until 5 PCI bridges can be cold plugged per VM. # This limitation could be a bug in qemu or in the kernel # Default number of bridges per SB/VM: # unspecified or 0 --> will be set to 1 # > 1 will be set to the specified number # > 5 --> will be set to 5 default_bridges = 1 # Default memory size in MiB for SB/VM. # If unspecified then it will be set 2048 MiB. default_memory = 2048 # # Default memory slots per SB/VM. # If unspecified then it will be set 10. # This is will determine the times that memory will be hotadded to sandbox/VM. #memory_slots = 10 # The size in MiB will be plused to max memory of hypervisor. # It is the memory address space for the NVDIMM devie. # If set block storage driver (block_device_driver) to \"nvdimm\", # should set memory_offset to the size of block device. # Default 0 #memory_offset = 0 # Disable block device from being used for a container's rootfs. # In case of a storage driver like devicemapper where a container's # root file system is backed by a block device, the block device is passed # directly to the hypervisor for performance reasons. # This flag prevents the block device from being passed to the hypervisor, # 9pfs is used instead to pass the rootfs. disable_block_device_use = false # Shared file system type: # - virtio-9p (default) # - virtio-fs shared_fs = \"virtio-9p\" # Path to vhost-user-fs daemon. virtio_fs_daemon = \"/usr/bin/virtiofsd-x86_64\" # Default size of DAX cache in MiB virtio_fs_cache_size = 1024 # Cache mode: # # - none # Metadata, data, and pathname lookup are not cached in guest. They are # always fetched from host and any changes are immediately pushed to host. # # - auto # Metadata and pathname lookup cache expires after a configured amount of # time (default is 1 second). Data is cached while the file is open (close # to open consistency). # # - always # Metadata, data, and pathname lookup are cached in guest and never expire. virtio_fs_cache = \"always\" # Block storage driver to be used for the hypervisor in case the container # rootfs is backed by a block device. This is virtio-scsi, virtio-blk # or nvdimm. block_device_driver = \"virtio-scsi\" # Specifies cache-related options will be set to block devices or not. # Default false #block_device_cache_set = true # Specifies cache-related options for block devices. # Denotes whether use of O_DIRECT (bypass the host page cache) is enabled. # Default false #block_device_cache_direct = true # Specifies cache-related options for block devices. # Denotes whether flush requests for the device are ignored. # Default false #block_device_cache_noflush = true # Enable iothreads (data-plane) to be used. This causes IO to be # handled in a separate IO thread. This is currently only implemented # for SCSI. # enable_iothreads = false # Enable pre allocation of VM RAM, default false # Enabling this will result in lower container density # as all of the memory will be allocated and locked # This is useful when you want to reserve all the memory # upfront or in the cases where you want memory latencies # to be very predictable # Default false #enable_mem_prealloc = true # Enable huge pages for VM RAM, default false # Enabling this will result in the VM memory # being allocated using huge pages. # This is useful when you want to use vhost-user network # stacks within the container. This will automatically # result in memory pre allocation #enable_hugepages = true # Enable swap of vm memory. Default false. # The behaviour is undefined if mem_prealloc is also set to true #enable_swap = true # This option changes the default hypervisor and kernel parameters # to enable debug output where available. This extra output is added # to the proxy logs, but only when proxy debug is also enabled. # # Default false #enable_debug = true # Disable the customizations done in the runtime when it detects # that it is running on top a VMM. This will result in the runtime # behaving as it would when running on bare metal. # #disable_nesting_checks = true # This is the msize used for 9p shares. It is the number of bytes # used for 9p packet payload. #msize_9p = 8192 # If true and vsocks are supported, use vsocks to communicate directly # with the agent and no proxy is started, otherwise use unix # sockets and start a proxy to communicate with the agent. # Default false #use_vsock = true # VFIO devices are hotplugged on a bridge by default. # Enable hotplugging on root bus. This may be required for devices with # a large PCI bar, as this is a current limitation with hotplugging on # a bridge. This value is valid for \"pc\" machine type. # Default false #hotplug_vfio_on_root_bus = true # If host doesn't support vhost_net, set to true. Thus we won't create vhost fds for nics. # Default false #disable_vhost_net = true # # Default entropy source. # The path to a host source of entropy (including a real hardware RNG) # /dev/urandom and /dev/random are two main options. # Be aware that /dev/random is a blocking source of entropy. If the host # runs out of entropy, the VMs boot time will increase leading to get startup # timeouts. # The source of entropy /dev/urandom is non-blocking and provides a # generally acceptable source of entropy. It should work well for pretty much # all practical purposes. #entropy_source= \"/dev/urandom\" # Path to OCI hook binaries in the *guest rootfs*. # This does not affect host-side hooks which must instead be added to # the OCI spec passed to the runtime. # # You can create a rootfs with hooks by customizing the osbuilder scripts: # https://github.com/kata-containers/osbuilder # # Hooks must be stored in a subdirectory of guest_hook_path according to their # hook type, i.e. \"guest_hook_path/{prestart,postart,poststop}\". # The agent will scan these directories for executable files and add them, in # lexicographical order, to the lifecycle of the guest container. # Hooks are executed in the runtime namespace of the guest. See the official documentation: # https://github.com/opencontainers/runtime-spec/blob/v1.0.1/config.md#posix-platform-hooks # Warnings will be logged if any error is encountered will scanning for hooks, # but it will not abort container execution. #guest_hook_path = \"/usr/share/oci/hooks\" [factory] # VM templating support. Once enabled, new VMs are created from template # using vm cloning. They will share the same initial kernel, initramfs and # agent memory by mapping it readonly. It helps speeding up new container # creation and saves a lot of memory if there are many kata containers running # on the same host. # # When disabled, new VMs are created from scratch. # # Note: Requires \"initrd=\" to be set (\"image=\" is not supported). # # Default false #enable_template = true # Specifies the path of template. # # Default \"/run/vc/vm/template\" #template_path = \"/run/vc/vm/template\" # The number of caches of VMCache: # unspecified or == 0 --> VMCache is disabled # > 0 --> will be set to the specified number # # VMCache is a function that creates VMs as caches before using it. # It helps speed up new container creation. # The function consists of a server and some clients communicating # through Unix socket. The protocol is gRPC in protocols/cache/cache.proto. # The VMCache server will create some VMs and cache them by factory cache. # It will convert the VM to gRPC format and transport it when gets # requestion from clients. # Factory grpccache is the VMCache client. It will request gRPC format # VM and convert it back to a VM. If VMCache function is enabled, # kata-runtime will request VM from factory grpccache when it creates # a new sandbox. # # Default 0 #vm_cache_number = 0 # Specify the address of the Unix socket that is used by VMCache. # # Default /var/run/kata-containers/cache.sock #vm_cache_endpoint = \"/var/run/kata-containers/cache.sock\" [proxy.kata] path = \"/usr/libexec/kata-containers/kata-proxy\" # If enabled, proxy messages will be sent to the system log # (default: disabled) #enable_debug = true [shim.kata] path = \"/usr/libexec/kata-containers/kata-shim\" # If enabled, shim messages will be sent to the system log # (default: disabled) #enable_debug = true # If enabled, the shim will create opentracing.io traces and spans. # (See https://www.jaegertracing.io/docs/getting-started). # # Note: By default, the shim runs in a separate network namespace. Therefore, # to allow it to send trace details to the Jaeger agent running on the host, # it is necessary to set 'disable_new_netns=true' so that it runs in the host # network namespace. # # (default: disabled) #enable_tracing = true [agent.kata] # If enabled, make the agent display debug-level messages. # (default: disabled) #enable_debug = true # Enable agent tracing. # # If enabled, the default trace mode is \"dynamic\" and the # default trace type is \"isolated\". The trace mode and type are set # explicity with the `trace_type=` and `trace_mode=` options. # # Notes: # # - Tracing is ONLY enabled when `enable_tracing` is set: explicitly # setting `trace_mode=` and/or `trace_type=` without setting `enable_tracing` # will NOT activate agent tracing. # # - See https://github.com/kata-containers/agent/blob/master/TRACING.md for # full details. # # (default: disabled) #enable_tracing = true # #trace_mode = \"dynamic\" #trace_type = \"isolated\" [netmon] # If enabled, the network monitoring process gets started when the # sandbox is created. This allows for the detection of some additional # network being added to the existing network namespace, after the # sandbox has been created. # (default: disabled) #enable_netmon = true # Specify the path to the netmon binary. path = \"/usr/libexec/kata-containers/kata-netmon\" # If enabled, netmon messages will be sent to the system log # (default: disabled) #enable_debug = true [runtime] # If enabled, the runtime will log additional debug messages to the # system log # (default: disabled) #enable_debug = true # # Internetworking model # Determines how the VM should be connected to the # the container network interface # Options: # # - bridged # Uses a linux bridge to interconnect the container interface to # the VM. Works for most cases except macvlan and ipvlan. # # - macvtap # Used when the Container network interface can be bridged using # macvtap. # # - none # Used when customize network. Only creates a tap device. No veth pair. # # - tcfilter # Uses tc filter rules to redirect traffic from the network interface # provided by plugin to a tap interface connected to the VM. # internetworking_model=\"tcfilter\" # disable guest seccomp # Determines whether container seccomp profiles are passed to the virtual # machine and applied by the kata agent. If set to true, seccomp is not applied # within the guest # (default: true) disable_guest_seccomp=true # If enabled, the runtime will create opentracing.io traces and spans. # (See https://www.jaegertracing.io/docs/getting-started). # (default: disabled) #enable_tracing = true # If enabled, the runtime will not create a network namespace for shim and hypervisor processes. # This option may have some potential impacts to your host. It should only be used when you know what you're doing. # `disable_new_netns` conflicts with `enable_netmon` # `disable_new_netns` conflicts with `internetworking_model=bridged` and `internetworking_model=macvtap`. It works only # with `internetworking_model=none`. The tap device will be in the host network namespace and can connect to a bridge # (like OVS) directly. # If you are using docker, `disable_new_netns` only works with `docker run --net=none` # (default: false) #disable_new_netns = true # Enabled experimental feature list, format: [\"a\", \"b\"]. # Experimental features are features not stable enough for production, # They may break compatibility, and are prepared for a big version bump. # Supported experimental features: # 1. \"newstore\": new persist storage driver which breaks backward compatibility, # expected to move out of experimental in 2.0.0. # (default: []) experimental=[] 参考： https://github.com/kata-containers/runtime#configuration Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"runtime/gpu/_index.html":{"url":"runtime/gpu/_index.html","title":"GPU","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"runtime/gpu/nvidia-device-plugin.html":{"url":"runtime/gpu/nvidia-device-plugin.html","title":"nvidia-device-plugin介绍","keywords":"","body":"1. 简介2. 要求3. 使用3.1. 安装NVIDIA drivers和nvidia-docker3.2. 配置docker runtime3.3. 部署nvidia-device-plugin3.4. 运行GPU任务4. 构建和运行nvidia-device-plugin4.1. docker方式4.1.1. 编译4.1.2. 运行4.2. 非docker方式4.2.1. 编译4.2.2. 本地运行1. 简介 NVIDIA device plugin 通过k8s daemonset的方式部署到每个k8s的node节点上，实现了Kubernetes device plugin的接口。 提供以下功能： 暴露每个节点的GPU数量给集群 跟踪GPU的健康情况 使在k8s的节点可以运行GPU容器 2. 要求 NVIDIA drivers ~= 384.81 nvidia-docker version > 2.0 (see how to install and it's prerequisites#prerequisites)) docker configured with nvidia as the default runtime. Kubernetes version >= 1.10 3. 使用 3.1. 安装NVIDIA drivers和nvidia-docker 提供GPU节点的机器，准备工作如下 安装NVIDIA drivers ~= 384.81 安装nvidia-docker version > 2.0 3.2. 配置docker runtime 配置nvidia runtime作为GPU节点的默认runtime。 修改文件/etc/docker/daemon.json，增加以下runtime内容。 { \"default-runtime\": \"nvidia\", \"runtimes\": { \"nvidia\": { \"path\": \"/usr/bin/nvidia-container-runtime\", \"runtimeArgs\": [] } } } 3.3. 部署nvidia-device-plugin $ kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/1.0.0-beta4/nvidia-device-plugin.yml nvidia-device-plugin的daemonset yaml文件如下： # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. apiVersion: apps/v1 kind: DaemonSet metadata: name: nvidia-device-plugin-daemonset namespace: kube-system spec: selector: matchLabels: name: nvidia-device-plugin-ds updateStrategy: type: RollingUpdate template: metadata: # This annotation is deprecated. Kept here for backward compatibility # See https://kubernetes.io/docs/tasks/administer-cluster/guaranteed-scheduling-critical-addon-pods/ annotations: scheduler.alpha.kubernetes.io/critical-pod: \"\" labels: name: nvidia-device-plugin-ds spec: tolerations: # This toleration is deprecated. Kept here for backward compatibility # See https://kubernetes.io/docs/tasks/administer-cluster/guaranteed-scheduling-critical-addon-pods/ - key: CriticalAddonsOnly operator: Exists - key: nvidia.com/gpu operator: Exists effect: NoSchedule # Mark this pod as a critical add-on; when enabled, the critical add-on # scheduler reserves resources for critical add-on pods so that they can # be rescheduled after a failure. # See https://kubernetes.io/docs/tasks/administer-cluster/guaranteed-scheduling-critical-addon-pods/ priorityClassName: \"system-node-critical\" containers: - image: nvidia/k8s-device-plugin:1.0.0-beta4 name: nvidia-device-plugin-ctr securityContext: allowPrivilegeEscalation: false capabilities: drop: [\"ALL\"] volumeMounts: - name: device-plugin mountPath: /var/lib/kubelet/device-plugins volumes: - name: device-plugin hostPath: path: /var/lib/kubelet/device-plugins 3.4. 运行GPU任务 创建一个GPU的pod，pod的资源类型指定为nvidia.com/gpu。 apiVersion: v1 kind: Pod metadata: name: gpu-pod spec: containers: - name: cuda-container image: nvidia/cuda:9.0-devel resources: limits: nvidia.com/gpu: 2 # requesting 2 GPUs - name: digits-container image: nvidia/digits:6.0 resources: limits: nvidia.com/gpu: 2 # requesting 2 GPUs 4. 构建和运行nvidia-device-plugin 4.1. docker方式 4.1.1. 编译 直接拉取dockerhub的镜像 $ docker pull nvidia/k8s-device-plugin:1.0.0-beta4 拉取代码构建镜像 $ docker build -t nvidia/k8s-device-plugin:1.0.0-beta4 https://github.com/NVIDIA/k8s-device-plugin.git#1.0.0-beta4 修改nvidia-device-plugin后构建镜像 $ git clone https://github.com/NVIDIA/k8s-device-plugin.git && cd k8s-device-plugin $ git checkout 1.0.0-beta4 $ docker build -t nvidia/k8s-device-plugin:1.0.0-beta4 . 4.1.2. 运行 docker本地运行 $ docker run --security-opt=no-new-privileges --cap-drop=ALL --network=none -it -v /var/lib/kubelet/device-plugins:/var/lib/kubelet/device-plugins nvidia/k8s-device-plugin:1.0.0-beta4 daemonset运行 $ kubectl create -f nvidia-device-plugin.yml 4.2. 非docker方式 4.2.1. 编译 $ C_INCLUDE_PATH=/usr/local/cuda/include LIBRARY_PATH=/usr/local/cuda/lib64 go build 4.2.2. 本地运行 $ ./k8s-device-plugin 参考： https://github.com/NVIDIA/k8s-device-plugin k8s-device-plugin gpu-support Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"etcd/etcd-introduction.html":{"url":"etcd/etcd-introduction.html","title":"Etcd介绍","keywords":"","body":"1. Etcd是什么（what）2. 为什么使用Etcd（why）2.1. Etcd的优势3. 如何实现Etcd架构（how）3.1. Etcd的相关名词解释3.2. Etcd的架构图1、HTTP Server:2、Raft:3、WAL:4、Store:1. Etcd是什么（what） etcd is a distributed, consistent key-value store for shared configuration and service discovery, with a focus on being: Secure: automatic TLS with optional client cert authentication[可选的SSL客户端证书认证：支持https访问 ] Fast: benchmarked 10,000 writes/sec[单实例每秒 1000 次写操作] Reliable: properly distributed using Raft[使用Raft保证一致性] etcd是一个分布式、一致性的键值存储系统，主要用于配置共享和服务发现。[以上内容来自etcd官网] 2. 为什么使用Etcd（why） 2.1. Etcd的优势 简单。使用Go语言编写部署简单；使用HTTP作为接口使用简单；使用Raft算法保证强一致性让用户易于理解。 数据持久化。etcd默认数据一更新就进行持久化。 安全。etcd支持SSL客户端安全认证。 3. 如何实现Etcd架构（how） 3.1. Etcd的相关名词解释 Raft：etcd所采用的保证分布式系统强一致性的算法。 Node：一个Raft状态机实例。 Member： 一个etcd实例。它管理着一个Node，并且可以为客户端请求提供服务。 Cluster：由多个Member构成可以协同工作的etcd集群。 Peer：对同一个etcd集群中另外一个Member的称呼。 Client： 向etcd集群发送HTTP请求的客户端。 WAL：预写式日志，etcd用于持久化存储的日志格式。 snapshot：etcd防止WAL文件过多而设置的快照，存储etcd数据状态。 Proxy：etcd的一种模式，为etcd集群提供反向代理服务。 Leader：Raft算法中通过竞选而产生的处理所有数据提交的节点。 Follower：竞选失败的节点作为Raft中的从属节点，为算法提供强一致性保证。 Candidate：当Follower超过一定时间接收不到Leader的心跳时转变为Candidate开始竞选。【候选人】 Term：某个节点成为Leader到下一次竞选时间，称为一个Term。【任期】 Index：数据项编号。Raft中通过Term和Index来定位数据。 3.2. Etcd的架构图 图片 - etcd的架构图 一个用户的请求发送过来，会经由HTTP Server转发给Store进行具体的事务处理，如果涉及到节点的修改，则交给Raft模块进行状态的变更、日志的记录，然后再同步给别的etcd节点以确认数据提交，最后进行数据的提交，再次同步。 1、HTTP Server: 用于处理用户发送的API请求以及其它etcd节点的同步与心跳信息请求。 2、Raft: Raft强一致性算法的具体实现，是etcd的核心。 3、WAL: Write Ahead Log（预写式日志），是etcd的数据存储方式，用于系统提供原子性和持久性的一系列技术。除了在内存中存有所有数据的状态以及节点的索引以外，etcd就通过WAL进行持久化存储。WAL中，所有的数据提交前都会事先记录日志。 Entry[日志内容]: 负责存储具体日志的内容。 Snapshot[快照内容]: Snapshot是为了防止数据过多而进行的状态快照，日志内容发生变化时保存Raft的状态。 4、Store: 用于处理etcd支持的各类功能的事务，包括数据索引、节点状态变更、监控与反馈、事件处理与执行等等，是etcd对用户提供的大多数API功能的具体实现。 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"etcd/raft.html":{"url":"etcd/raft.html","title":"Raft算法","keywords":"","body":"1. Raft协议[分布式一致性算法]2. 过程2.1. 选举2.2. 同步2.3. 网络分区1. Raft协议[分布式一致性算法] 图片 - raft raft算法中涉及三种角色，分别是： follower: 跟随者 candidate: 候选者，选举过程中的中间状态角色 leader: 领导者 2. 过程 2.1. 选举 有两个timeout来控制选举，第一个是election timeout，该时间是节点从follower到成为candidate的时间，该时间是150到300毫秒之间的随机值。另一个是heartbeat timeout。 当某个节点经历完election timeout成为candidate后，开启新的一个选举周期，他向其他节点发起投票请求（Request Vote），如果接收到消息的节点在该周期内还没投过票则给这个candidate投票，然后节点重置他的election timeout。 当该candidate获得大部分的选票，则可以当选为leader。 leader就开始发送append entries给其他follower节点，这个消息会在内部指定的heartbeat timeout时间内发出，follower收到该信息则响应给leader。 这个选举周期会继续，直到某个follower没有收到心跳，并成为candidate。 如果某个选举周期内，有两个candidate同时获得相同多的选票，则会等待一个新的周期重新选举。 2.2. 同步 当选举过程结束，选出了leader，则leader需要把所有的变更同步的系统中的其他节点，该同步也是通过发送Append Entries的消息的方式。 首先一个客户端发送一个更新给leader，这个更新会添加到leader的日志中。 然后leader会在给follower的下次心跳探测中发送该更新。 一旦大多数follower收到这个更新并返回给leader，leader提交这个更新，然后返回给客户端。 2.3. 网络分区 当发生网络分区的时候，在不同分区的节点接收不到leader的心跳，则会开启一轮选举，形成不同leader的多个分区集群。 当客户端给不同leader的发送更新消息时，不同分区集群中的节点个数小于原先集群的一半时，更新不会被提交，而节点个数大于集群数一半时，更新会被提交。 当网络分区恢复后，被提交的更新会同步到其他的节点上，其他节点未提交的日志会被回滚并匹配新leader的日志，保证全局的数据是一致的。 参考： http://thesecretlivesofdata.com/raft/ https://raft.github.io/raft.pdf https://raft.github.io/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"etcd/etcd-setup-flags.html":{"url":"etcd/etcd-setup-flags.html","title":"Etcd启动配置参数","keywords":"","body":"1. Etcd配置参数1.1. member flags1.2. clustering flags1.3. proxy flags1.4. security flags1.5. logging flags1.6. unsafe flags1.7. profiling flags1.8. auth flags1.9. experimental flags1. Etcd配置参数 / # etcd --help usage: etcd [flags] start an etcd server etcd --version show the version of etcd etcd -h | --help show the help information about etcd etcd --config-file path to the server configuration file etcd gateway run the stateless pass-through etcd TCP connection forwarding proxy etcd grpc-proxy run the stateless etcd v3 gRPC L7 reverse proxy 1.1. member flags member flags: --name 'default' human-readable name for this member. --data-dir '${name}.etcd' path to the data directory. --wal-dir '' path to the dedicated wal directory. --snapshot-count '100000' number of committed transactions to trigger a snapshot to disk. --heartbeat-interval '100' time (in milliseconds) of a heartbeat interval. --election-timeout '1000' time (in milliseconds) for an election to timeout. See tuning documentation for details. --initial-election-tick-advance 'true' whether to fast-forward initial election ticks on boot for faster election. --listen-peer-urls 'http://localhost:2380' list of URLs to listen on for peer traffic. --listen-client-urls 'http://localhost:2379' list of URLs to listen on for client traffic. --max-snapshots '5' maximum number of snapshot files to retain (0 is unlimited). --max-wals '5' maximum number of wal files to retain (0 is unlimited). --cors '' comma-separated whitelist of origins for CORS (cross-origin resource sharing). --quota-backend-bytes '0' raise alarms when backend size exceeds the given quota (0 defaults to low space quota). --max-txn-ops '128' maximum number of operations permitted in a transaction. --max-request-bytes '1572864' maximum client request size in bytes the server will accept. --grpc-keepalive-min-time '5s' minimum duration interval that a client should wait before pinging server. --grpc-keepalive-interval '2h' frequency duration of server-to-client ping to check if a connection is alive (0 to disable). --grpc-keepalive-timeout '20s' additional duration of wait before closing a non-responsive connection (0 to disable). 1.2. clustering flags clustering flags: --initial-advertise-peer-urls 'http://localhost:2380' list of this member's peer URLs to advertise to the rest of the cluster. --initial-cluster 'default=http://localhost:2380' initial cluster configuration for bootstrapping. --initial-cluster-state 'new' initial cluster state ('new' or 'existing'). --initial-cluster-token 'etcd-cluster' initial cluster token for the etcd cluster during bootstrap. Specifying this can protect you from unintended cross-cluster interaction when running multiple clusters. --advertise-client-urls 'http://localhost:2379' list of this member's client URLs to advertise to the public. The client URLs advertised should be accessible to machines that talk to etcd cluster. etcd client libraries parse these URLs to connect to the cluster. --discovery '' discovery URL used to bootstrap the cluster. --discovery-fallback 'proxy' expected behavior ('exit' or 'proxy') when discovery services fails. \"proxy\" supports v2 API only. --discovery-proxy '' HTTP proxy to use for traffic to discovery service. --discovery-srv '' dns srv domain used to bootstrap the cluster. --strict-reconfig-check 'true' reject reconfiguration requests that would cause quorum loss. --auto-compaction-retention '0' auto compaction retention length. 0 means disable auto compaction. --auto-compaction-mode 'periodic' interpret 'auto-compaction-retention' one of: periodic|revision. 'periodic' for duration based retention, defaulting to hours if no time unit is provided (e.g. '5m'). 'revision' for revision number based retention. --enable-v2 'true' Accept etcd V2 client requests. 1.3. proxy flags proxy flags: \"proxy\" supports v2 API only. --proxy 'off' proxy mode setting ('off', 'readonly' or 'on'). --proxy-failure-wait 5000 time (in milliseconds) an endpoint will be held in a failed state. --proxy-refresh-interval 30000 time (in milliseconds) of the endpoints refresh interval. --proxy-dial-timeout 1000 time (in milliseconds) for a dial to timeout. --proxy-write-timeout 5000 time (in milliseconds) for a write to timeout. --proxy-read-timeout 0 time (in milliseconds) for a read to timeout. 1.4. security flags security flags: --ca-file '' [DEPRECATED] path to the client server TLS CA file. '-ca-file ca.crt' could be replaced by '-trusted-ca-file ca.crt -client-cert-auth' and etcd will perform the same. --cert-file '' path to the client server TLS cert file. --key-file '' path to the client server TLS key file. --client-cert-auth 'false' enable client cert authentication. --client-crl-file '' path to the client certificate revocation list file. --trusted-ca-file '' path to the client server TLS trusted CA cert file. --auto-tls 'false' client TLS using generated certificates. --peer-ca-file '' [DEPRECATED] path to the peer server TLS CA file. '-peer-ca-file ca.crt' could be replaced by '-peer-trusted-ca-file ca.crt -peer-client-cert-auth' and etcd will perform the same. --peer-cert-file '' path to the peer server TLS cert file. --peer-key-file '' path to the peer server TLS key file. --peer-client-cert-auth 'false' enable peer client cert authentication. --peer-trusted-ca-file '' path to the peer server TLS trusted CA file. --peer-auto-tls 'false' peer TLS using self-generated certificates if --peer-key-file and --peer-cert-file are not provided. --peer-crl-file '' path to the peer certificate revocation list file. 1.5. logging flags logging flags --debug 'false' enable debug-level logging for etcd. --log-package-levels '' specify a particular log level for each etcd package (eg: 'etcdmain=CRITICAL,etcdserver=DEBUG'). --log-output 'default' specify 'stdout' or 'stderr' to skip journald logging even when running under systemd. 1.6. unsafe flags unsafe flags: Please be CAUTIOUS when using unsafe flags because it will break the guarantees given by the consensus protocol. --force-new-cluster 'false' force to create a new one-member cluster. 1.7. profiling flags profiling flags: --enable-pprof 'false' Enable runtime profiling data via HTTP server. Address is at client URL + \"/debug/pprof/\" --metrics 'basic' Set level of detail for exported metrics, specify 'extensive' to include histogram metrics. --listen-metrics-urls '' List of URLs to listen on for metrics. 1.8. auth flags auth flags: --auth-token 'simple' Specify a v3 authentication token type and its options ('simple' or 'jwt'). 1.9. experimental flags experimental flags: --experimental-initial-corrupt-check 'false' enable to check data corruption before serving any client/peer traffic. --experimental-corrupt-check-time '0s' duration of time between cluster corruption check passes. --experimental-enable-v2v3 '' serve v2 requests through the v3 backend under a given prefix. Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"etcd/etcd-auth-and-security.html":{"url":"etcd/etcd-auth-and-security.html","title":"Etcd访问控制","keywords":"","body":"1. ETCD资源类型2. 权限资源3. ETCD访问控制3.1. 访问控制相关命令3.2. user相关命令3.2.1. 添加root用户并设置密码3.2.2. 添加非root用户并设置密码3.2.3. 查看当前所有用户3.2.4. 将用户添加到对应角色3.2.5. 查看用户拥有哪些角色3.3. role相关命令3.3.1. 添加角色3.3.2. 查看所有角色3.3.3. 给角色分配权限3.3.4. 查看角色所拥有的权限3.4. auth相关操作3.4.1. 开启认证4. 访问控制设置步骤5. 访问认证的API调用1. ETCD资源类型 There are three types of resources in etcd permission resources: users and roles in the user store key-value resources: key-value pairs in the key-value store settings resources: security settings, auth settings, and dynamic etcd cluster settings (election/heartbeat) 2. 权限资源 Users：user用来设置身份认证（user：passwd），一个用户可以拥有多个角色，每个角色被分配一定的权限（只读、只写、可读写），用户分为root用户和非root用户。 Roles：角色用来关联权限，角色主要三类：root角色。默认创建root用户时即创建了root角色，该角色拥有所有权限；guest角色，默认自动创建，主要用于非认证使用。普通角色，由root用户创建角色，并分配指定权限。 注意：如果没有指定任何验证方式，即没显示指定以什么用户进行访问，那么默认会设定为 guest 角色。默认情况下 guest 也是具有全局访问权限的。如果不希望未授权就获取或修改etcd的数据，则可收回guest角色的权限或删除该角色，etcdctl role revoke 。 Permissions:权限分为只读、只写、可读写三种权限，权限即对指定目录或key的读写权限。 3. ETCD访问控制 3.1. 访问控制相关命令 NAME: etcdctl - A simple command line client for etcd. USAGE: etcdctl [global options] command [command options] [arguments...] VERSION: 2.2.0 COMMANDS: user user add, grant and revoke subcommands role role add, grant and revoke subcommands auth overall auth controls GLOBAL OPTIONS: --peers, -C a comma-delimited list of machine addresses in the cluster (default: \"http://127.0.0.1:4001,http://127.0.0.1:2379\") --endpoint a comma-delimited list of machine addresses in the cluster (default: \"http://127.0.0.1:4001,http://127.0.0.1:2379\") --cert-file identify HTTPS client using this SSL certificate file --key-file identify HTTPS client using this SSL key file --ca-file verify certificates of HTTPS-enabled servers using this CA bundle --username, -u provide username[:password] and prompt if password is not supplied. --timeout '1s' connection timeout per request 3.2. user相关命令 [root@localhost etcd]# etcdctl user --help NAME: etcdctl user - user add, grant and revoke subcommands USAGE: etcdctl user command [command options] [arguments...] COMMANDS: add add a new user for the etcd cluster get get details for a user list list all current users remove remove a user for the etcd cluster grant grant roles to an etcd user revoke revoke roles for an etcd user passwd change password for a user help, h Shows a list of commands or help for one command OPTIONS: --help, -h show help 3.2.1. 添加root用户并设置密码 etcdctl --endpoints http://172.16.22.36:2379 user add root 3.2.2. 添加非root用户并设置密码 etcdctl --endpoints http://172.16.22.36:2379 --username root:123 user add huwh 3.2.3. 查看当前所有用户 etcdctl --endpoints http://172.16.22.36:2379 --username root:123 user list 3.2.4. 将用户添加到对应角色 etcdctl --endpoints http://172.16.22.36:2379 --username root:123 user grant --roles test1 phpor 3.2.5. 查看用户拥有哪些角色 etcdctl --endpoints http://172.16.22.36:2379 --username root:123 user get phpor 3.3. role相关命令 [root@localhost etcd]# etcdctl role --help NAME: etcdctl role - role add, grant and revoke subcommands USAGE: etcdctl role command [command options] [arguments...] COMMANDS: add add a new role for the etcd cluster get get details for a role list list all roles remove remove a role from the etcd cluster grant grant path matches to an etcd role revoke revoke path matches for an etcd role help, h Shows a list of commands or help for one command OPTIONS: --help, -h show help 3.3.1. 添加角色 etcdctl --endpoints http://172.16.22.36:2379 --username root:2379 role add test1 3.3.2. 查看所有角色 etcdctl --endpoints http://172.16.22.36:2379 --username root:123 role list 3.3.3. 给角色分配权限 [root@localhost etcd]# etcdctl role grant --help NAME: grant - grant path matches to an etcd role USAGE: command grant [command options] [arguments...] OPTIONS: --path Path granted for the role to access --read Grant read-only access --write Grant write-only access --readwrite Grant read-write access 1、只包含目录 etcdctl --endpoints http://172.16.22.36:2379 --username root:123 role grant --readwrite --path /test1 test1 2、包括目录和子目录或文件 etcdctl --endpoints http://172.16.22.36:2379 --username root:123 role grant --readwrite --path /test1/* test1 3.3.4. 查看角色所拥有的权限 etcdctl --endpoints http://172.16.22.36:2379 --username root:2379 role get test1 3.4. auth相关操作 [root@localhost etcd]# etcdctl auth --help NAME: etcdctl auth - overall auth controls USAGE: etcdctl auth command [command options] [arguments...] COMMANDS: enable enable auth access controls disable disable auth access controls help, h Shows a list of commands or help for one command OPTIONS: --help, -h show help 3.4.1. 开启认证 etcdctl --endpoints http://172.16.22.36:2379 auth enable 4. 访问控制设置步骤 顺序 步骤 命令 1 添加root用户 etcdctl --endpoints http://: user add root 2 开启认证 etcdctl --endpoints http://: auth enable 3 添加非root用户 etcdctl --endpoints http://: –username root: user add 4 添加角色 etcdctl --endpoints http://: –username root: role add 5 给角色授权（只读、只写、可读写） etcdctl --endpoints http://: –username root: role grant --readwrite --path 6 给用户分配角色（即分配了角色对应的权限） etcdctl --endpoints http://: –username root: user grant --roles 5. 访问认证的API调用 更多参考 https://coreos.com/etcd/docs/latest/v2/auth_api.html https://coreos.com/etcd/docs/latest/v2/authentication.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"etcd/etcdctl/_index.html":{"url":"etcd/etcdctl/_index.html","title":"etcdctl命令工具","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"etcd/etcdctl/etcdctl-v3.html":{"url":"etcd/etcdctl/etcdctl-v3.html","title":"etcdctl命令工具-V3","keywords":"","body":"1. etcdctl的安装2. etcdctl V33. etcdctl 常用命令3.1. 指定etcd集群3.2. 增删改查3.3. 集群状态3.4. 集群成员4. etcdctl get etcdctl的v3版本与v2版本使用命令有所不同，本文介绍etcdctl v3版本的命令工具的使用方式。 1. etcdctl的安装 etcdctl的二进制文件可以在 github.com/coreos/etcd/releases 选择对应的版本下载，例如可以执行以下install_etcdctl.sh的脚本，修改其中的版本信息。 #!/bin/bash ETCD_VER=v3.3.4 ETCD_DIR=etcd-download DOWNLOAD_URL=https://github.com/coreos/etcd/releases/download # Download mkdir ${ETCD_DIR} cd ${ETCD_DIR} wget ${DOWNLOAD_URL}/${ETCD_VER}/etcd-${ETCD_VER}-linux-amd64.tar.gz tar -xzvf etcd-${ETCD_VER}-linux-amd64.tar.gz # install cd etcd-${ETCD_VER}-linux-amd64 cp etcdctl /usr/local/bin/ 2. etcdctl V3 使用etcdctlv3的版本时，需设置环境变量ETCDCTL_API=3。 export ETCDCTL_API=3 # 或者在`/etc/profile`文件中添加环境变量 vi /etc/profile ... export ETCDCTL_API=3 ... source /etc/profile # 或者在命令执行前加 ETCDCTL_API=3 ETCDCTL_API=3 etcdctl --endpoints=$ENDPOINTS member list 查看当前etcdctl的版本信息etcdctl version。 [root@k8s-dbg-master-1 etcd]# etcdctl version etcdctl version: 3.3.4 API version: 3.3 更多命令帮助可以查询etcdctl —help。 [root@k8s-dbg-master-1 etcd]# etcdctl --help NAME: etcdctl - A simple command line client for etcd3. USAGE: etcdctl VERSION: 3.3.4 API VERSION: 3.3 COMMANDS: get Gets the key or a range of keys put Puts the given key into the store del Removes the specified key or range of keys [key, range_end) txn Txn processes all the requests in one transaction compaction Compacts the event history in etcd alarm disarm Disarms all alarms alarm list Lists all alarms defrag Defragments the storage of the etcd members with given endpoints endpoint health Checks the healthiness of endpoints specified in `--endpoints` flag endpoint status Prints out the status of endpoints specified in `--endpoints` flag endpoint hashkv Prints the KV history hash for each endpoint in --endpoints move-leader Transfers leadership to another etcd cluster member. watch Watches events stream on keys or prefixes version Prints the version of etcdctl lease grant Creates leases lease revoke Revokes leases lease timetolive Get lease information lease list List all active leases lease keep-alive Keeps leases alive (renew) member add Adds a member into the cluster member remove Removes a member from the cluster member update Updates a member in the cluster member list Lists all members in the cluster snapshot save Stores an etcd node backend snapshot to a given file snapshot restore Restores an etcd member snapshot to an etcd directory snapshot status Gets backend snapshot status of a given file make-mirror Makes a mirror at the destination etcd cluster migrate Migrates keys in a v2 store to a mvcc store lock Acquires a named lock elect Observes and participates in leader election auth enable Enables authentication auth disable Disables authentication user add Adds a new user user delete Deletes a user user get Gets detailed information of a user user list Lists all users user passwd Changes password of user user grant-role Grants a role to a user user revoke-role Revokes a role from a user role add Adds a new role role delete Deletes a role role get Gets detailed information of a role role list Lists all roles role grant-permission Grants a key to a role role revoke-permission Revokes a key from a role check perf Check the performance of the etcd cluster help Help about any command OPTIONS: --cacert=\"\" verify certificates of TLS-enabled secure servers using this CA bundle --cert=\"\" identify secure client using this TLS certificate file --command-timeout=5s timeout for short running command (excluding dial timeout) --debug[=false] enable client-side debug logging --dial-timeout=2s dial timeout for client connections -d, --discovery-srv=\"\" domain name to query for SRV records describing cluster endpoints --endpoints=[127.0.0.1:2379] gRPC endpoints --hex[=false] print byte strings as hex encoded strings --insecure-discovery[=true] accept insecure SRV records describing cluster endpoints --insecure-skip-tls-verify[=false] skip server certificate verification --insecure-transport[=true] disable transport security for client connections --keepalive-time=2s keepalive time for client connections --keepalive-timeout=6s keepalive timeout for client connections --key=\"\" identify secure client using this TLS key file --user=\"\" username[:password] for authentication (prompt if password is not supplied) -w, --write-out=\"simple\" set the output format (fields, json, protobuf, simple, table) 3. etcdctl 常用命令 3.1. 指定etcd集群 HOST_1=10.240.0.17 HOST_2=10.240.0.18 HOST_3=10.240.0.19 ENDPOINTS=$HOST_1:2379,$HOST_2:2379,$HOST_3:2379 etcdctl --endpoints=$ENDPOINTS member list 如果etcd设置了证书访问，则需要添加证书相关参数： ETCDCTL_API=3 etcdctl --endpoints=$ENDPOINTS --cacert= --cert= --key= 参数说明如下： --cacert=\"\" verify certificates of TLS-enabled secure servers using this CA bundle --cert=\"\" identify secure client using this TLS certificate file --key=\"\" identify secure client using this TLS key file --endpoints=[127.0.0.1:2379] gRPC endpoints 可以自定义alias命令 # alias 命令，避免每次需要输入证书参数 alias ectl='ETCDCTL_API=3 etcdctl --endpoints=$ENDPOINTS --cacert= --cert= --key=' # 直接使用别名执行命令 ectl 3.2. 增删改查 1、增 etcdctl --endpoints=$ENDPOINTS put foo \"Hello World!\" 2、查 etcdctl --endpoints=$ENDPOINTS get foo etcdctl --endpoints=$ENDPOINTS --write-out=\"json\" get foo 基于相同前缀查找 etcdctl --endpoints=$ENDPOINTS put web1 value1 etcdctl --endpoints=$ENDPOINTS put web2 value2 etcdctl --endpoints=$ENDPOINTS put web3 value3 etcdctl --endpoints=$ENDPOINTS get web --prefix 列出所有的key etcdctl --endpoints=$ENDPOINTS get / --prefix --keys-only 3、删 etcdctl --endpoints=$ENDPOINTS put key myvalue etcdctl --endpoints=$ENDPOINTS del key etcdctl --endpoints=$ENDPOINTS put k1 value1 etcdctl --endpoints=$ENDPOINTS put k2 value2 etcdctl --endpoints=$ENDPOINTS del k --prefix 3.3. 集群状态 集群状态主要是etcdctl endpoint status 和etcdctl endpoint health两条命令。 etcdctl --write-out=table --endpoints=$ENDPOINTS endpoint status +------------------+------------------+---------+---------+-----------+-----------+------------+ | ENDPOINT | ID | VERSION | DB SIZE | IS LEADER | RAFT TERM | RAFT INDEX | +------------------+------------------+---------+---------+-----------+-----------+------------+ | 10.240.0.17:2379 | 4917a7ab173fabe7 | 3.0.0 | 45 kB | true | 4 | 16726 | | 10.240.0.18:2379 | 59796ba9cd1bcd72 | 3.0.0 | 45 kB | false | 4 | 16726 | | 10.240.0.19:2379 | 94df724b66343e6c | 3.0.0 | 45 kB | false | 4 | 16726 | +------------------+------------------+---------+---------+-----------+-----------+------------+ etcdctl --endpoints=$ENDPOINTS endpoint health 10.240.0.17:2379 is healthy: successfully committed proposal: took = 3.345431ms 10.240.0.19:2379 is healthy: successfully committed proposal: took = 3.767967ms 10.240.0.18:2379 is healthy: successfully committed proposal: took = 4.025451ms 3.4. 集群成员 跟集群成员相关的命令如下： member add Adds a member into the cluster member remove Removes a member from the cluster member update Updates a member in the cluster member list Lists all members in the cluster 例如 etcdctl member list列出集群成员的命令。 etcdctl --endpoints=http://172.16.5.4:12379 member list -w table +-----------------+---------+-------+------------------------+-----------------------------------------------+ | ID | STATUS | NAME | PEER ADDRS | CLIENT ADDRS | +-----------------+---------+-------+------------------------+-----------------------------------------------+ | c856d92a82ba66a | started | etcd0 | http://172.16.5.4:2380 | http://172.16.5.4:2379,http://172.16.5.4:4001 | +-----------------+---------+-------+------------------------+-----------------------------------------------+ 4. etcdctl get 使用etcdctl {command} --help可以查看具体命令的帮助信息。 # etcdctl get --help NAME: get - Gets the key or a range of keys USAGE: etcdctl get [options] [range_end] OPTIONS: --consistency=\"l\" Linearizable(l) or Serializable(s) --from-key[=false] Get keys that are greater than or equal to the given key using byte compare --keys-only[=false] Get only the keys --limit=0 Maximum number of results --order=\"\" Order of results; ASCEND or DESCEND (ASCEND by default) --prefix[=false] Get keys with matching prefix --print-value-only[=false] Only write values when using the \"simple\" output format --rev=0 Specify the kv revision --sort-by=\"\" Sort target; CREATE, KEY, MODIFY, VALUE, or VERSION GLOBAL OPTIONS: --cacert=\"\" verify certificates of TLS-enabled secure servers using this CA bundle --cert=\"\" identify secure client using this TLS certificate file --command-timeout=5s timeout for short running command (excluding dial timeout) --debug[=false] enable client-side debug logging --dial-timeout=2s dial timeout for client connections --endpoints=[127.0.0.1:2379] gRPC endpoints --hex[=false] print byte strings as hex encoded strings --insecure-skip-tls-verify[=false] skip server certificate verification --insecure-transport[=true] disable transport security for client connections --key=\"\" identify secure client using this TLS key file --user=\"\" username[:password] for authentication (prompt if password is not supplied) -w, --write-out=\"simple\" set the output format (fields, json, protobuf, simple, table) 文章参考： https://coreos.com/etcd/docs/latest/demo.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"etcd/etcdctl/etcdctl-v2.html":{"url":"etcd/etcdctl/etcdctl-v2.html","title":"etcdctl命令工具-V2","keywords":"","body":"1. etcdctl介绍2. Etcd常用命令2.1. 数据库操作命令2.1.1. 对象为键值set[增:无论是否存在]:etcdctl set key valuemk[增:必须不存在]:etcdctl mk key valuerm[删]:etcdctl rm keyupdate[改]:etcdctl update key valueget[查]:etcdctl get key2.1.2. 对象为目录setdir[增:无论是否存在]:etcdctl setdir dirmkdir[增:必须不存在]: etcdctl mkdir dirrmdir[删]:etcdctl rmdir dirupdatedir[改]:etcdctl updatedir dirls[查]:etcdclt ls2.2. 非数据库操作命令backup[备份 etcd 的数据]watch[监测一个键值的变化，一旦键值发生更新，就会输出最新的值并退出]exec-watch[监测一个键值的变化，一旦键值发生更新，就执行给定命令]member[通过 list、add、remove、update 命令列出、添加、删除 、更新etcd 实例到 etcd 集群中]etcdctl cluster-health[检查集群健康状态]2.3. 常用配置参数1. etcdctl介绍 etcdctl是一个命令行的客户端，它提供了一下简洁的命令，可理解为命令工具集，可以方便我们在对服务进行测试或者手动修改数据库内容。etcdctl与其他xxxctl的命令原理及操作类似（例如kubectl，systemctl）。 用法：etcdctl [global options] command [command options][args...] 2. Etcd常用命令 2.1. 数据库操作命令 etcd 在键的组织上采用了层次化的空间结构（类似于文件系统中目录的概念），数据库操作围绕对键值和目录的 CRUD [增删改查]（符合 REST 风格的一套操作：Create, Read, Update, Delete）完整生命周期的管理。 具体的命令选项参数可以通过 etcdctl command --help来获取相关帮助。 2.1.1. 对象为键值 set[增:无论是否存在]:etcdctl set key value mk[增:必须不存在]:etcdctl mk key value rm[删]:etcdctl rm key update[改]:etcdctl update key value get[查]:etcdctl get key 2.1.2. 对象为目录 setdir[增:无论是否存在]:etcdctl setdir dir mkdir[增:必须不存在]: etcdctl mkdir dir rmdir[删]:etcdctl rmdir dir updatedir[改]:etcdctl updatedir dir ls[查]:etcdclt ls 2.2. 非数据库操作命令 backup[备份 etcd 的数据] etcdctl backup watch[监测一个键值的变化，一旦键值发生更新，就会输出最新的值并退出] etcdctl watch key exec-watch[监测一个键值的变化，一旦键值发生更新，就执行给定命令] etcdctl exec-watch key --sh -c \"ls\" member[通过 list、add、remove、update 命令列出、添加、删除 、更新etcd 实例到 etcd 集群中] etcdctl member list；etcdctl member add 实例；etcdctl member remove 实例；etcdctl member update 实例。 etcdctl cluster-health[检查集群健康状态] 2.3. 常用配置参数 设置配置文件，默认为/etc/etcd/etcd.conf。 配置参数 参数说明 配置参数 参数说明 -name 节点名称 -data-dir 保存日志和快照的目录，默认为当前工作目录，指定节点的数据存储目录 -addr 公布的ip地址和端口。 默认为127.0.0.1:2379 -bind-addr 用于客户端连接的监听地址，默认为-addr配置 -peers 集群成员逗号分隔的列表，例如 127.0.0.1:2380,127.0.0.1:2381 -peer-addr 集群服务通讯的公布的IP地址，默认为 127.0.0.1:2380. -peer-bind-addr 集群服务通讯的监听地址，默认为-peer-addr配置 -wal-dir 指定节点的was文件的存储目录，若指定了该参数，wal文件会和其他数据文件分开存储 -listen-client-urls -listen-peer-urls 监听URL，用于与其他节点通讯 -initial-advertise-peer-urls 告知集群其他节点url. -advertise-client-urls 告知客户端url, 也就是服务的url -initial-cluster-token 集群的ID -initial-cluster 集群中所有节点 -initial-cluster-state -initial-cluster-state=new 表示从无到有搭建etcd集群 -discovery-srv 用于DNS动态服务发现，指定DNS SRV域名 -discovery 用于etcd动态发现，指定etcd发现服务的URL [https://discovery.etcd.io/],用环境变量表示 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"etcd/k8s-etcd-data.html":{"url":"etcd/k8s-etcd-data.html","title":"Etcd中的k8s数据","keywords":"","body":"1. 读取数据key2. 集群数据2.1. node3. k8s对象数据3.1. namespace3.2. namespace级别对象4. 读取数据value5. 注意事项6. 快捷命令6.1. etcdhelper的使用7. RBAC1. 读取数据key 使用以下命令列出所有的key。 ETCDCTL_API=3 etcdctl --endpoints=:2379,:2379,:2379 --cacert=/etc/kubernetes/pki/etcd/ca.crt --key=/etc/kubernetes/pki/apiserver-etcd-client.key --cert=/etc/kubernetes/pki/apiserver-etcd-client.crt get / --prefix --keys-only 参数说明： --cacert=\"\" verify certificates of TLS-enabled secure servers using this CA bundle --cert=\"\" identify secure client using this TLS certificate file --key=\"\" identify secure client using this TLS key file --endpoints=[127.0.0.1:2379] gRPC endpoints 可以使用alias来重命名etcdctl一串的命令 alias ectl='ETCDCTL_API=3 etcdctl --endpoints=:2379,:2379,:2379 --cacert=/etc/kubernetes/pki/etcd/ca.crt --key=/etc/kubernetes/pki/apiserver-etcd-client.key --cert=/etc/kubernetes/pki/apiserver-etcd-client.crt' 2. 集群数据 2.1. node /registry/minions/ /registry/minions/ /registry/minions/ 其他信息： /registry/leases/kube-node-lease/ /registry/leases/kube-node-lease/ /registry/leases/kube-node-lease/ /registry/masterleases/ /registry/masterleases/ 3. k8s对象数据 k8s对象数据的格式 3.1. namespace /registry/namespaces/default /registry/namespaces/game /registry/namespaces/kube-node-lease /registry/namespaces/kube-public /registry/namespaces/kube-system 3.2. namespace级别对象 /registry/{resource}/{namespace}/{resource_name} 以下以常见k8s对象为例： # deployment /registry/deployments/default/game-2048 /registry/deployments/kube-system/prometheus-operator # replicasets /registry/replicasets/default/game-2048-c7d589ccf # pod /registry/pods/default/game-2048-c7d589ccf-8lsbw # statefulsets /registry/statefulsets/kube-system/prometheus-k8s # daemonsets /registry/daemonsets/kube-system/kube-proxy # secrets /registry/secrets/default/default-token-tbfmb # serviceaccounts /registry/serviceaccounts/default/default service # service /registry/services/specs/default/game-2048 # endpoints /registry/services/endpoints/default/game-2048 4. 读取数据value 由于k8s默认etcd中的数据是通过protobuf格式存储，因此看到的key和value的值是一串字符串。 alias ectl='ETCDCTL_API=3 etcdctl --endpoints=:2379,:2379,:2379 --cacert=/etc/kubernetes/pki/etcd/ca.crt --key=/etc/kubernetes/pki/apiserver-etcd-client.key --cert=/etc/kubernetes/pki/apiserver-etcd-client.crt' # ectl get /registry/namespaces/test -w json |jq { \"header\": { \"cluster_id\": 12113422651334595000, \"member_id\": 8381627376898157000, \"revision\": 12321629, \"raft_term\": 20 }, \"kvs\": [ { \"key\": \"L3JlZ2lzdHJ5L25hbWVzcGFjZXMvdGVzdA==\", \"create_revision\": 11670741, \"mod_revision\": 11670741, \"version\": 1, \"value\": \"azhzAAoPCgJ2MRIJTmFtZXNwYWNlElwKQgoEdGVzdBIAGgAiACokYWM1YmJjOTQtNTkxZi0xMWVhLWJiOTQtNmM5MmJmM2I3NmI1MgA4AEIICJuf3fIFEAB6ABIMCgprdWJlcm5ldGVzGggKBkFjdGl2ZRoAIgA=\" } ], \"count\": 1 } 其中key可以通过base64解码出来 echo \"L3JlZ2lzdHJ5L25hbWVzcGFjZXMvdGVzdA==\" | base64 --decode # output /registry/namespaces/test value是值可以通过安装etcdhelper工具解析出来。 alias ehelper='etcdhelper -key /etc/kubernetes/pki/apiserver-etcd-client.key -cert /etc/kubernetes/pki/apiserver-etcd-client.crt -cacert /etc/kubernetes/pki/etcd/ca.crt' # ehelper get /registry/namespaces/test /v1, Kind=Namespace { \"kind\": \"Namespace\", \"apiVersion\": \"v1\", \"metadata\": { \"name\": \"test\", \"uid\": \"ac5bbc94-591f-11ea-bb94-6c92bf3b76b5\", \"creationTimestamp\": \"2020-02-27T05:11:55Z\" }, \"spec\": { \"finalizers\": [ \"kubernetes\" ] }, \"status\": { \"phase\": \"Active\" } } 5. 注意事项 由于k8s的etcd数据为了性能考虑，默认通过protobuf格式存储，不要通过手动的方式去修改或添加k8s数据。 不推荐使用json格式存储etcd数据，如果需要json格式，可以使用--storage-media-type=application/json参数存储，参考：https://github.com/kubernetes/kubernetes/issues/44670 6. 快捷命令 由于etcdctl的命令需要添加很多认证参数和endpoints的参数，因此可以使用别名的方式来简化命令。 # etcdctl alias ectl='ETCDCTL_API=3 etcdctl --endpoints=:2379,:2379,:2379 --cacert=/etc/kubernetes/pki/etcd/ca.crt --key=/etc/kubernetes/pki/apiserver-etcd-client.key --cert=/etc/kubernetes/pki/apiserver-etcd-client.crt' # etcdhelper alias ehelper='etcdhelper -key /etc/kubernetes/pki/apiserver-etcd-client.key -cert /etc/kubernetes/pki/apiserver-etcd-client.crt -cacert /etc/kubernetes/pki/etcd/ca.crt' 6.1. etcdhelper的使用 etcdhelper文档参考：https://github.com/openshift/origin/tree/master/tools/etcdhelper # 必要的认证参数 -key - points to master.etcd-client.key -cert - points to master.etcd-client.crt -cacert - points to ca.crt # 命令操作参数 ls - list all keys starting with prefix get - get the specific value of a key dump - dump the entire contents of the etcd 示例 $ ehelper ls /registry/leases/ /registry/leases/kube-node-lease/ /registry/leases/kube-node-lease/ /registry/leases/kube-node-lease/ $ ehelper get 7. RBAC 附RBAC相关的key。 clusterrolebindings /registry/clusterrolebindings/cluster-admin /registry/clusterrolebindings/flannel /registry/clusterrolebindings/galaxy /registry/clusterrolebindings/helm /registry/clusterrolebindings/kube-state-metrics /registry/clusterrolebindings/kubeadm:kubelet-bootstrap /registry/clusterrolebindings/kubeadm:node-autoapprove-bootstrap /registry/clusterrolebindings/kubeadm:node-autoapprove-certificate-rotation /registry/clusterrolebindings/kubeadm:node-proxier /registry/clusterrolebindings/lbcf-controller /registry/clusterrolebindings/prometheus-k8s /registry/clusterrolebindings/prometheus-operator /registry/clusterrolebindings/system:aws-cloud-provider /registry/clusterrolebindings/system:basic-user /registry/clusterrolebindings/system:controller:attachdetach-controller /registry/clusterrolebindings/system:controller:certificate-controller /registry/clusterrolebindings/system:controller:clusterrole-aggregation-controller /registry/clusterrolebindings/system:controller:cronjob-controller /registry/clusterrolebindings/system:controller:daemon-set-controller /registry/clusterrolebindings/system:controller:deployment-controller /registry/clusterrolebindings/system:controller:disruption-controller /registry/clusterrolebindings/system:controller:endpoint-controller /registry/clusterrolebindings/system:controller:expand-controller /registry/clusterrolebindings/system:controller:generic-garbage-collector /registry/clusterrolebindings/system:controller:horizontal-pod-autoscaler /registry/clusterrolebindings/system:controller:job-controller /registry/clusterrolebindings/system:controller:namespace-controller /registry/clusterrolebindings/system:controller:node-controller /registry/clusterrolebindings/system:controller:persistent-volume-binder /registry/clusterrolebindings/system:controller:pod-garbage-collector /registry/clusterrolebindings/system:controller:pv-protection-controller /registry/clusterrolebindings/system:controller:pvc-protection-controller /registry/clusterrolebindings/system:controller:replicaset-controller /registry/clusterrolebindings/system:controller:replication-controller /registry/clusterrolebindings/system:controller:resourcequota-controller /registry/clusterrolebindings/system:controller:route-controller /registry/clusterrolebindings/system:controller:service-account-controller /registry/clusterrolebindings/system:controller:service-controller /registry/clusterrolebindings/system:controller:statefulset-controller /registry/clusterrolebindings/system:controller:ttl-controller /registry/clusterrolebindings/system:coredns /registry/clusterrolebindings/system:discovery /registry/clusterrolebindings/system:kube-controller-manager /registry/clusterrolebindings/system:kube-dns /registry/clusterrolebindings/system:kube-scheduler /registry/clusterrolebindings/system:node /registry/clusterrolebindings/system:node-proxier /registry/clusterrolebindings/system:public-info-viewer /registry/clusterrolebindings/system:volume-scheduler clusterroles /registry/clusterroles/admin /registry/clusterroles/cluster-admin /registry/clusterroles/edit /registry/clusterroles/flannel /registry/clusterroles/kube-state-metrics /registry/clusterroles/lbcf-controller /registry/clusterroles/prometheus-k8s /registry/clusterroles/prometheus-operator /registry/clusterroles/system:aggregate-to-admin /registry/clusterroles/system:aggregate-to-edit /registry/clusterroles/system:aggregate-to-view /registry/clusterroles/system:auth-delegator /registry/clusterroles/system:aws-cloud-provider /registry/clusterroles/system:basic-user /registry/clusterroles/system:certificates.k8s.io:certificatesigningrequests:nodeclient /registry/clusterroles/system:certificates.k8s.io:certificatesigningrequests:selfnodeclient /registry/clusterroles/system:controller:attachdetach-controller /registry/clusterroles/system:controller:certificate-controller /registry/clusterroles/system:controller:clusterrole-aggregation-controller /registry/clusterroles/system:controller:cronjob-controller /registry/clusterroles/system:controller:daemon-set-controller /registry/clusterroles/system:controller:deployment-controller /registry/clusterroles/system:controller:disruption-controller /registry/clusterroles/system:controller:endpoint-controller /registry/clusterroles/system:controller:expand-controller /registry/clusterroles/system:controller:generic-garbage-collector /registry/clusterroles/system:controller:horizontal-pod-autoscaler /registry/clusterroles/system:controller:job-controller /registry/clusterroles/system:controller:namespace-controller /registry/clusterroles/system:controller:node-controller /registry/clusterroles/system:controller:persistent-volume-binder /registry/clusterroles/system:controller:pod-garbage-collector /registry/clusterroles/system:controller:pv-protection-controller /registry/clusterroles/system:controller:pvc-protection-controller /registry/clusterroles/system:controller:replicaset-controller /registry/clusterroles/system:controller:replication-controller /registry/clusterroles/system:controller:resourcequota-controller /registry/clusterroles/system:controller:route-controller /registry/clusterroles/system:controller:service-account-controller /registry/clusterroles/system:controller:service-controller /registry/clusterroles/system:controller:statefulset-controller /registry/clusterroles/system:controller:ttl-controller /registry/clusterroles/system:coredns /registry/clusterroles/system:csi-external-attacher /registry/clusterroles/system:csi-external-provisioner /registry/clusterroles/system:discovery /registry/clusterroles/system:heapster /registry/clusterroles/system:kube-aggregator /registry/clusterroles/system:kube-controller-manager /registry/clusterroles/system:kube-dns /registry/clusterroles/system:kube-scheduler /registry/clusterroles/system:kubelet-api-admin /registry/clusterroles/system:node /registry/clusterroles/system:node-bootstrapper /registry/clusterroles/system:node-problem-detector /registry/clusterroles/system:node-proxier /registry/clusterroles/system:persistent-volume-provisioner /registry/clusterroles/system:public-info-viewer /registry/clusterroles/system:volume-scheduler /registry/clusterroles/view rolebindings /registry/rolebindings/kube-public/kubeadm:bootstrap-signer-clusterinfo /registry/rolebindings/kube-public/system:controller:bootstrap-signer /registry/rolebindings/kube-system/kube-proxy /registry/rolebindings/kube-system/kube-state-metrics /registry/rolebindings/kube-system/kubeadm:kubeadm-certs /registry/rolebindings/kube-system/kubeadm:kubelet-config-1.14 /registry/rolebindings/kube-system/kubeadm:nodes-kubeadm-config /registry/rolebindings/kube-system/system::extension-apiserver-authentication-reader /registry/rolebindings/kube-system/system::leader-locking-kube-controller-manager /registry/rolebindings/kube-system/system::leader-locking-kube-scheduler /registry/rolebindings/kube-system/system:controller:bootstrap-signer /registry/rolebindings/kube-system/system:controller:cloud-provider /registry/rolebindings/kube-system/system:controller:token-cleaner roles /registry/roles/kube-public/kubeadm:bootstrap-signer-clusterinfo /registry/roles/kube-public/system:controller:bootstrap-signer /registry/roles/kube-system/extension-apiserver-authentication-reader /registry/roles/kube-system/kube-proxy /registry/roles/kube-system/kube-state-metrics-resizer /registry/roles/kube-system/kubeadm:kubeadm-certs /registry/roles/kube-system/kubeadm:kubelet-config-1.14 /registry/roles/kube-system/kubeadm:nodes-kubeadm-config /registry/roles/kube-system/system::leader-locking-kube-controller-manager /registry/roles/kube-system/system::leader-locking-kube-scheduler /registry/roles/kube-system/system:controller:bootstrap-signer /registry/roles/kube-system/system:controller:cloud-provider /registry/roles/kube-system/system:controller:token-cleaner 参考： https://github.com/etcd-io/etcd/tree/master/etcdctl https://github.com/openshift/origin/tree/master/tools/etcdhelper using-etcdctl-to-access-kubernetes-data 如何读取Kubernetes存储在etcd上的数据 https://github.com/kubernetes/kubernetes/issues/44670 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"etcd/etcd-operator-usage.html":{"url":"etcd/etcd-operator-usage.html","title":"Etcd-Operator的使用","keywords":"","body":"1. 部署RBAC1.1. create_role.sh 脚本1.2. cluster-role-binding-template.yaml1.3. cluster-role-template.yaml2. 部署etcd-operator3. 部署etcd集群4. 访问etcd集群5. 销毁etcd-operator 本文主要介绍etcd-operator的部署及使用 1. 部署RBAC 下载create_role.sh、cluster-role-binding-template.yaml、cluster-role-template.yaml 例如： |-- cluster-role-binding-template.yaml |-- cluster-role-template.yaml |-- create_role.sh # 部署rbac kubectl create ns operator bash create_role.sh --namespace=operator # namespace与etcd-operator的ns一致 示例： bash create_role.sh --namespace=operator + ROLE_NAME=etcd-operator + ROLE_BINDING_NAME=etcd-operator + NAMESPACE=default + for i in '\"$@\"' + case $i in + NAMESPACE=operator + echo 'Creating role with ROLE_NAME=etcd-operator, NAMESPACE=operator' Creating role with ROLE_NAME=etcd-operator, NAMESPACE=operator + sed -e 's//etcd-operator/g' -e 's//operator/g' cluster-role-template.yaml + kubectl create -f - clusterrole.rbac.authorization.k8s.io/etcd-operator created + echo 'Creating role binding with ROLE_NAME=etcd-operator, ROLE_BINDING_NAME=etcd-operator, NAMESPACE=operator' Creating role binding with ROLE_NAME=etcd-operator, ROLE_BINDING_NAME=etcd-operator, NAMESPACE=operator + sed -e 's//etcd-operator/g' -e 's//etcd-operator/g' -e 's//operator/g' cluster-role-binding-template.yaml + kubectl create -f - clusterrolebinding.rbac.authorization.k8s.io/etcd-operator created 1.1. create_role.sh 脚本 create_role.sh有三个入参，可以指定--namespace参数，该参数与etcd-operator部署的namespace应一致。默认为default。 #!/usr/bin/env bash set -o errexit set -o nounset set -o pipefail ETCD_OPERATOR_ROOT=$(dirname \"${BASH_SOURCE}\")/../.. print_usage() { echo \"$(basename \"$0\") - Create Kubernetes RBAC role and role bindings for etcd-operator Usage: $(basename \"$0\") [options...] Options: --role-name=STRING Name of ClusterRole to create (default=\\\"etcd-operator\\\", environment variable: ROLE_NAME) --role-binding-name=STRING Name of ClusterRoleBinding to create (default=\\\"etcd-operator\\\", environment variable: ROLE_BINDING_NAME) --namespace=STRING namespace to create role and role binding in. Must already exist. (default=\\\"default\\\", environment variable: NAMESPACE) \" >&2 } ROLE_NAME=\"${ROLE_NAME:-etcd-operator}\" ROLE_BINDING_NAME=\"${ROLE_BINDING_NAME:-etcd-operator}\" NAMESPACE=\"${NAMESPACE:-default}\" for i in \"$@\" do case $i in --role-name=*) ROLE_NAME=\"${i#*=}\" ;; --role-binding-name=*) ROLE_BINDING_NAME=\"${i#*=}\" ;; --namespace=*) NAMESPACE=\"${i#*=}\" ;; -h|--help) print_usage exit 0 ;; *) print_usage exit 1 ;; esac done echo \"Creating role with ROLE_NAME=${ROLE_NAME}, NAMESPACE=${NAMESPACE}\" sed -e \"s//${ROLE_NAME}/g\" \\ -e \"s//${NAMESPACE}/g\" \\ \"cluster-role-template.yaml\" | \\ kubectl create -f - echo \"Creating role binding with ROLE_NAME=${ROLE_NAME}, ROLE_BINDING_NAME=${ROLE_BINDING_NAME}, NAMESPACE=${NAMESPACE}\" sed -e \"s//${ROLE_NAME}/g\" \\ -e \"s//${ROLE_BINDING_NAME}/g\" \\ -e \"s//${NAMESPACE}/g\" \\ \"cluster-role-binding-template.yaml\" | \\ kubectl create -f - 1.2. cluster-role-binding-template.yaml apiVersion: rbac.authorization.k8s.io/v1beta1 kind: ClusterRoleBinding metadata: name: roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: subjects: - kind: ServiceAccount name: default namespace: 1.3. cluster-role-template.yaml apiVersion: rbac.authorization.k8s.io/v1beta1 kind: ClusterRole metadata: name: rules: - apiGroups: - etcd.database.coreos.com resources: - etcdclusters - etcdbackups - etcdrestores verbs: - \"*\" - apiGroups: - apiextensions.k8s.io resources: - customresourcedefinitions verbs: - \"*\" - apiGroups: - \"\" resources: - pods - services - endpoints - persistentvolumeclaims - events verbs: - \"*\" - apiGroups: - apps resources: - deployments verbs: - \"*\" # The following permissions can be removed if not using S3 backup and TLS - apiGroups: - \"\" resources: - secrets verbs: - get 2. 部署etcd-operator kubectl create -f etcd-operator.yaml etcd-operator.yaml如下： apiVersion: apps/v1 kind: Deployment metadata: name: etcd-operator namespace: operator # 与rbac指定的ns一致 labels: app: etcd-operator spec: replicas: 1 selector: matchLabels: app: etcd-operator template: metadata: labels: app: etcd-operator spec: containers: - name: etcd-operator image: registry.cn-shenzhen.aliyuncs.com/huweihuang/etcd-operator:v0.9.4 command: - etcd-operator # Uncomment to act for resources in all namespaces. More information in doc/user/clusterwide.md - -cluster-wide env: - name: MY_POD_NAMESPACE valueFrom: fieldRef: fieldPath: metadata.namespace - name: MY_POD_NAME valueFrom: fieldRef: fieldPath: metadata.name 查看CRD #kubectl get customresourcedefinitions NAME CREATED AT etcdclusters.etcd.database.coreos.com 2020-08-01T13:02:18Z 查看etcd-operator的日志是否OK。 k logs -f etcd-operator-545df8d445-qpf6n -n operator time=\"2020-08-01T13:02:18Z\" level=info msg=\"etcd-operator Version: 0.9.4\" time=\"2020-08-01T13:02:18Z\" level=info msg=\"Git SHA: c8a1c64\" time=\"2020-08-01T13:02:18Z\" level=info msg=\"Go Version: go1.11.5\" time=\"2020-08-01T13:02:18Z\" level=info msg=\"Go OS/Arch: linux/amd64\" time=\"2020-08-01T13:02:18Z\" level=info msg=\"Event(v1.ObjectReference{Kind:\\\"Endpoints\\\", Namespace:\\\"operator\\\", Name:\\\"etcd-operator\\\", UID:\\\"7de38cff-1b7b-4bf2-9837-473fa66c9366\\\", APIVersion:\\\"v1\\\", ResourceVersion:\\\"41195930\\\", FieldPath:\\\"\\\"}): type: 'Normal' reason: 'LeaderElection' etcd-operator-545df8d445-qpf6n became leader\" 以上内容表示etcd-operator运行正常。 3. 部署etcd集群 kubectl create -f etcd-cluster.yaml 当开启clusterwide则etcd集群与etcd-operator的ns可不同。 etcd-cluster.yaml apiVersion: \"etcd.database.coreos.com/v1beta2\" kind: \"EtcdCluster\" metadata: name: \"default-etcd-cluster\" ## Adding this annotation make this cluster managed by clusterwide operators ## namespaced operators ignore it annotations: etcd.database.coreos.com/scope: clusterwide namespace: etcd # 此处的ns表示etcd集群部署在哪个ns下 spec: size: 3 version: \"v3.3.18\" repository: registry.cn-shenzhen.aliyuncs.com/huweihuang/etcd pod: busyboxImage: registry.cn-shenzhen.aliyuncs.com/huweihuang/busybox:1.28.0-glibc 查看集群部署结果 $ kgpo -n etcd NAME READY STATUS RESTARTS AGE default-etcd-cluster-b6phnpf8z8 1/1 Running 0 3m3s default-etcd-cluster-hhgq4sbtgr 1/1 Running 0 109s default-etcd-cluster-ttfh5fj92b 1/1 Running 0 2m29s 4. 访问etcd集群 查看service $ kgsvc -n etcd NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE default-etcd-cluster ClusterIP None 2379/TCP,2380/TCP 5m37s default-etcd-cluster-client ClusterIP 192.168.255.244 2379/TCP 5m37s 使用service地址访问 # 查看集群健康状态 $ ETCDCTL_API=3 etcdctl --endpoints 192.168.255.244:2379 endpoint health 192.168.255.244:2379 is healthy: successfully committed proposal: took = 1.96126ms # 写数据 $ ETCDCTL_API=3 etcdctl --endpoints 192.168.255.244:2379 put foo bar OK # 读数据 $ ETCDCTL_API=3 etcdctl --endpoints 192.168.255.244:2379 get foo foo bar 5. 销毁etcd-operator kubectl delete -f example/deployment.yaml kubectl delete endpoints etcd-operator kubectl delete crd etcdclusters.etcd.database.coreos.com kubectl delete clusterrole etcd-operator kubectl delete clusterrolebinding etcd-operator 参考： https://github.com/coreos/etcd-operator https://github.com/coreos/etcd-operator/blob/master/doc/user/install_guide.md https://github.com/coreos/etcd-operator/blob/master/doc/user/client_service.md https://github.com/coreos/etcd-operator/blob/master/doc/user/spec_examples.md https://github.com/coreos/etcd-operator/blob/master/doc/user/cluster_tls.md Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"multi-cluster/k8s-multi-cluster-thinking.html":{"url":"multi-cluster/k8s-multi-cluster-thinking.html","title":"k8s多集群管理的思考","keywords":"","body":"k8s多集群的思考1. 为什么需要多集群2. 多集群部署需要解决哪些问题2.1. 多集群工作负载的分发2.2. 跨集群自动迁移与调度3. 目前的多集群方案3.1. Kubefed[Federation v2]3.2. virtual kubelet3.3. Karmadak8s多集群的思考 1. 为什么需要多集群 1、k8s单集群的承载能力有限。 Kubernetes v1.21 支持的最大节点数为 5000。 更具体地说，Kubernetes旨在适应满足以下所有标准的配置： 每个节点的 Pod 数量不超过 100 节点数不超过 5000 Pod 总数不超过 150000 容器总数不超过 300000 参考：https://kubernetes.io/zh/docs/setup/best-practices/cluster-large/ 且当节点数量较大时，会出现调度延迟，etcd读写延迟，apiserver负载高等问题，影响服务的正常创建。 2、分散集群服务风险。 全部服务都放在一个k8s集群中，当该集群出现异常，短期无法恢复的情况下，则影响全部服务和影响部署。为了避免机房等故障导致单集群异常，建议将k8s的master在分散在延迟较低的不同可用区部署，且在不同region部署多个k8s集群来进行集群级别的容灾。 3、当前混合云的使用方式和架构 当前部分公司会存在自建机房+不同云厂商的公有云从而来实现混部云的运营模式，那么自然会引入多集群管理的问题。 2. 多集群部署需要解决哪些问题 目标：让用户像使用单集群一样来使用多集群。 扩展集群的边界，服务的边界从单台物理机多个进程，发展到通过k8s集群来管理多台的物理机，再发展到管理多个的k8s集群。服务的边界从物理机发展到集群。 而多集群管理需要解决以下问题： 多集群服务的分发部署（deployment、daemonset等） 跨集群自动迁移与调度（当某个集群异常，服务可以在其他集群自动部署） 多集群服务发现，网络通信及负载均衡（service，ingress等） 而多集群服务的网络通信可以由Service mesh等来解决，本文不做重点讨论。 以上几个问题，可以先从k8s管理节点的思维进行分析 物理机视角 单集群视角 多集群视角 进程的边界 物理机 k8s集群 多集群 调度单元 进程或线程 容器或pod 工作负载（deployment） 服务的集合 工作负载（deployment） 不同集群工作负载的集合体（workloadGroup） 服务发现 service 不同集群service的集合体 服务迁移 工作负载（deployment）控制器 不同集群工作负载的集合体控制器 服务调度 nodename或者node selector clustername或cluster selector pod的反亲和（相同deployment下的pod不调度在相同节点） workload反亲和（相同workloadGroup分散在不同集群） 2.1. 多集群工作负载的分发 单集群中k8s的调度单元是pod，即一个pod只能跑在一个节点上，一个节点可以运行多个pod，而不同节点上的一组pod是通过一个workload来控制和分发。类似这个逻辑，那么在多集群的视角下，多集群的调度单元是一个集群的workload，一个workload只能跑在一个集群中，一个集群可以运行多个workload。 那么就需要有一个控制器来管理不同k8s集群的相同workload。例如 workloadGroup。而该workloadGroup在不侵入k8s原生API的情况下，主要包含两个部分。 workloadGroup: 资源模板（Resource Template）：服务的描述（workload） 分发策略（Propagaion Policy）：服务分发的集群（即多个workload应该被分发到哪些集群运行） workload描述的是什么服务运行在什么节点，workloadGroup描述的是什么服务运行在什么集群。 实现workloadGroup有两种方式： 一种是自定义API将workloadGroup中的Resource Template和Propagaion Policy合成在一个自定义的对象中，由用户直接指定该workloadGroup信息，从而将不同的workload分发到不同的集群中。 另一种方式是通过一个k8s载体来记录一个具体的workload对象，再由用户指定Propagaion Policy关联该workload对象，从而让控制器自动根据用户指定的Propagaion Policy将workload分发到不同的集群中。 2.2. 跨集群自动迁移与调度 单集群中k8s中通过workload中的nodeselector或者nodename以及亲和性来控制pod运行在哪个节点上。而多集群的视角下，则需要有一个控制器来实现集群级别的调度逻辑，例如clustername，cluster selector，cluster AntiAffinity，从而来自动控制workloadGroup下的workload分散在什么集群上。 3. 目前的多集群方案 3.1. Kubefed[Federation v2] 简介 基本思想 3.2. virtual kubelet 简介 基本思想 3.3. Karmada 简介 基本思想 参考： https://kubernetes.io/zh/docs/setup/best-practices/cluster-large/ CoreOS 是如何将 Kubernetes 的性能提高 10 倍的? 当 K8s 集群达到万级规模，阿里巴巴如何解决系统各组件性能问题？ https://github.com/kubernetes-sigs/kubefed https://jimmysong.io/kubernetes-handbook/practice/federation.html https://kubernetes.io/blog/2018/12/12/kubernetes-federation-evolution/ https://zhuanlan.zhihu.com/p/355193315 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"multi-cluster/virtual-kubelet/_index.html":{"url":"multi-cluster/virtual-kubelet/_index.html","title":"Virtual Kubelet","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"multi-cluster/virtual-kubelet/virtual-kubelet.html":{"url":"multi-cluster/virtual-kubelet/virtual-kubelet.html","title":"Virtual Kubelet介绍","keywords":"","body":"1. 简介2. 架构图3. 功能4. Providers5. 自定义provider5.1. PodLifecylceHandler5.2. PodNotifier(optional)5.3. NodeProvider5.4. 测试5.5. 示例代码1. 简介 Virtual Kubelet是 Kubernetes kubelet 的一种实现，作为一种虚拟的kubelet用来连接k8s集群和其他平台的API。这允许k8s的节点由其他提供者（provider）提供支持，这些提供者例如serverless平台（ACI, AWS Fargate）、IoT Edge等。 一句话概括：Kubernetes API on top, programmable back。 2. 架构图 3. 功能 virtual kubelet提供一个可以自定义k8s node的依赖库。 目前支持的功能如下： 创建、删除、更新 pod 容器的日志、exec命令、metrics 获取pod、pod列表、pod status node的地址、容量、daemon 操作系统 自定义virtual network 4. Providers virtual kubelet提供一个插件式的provider接口，让开发者可以自定义实现传统kubelet的功能。自定义的provider可以用自己的配置文件和环境参数。 自定义的provider必须提供以下功能： 提供pod、容器、资源的生命周期管理的功能 符合virtual kubelet提供的API 不直接访问k8s apiserver，定义获取数据的回调机制，例如configmap、secrets 开源的provider Alibaba Cloud ECI Provider Azure Container Instances Provider AWS Fargate Provider 5. 自定义provider 创建自定义provider的目录。 git clone https://github.com/virtual-kubelet/virtual-kubelet cd virtual-kubelet mkdir providers/my-provider 5.1. PodLifecylceHandler 当pod被k8s创建、更新、删除时，会调用以下方法。 type PodLifecycleHandler interface { // CreatePod takes a Kubernetes Pod and deploys it within the provider. CreatePod(ctx context.Context, pod *corev1.Pod) error // UpdatePod takes a Kubernetes Pod and updates it within the provider. UpdatePod(ctx context.Context, pod *corev1.Pod) error // DeletePod takes a Kubernetes Pod and deletes it from the provider. DeletePod(ctx context.Context, pod *corev1.Pod) error // GetPod retrieves a pod by name from the provider (can be cached). GetPod(ctx context.Context, namespace, name string) (*corev1.Pod, error) // GetPodStatus retrieves the status of a pod by name from the provider. GetPodStatus(ctx context.Context, namespace, name string) (*corev1.PodStatus, error) // GetPods retrieves a list of all pods running on the provider (can be cached). GetPods(context.Context) ([]*corev1.Pod, error) } PodLifecycleHandler是被PodController来调用，来管理被分配到node上的pod。 pc, _ := node.NewPodController(podControllerConfig) // 5.2. PodNotifier(optional) PodNotifier是可选实现，该接口主要用来通知virtual kubelet的pod状态变化。如果没有实现该接口，virtual-kubelet会定期检查所有pod的状态。 type PodNotifier interface { // NotifyPods instructs the notifier to call the passed in function when // the pod status changes. // // NotifyPods should not block callers. NotifyPods(context.Context, func(*corev1.Pod)) } 5.3. NodeProvider NodeProvider用来通知virtual-kubelet关于node状态的变化，virtual-kubelet会定期检查node是状态并相应地更新k8s。 type NodeProvider interface { // Ping checks if the node is still active. // This is intended to be lightweight as it will be called periodically as a // heartbeat to keep the node marked as ready in Kubernetes. Ping(context.Context) error // NotifyNodeStatus is used to asynchronously monitor the node. // The passed in callback should be called any time there is a change to the // node's status. // This will generally trigger a call to the Kubernetes API server to update // the status. // // NotifyNodeStatus should not block callers. NotifyNodeStatus(ctx context.Context, cb func(*corev1.Node)) } NodeProvider是被NodeController调用，来管理k8s中的node对象。 nc, _ := node.NewNodeController(nodeProvider, nodeSpec) // 5.4. 测试 进入到项目根目录 make test 5.5. 示例代码 Azure Container Instances Provider https://github.com/virtual-kubelet/azure-aci/blob/master/aci.go#L541 Alibaba Cloud ECI Provider https://github.com/virtual-kubelet/alibabacloud-eci/blob/master/eci.go#L177 AWS Fargate Provider https://github.com/virtual-kubelet/aws-fargate/blob/master/provider.go#L110 参考： https://github.com/virtual-kubelet/virtual-kubelet https://virtual-kubelet.io/docs/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"multi-cluster/virtual-kubelet/virtual-kubelet-cmd.html":{"url":"multi-cluster/virtual-kubelet/virtual-kubelet-cmd.html","title":"Virtual Kubelet 命令","keywords":"","body":"virtual-kubelet --helpvirtual-kubelet --help #./virtual-kubelet --help virtual-kubelet implements the Kubelet interface with a pluggable backend implementation allowing users to create kubernetes nodes without running the kubelet. This allows users to schedule kubernetes workloads on nodes that aren't running Kubernetes. Usage: virtual-kubelet [flags] virtual-kubelet [command] Available Commands: help Help about any command providers Show the list of supported providers version Show the version of the program Flags: --cluster-domain string kubernetes cluster-domain (default is 'cluster.local') (default \"cluster.local\") --disable-taint disable the virtual-kubelet node taint --enable-node-lease use node leases (1.13) for node heartbeats --full-resync-period duration how often to perform a full resync of pods between kubernetes and the provider (default 1m0s) -h, --help help for virtual-kubelet --klog.alsologtostderr log to standard error as well as files --klog.log_backtrace_at traceLocation when logging hits line file:N, emit a stack trace (default :0) --klog.log_dir string If non-empty, write log files in this directory --klog.log_file string If non-empty, use this log file --klog.log_file_max_size uint Defines the maximum size a log file can grow to. Unit is megabytes. If the value is 0, the maximum file size is unlimited. (default 1800) --klog.logtostderr log to standard error instead of files (default true) --klog.skip_headers If true, avoid header prefixes in the log messages --klog.skip_log_headers If true, avoid headers when opening log files --klog.stderrthreshold severity logs at or above this threshold go to stderr (default 2) --klog.v Level number for the log level verbosity --klog.vmodule moduleSpec comma-separated list of pattern=N settings for file-filtered logging --kubeconfig string kube config file to use for connecting to the Kubernetes API server (default \"/root/.kube/config\") --log-level string set the log level, e.g. \"debug\", \"info\", \"warn\", \"error\" (default \"info\") --metrics-addr string address to listen for metrics/stats requests (default \":10255\") --namespace string kubernetes namespace (default is 'all') --nodename string kubernetes node name (default \"virtual-kubelet\") --os string Operating System (Linux/Windows) (default \"Linux\") --pod-sync-workers int set the number of pod synchronization workers (default 10) --provider string cloud provider --provider-config string cloud provider configuration file --startup-timeout duration How long to wait for the virtual-kubelet to start --trace-exporter strings sets the tracing exporter to use, available exporters: [jaeger ocagent] --trace-sample-rate string set probability of tracing samples --trace-service-name string sets the name of the service used to register with the trace exporter (default \"virtual-kubelet\") --trace-tag map add tags to include with traces in key=value form Use \"virtual-kubelet [command] --help\" for more information about a command. Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"multi-cluster/karmada/_index.html":{"url":"multi-cluster/karmada/_index.html","title":"Karmada","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"multi-cluster/karmada/karmada-introduction.html":{"url":"multi-cluster/karmada/karmada-introduction.html","title":"Karmada介绍","keywords":"","body":"简介特性框架结构资源分发流程 本文由网络资源整理以作记录 简介 Karmada（Kubernetes Armada）是基于Kubernetes原生API的多集群管理系统。在多云和混合云场景下，Karmada提供可插拔，全自动化管理多集群应用，实现多云集中管理、高可用性、故障恢复和流量调度。 特性 基于K8s原生API的跨集群应用管理，用户可以方便快捷地将应用从单集群迁移到多集群。 中心式操作和管理Kubernetes集群。 跨集群应用可在多集群上自动扩展，故障转移和负载均衡。 高级的调度策略：区域，可用区，云提供商，集群亲和性/反亲和性。 支持创建分发用户自定义（CustomResourceDefinitions）资源。 框架结构 图片 - img ETCD：存储Karmada API对象。 Karmada Scheduler：提供高级的多集群调度策略。 Karmada Controller Manager: 包含多个Controller，Controller监听karmada对象并且与成员集群API server进行通信并创建成员集群的k8s对象。 Cluster Controller：成员集群的生命周期管理与对象管理。 Policy Controller：监听PropagationPolicy对象，创建ResourceBinding，配置资源分发策略。 Binding Controller：监听ResourceBinding对象，并创建work对象响应资源清单。 Execution Controller：监听work对象，并将资源分发到成员集群中。 资源分发流程 基本概念 资源模板（Resource Template）：Karmada使用K8s原生API定义作为资源模板，便于快速对接K8s生态工具链。 分发策略（Propagaion Policy）：Karmada提供独立的策略API，用来配置资源分发策略。 差异化策略（Override Policy）：Karmada提供独立的差异化API，用来配置与集群相关的差异化配置。比如配置不同集群使用不同的镜像。 Karmada资源分发流程图： 图片 - img 参考： https://github.com/karmada-io/karmada https://support.huaweicloud.com/productdesc-mcp/mcp_productdesc_0001.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"edge/kubeedge/kubeedge-arch.html":{"url":"edge/kubeedge/kubeedge-arch.html","title":"KubeEdge介绍","keywords":"","body":"1. KubeEdge简介2. 功能3. 组件3.1. 云端3.2. 边缘端4. 架构图1. KubeEdge简介 KubeEdge是基于kubernetes之上将容器化应用的编排能力拓展到边缘主机或边缘设备，在云端和边缘端提供网络通信，应用部署、元数据同步等功能。同时支持MQTT协议，允许开发者在边缘端自定义接入边缘设备。 2. 功能 边缘计算：提供边缘节点自治能力，边缘节点数据处理能力。 便捷部署：开发者可以开发http或mqtt协议的应用，运行在云端和边缘端。 k8s原生支持：可以通过k8s管理和监控边缘设备和边缘节点。 丰富的应用类型：可以在边缘端部署机器学习、图片识别、事件处理等应用。 3. 组件 3.1. 云端 CloudHub：一个web socket服务器，负责监听云端的更新、缓存及向EdgeHub发送消息。 EdgeController：一个扩展的k8s控制器，负责管理边缘节点和pod元数据，同步边缘节点的数据，是k8s-apiserver 与EdgeCore的通信桥梁。 DeviceController：一个扩展的k8s控制器，负责管理节点设备，同步云端和边缘端的设备元数据和状态。 3.2. 边缘端 EdgeHub：一个web socket客户端，负责云端与边缘端的信息交互，其中包括将云端的资源变更同步到边缘端及边缘端的状态变化同步到云端。 Edged：运行在边缘节点，管理容器化应用的agent，负责pod生命周期的管理，类似kubelet。 EventBus：一个MQTT客户端，与MQTT服务端交互，提供发布/订阅的能力。 ServiceBus：一个HTTP客户端，与HTTP服务端交互。为云组件提供HTTP客户端功能，以访问在边缘运行的HTTP服务器。 DeviceTwin：负责存储设备状态并同步设备状态到云端，同时提供应用的接口查询。 MetaManager：edged和edgehub之间的消息处理器，负责向轻量数据库（SQLite）存储或查询元数据。 4. 架构图 图片 - kubeedge-arch 参考： https://github.com/kubeedge/kubeedge https://kubeedge.readthedocs.io/en/latest/modules/kubeedge.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"edge/kubeedge/code-analysis/_index.html":{"url":"edge/kubeedge/code-analysis/_index.html","title":"KubeEdge源码分析","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"edge/kubeedge/code-analysis/cloudcore.html":{"url":"edge/kubeedge/code-analysis/cloudcore.html","title":"cloudcore","keywords":"","body":"kubeedge源码分析之cloudcore1. main函数2. NewCloudCoreCommand3. registerModules4. core.Run5. StartModules6. GracefulShutdownkubeedge源码分析之cloudcore 本文源码分析基于kubeedge v1.1.0 本文主要分析cloudcore中CloudCoreCommand的基本流程，具体的cloudhub、edgecontroller、devicecontroller模块的实现逻辑待后续单独文章分析。 目录结构： cloud/cmd/cloudcore cloudcore ├── app │ ├── options │ │ └── options.go │ └── server.go # NewCloudCoreCommand、registerModules └── cloudcore.go # main函数 cloudcore部分包含以下模块： cloudhub edgecontroller devicecontroller 1. main函数 kubeedge的代码采用cobra命令框架，代码风格与k8s源码风格类似。cmd目录主要为cobra command的基本内容及参数解析，pkg目录包含具体的实现逻辑。 cloud/cmd/cloudcore/cloudcore.go func main() { command := app.NewCloudCoreCommand() logs.InitLogs() defer logs.FlushLogs() if err := command.Execute(); err != nil { os.Exit(1) } } 2. NewCloudCoreCommand NewCloudCoreCommand为cobra command的构造函数，该类函数一般包含以下部分： 构造option 添加Flags 运行Run函数（核心） cloud/cmd/cloudcore/app/server.go func NewCloudCoreCommand() *cobra.Command { opts := options.NewCloudCoreOptions() cmd := &cobra.Command{ Use: \"cloudcore\", Long: `CloudCore is the core cloud part of KubeEdge, which contains three modules: cloudhub, edgecontroller, and devicecontroller. Cloudhub is a web server responsible for watching changes at the cloud side, caching and sending messages to EdgeHub. EdgeController is an extended kubernetes controller which manages edge nodes and pods metadata so that the data can be targeted to a specific edge node. DeviceController is an extended kubernetes controller which manages devices so that the device metadata/status date can be synced between edge and cloud.`, Run: func(cmd *cobra.Command, args []string) { verflag.PrintAndExitIfRequested() flag.PrintFlags(cmd.Flags()) // To help debugging, immediately log version klog.Infof(\"Version: %+v\", version.Get()) registerModules() // start all modules core.Run() }, } fs := cmd.Flags() namedFs := opts.Flags() verflag.AddFlags(namedFs.FlagSet(\"global\")) globalflag.AddGlobalFlags(namedFs.FlagSet(\"global\"), cmd.Name()) for _, f := range namedFs.FlagSets { fs.AddFlagSet(f) } usageFmt := \"Usage:\\n %s\\n\" cols, _, _ := term.TerminalSize(cmd.OutOrStdout()) cmd.SetUsageFunc(func(cmd *cobra.Command) error { fmt.Fprintf(cmd.OutOrStderr(), usageFmt, cmd.UseLine()) cliflag.PrintSections(cmd.OutOrStderr(), namedFs, cols) return nil }) cmd.SetHelpFunc(func(cmd *cobra.Command, args []string) { fmt.Fprintf(cmd.OutOrStdout(), \"%s\\n\\n\"+usageFmt, cmd.Long, cmd.UseLine()) cliflag.PrintSections(cmd.OutOrStdout(), namedFs, cols) }) return cmd } 核心代码： // 构造option opts := options.NewCloudCoreOptions() // 执行run函数 registerModules() core.Run() // 添加flags fs.AddFlagSet(f) 3. registerModules 由于kubeedge的代码的大部分模块都采用了基于go-channel的消息通信框架Beehive（待后续单独文章分析），因此在各模块启动之前，需要将该模块注册到beehive的框架中。 其中cloudcore部分涉及的模块有： cloudhub edgecontroller devicecontroller cloud/cmd/cloudcore/app/server.go // registerModules register all the modules started in cloudcore func registerModules() { cloudhub.Register() edgecontroller.Register() devicecontroller.Register() } 以下以cloudhub为例说明注册的过程。 cloudhub结构体主要包含： context：上下文，用来传递消息上下文 stopChan：go channel通信 beehive框架中的模块需要实现Module接口，因此cloudhub也实现了该接口，其中核心方法为Start，用来启动相应模块的运行。 vendor/github.com/kubeedge/beehive/pkg/core/module.go // Module interface type Module interface { Name() string Group() string Start(c *context.Context) Cleanup() } 以下为cloudHub结构体及注册函数。 cloud/pkg/cloudhub/cloudhub.go type cloudHub struct { context *context.Context stopChan chan bool } func Register() { core.Register(&cloudHub{}) } 具体的注册实现函数为core.Register，注册过程实际上就是将具体的模块结构体放入一个以模块名为key的map映射中，待后续调用。 vendor/github.com/kubeedge/beehive/pkg/core/module.go // Register register module func Register(m Module) { if isModuleEnabled(m.Name()) { modules[m.Name()] = m //将具体的模块结构体放入一个以模块名为key的map映射中 log.LOGGER.Info(\"module \" + m.Name() + \" registered\") } else { disabledModules[m.Name()] = m log.LOGGER.Info(\"module \" + m.Name() + \" is not register, please check modules.yaml\") } } 4. core.Run CloudCoreCommand命令的Run函数实际上是运行beehive框架中注册的所有模块。 其中包括两部分逻辑： 启动运行所有注册模块 监听信号并做优雅清理 vendor/github.com/kubeedge/beehive/pkg/core/core.go //Run starts the modules and in the end does module cleanup func Run() { //Address the module registration and start the core StartModules() // monitor system signal and shutdown gracefully GracefulShutdown() } 5. StartModules StartModules获取context上下文，并以goroutine的方式运行所有已注册的模块。其中Start函数即每个模块的具体实现Module接口中的Start方法。不同模块各自定义自己的具体Start方法实现。 coreContext := context.GetContext(context.MsgCtxTypeChannel) go module.Start(coreContext) 具体实现如下： vendor/github.com/kubeedge/beehive/pkg/core/core.go // StartModules starts modules that are registered func StartModules() { coreContext := context.GetContext(context.MsgCtxTypeChannel) modules := GetModules() for name, module := range modules { //Init the module coreContext.AddModule(name) //Assemble typeChannels for send2Group coreContext.AddModuleGroup(name, module.Group()) go module.Start(coreContext) log.LOGGER.Info(\"starting module \" + name) } } 6. GracefulShutdown 当收到相关信号，则执行各个模块实现的Cleanup方法。 vendor/github.com/kubeedge/beehive/pkg/core/core.go // GracefulShutdown is if it gets the special signals it does modules cleanup func GracefulShutdown() { c := make(chan os.Signal) signal.Notify(c, syscall.SIGINT, syscall.SIGHUP, syscall.SIGTERM, syscall.SIGQUIT, syscall.SIGILL, syscall.SIGTRAP, syscall.SIGABRT) select { case s := 参考： https://github.com/kubeedge/kubeedge/tree/release-1.1/cloud/cmd/cloudcore https://github.com/kubeedge/kubeedge/tree/release-1.1/vendor/github.com/kubeedge/beehive/pkg/core Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"edge/kubeedge/code-analysis/edgecore.html":{"url":"edge/kubeedge/code-analysis/edgecore.html","title":"edgecore","keywords":"","body":"kubeedge源码分析之edgecore1. main函数2. NewEdgeCoreCommand3. registerModules4. core.Runkubeedge源码分析之edgecore 本文源码分析基于kubeedge v1.1.0 本文主要分析edgecore中EdgeCoreCommand的基本流程，具体的edged、edgehub、metamanager等模块的实现逻辑待后续单独文章分析。 目录结构： edgecore ├── app │ ├── options │ │ └── options.go │ └── server.go # NewEdgeCoreCommand 、registerModules └── edgecore.go # main edgecore模块包含： edged edgehub metamanager eventbus servicebus devicetwin edgemesh 1. main函数 main入口函数，仍然是cobra命令框架格式。 edge/cmd/edgecore/edgecore.go func main() { command := app.NewEdgeCoreCommand() logs.InitLogs() defer logs.FlushLogs() if err := command.Execute(); err != nil { os.Exit(1) } } 2. NewEdgeCoreCommand NewEdgeCoreCommand与NewCloudCoreCommand一样构造对应的cobra command结构体。 edge/cmd/edgecore/app/server.go // NewEdgeCoreCommand create edgecore cmd func NewEdgeCoreCommand() *cobra.Command { opts := options.NewEdgeCoreOptions() cmd := &cobra.Command{ Use: \"edgecore\", Long: `Edgecore is the core edge part of KubeEdge, which contains six modules: devicetwin, edged, edgehub, eventbus, metamanager, and servicebus. DeviceTwin is responsible for storing device status and syncing device status to the cloud. It also provides query interfaces for applications. Edged is an agent that runs on edge nodes and manages containerized applications and devices. Edgehub is a web socket client responsible for interacting with Cloud Service for the edge computing (like Edge Controller as in the KubeEdge Architecture). This includes syncing cloud-side resource updates to the edge, and reporting edge-side host and device status changes to the cloud. EventBus is a MQTT client to interact with MQTT servers (mosquito), offering publish and subscribe capabilities to other components. MetaManager is the message processor between edged and edgehub. It is also responsible for storing/retrieving metadata to/from a lightweight database (SQLite).ServiceBus is a HTTP client to interact with HTTP servers (REST), offering HTTP client capabilities to components of cloud to reach HTTP servers running at edge. `, Run: func(cmd *cobra.Command, args []string) { verflag.PrintAndExitIfRequested() flag.PrintFlags(cmd.Flags()) // To help debugging, immediately log version klog.Infof(\"Version: %+v\", version.Get()) registerModules() // start all modules core.Run() }, } fs := cmd.Flags() namedFs := opts.Flags() verflag.AddFlags(namedFs.FlagSet(\"global\")) globalflag.AddGlobalFlags(namedFs.FlagSet(\"global\"), cmd.Name()) for _, f := range namedFs.FlagSets { fs.AddFlagSet(f) } usageFmt := \"Usage:\\n %s\\n\" cols, _, _ := term.TerminalSize(cmd.OutOrStdout()) cmd.SetUsageFunc(func(cmd *cobra.Command) error { fmt.Fprintf(cmd.OutOrStderr(), usageFmt, cmd.UseLine()) cliflag.PrintSections(cmd.OutOrStderr(), namedFs, cols) return nil }) cmd.SetHelpFunc(func(cmd *cobra.Command, args []string) { fmt.Fprintf(cmd.OutOrStdout(), \"%s\\n\\n\"+usageFmt, cmd.Long, cmd.UseLine()) cliflag.PrintSections(cmd.OutOrStdout(), namedFs, cols) }) return cmd } 核心代码： opts := options.NewEdgeCoreOptions() registerModules() core.Run() 3. registerModules edgecore仍然采用Beehive通信框架，模块调用前先注册对应的模块。具体参考cloudcore.registerModules处的分析，此处不再展开分析注册流程。此处注册的是edgecore中涉及的组件。 edge/cmd/edgecore/app/server.go // registerModules register all the modules started in edgecore func registerModules() { devicetwin.Register() edged.Register() edgehub.Register() eventbus.Register() edgemesh.Register() metamanager.Register() servicebus.Register() test.Register() dbm.InitDBManager() } 4. core.Run core.Run与cloudcore.run处逻辑一致不再展开分析。 vendor/github.com/kubeedge/beehive/pkg/core/core.go //Run starts the modules and in the end does module cleanup func Run() { //Address the module registration and start the core StartModules() // monitor system signal and shutdown gracefully GracefulShutdown() } 参考： https://github.com/kubeedge/kubeedge/tree/release-1.1/edge/cmd/edgecore Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"edge/openyurt/_index.html":{"url":"edge/openyurt/_index.html","title":"OpenYurt","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"edge/openyurt/install-openyurt.html":{"url":"edge/openyurt/install-openyurt.html","title":"OpenYurt部署","keywords":"","body":"1. 给云端节点和边缘节点打标签1.1. openyurt.io/is-edge-worker节点标签1.2. 给边缘节点开启自治模式2. 安装准备2.1. 调整k8s组件的配置2.2. 部署tunnel-dns3. 部署openyurt控制面3.1. yurt-app-manager3.2. openyurt4. 部署 Yurthub(edge)5. 重置 Kubelet6. yurthub部署脚本 本文主要介绍部署openyurt组件到k8s集群中。 1. 给云端节点和边缘节点打标签 openyurt将k8s节点分为云端节点和边缘节点，云端节点主要运行一些云端的业务，边缘节点运行边缘业务。当与 apiserver 断开连接时，只有运行在边缘自治的节点上的Pod才不会被驱逐。通过打 openyurt.io/is-edge-worker 的标签的方式来区分，false表示云端节点，true表示边缘节点。 云端组件： yurt-controller-manager yurt-tunnel-server 边缘组件： yurt-hub yurt-tunnel-agent 1.1. openyurt.io/is-edge-worker节点标签 # 云端节点，值为false kubectl label node us-west-1.192.168.0.87 openyurt.io/is-edge-worker=false # 边缘节点，值为true kubectl label node us-west-1.192.168.0.88 openyurt.io/is-edge-worker=true 1.2. 给边缘节点开启自治模式 kubectl annotate node us-west-1.192.168.0.88 node.beta.openyurt.io/autonomy=true 2. 安装准备 2.1. 调整k8s组件的配置 参考调整k8s组件的配置 2.2. 部署tunnel-dns wget https://raw.githubusercontent.com/openyurtio/openyurt/master/config/setup/yurt-tunnel-dns.yaml kubectl apply -f yurt-tunnel-dns.yaml 获取clusterIP，作为kube-apiserver的专用nameserver地址。 kubectl -n kube-system get svc yurt-tunnel-dns -o=jsonpath='{.spec.clusterIP}' 3. 部署openyurt控制面 通过helm来部署控制面，所有helm charts都可以在openyurt-helm 仓库中找到。 快捷安装可参考脚本：helm-install-openyurt.sh helm repo add openyurt https://openyurtio.github.io/openyurt-helm 3.1. yurt-app-manager helm upgrade --install yurt-app-manager -n kube-system openyurt/yurt-app-manager 3.2. openyurt 在openyurt/openyurt中的组件包括： yurt-controller-manager: 防止apiserver在断开连接时驱逐运行在边缘节点上的pod yurt-tunnel-server: 在云端构建云边隧道 yurt-tunnel-agent: 在边缘侧构建云边隧道 由于yurt-tunnel-server默认使用host模式，因此可能存在边缘端的agent无法访问云端的tunnel-server，需要为tunnel-server配置一个可访问的地址。 # 下载并解压 helm pull openyurt/openyurt --untar # 修改tunnel相关配置 cd openyurt vi values.yaml # 示例： yurtTunnelServer: replicaCount: 1 tolerations: [] parameters: certDnsNames: \"\" tunnelAgentConnectPort: certIps: \"\" yurtTunnelAgent: replicaCount: 1 tolerations: [] parameters: tunnelserverAddr: \"\" # install helm install openyurt ./openyurt 4. 部署 Yurthub(edge) 在 yurt-controller-manager 启动并正常运行后，以静态 pod 的方式部署 Yurthub。 为 yurthub 创建全局配置(即RBAC, configmap) wget https://raw.githubusercontent.com/openyurtio/openyurt/master/config/setup/yurthub-cfg.yaml kubectl apply -f yurthub-cfg.yaml 在边缘节点以static pod方式创建yurthub mkdir -p /etc/kubernetes/manifests/ cd /etc/kubernetes/manifests/ wget https://raw.githubusercontent.com/openyurtio/openyurt/master/config/setup/yurthub.yaml # 获取bootstrap token kubeadm token create # 假设 apiserver 的地址是 1.2.3.4:6443，bootstrap token 是 07401b.f395accd246ae52d sed -i 's|__kubernetes_master_address__|1.2.3.4:6443|; s|__bootstrap_token__|07401b.f395accd246ae52d|' /etc/kubernetes/manifests/yurthub.yaml 5. 重置 Kubelet 重置 kubelet 服务，让它通过 yurthub 访问apiserver。为 kubelet 服务创建一个新的 kubeconfig 文件来访问apiserver。 mkdir -p /var/lib/openyurt cat /var/lib/openyurt/kubelet.conf apiVersion: v1 clusters: - cluster: server: http://127.0.0.1:10261 name: default-cluster contexts: - context: cluster: default-cluster namespace: default user: default-auth name: default-context current-context: default-context kind: Config preferences: {} EOF 修改/etc/systemd/system/kubelet.service.d/10-kubeadm.conf sed -i \"s|KUBELET_KUBECONFIG_ARGS=--bootstrap-kubeconfig=\\/etc\\/kubernetes\\/bootstrap-kubelet.conf\\ --kubeconfig=\\/etc\\/kubernetes\\/kubelet.conf|KUBELET_KUBECONFIG_ARGS=--kubeconfig=\\/var\\/lib\\/openyurt\\/kubelet.conf|g\" \\ /etc/systemd/system/kubelet.service.d/10-kubeadm.conf 重启kubelet服务 systemctl daemon-reload && systemctl restart kubelet 6. yurthub部署脚本 根据以上部署步骤，整理部署脚本。需要修改脚本内容的master-addr和token字段。 #!/bin/bash set -e set -x ### install yurthub ### mkdir -p /etc/kubernetes/manifests/ cd /etc/kubernetes/manifests/ wget https://raw.githubusercontent.com/openyurtio/openyurt/master/config/setup/yurthub.yaml ### 修改master和token字段 sed -i 's|__kubernetes_master_address__|:6443|; s|__bootstrap_token__||' /etc/kubernetes/manifests/yurthub.yaml mkdir -p /var/lib/openyurt cat /var/lib/openyurt/kubelet.conf apiVersion: v1 clusters: - cluster: server: http://127.0.0.1:10261 name: default-cluster contexts: - context: cluster: default-cluster namespace: default user: default-auth name: default-context current-context: default-context kind: Config preferences: {} EOF cp /etc/systemd/system/kubelet.service.d/10-kubeadm.conf /etc/systemd/system/kubelet.service.d/10-kubeadm.conf.bak sed -i \"s|KUBELET_KUBECONFIG_ARGS=--bootstrap-kubeconfig=\\/etc\\/kubernetes\\/bootstrap-kubelet.conf\\ --kubeconfig=\\/etc\\/kubernetes\\/kubelet.conf|KUBELET_KUBECONFIG_ARGS=--kubeconfig=\\/var\\/lib\\/openyurt\\/kubelet.conf|g\" \\ /etc/systemd/system/kubelet.service.d/10-kubeadm.conf systemctl daemon-reload && systemctl restart kubelet 参考： https://openyurt.io/zh/docs/installation/manually-setup https://openyurt.io/zh/docs/installation/openyurt-prepare 在存量的K8s节点上安装OpenYurt Node组件 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"edge/openyurt/update-k8s-for-openyurt.html":{"url":"edge/openyurt/update-k8s-for-openyurt.html","title":"OpenYurt部署之调整k8s配置","keywords":"","body":"1. kube-apiserver2. kube-controller-manager3. CoreDNS4. kube-proxy安装openyurt，为了适配边缘场景，需要对k8s组件进行调整。其中包括： kube-apiserver kube-controller-manager kube-proxy CoreDNS 1. kube-apiserver 为了实现云边通信，即用户可以正常使用kubectl exec/logs的功能来登录或查看边缘容器的信息。需要将kube-apiserver访问kubelet的地址调整为hostname优先。 $ vi /etc/kubernetes/manifests/kube-apiserver.yaml apiVersion: v1 kind: Pod ... spec: dnsPolicy: \"None\" # 1. dnsPolicy修改为None dnsConfig: # 2. 增加dnsConfig配置 nameservers: - 1.2.3.4 # 使用yurt-tunnel-dns service的clusterIP替换 searches: - kube-system.svc.cluster.local - svc.cluster.local - cluster.local options: - name: ndots value: \"5\" containers: - command: - kube-apiserver ... - --kubelet-preferred-address-types=Hostname,InternalIP,ExternalIP # 3. 把Hostname放在第一位 ... 2. kube-controller-manager 禁用默认的 nodelifecycle 控制器，当节点断连时不驱逐pod。 nodelifecycle控制器主要用来根据node的status及lease的更新时间来决定是否要驱逐节点上的pod。为了让 yurt-controller-mamanger 能够正常工作，因此需要禁用controller的驱逐功能。 vim /etc/kubernetes/manifests/kube-controller-manager.yaml # 在--controllers=*,bootstrapsigner,tokencleaner后面添加,-nodelifecycle # 即参数为： --controllers=*,bootstrapsigner,tokencleaner,-nodelifecycle # 如果kube-controller-manager是以static pod部署，修改yaml文件后会自动重启。 3. CoreDNS 将coredns从deployment部署改为daemonset部署。 将deployment的coredns副本数调整为0。 kubectl scale --replicas=0 deployment/coredns -n kube-system 创建daemonset的coredns。 wget https://raw.githubusercontent.com/huweihuang/kubeadm-scripts/main/openyurt/yurt-tunnel/coredns.ds.yaml kubectl apply -f 支持流量拓扑： # 利用openyurt实现endpoint过滤 kubectl annotate svc kube-dns -n kube-system openyurt.io/topologyKeys='openyurt.io/nodepool' 4. kube-proxy 云边端场景下，边缘节点间很有可能无法互通，因此需要endpoints基于nodepool进行拓扑。直接将kube-proxy的kubeconfig配置删除，将apiserver请求经过yurthub即可解决服务拓扑问题。 kubectl edit cm -n kube-system kube-proxy 示例： apiVersion: v1 data: config.conf: |- apiVersion: kubeproxy.config.k8s.io/v1alpha1 bindAddress: 0.0.0.0 bindAddressHardFail: false clientConnection: acceptContentTypes: \"\" burst: 0 contentType: \"\" #kubeconfig: /var/lib/kube-proxy/kubeconfig.conf 参考： https://openyurt.io/zh/docs/installation/openyurt-prepare/ Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"edge/openyurt/code-analysis/_index.html":{"url":"edge/openyurt/code-analysis/_index.html","title":"OpenYurt源码分析","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"edge/openyurt/code-analysis/yurthub-code-analysis-1.html":{"url":"edge/openyurt/code-analysis/yurthub-code-analysis-1.html","title":"YurtHub源码分析（1)","keywords":"","body":"1. NewCmdStartYurtHub2. Run(ctx, yurtHubCfg)3. RunYurtHubServers3.1. YurtHubServerServing3.2. YurtHubProxyServerServing3.3. NewYurtReverseProxyHandler4. yurtReverseProxy5. LoadBalancer5.1. NewLoadBalancer5.2. loadBalancer.ServeHTTP5.3. errorHandler5.4. QueryCache5.5. 查询storage中的数据6. RemoteProxy7. LocalProxy7.1. localReqCache总结 本文分析yurthub源码，第一部分。 本文以commit id：180282663457080119a1bc6076cce20c922b5c50， 对应版本tag: v1.2.1 的源码分析yurthub的实现逻辑。 yurthub是部署在每个边缘节点上用来实现边缘自治的组件。在云边通信正常的情况下实现apiserver的请求转发，断网的情况下通过本地的缓存数据保证节点上容器的正常运行。 基本架构图： pkg包中yurthub代码目录结构； yurthub ├── cachemanager # cache 管理器， ├── certificate # 证书token管理 ├── filter ├── gc # GCManager ├── healthchecker # cloud apiserver 探火机制 ├── kubernetes # ├── metrics ├── network # 网络iptables配置 ├── otaupdate ├── poolcoordinator ├── proxy # 核心代码，反向代理机制，包括remote proxy和local proxy ├── server # yurthub server ├── storage # 本地存储的实现 ├── tenant ├── transport └── util 1. NewCmdStartYurtHub openyurt的代码风格与k8s的一致，由cmd为入口，pkg为主要的实现逻辑。 以下是cmd的main函数。 func main() { newRand := rand.New(rand.NewSource(time.Now().UnixNano())) newRand.Seed(time.Now().UnixNano()) cmd := app.NewCmdStartYurtHub(server.SetupSignalContext()) cmd.Flags().AddGoFlagSet(flag.CommandLine) if err := cmd.Execute(); err != nil { panic(err) } } main 函数主要创建 NewCmdStartYurtHub 对象。NewCmd的函数一般都包含以下的几个部分，运行顺序从上到下： NewYurtHubOptions：创建option参数对象，主要用于flag参数解析到option的结构体。 yurtHubOptions.AddFlags(cmd.Flags())：添加AddFlags，设置flag参数信息。 yurtHubOptions.Validate()：校验flag解析后的option的参数合法性。 yurtHubCfg, err := config.Complete(yurtHubOptions)：将option的参数转换为config的对象。 Run(ctx, yurtHubCfg)：基于config执行run函数，运行cmd的核心逻辑。 // NewCmdStartYurtHub creates a *cobra.Command object with default parameters func NewCmdStartYurtHub(ctx context.Context) *cobra.Command { yurtHubOptions := options.NewYurtHubOptions() cmd := &cobra.Command{ Use: projectinfo.GetHubName(), Short: \"Launch \" + projectinfo.GetHubName(), Long: \"Launch \" + projectinfo.GetHubName(), Run: func(cmd *cobra.Command, args []string) { if yurtHubOptions.Version { fmt.Printf(\"%s: %#v\\n\", projectinfo.GetHubName(), projectinfo.Get()) return } fmt.Printf(\"%s version: %#v\\n\", projectinfo.GetHubName(), projectinfo.Get()) cmd.Flags().VisitAll(func(flag *pflag.Flag) { klog.V(1).Infof(\"FLAG: --%s=%q\", flag.Name, flag.Value) }) if err := yurtHubOptions.Validate(); err != nil { klog.Fatalf(\"validate options: %v\", err) } yurtHubCfg, err := config.Complete(yurtHubOptions) if err != nil { klog.Fatalf(\"complete %s configuration error, %v\", projectinfo.GetHubName(), err) } klog.Infof(\"%s cfg: %#+v\", projectinfo.GetHubName(), yurtHubCfg) if err := Run(ctx, yurtHubCfg); err != nil { klog.Fatalf(\"run %s failed, %v\", projectinfo.GetHubName(), err) } }, } yurtHubOptions.AddFlags(cmd.Flags()) return cmd } 以上flag、option、config的构建函数此处不做分析，以下分析run函数的逻辑。 2. Run(ctx, yurtHubCfg) Run函数部分主要构建了几个manager，每个manager各司其职，负责对应的逻辑。有的manager在该函数中直接构建后执行manager.run的逻辑。有的则作为参数传入下一级函数中再执行manager.run函数。 主要包括以下的manager： transportManager cloudHealthChecker restConfigMgr cacheMgr gcMgr tenantMgr NetworkMgr 每个manager的实现细节此处暂不做分析。 此处先贴一下完整源码，避免读者还需要去翻代码。 代码：/cmd/yurthub/app/start.go // Run runs the YurtHubConfiguration. This should never exit func Run(ctx context.Context, cfg *config.YurtHubConfiguration) error { defer cfg.CertManager.Stop() trace := 1 klog.Infof(\"%d. new transport manager\", trace) // 构造NewTransportManager transportManager, err := transport.NewTransportManager(cfg.CertManager, ctx.Done()) if err != nil { return fmt.Errorf(\"could not new transport manager, %w\", err) } trace++ klog.Infof(\"%d. prepare cloud kube clients\", trace) cloudClients, err := createClients(cfg.HeartbeatTimeoutSeconds, cfg.RemoteServers, cfg.CoordinatorServerURL, transportManager) if err != nil { return fmt.Errorf(\"failed to create cloud clients, %w\", err) } trace++ var cloudHealthChecker healthchecker.MultipleBackendsHealthChecker if cfg.WorkingMode == util.WorkingModeEdge { klog.Infof(\"%d. create health checkers for remote servers and pool coordinator\", trace) cloudHealthChecker, err = healthchecker.NewCloudAPIServerHealthChecker(cfg, cloudClients, ctx.Done()) if err != nil { return fmt.Errorf(\"could not new cloud health checker, %w\", err) } } else { klog.Infof(\"%d. disable health checker for node %s because it is a cloud node\", trace, cfg.NodeName) // In cloud mode, cloud health checker is not needed. // This fake checker will always report that the cloud is healthy and pool coordinator is unhealthy. cloudHealthChecker = healthchecker.NewFakeChecker(true, make(map[string]int)) } trace++ klog.Infof(\"%d. new restConfig manager\", trace) restConfigMgr, err := hubrest.NewRestConfigManager(cfg.CertManager, cloudHealthChecker) if err != nil { return fmt.Errorf(\"could not new restConfig manager, %w\", err) } trace++ var cacheMgr cachemanager.CacheManager if cfg.WorkingMode == util.WorkingModeEdge { klog.Infof(\"%d. new cache manager with storage wrapper and serializer manager\", trace) cacheMgr = cachemanager.NewCacheManager(cfg.StorageWrapper, cfg.SerializerManager, cfg.RESTMapperManager, cfg.SharedFactory) } else { klog.Infof(\"%d. disable cache manager for node %s because it is a cloud node\", trace, cfg.NodeName) } trace++ if cfg.WorkingMode == util.WorkingModeEdge { klog.Infof(\"%d. new gc manager for node %s, and gc frequency is a random time between %d min and %d min\", trace, cfg.NodeName, cfg.GCFrequency, 3*cfg.GCFrequency) gcMgr, err := gc.NewGCManager(cfg, restConfigMgr, ctx.Done()) if err != nil { return fmt.Errorf(\"could not new gc manager, %w\", err) } // 直接运行manager gcMgr.Run() } else { klog.Infof(\"%d. disable gc manager for node %s because it is a cloud node\", trace, cfg.NodeName) } trace++ klog.Infof(\"%d. new tenant sa manager\", trace) tenantMgr := tenant.New(cfg.TenantNs, cfg.SharedFactory, ctx.Done()) trace++ var coordinatorHealthCheckerGetter func() healthchecker.HealthChecker = getFakeCoordinatorHealthChecker var coordinatorTransportManagerGetter func() transport.Interface = getFakeCoordinatorTransportManager var coordinatorGetter func() poolcoordinator.Coordinator = getFakeCoordinator if cfg.EnableCoordinator { klog.Infof(\"%d. start to run coordinator\", trace) trace++ coordinatorInformerRegistryChan := make(chan struct{}) // coordinatorRun will register secret informer into sharedInformerFactory, and start a new goroutine to periodically check // if certs has been got from cloud APIServer. It will close the coordinatorInformerRegistryChan if the secret channel has // been registered into informer factory. coordinatorHealthCheckerGetter, coordinatorTransportManagerGetter, coordinatorGetter = coordinatorRun(ctx, cfg, restConfigMgr, cloudHealthChecker, coordinatorInformerRegistryChan) // wait for coordinator informer registry klog.Infof(\"waiting for coordinator informer registry\") 除了上述的各种manager的构造及运行外，run函数中还构建了yurtProxyHandler，最终执行RunYurtHubServers运行一组不会退出的http server。以下先不对manager的实现做展开，而直接分析RunYurtHubServers的逻辑。RunYurtHubServers的代码在pkg包中。 3. RunYurtHubServers RunYurtHubServers就是一个传统的http server的运行逻辑，主要包括几个不同类型的http server。http server的运行逻辑可以概括如下： hubServerHandler := mux.NewRouter()： 新建路由创建handler registerHandlers(hubServerHandler, cfg, rest)： 注册路由 YurtHubServerServing.Serve：执行http server.Serve函数启动一个server服务。 http server分为两类： yurthub http server: yurthub metrics, healthz的接口。 yurthub proxy server: 代理kube-apiserver的请求。 3.1. YurtHubServerServing hubServerHandler := mux.NewRouter() registerHandlers(hubServerHandler, cfg, rest) // start yurthub http server for serving metrics, pprof. if cfg.YurtHubServerServing != nil { if err := cfg.YurtHubServerServing.Serve(hubServerHandler, 0, stopCh); err != nil { return err } } registerHandlers的路由内容如下： // registerHandler registers handlers for yurtHubServer, and yurtHubServer can handle requests like profiling, healthz, update token. func registerHandlers(c *mux.Router, cfg *config.YurtHubConfiguration, rest *rest.RestConfigManager) { // register handlers for update join token c.Handle(\"/v1/token\", updateTokenHandler(cfg.CertManager)).Methods(\"POST\", \"PUT\") // register handler for health check c.HandleFunc(\"/v1/healthz\", healthz).Methods(\"GET\") c.Handle(\"/v1/readyz\", readyz(cfg.CertManager)).Methods(\"GET\") // register handler for profile if cfg.EnableProfiling { profile.Install(c) } // register handler for metrics c.Handle(\"/metrics\", promhttp.Handler()) // register handler for ota upgrade c.Handle(\"/pods\", ota.GetPods(cfg.StorageWrapper)).Methods(\"GET\") c.Handle(\"/openyurt.io/v1/namespaces/{ns}/pods/{podname}/upgrade\", ota.HealthyCheck(rest, cfg.NodeName, ota.UpdatePod)).Methods(\"POST\") } 以上路由不做深入分析。 3.2. YurtHubProxyServerServing YurtHubProxyServerServing主要代理kube-apiserver的转发请求。 // start yurthub proxy servers for forwarding requests to cloud kube-apiserver if cfg.WorkingMode == util.WorkingModeEdge { proxyHandler = wrapNonResourceHandler(proxyHandler, cfg, rest) } if cfg.YurtHubProxyServerServing != nil { if err := cfg.YurtHubProxyServerServing.Serve(proxyHandler, 0, stopCh); err != nil { return err } } 以下分析yurtProxyHandler的逻辑。 3.3. NewYurtReverseProxyHandler NewYurtReverseProxyHandler主要创建了http handler 代理所有转发请求。 1、创建Load Balancer，主要用来转发apiserver的请求。 lb, err := remote.NewLoadBalancer( yurtHubCfg.LBMode, yurtHubCfg.RemoteServers, localCacheMgr, transportMgr, coordinatorGetter, cloudHealthChecker, yurtHubCfg.FilterManager, yurtHubCfg.WorkingMode, stopCh) 2、创建local Proxy，主要用来转发本地缓存的请求。 // When yurthub works in Edge mode, we may use local proxy or pool proxy to handle // the request when offline. localProxy = local.NewLocalProxy(localCacheMgr, cloudHealthChecker.IsHealthy, isCoordinatorHealthy, yurtHubCfg.MinRequestTimeout, ) localProxy = local.WithFakeTokenInject(localProxy, yurtHubCfg.SerializerManager) 3、创建yurtReverseProxy yurtProxy := &yurtReverseProxy{ resolver: resolver, loadBalancer: lb, cloudHealthChecker: cloudHealthChecker, coordinatorHealtCheckerGetter: coordinatorHealthCheckerGetter, localProxy: localProxy, poolProxy: poolProxy, maxRequestsInFlight: yurtHubCfg.MaxRequestInFlight, isCoordinatorReady: isCoordinatorReady, enablePoolCoordinator: yurtHubCfg.EnableCoordinator, tenantMgr: tenantMgr, workingMode: yurtHubCfg.WorkingMode, } return yurtProxy.buildHandlerChain(yurtProxy), nil 4. yurtReverseProxy yurtReverseProxy主要是作为实现反向代理的结构体。 type yurtReverseProxy struct { resolver apirequest.RequestInfoResolver loadBalancer remote.LoadBalancer cloudHealthChecker healthchecker.MultipleBackendsHealthChecker coordinatorHealtCheckerGetter func() healthchecker.HealthChecker localProxy http.Handler poolProxy http.Handler maxRequestsInFlight int tenantMgr tenant.Interface isCoordinatorReady func() bool workingMode hubutil.WorkingMode enablePoolCoordinator bool } 反向代理服务 func (p *yurtReverseProxy) ServeHTTP(rw http.ResponseWriter, req *http.Request) { if p.workingMode == hubutil.WorkingModeCloud { p.loadBalancer.ServeHTTP(rw, req) return } switch { case util.IsKubeletLeaseReq(req): p.handleKubeletLease(rw, req) case util.IsEventCreateRequest(req): p.eventHandler(rw, req) case util.IsPoolScopedResouceListWatchRequest(req): p.poolScopedResouceHandler(rw, req) case util.IsSubjectAccessReviewCreateGetRequest(req): p.subjectAccessReviewHandler(rw, req) default: // For resource request that do not need to be handled by pool-coordinator, // handling the request with cloud apiserver or local cache. if p.cloudHealthChecker.IsHealthy() { p.loadBalancer.ServeHTTP(rw, req) } else { p.localProxy.ServeHTTP(rw, req) } } } 核心逻辑：如果是云端apiserver可以访问的通，则通过loadbalaner来转发，否则就通过localproxy来转发读取本地节点的数据。 5. LoadBalancer LoadBalancer是个本地的负载均衡逻辑，通过轮询的方式去请求cloud的apiserver，当云边网络通信是正常的时候反向代理apiserver的请求，并做本地缓存持久化。断网的时候则读取本地的缓存数据。 backends：真实反向代理的后端 algo: 处理负载均衡策略，轮询或者按优先级 localCacheMgr: 本地缓存管理的manager 代码：/pkg/yurthub/proxy/remote/loadbalancer.go type loadBalancer struct { backends []*util.RemoteProxy algo loadBalancerAlgo localCacheMgr cachemanager.CacheManager filterManager *manager.Manager coordinatorGetter func() poolcoordinator.Coordinator workingMode hubutil.WorkingMode stopCh 5.1. NewLoadBalancer NewLoadBalancer构建一个remote的反向代理，主要包含添加romote server proxy和处理负载均衡策略两部分。 1、添加多个apiserver的地址，创建remote proxy实现反向代理操作。 backends := make([]*util.RemoteProxy, 0, len(remoteServers)) for i := range remoteServers { b, err := util.NewRemoteProxy(remoteServers[i], lb.modifyResponse, lb.errorHandler, transportMgr, stopCh) if err != nil { klog.Errorf(\"could not new proxy backend(%s), %v\", remoteServers[i].String(), err) continue } backends = append(backends, b) } 2、处理负载均衡策略： var algo loadBalancerAlgo switch lbMode { case \"rr\": algo = &rrLoadBalancerAlgo{backends: backends, checker: healthChecker} case \"priority\": algo = &priorityLoadBalancerAlgo{backends: backends, checker: healthChecker} default: algo = &rrLoadBalancerAlgo{backends: backends, checker: healthChecker} } 5.2. loadBalancer.ServeHTTP loadBalancer实现ServeHTTP的接口，通过负载均衡策略挑选出一个可用的反向代理backend。再调用backend的ServeHTTP方法实现具体的反向代理操作。 // pick a remote proxy based on the load balancing algorithm. rp := lb.algo.PickOne() rp.ServeHTTP(rw, req) 5.3. errorHandler 如果请求apiserver失败，当verb=get/list, 则读取cache中的内容。 func (lb *loadBalancer) errorHandler(rw http.ResponseWriter, req *http.Request, err error) { klog.Errorf(\"remote proxy error handler: %s, %v\", hubutil.ReqString(req), err) if lb.localCacheMgr == nil || !lb.localCacheMgr.CanCacheFor(req) { rw.WriteHeader(http.StatusBadGateway) return } ctx := req.Context() if info, ok := apirequest.RequestInfoFrom(ctx); ok { if info.Verb == \"get\" || info.Verb == \"list\" { # 读取cache内容 if obj, err := lb.localCacheMgr.QueryCache(req); err == nil { hubutil.WriteObject(http.StatusOK, obj, rw, req) return } } } rw.WriteHeader(http.StatusBadGateway) } 5.4. QueryCache // QueryCache get runtime object from backend storage for request func (cm *cacheManager) QueryCache(req *http.Request) (runtime.Object, error) { ctx := req.Context() info, ok := apirequest.RequestInfoFrom(ctx) if !ok || info == nil || info.Resource == \"\" { return nil, fmt.Errorf(\"failed to get request info for request %s\", util.ReqString(req)) } if !info.IsResourceRequest { return nil, fmt.Errorf(\"failed to QueryCache for getting non-resource request %s\", util.ReqString(req)) } # 根据verb查询storage中的数据 switch info.Verb { case \"list\": return cm.queryListObject(req) case \"get\", \"patch\", \"update\": return cm.queryOneObject(req) default: return nil, fmt.Errorf(\"failed to QueryCache, unsupported verb %s of request %s\", info.Verb, util.ReqString(req)) } } 5.5. 查询storage中的数据 func (cm *cacheManager) queryOneObject(req *http.Request) (runtime.Object, error) { ... klog.V(4).Infof(\"component: %s try to get key: %s\", comp, key.Key()) obj, err := cm.storage.Get(key) if err != nil { klog.Errorf(\"failed to get obj %s from storage, %v\", key.Key(), err) return nil, err } ... } 目前存储有两种接口实现，一个是本地磁盘存储，一个是etcd存储。以下以磁盘存储为例分析。 代码：/pkg/yurthub/storage/disk/storage.go // Get will get content from the regular file that specified by key. // If key points to a dir, return ErrKeyHasNoContent. func (ds *diskStorage) Get(key storage.Key) ([]byte, error) { if err := utils.ValidateKey(key, storageKey{}); err != nil { return []byte{}, storage.ErrKeyIsEmpty } storageKey := key.(storageKey) if !ds.lockKey(storageKey) { return nil, storage.ErrStorageAccessConflict } defer ds.unLockKey(storageKey) path := filepath.Join(ds.baseDir, storageKey.Key()) buf, err := ds.fsOperator.Read(path) switch err { case nil: return buf, nil case fs.ErrNotExists: return nil, storage.ErrStorageNotFound case fs.ErrIsNotFile: return nil, storage.ErrKeyHasNoContent default: return buf, fmt.Errorf(\"failed to read file at %s, %v\", path, err) } } 6. RemoteProxy RemoteProxy实现一个具体的反向代理操作。 字段说明： reverseProxy：http的ReverseProxy remoteServer：apiserver的地址 代码：/pkg/yurthub/proxy/util/remote.go // RemoteProxy is an reverse proxy for remote server type RemoteProxy struct { reverseProxy *httputil.ReverseProxy remoteServer *url.URL currentTransport http.RoundTripper bearerTransport http.RoundTripper upgradeHandler *proxy.UpgradeAwareHandler bearerUpgradeHandler *proxy.UpgradeAwareHandler stopCh 实现ReverseProxy的ServeHTTP接口。 func (rp *RemoteProxy) ServeHTTP(rw http.ResponseWriter, req *http.Request) { if httpstream.IsUpgradeRequest(req) { klog.V(5).Infof(\"get upgrade request %s\", req.URL) if isBearerRequest(req) { rp.bearerUpgradeHandler.ServeHTTP(rw, req) } else { rp.upgradeHandler.ServeHTTP(rw, req) } return } rp.reverseProxy.ServeHTTP(rw, req) } 实现错误处理的接口。 func (r *responder) Error(w http.ResponseWriter, req *http.Request, err error) { klog.Errorf(\"failed while proxying request %s, %v\", req.URL, err) http.Error(w, err.Error(), http.StatusInternalServerError) } 7. LocalProxy LocalProxy是一个当云边网络断开的时候，用于处理本地kubelet请求的数据的代理。 字段说明： cacheMgr：主要包含本地cache的一个处理管理器。 代码：/pkg/yurthub/proxy/local/local.go // LocalProxy is responsible for handling requests when remote servers are unhealthy type LocalProxy struct { cacheMgr manager.CacheManager isCloudHealthy IsHealthy isCoordinatorReady IsHealthy minRequestTimeout time.Duration } LocalProxy实现ServeHTTP接口，根据不同的k8s请求类型，执行不同的操作： watch：lp.localWatch(w, req) create：lp.localPost(w, req) delete, deletecollection: localDelete(w, req) list., get, update：lp.localReqCache(w, req) // ServeHTTP implements http.Handler for LocalProxy func (lp *LocalProxy) ServeHTTP(w http.ResponseWriter, req *http.Request) { var err error ctx := req.Context() if reqInfo, ok := apirequest.RequestInfoFrom(ctx); ok && reqInfo != nil && reqInfo.IsResourceRequest { klog.V(3).Infof(\"go into local proxy for request %s\", hubutil.ReqString(req)) switch reqInfo.Verb { case \"watch\": err = lp.localWatch(w, req) case \"create\": err = lp.localPost(w, req) case \"delete\", \"deletecollection\": err = localDelete(w, req) default: // list, get, update err = lp.localReqCache(w, req) } if err != nil { klog.Errorf(\"could not proxy local for %s, %v\", hubutil.ReqString(req), err) util.Err(err, w, req) } } else { klog.Errorf(\"local proxy does not support request(%s), requestInfo: %s\", hubutil.ReqString(req), hubutil.ReqInfoString(reqInfo)) util.Err(apierrors.NewBadRequest(fmt.Sprintf(\"local proxy does not support request(%s)\", hubutil.ReqString(req))), w, req) } } 7.1. localReqCache 当边缘网络断连的时候，kubelet执行get list的操作时，通过localReqCache请求本地缓存的数据，返回给kubelet对应的k8s元数据。 // localReqCache handles Get/List/Update requests when remote servers are unhealthy func (lp *LocalProxy) localReqCache(w http.ResponseWriter, req *http.Request) error { if !lp.cacheMgr.CanCacheFor(req) { klog.Errorf(\"can not cache for %s\", hubutil.ReqString(req)) return apierrors.NewBadRequest(fmt.Sprintf(\"can not cache for %s\", hubutil.ReqString(req))) } obj, err := lp.cacheMgr.QueryCache(req) if errors.Is(err, storage.ErrStorageNotFound) || errors.Is(err, hubmeta.ErrGVRNotRecognized) { klog.Errorf(\"object not found for %s\", hubutil.ReqString(req)) reqInfo, _ := apirequest.RequestInfoFrom(req.Context()) return apierrors.NewNotFound(schema.GroupResource{Group: reqInfo.APIGroup, Resource: reqInfo.Resource}, reqInfo.Name) } else if err != nil { klog.Errorf(\"failed to query cache for %s, %v\", hubutil.ReqString(req), err) return apierrors.NewInternalError(err) } else if obj == nil { klog.Errorf(\"no cache object for %s\", hubutil.ReqString(req)) return apierrors.NewInternalError(fmt.Errorf(\"no cache object for %s\", hubutil.ReqString(req))) } return util.WriteObject(http.StatusOK, obj, w, req) } 核心代码为： 查询本地缓存，返回缓存数据。 obj, err := lp.cacheMgr.QueryCache(req) return util.WriteObject(http.StatusOK, obj, w, req) 总结 yurthub是实现边缘断网自治的核心组件，核心逻辑是kubelet向apiserver的请求会通过yurhub进行转发，如果apiserver的接口可通，则将请求结果返回，并存储到本地，如果接口不可通，则读取本地的数据。 yurthub本质是一个反向代理的http server, 核心逻辑主要包括 ： - proxy: 反向代理的实现 - cachemanager：cache的实现 - storage：本地存储的实现 参考： https://github.com/openyurtio/openyurt Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"edge/openyurt/code-analysis/tunnel-server-code-analysis.html":{"url":"edge/openyurt/code-analysis/tunnel-server-code-analysis.html","title":"TunnelServer源码分析（1）","keywords":"","body":"1. Tunnel-server简介2. NewYurttunnelServerCommand3. Run(cfg.Complete(), stopCh)3. TunnelServer4. runAgentServer5. Interceptor 本文以commit id：180282663457080119a1bc6076cce20c922b5c50， 对应版本tag: v1.2.1 的源码分析tunnel-server的实现逻辑。 1. Tunnel-server简介 云与边一般位于不同网络平面，同时边缘节点普遍位于防火墙内部，采用云(中心)边协同架构，将导致原生 K8s 系统的运维监控能力面临如下挑战: K8s 原生运维能力缺失(如 kubectl logs/exec 等无法执行) 社区主流监控运维组件无法工作(如 Prometheus/metrics-server ) 在 OpenYurt 中，引入了专门的组件 YurtTunnel 负责解决云边通信问题。反向通道是解决跨网络通信的一种常见方式，而 YurtTunnel 的本质也是一个反向通道。 它是一个典型的C/S结构的组件，由部署于云端的 YurtTunnelServer 和部署于边缘节点上的 YurtTunnelAgent组成。 本文主要分析tunnel-server的代理逻辑。 以下是基本架构图： pkg中的目录结构： yurttunnel ├── agent # tunnel agent代码 ├── constants # 常量值 ├── handlerwrapper ├── informers ├── kubernetes # k8s clientset工具包 ├── server # 核心代码： tunnel server逻辑 ├── trafficforward # iptables和dns操作 └── util 2. NewYurttunnelServerCommand main函数入口： func main() { cmd := app.NewYurttunnelServerCommand(stop) cmd.Flags().AddGoFlagSet(flag.CommandLine) if err := cmd.Execute(); err != nil { klog.Fatalf(\"%s failed: %s\", projectinfo.GetServerName(), err) } } 以下是NewYurttunnelServerCommand构造函数，常见的三件套，不做展开： 读取参数：serverOptions.AddFlags(cmd.Flags()) 生成配置：cfg.Complete() 执行Run函数：核心逻辑 func NewYurttunnelServerCommand(stopCh 3. Run(cfg.Complete(), stopCh) Run函数最终是运行一个tunnelserver的常驻进程。在之前会做一些controller或manager的准备工作。 其中包括： DNS controller IP table manager certificate manager RegisterInformersForTunnelServer 首先是构建并运行上述的manager或controller， 源码中的注释也大概描述了主要流程： 注册tunnel所需的SharedInformerFactory 运行dns controller 运行ip table manager 给tunnel server创建certificate manager 给tunnel agent 创建certificate manager 创建handler wrappers 生成TLS 证书 运行tunnel server 以下是部分代码，已删除无关紧要的代码： // run starts the yurttunel-server func Run(cfg *config.CompletedConfig, stopCh 3. TunnelServer anpTunnelServer实现了TunnelServer的接口，以下分析TunnelServer.Run的部分。 run部分主要运行了三个server proxyServer： 主要是重定向apiserver的请求到tunnel server MasterServer： AgentServer：主要运行一个grpc server与tunnel agent连接，即云边反向隧道 代码参考：/pkg/yurttunnel/server/anpserver.go // Run runs the yurttunnel-server func (ats *anpTunnelServer) Run() error { proxyServer := anpserver.NewProxyServer(uuid.New().String(), []anpserver.ProxyStrategy{anpserver.ProxyStrategy(ats.proxyStrategy)}, ats.serverCount, &anpserver.AgentTokenAuthenticationOptions{}) // 1. start the proxier proxierErr := runProxier( &anpserver.Tunnel{Server: proxyServer}, ats.egressSelectorEnabled, ats.interceptorServerUDSFile, ats.tlsCfg) wrappedHandler, err := wh.WrapHandler( NewRequestInterceptor(ats.interceptorServerUDSFile, ats.proxyClientTlsCfg), ats.wrappers, ) // 2. start the master server masterServerErr := runMasterServer( wrappedHandler, ats.egressSelectorEnabled, ats.serverMasterAddr, ats.serverMasterInsecureAddr, ats.tlsCfg) // 3. start the agent server agentServerErr := runAgentServer(ats.tlsCfg, ats.serverAgentAddr, proxyServer) return nil } 4. runAgentServer runAgentServer主要运行一个grpc server与edge端的agent形成grpc连接。 // runAgentServer runs a grpc server that handles connections from the yurttunel-agent // NOTE agent server is responsible for managing grpc connection yurttunel-server // and yurttunnel-agent, and the proxy server is responsible for redirecting requests // to corresponding yurttunel-agent func runAgentServer(tlsCfg *tls.Config, agentServerAddr string, proxyServer *anpserver.ProxyServer) error { serverOption := grpc.Creds(credentials.NewTLS(tlsCfg)) ka := keepalive.ServerParameters{ // Ping the client if it is idle for `Time` seconds to ensure the // connection is still active Time: constants.YurttunnelANPGrpcKeepAliveTimeSec * time.Second, // Wait `Timeout` second for the ping ack before assuming the // connection is dead Timeout: constants.YurttunnelANPGrpcKeepAliveTimeoutSec * time.Second, } grpcServer := grpc.NewServer(serverOption, grpc.KeepaliveParams(ka)) anpagent.RegisterAgentServiceServer(grpcServer, proxyServer) listener, err := net.Listen(\"tcp\", agentServerAddr) klog.Info(\"start handling connection from agents\") if err != nil { return fmt.Errorf(\"fail to listen to agent on %s: %w\", agentServerAddr, err) } go grpcServer.Serve(listener) return nil } 5. Interceptor Interceptor（请求拦截器）拦截kube-apiserver的请求转发给tunnel，通过tunnel请求kubelet。 流程图： 代码地址：/pkg/yurttunnel/server/interceptor.go // NewRequestInterceptor creates a interceptor object that intercept request from kube-apiserver func NewRequestInterceptor(udsSockFile string, cfg *tls.Config) *RequestInterceptor { if len(udsSockFile) == 0 || cfg == nil { return nil } cfg.InsecureSkipVerify = true contextDialer := func(addr string, header http.Header, isTLS bool) (net.Conn, error) { klog.V(4).Infof(\"Sending request to %q.\", addr) proxyConn, err := net.Dial(\"unix\", udsSockFile) if err != nil { return nil, fmt.Errorf(\"dialing proxy %q failed: %w\", udsSockFile, err) } var connectHeaders string for _, h := range supportedHeaders { if v := header.Get(h); len(v) != 0 { connectHeaders = fmt.Sprintf(\"%s\\r\\n%s: %s\", connectHeaders, h, v) } } fmt.Fprintf(proxyConn, \"CONNECT %s HTTP/1.1\\r\\nHost: localhost%s\\r\\n\\r\\n\", addr, connectHeaders) br := newBufioReader(proxyConn) defer putBufioReader(br) res, err := http.ReadResponse(br, nil) if err != nil { proxyConn.Close() return nil, fmt.Errorf(\"reading HTTP response from CONNECT to %s via proxy %s failed: %w\", addr, udsSockFile, err) } if res.StatusCode != 200 { proxyConn.Close() return nil, fmt.Errorf(\"proxy error from %s while dialing %s, code %d: %v\", udsSockFile, addr, res.StatusCode, res.Status) } // if the request scheme is https, setup a tls connection over the // proxy tunnel (i.e. interceptor kubelet) if isTLS { tlsTunnelConn := tls.Client(proxyConn, cfg) if err := tlsTunnelConn.Handshake(); err != nil { proxyConn.Close() return nil, fmt.Errorf(\"fail to setup TLS handshake through the Tunnel: %w\", err) } klog.V(4).Infof(\"successfully setup TLS connection to %q with headers: %s\", addr, connectHeaders) return tlsTunnelConn, nil } klog.V(2).Infof(\"successfully setup connection to %q with headers: %q\", addr, connectHeaders) return proxyConn, nil } return &RequestInterceptor{ contextDialer: contextDialer, } } 参考： https://openyurt.io/zh/docs/core-concepts/yurttunnel Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"edge/openyurt/code-analysis/tennel-agent-code-analysis.html":{"url":"edge/openyurt/code-analysis/tennel-agent-code-analysis.html","title":"Tunnel-Agent源码分析","keywords":"","body":"1. Tunnel-Agent简介2. NewYurttunnelAgentCommand3. Run4. TunnelAgent5. anpTunnelAgent.Run1. Tunnel-Agent简介 tunnel-agent是通过daemonset部署在每个worker节点，通过grpc协议与云端的tunnel-server建立连接。以下分析tunnel-agent的源码逻辑。 常用的启动参数： - args: - --node-name=$(NODE_NAME) - --node-ip=$(POD_IP) - --tunnelserver-addr=tunnel-server-address[ip:port] - --v=2 command: - yurt-tunnel-agent 2. NewYurttunnelAgentCommand NewYurttunnelAgentCommand还是常用命令代码三板斧，此处不做展开，直接分析Run函数。 // NewYurttunnelAgentCommand creates a new yurttunnel-agent command func NewYurttunnelAgentCommand(stopCh 3. Run Run函数即启动一个agent服务，主要包含以下几个步骤： 先获取配置项tunnelserver-addr中的地址，如果地址不存在，则获取x-tunnel-server-svc的service 地址。（说明：一般情况下，tunnel-server跟agent不在同一个网络域，因此网络会不通，所以一般需要配置独立且可连通的地址，可以通过Nginx转发） agent通过host的网络模式运行在宿主机上，启动证书manager。并等待证书生成。 生成连接tunnel-server的证书。 启动 yurttunnel-agent。 启动meta server。 // Run starts the yurttunel-agent func Run(cfg *config.CompletedConfig, stopCh 4. TunnelAgent TunnelAgent与tunnel-server建立tunnel，接收server的请求，并转发给kubelet。 // TunnelAgent sets up tunnel to TunnelServer, receive requests // from tunnel, and forwards requests to kubelet type TunnelAgent interface { Run( 5. anpTunnelAgent.Run anpTunnelAgent使用apiserver-network-proxy包来实现tunnel逻辑。项目具体参考：https://github.com/kubernetes-sigs/apiserver-network-proxy) 代码：/pkg/yurttunnel/agent/anpagent.go // RunAgent runs the yurttunnel-agent which will try to connect yurttunnel-server func (ata *anpTunnelAgent) Run(stopChan 以下是apiserver-network-proxy的源码分析，待补充。 参考： /pkg/yurttunnel/agent/anpagent.go Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"kvm/vm-concept.html":{"url":"kvm/vm-concept.html","title":"虚拟化相关概念","keywords":"","body":"1. 虚拟化2. 虚拟化的类型3. hypervisor4. kvm5. qemu6. libvirt1. 虚拟化 借助虚拟化技术，用户能以单个物理硬件系统为基础，创建多个模拟环境或专用资源，并使用一款名为“Hypervisor”（虚拟机监控程序）的软件直接连接到硬件，从而将一个系统划分为不同、单独而安全的环境，即虚拟机 (VM)。 虚拟化技术可以重新划分IT资源，提高资源的利用率。 2. 虚拟化的类型 全虚拟化（Full virtualization） 全虚拟化使用未修改的guest操作系统版本，guest直接与CPU通信，是最快的虚拟化方法。 半虚拟化（Paravirtualization） 半虚拟化使用修改过的guest操作系统，guest与hypervisor通信，hypervisor将guest的调用传递给CPU和其他接口。因为通信经过hypervisor，因此比全虚拟化慢。 3. hypervisor hypervisor又称为 virtual machine monitor (VMM)，是一个创建和运行虚拟机的程序。被 hypervisor 用来运行一个或多个虚拟机的计算机称为宿主机（host machine），这些虚拟机则称为客户机（guest machine）。 4. kvm kvm(Kernel-based Virtual Machine)是Linux内核的虚拟化模块，可以利用Linux内核的功能来作为hypervisor。 KVM本身不进行模拟，而是暴露一个/dev/kvm接口。 使用KVM，可以在Linux的镜像上 5. qemu QEMU（quick emulator） 待补充 6. libvirt libvirt是一个硬件虚拟化的管理工具API，可用于KVM、QEMU等虚拟化技术， 参考： https://access.redhat.com/documentation/en-us/red_hat_enterprise_linux/6/html/virtualization_getting_started_guide/chap-virtualization_getting_started-what_is_it https://en.wikipedia.org/wiki/Hypervisor https://www.linux-kvm.org/page/Main_Page http://www.linux-kvm.org/page/Documents https://en.wikipedia.org/wiki/Kernel-based_Virtual_Machine https://wiki.qemu.org/Index.html Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"kvm/kubevirt/_index.html":{"url":"kvm/kubevirt/_index.html","title":"KubeVirt","keywords":"","body":"Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"kvm/kubevirt/kubevirt-introduction.html":{"url":"kvm/kubevirt/kubevirt-introduction.html","title":"KubeVirt的介绍","keywords":"","body":"1. kubevirt简介2. 架构图2.1. 组件说明2.2. 自定义CRD对象3. 创建虚拟机流程 本文主要由云原生虚拟化：基于 Kubevirt 构建边缘计算实例文章重新整理而成。 1. kubevirt简介 kubevirt是基于k8s之上，提供了一种通过k8s来编排和管理虚拟机的方式。 2. 架构图 图片 - arch 2.1. 组件说明 分类 组件 部署方式 功能说明 控制面 virt-api deployment 自定义API，开机、关机、重启等，作为apiserver的插件，业务通过k8s apiserver请求virt-api。 virt-controller deployment 管理和监控VMI对象的状态，控制VMI下的pod。 节点侧 virt-handler daemonset 类似kubelet，管理宿主机上的所有虚拟机实例。 virt-launcher virt-handler pod 调用libvirt和qemu创建虚拟机进程。 virt-launcher与libvirt逻辑： 2.2. 自定义CRD对象 分类 CRD对象 功能说明 虚机 VirtualMachineInstance（VMI） 代表运行的虚拟机实例 VirtualMachine（VM） 虚机对象，提供开机、关机、重启，管理VMI实例，与VMI的关系是1：1 3. 创建虚拟机流程 待补充 参考： https://github.com/kubevirt/kubevirt Architecture - KubeVirt User-Guide https://mp.weixin.qq.com/s/IwA1QcGaooZAL96YjvTqjA Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"kvm/kubevirt/kubevirt-installation.html":{"url":"kvm/kubevirt/kubevirt-installation.html","title":"KubeVirt的使用","keywords":"","body":"1. 安装kubevirt1.1. 修改镜像仓库1.2. 部署virt-operator1.3. 部署virtctl2. kubevirt部署产物3. 创建虚拟机1. 安装kubevirt 1.1. 修改镜像仓库 针对私有环境，需要将所需镜像上传到自己的镜像仓库中。 涉及的镜像组件有 virt-operator virt-api virt-controller virt-launcher 重命名镜像脚本如下: #!/bin/bash # kubevirt组件版本 version=$1 # 私有镜像仓库 registry=$2 # 私有镜像仓库的namespace namespace=$3 kubevirtRegistry=\"quay.io/kubevirt\" readonly APPLIST=( virt-operator virt-api virt-controller virt-launcher ) for app in \"${APPLIST[@]}\"; do # 拉取镜像 docker pull ${kubevirtRegistry}/${app}:${version} # 重命名 docker tag ${kubevirtRegistry}/${app}:${version} ${registry}/${namespace}/${app}:${version} # 推送镜像 docker push ${registry}/${namespace}/${app}:${version} done echo \"重新命名成功\" 1.2. 部署virt-operator 通过kubevirt operator安装kubevirt相关组件，选择指定版本，下载kubevirt-operator.yaml和kubevirt-cr.yaml文件，并创建k8s相关对象。 如果是私有镜像仓库，则需要将kubevirt-operator.yaml文件中镜像的名字替换为私有镜像仓库的地址，并提前按步骤1推送所需镜像到私有镜像仓库。 # Pick an upstream version of KubeVirt to install $ export RELEASE=v0.52.0 # Deploy the KubeVirt operator $ kubectl apply -f https://github.com/kubevirt/kubevirt/releases/download/${RELEASE}/kubevirt-operator.yaml # Create the KubeVirt CR (instance deployment request) which triggers the actual installation $ kubectl apply -f https://github.com/kubevirt/kubevirt/releases/download/${RELEASE}/kubevirt-cr.yaml # wait until all KubeVirt components are up $ kubectl -n kubevirt wait kv kubevirt --for condition=Available 1.3. 部署virtctl virtctl用来启动和关闭虚拟机。 VERSION=$(kubectl get kubevirt.kubevirt.io/kubevirt -n kubevirt -o=jsonpath=\"{.status.observedKubeVirtVersion}\") ARCH=$(uname -s | tr A-Z a-z)-$(uname -m | sed 's/x86_64/amd64/') || windows-amd64.exe echo ${ARCH} curl -L -o virtctl https://github.com/kubevirt/kubevirt/releases/download/${VERSION}/virtctl-${VERSION}-${ARCH} chmod +x virtctl sudo install virtctl /usr/local/bin 2. kubevirt部署产物 通过手动部署virt-operator，会自动部署以下组件 组件 部署方式 副本数 virt-api deployment 2 virt-controller deployment 2 virt-handler daemonset - 具体参考: #kg all -n kubevirt NAME READY STATUS RESTARTS AGE pod/virt-api-5fb5cffb7f-hgjjh 1/1 Running 0 23h pod/virt-api-5fb5cffb7f-jcp7x 1/1 Running 0 23h pod/virt-controller-844cd4f58c-h8vsx 1/1 Running 0 23h pod/virt-controller-844cd4f58c-vlxqs 1/1 Running 0 23h pod/virt-handler-lb5ft 1/1 Running 0 23h pod/virt-handler-mtr4d 1/1 Running 0 22h pod/virt-handler-sxd2t 1/1 Running 0 23h pod/virt-operator-8595f577cd-b9txg 1/1 Running 0 23h pod/virt-operator-8595f577cd-p2f69 1/1 Running 0 23h NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/kubevirt-operator-webhook ClusterIP 10.254.159.81 443/TCP 23h service/kubevirt-prometheus-metrics ClusterIP 10.254.7.231 443/TCP 23h service/virt-api ClusterIP 10.254.244.139 443/TCP 23h NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE daemonset.apps/virt-handler 3 3 3 3 3 kubernetes.io/os=linux 23h NAME READY UP-TO-DATE AVAILABLE AGE deployment.apps/virt-api 2/2 2 2 23h deployment.apps/virt-controller 2/2 2 2 23h deployment.apps/virt-operator 2/2 2 2 23h NAME DESIRED CURRENT READY AGE replicaset.apps/virt-api-5fb5cffb7f 2 2 2 23h replicaset.apps/virt-controller-844cd4f58c 2 2 2 23h replicaset.apps/virt-operator-8595f577cd 2 2 2 23h NAME AGE PHASE kubevirt.kubevirt.io/kubevirt 23h Deployed 3. 创建虚拟机 通过vm.yaml创建虚拟机 # 下载vm.yaml wget https://kubevirt.io/labs/manifests/vm.yaml # 创建虚拟机 kubectl apply -f https://kubevirt.io/labs/manifests/vm.yaml vm.yaml文件 apiVersion: kubevirt.io/v1 kind: VirtualMachine metadata: name: testvm spec: running: false template: metadata: labels: kubevirt.io/size: small kubevirt.io/domain: testvm spec: domain: devices: disks: - name: containerdisk disk: bus: virtio - name: cloudinitdisk disk: bus: virtio interfaces: - name: default masquerade: {} resources: requests: memory: 64M networks: - name: default pod: {} volumes: - name: containerdisk containerDisk: image: quay.io/kubevirt/cirros-container-disk-demo - name: cloudinitdisk cloudInitNoCloud: userDataBase64: SGkuXG4= 查看虚拟机 kubectl get vms kubectl get vms -o yaml testvm 启动或暂停虚拟机 # 启动虚拟机 virtctl start testvm # 关闭虚拟机 virtctl stop testvm # 进入虚拟机 virtctl console testvm 删除虚拟机 kubectl delete vm testvm 参考： Installation - KubeVirt User-Guide KubeVirt quickstart with Minikube | KubeVirt.io Use KubeVirt | KubeVirt.io Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"monitor/kubernetes-cluster-monitoring.html":{"url":"monitor/kubernetes-cluster-monitoring.html","title":"监控体系介绍","keywords":"","body":"1. 概述1.1. cAdvisor1.2. Heapster1.3. InfluxDB1.4. Grafana1.5. 总体架构图2. 部署与使用2.1. cAdvisor2.2. Service2.2.1. heapster-service2.2.2. influxdb-service2.2.3. grafana-service2.2.4. 创建service2.3. ReplicationController2.3.1. influxdb-grafana-controller2.3.2. heapster-controller2.3.3. 创建ReplicationController3. 查看界面及数据3.1. InfluxDB3.2. Grafana4. 容器化部署4.1. 拉取镜像4.2. 运行容器4.2.1. influxdb4.2.2. cadvisor4.2.3. grafana4.2.4. heapster4.3. 访问1. 概述 1.1. cAdvisor cAdvisor对Node机器上的资源及容器进行实时监控和性能数据采集，包括CPU使用情况、内存使用情况、网络吞吐量及文件系统使用情况，cAdvisor集成在Kubelet中，当kubelet启动时会自动启动cAdvisor，即一个cAdvisor仅对一台Node机器进行监控。kubelet的启动参数--cadvisor-port可以定义cAdvisor对外提供服务的端口，默认为4194。可以通过浏览器Node_IP:port访问。项目主页：http://github.com/google/cadvisor。 1.2. Heapster 是对集群中的各个Node、Pod的资源使用数据进行采集，通过访问每个Node上Kubelet的API，再通过Kubelet调用cAdvisor的API来采集该节点上所有容器的性能数据。由Heapster进行数据汇聚，保存到后端存储系统中，例如InfluxDB，Google Cloud Logging等。项目主页为：https://github.com/kubernetes/heapster。 1.3. InfluxDB 是分布式时序数据库（每条记录带有时间戳属性），主要用于实时数据采集、事件跟踪记录、存储时间图表、原始数据等。提供REST API用于数据的存储和查询。项目主页为http://InfluxDB.com。 1.4. Grafana 通过Dashboard将InfluxDB的时序数据展现成图表形式，便于查看集群运行状态。项目主页为http://Grafana.org。 1.5. 总体架构图 图片 - k8s监控架构图 其中当前Kubernetes中，Heapster、InfluxDB、Grafana均以Pod的形式启动和运行。Heapster与Master需配置安全连接。 2. 部署与使用 2.1. cAdvisor kubelet的启动参数--cadvisor-port可以定义cAdvisor对外提供服务的端口，默认为4194。可以通过浏览器Node_IP:port访问。也提供了REST API供客户端远程调用，API返回的格式为JSON，可以采用URL访问：http://`hostname`:`port`/api/`version`/`request`/ 例如：http://14.152.49.100:4194/api/v1.3/machine 获取主机信息。 2.2. Service 2.2.1. heapster-service heapster-service.yaml apiVersion:v1 kind:Service metadata: label: kubenetes.io/cluster-service:\"true\" kubernetes.io/name:Heapster name:heapster namespace:kube-system spec: ports: - port:80 targetPort:8082 selector: k8s-app:heapster 2.2.2. influxdb-service influxdb-service.yaml apiVersion:v1 kind:Service metadata: label:null name:monitoring-InfluxDB namespace:kube-system spec: type:Nodeport ports: - name:http port:80 targetPort:8083 - name:api port:8086 targetPort:8086 Nodeport:8086 selector: name:influxGrafana 2.2.3. grafana-service grafana-service.yaml apiVersion:v1 kind:Service metadata: label: kubenetes.io/cluster-service:\"true\" kubernetes.io/name:monitoring-Grafana name:monitoring-Grafana namespace:kube-system spec: type:Nodeport ports: port:80 targetPort:8080 Nodeport:8085 selector: name:influxGrafana 使用type=NodePort将InfluxDB和Grafana暴露在Node的端口上，以便通过浏览器进行访问。 2.2.4. 创建service kubectl create -f heapster-service.yaml kubectl create -f InfluxDB-service.yaml kubectl create -f Grafana-service.yaml 2.3. ReplicationController 2.3.1. influxdb-grafana-controller influxdb-grafana-controller-v3.yaml apiVersion:v1 kind:ReplicationController metadata: name:monitoring-influxdb-grafana-v3 namespace:kube-system labels: k8s-app:influxGrafana version:v3 kubernetes.io/cluster-service:\"true spec: replicas:1 selector: k8s-app:influxGrafana version:v3 template: metadata: labels: k8s-app:influxGrafana version:v3 kubernetes.io/cluster-service:\"true spec: containers: - image:gcr.io/google_containers/heapster_influxdb:v0.5 name:influxdb resources: limits: cpu:100m memory:500Mi requests: cpu:100m memory:500Mi ports: - containerPort:8083 - containerPort:8086 volumeMounts: -name:influxdb-persistent-storage mountPath:/data - image:grc.io/google_containers/heapster_grafana:v2.6.0-2 name:grafana resources: limits: cpu:100m memory:100Mi requests: cpu:100m memory:100Mi env: - name:INFLUXDB_SERVICE_URL value:http://monitoring-influxdb:8086 - name:GF_AUTH_BASIC_ENABLED value:\"false\" - name:GF_AUTH_ANONYMOUS_ENABLED value:\"true\" - name:GF_AUTH_ANONYMOUS_ORG_ROLE value:Admin - name:GF_SERVER_ROOT_URL value:/api/v1/proxy/namespace/kube-system/services/monitoring-grafana/ volumeMounts: - name:grafana-persistent-storage mountPath:/var volumes: - name:influxdb-persistent-storage emptyDir{} - name:grafana-persistent-storage emptyDir{} 2.3.2. heapster-controller heapster-controller.yaml apiVersion:v1 kind:ReplicationController metadata: labels: k8s-app:heapster name:heapster version:v6 name:heapster namespace:kube-system spec: replicas:1 selector: name:heapster k8s-app:heapster version:v6 template: metadata: labels: k8s-app:heapster version:v6 spec: containers: - image:gcr.io/google_containers/heapster:v0.17.0 name:heapster command: - /heapster - --source=kubernetes:http://192.168.1.128:8080?inClusterConfig=flase&kubeletHttps=true&useServiceAccount=true&auth= - --sink=InfluxDB:http://monitoring-InfluxDB:8086 Heapster设置启动参数说明： 1、–source 配置监控来源，本例中表示从k8s-Master获取各个Node的信息。在URL的参数部分，修改kubeletHttps、inClusterConfig、useServiceAccount的值。 2、–sink 配置后端的存储系统，本例中使用InfluxDB。URL中主机名的地址是InfluxDB的Service名字，需要DNS服务正常工作，如果没有配置DNS服务可使用Service的ClusterIP地址。 2.3.3. 创建ReplicationController kubelet create -f InfluxDB-Grafana-controller.yaml kubelet create -f heapster-controller.yaml 3. 查看界面及数据 3.1. InfluxDB 访问任意一台Node机器的30083端口。 3.2. Grafana 访问任意一台Node机器的30080端口。 4. 容器化部署 4.1. 拉取镜像 docker pull influxdb:latest docker pull cadvisor:latest docker pull grafana:latest docker pull heapster:latest 4.2. 运行容器 4.2.1. influxdb #influxdb docker run -d -p 8083:8083 -p 8086:8086 --expose 8090 --expose 8099 --volume=/opt/data/influxdb:/data --name influxsrv influxdb:latest 4.2.2. cadvisor #cadvisor docker run --volume=/:/rootfs:ro --volume=/var/run:/var/run:rw --volume=/sys:/sys:ro --volume=/var/lib/docker/:/var/lib/docker:ro --publish=8080:8080 --detach=true --link influxsrv:influxsrv --name=cadvisor cadvisor:latest -storage_driver=influxdb -storage_driver_db=cadvisor -storage_driver_host=influxsrv:8086 4.2.3. grafana #grafana docker run -d -p 3000:3000 -e INFLUXDB_HOST=influxsrv -e INFLUXDB_PORT=8086 -e INFLUXDB_NAME=cadvisor -e INFLUXDB_USER=root -e INFLUXDB_PASS=root --link influxsrv:influxsrv --name grafana grafana:latest 4.2.4. heapster docker run -d -p 8082:8082 --net=host heapster:canary --source=kubernetes:http://`k8s-server-ip`:8080?inClusterConfig=false/&useServiceAccount=false --sink=influxdb:http://`influxdb-ip`:8086 4.3. 访问 在浏览器输入IP:PORT Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"monitor/kube-promethus-stack.html":{"url":"monitor/kube-promethus-stack.html","title":"kube-prometheus-stack的使用","keywords":"","body":"1. kube-prometheus-stack简介2. 安装kube-prometheus-stack3. 查看安装结果4. 登录grafana1. kube-prometheus-stack简介 kube-prometheus-stack是prometheus监控k8s集群的套件，可以通过helm一键安装，同时带有监控的模板。 各组件包括 grafana kube-state-metrics prometheus alertmanager node-exporter 2. 安装kube-prometheus-stack 执行以下命令 helm repo add prometheus-community https://prometheus-community.github.io/helm-charts helm repo update helm install kube-prometheus-stack prometheus-community/kube-prometheus-stack -n prometheus 示例： # helm install kube-prometheus-stack prometheus-community/kube-prometheus-stack -n prometheus NAME: kube-prometheus-stack LAST DEPLOYED: Wed May 17 17:12:24 2023 NAMESPACE: prometheus STATUS: deployed REVISION: 1 NOTES: kube-prometheus-stack has been installed. Check its status by running: kubectl --namespace prometheus get pods -l \"release=kube-prometheus-stack\" Visit https://github.com/prometheus-operator/kube-prometheus for instructions on how to create & configure Alertmanager and Prometheus instances using the Operator. 3. 查看安装结果 deployment kube-prometheus-stack-grafana kube-prometheus-stack-kube-state-metrics kube-prometheus-stack-operator statefulset prometheus-kube-prometheus-stack-prometheus alertmanager-kube-prometheus-stack-alertmanager daemonset kube-prometheus-stack-prometheus-node-exporter # kg all -n prometheus NAME READY STATUS RESTARTS AGE pod/alertmanager-kube-prometheus-stack-alertmanager-0 2/2 Running 0 9m34s pod/kube-prometheus-stack-grafana-5bb7689dc8-lgrws 3/3 Running 0 9m35s pod/kube-prometheus-stack-kube-state-metrics-5d6578867c-25xbq 1/1 Running 0 9m35s pod/kube-prometheus-stack-operator-9c5fbdc68-nrn7h 1/1 Running 0 9m35s pod/kube-prometheus-stack-prometheus-node-exporter-8ghd8 1/1 Running 0 48s pod/kube-prometheus-stack-prometheus-node-exporter-brtp9 1/1 Running 0 29s pod/kube-prometheus-stack-prometheus-node-exporter-n4kdp 1/1 Running 0 88s pod/kube-prometheus-stack-prometheus-node-exporter-ttksv 1/1 Running 0 35s pod/prometheus-kube-prometheus-stack-prometheus-0 2/2 Running 0 9m34s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/alertmanager-operated ClusterIP None 9093/TCP,9094/TCP,9094/UDP 9m34s service/kube-prometheus-stack-alertmanager ClusterIP 10.99.108.180 9093/TCP 9m36s service/kube-prometheus-stack-grafana ClusterIP 10.110.62.28 80/TCP 9m36s service/kube-prometheus-stack-kube-state-metrics ClusterIP 10.110.105.139 8080/TCP 9m35s service/kube-prometheus-stack-operator ClusterIP 10.96.147.204 443/TCP 9m36s service/kube-prometheus-stack-prometheus ClusterIP 10.98.235.203 9090/TCP 9m36s service/kube-prometheus-stack-prometheus-node-exporter ClusterIP 10.105.99.77 9100/TCP 9m36s service/prometheus-operated ClusterIP None 9090/TCP 9m34s NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE daemonset.apps/kube-prometheus-stack-prometheus-node-exporter 4 4 4 4 4 9m35s NAME READY UP-TO-DATE AVAILABLE AGE deployment.apps/kube-prometheus-stack-grafana 1/1 1 1 9m35s deployment.apps/kube-prometheus-stack-kube-state-metrics 1/1 1 1 9m35s deployment.apps/kube-prometheus-stack-operator 1/1 1 1 9m35s NAME READY AGE statefulset.apps/alertmanager-kube-prometheus-stack-alertmanager 1/1 9m34s statefulset.apps/prometheus-kube-prometheus-stack-prometheus 1/1 9m34s 4. 登录grafana 默认账号密码 账号：admin 密码：prom-operator 默认账号密码位于secret中，通过base64解码可得上述密码。 kg secret -n prometheus kube-prometheus-stack-grafana -oyaml apiVersion: v1 data: admin-password: cHJvbS1vcGVyYXRvcg== admin-user: YWRtaW4= 模板内容： pod数据： 参考： https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack https://github.com/prometheus-operator/kube-prometheus Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"monitor/cadvisor-introduction.html":{"url":"monitor/cadvisor-introduction.html","title":"cAdvisor介绍","keywords":"","body":"1. cAdvisor简介2. cAdvisor结构图3. Metrics4. cAdvisor源码4.1. cAdvisor入口函数4.2. cAdvisor Client的使用4.2.1 client定义4.2.2. client方法1. cAdvisor简介 ​ cAdvisor对Node机器上的资源及容器进行实时监控和性能数据采集，包括CPU使用情况、内存使用情况、网络吞吐量及文件系统使用情况，cAdvisor集成在Kubelet中，当kubelet启动时会自动启动cAdvisor，即一个cAdvisor仅对一台Node机器进行监控。kubelet的启动参数--cadvisor-port可以定义cAdvisor对外提供服务的端口，默认为4194。可以通过浏览器访问。项目主页：http://github.com/google/cadvisor。 2. cAdvisor结构图 图片 - cAdvisor 3. Metrics 分类 字段 描述 cpu cpu_usage_total cpu_usage_system cpu_usage_user cpu_usage_per_cpu load_average Smoothed average of number of runnable threads x 1000 memory memory_usage Memory Usage memory_working_set Working set size network rx_bytes Cumulative count of bytes received rx_errors Cumulative count of receive errors encountered tx_bytes Cumulative count of bytes transmitted tx_errors Cumulative count of transmit errors encountered filesystem fs_device Filesystem device fs_limit Filesystem limit fs_usage Filesystem usage 4. cAdvisor源码 4.1. cAdvisor入口函数 cadvisor.go func main() { defer glog.Flush() flag.Parse() if *versionFlag { fmt.Printf(\"cAdvisor version %s (%s)/n\", version.Info[\"version\"], version.Info[\"revision\"]) os.Exit(0) } setMaxProcs() memoryStorage, err := NewMemoryStorage() if err != nil { glog.Fatalf(\"Failed to initialize storage driver: %s\", err) } sysFs, err := sysfs.NewRealSysFs() if err != nil { glog.Fatalf(\"Failed to create a system interface: %s\", err) } collectorHttpClient := createCollectorHttpClient(*collectorCert, *collectorKey) containerManager, err := manager.New(memoryStorage, sysFs, *maxHousekeepingInterval, *allowDynamicHousekeeping, ignoreMetrics.MetricSet, &collectorHttpClient) if err != nil { glog.Fatalf(\"Failed to create a Container Manager: %s\", err) } mux := http.NewServeMux() if *enableProfiling { mux.HandleFunc(\"/debug/pprof/\", pprof.Index) mux.HandleFunc(\"/debug/pprof/cmdline\", pprof.Cmdline) mux.HandleFunc(\"/debug/pprof/profile\", pprof.Profile) mux.HandleFunc(\"/debug/pprof/symbol\", pprof.Symbol) } // Register all HTTP handlers. err = cadvisorhttp.RegisterHandlers(mux, containerManager, *httpAuthFile, *httpAuthRealm, *httpDigestFile, *httpDigestRealm) if err != nil { glog.Fatalf(\"Failed to register HTTP handlers: %v\", err) } cadvisorhttp.RegisterPrometheusHandler(mux, containerManager, *prometheusEndpoint, nil) // Start the manager. if err := containerManager.Start(); err != nil { glog.Fatalf(\"Failed to start container manager: %v\", err) } // Install signal handler. installSignalHandler(containerManager) glog.Infof(\"Starting cAdvisor version: %s-%s on port %d\", version.Info[\"version\"], version.Info[\"revision\"], *argPort) addr := fmt.Sprintf(\"%s:%d\", *argIp, *argPort) glog.Fatal(http.ListenAndServe(addr, mux)) } 核心代码： memoryStorage, err := NewMemoryStorage() sysFs, err := sysfs.NewRealSysFs() #创建containerManager containerManager, err := manager.New(memoryStorage, sysFs, *maxHousekeepingInterval, *allowDynamicHousekeeping, ignoreMetrics.MetricSet, &collectorHttpClient) #启动containerManager err := containerManager.Start() 4.2. cAdvisor Client的使用 import \"github.com/google/cadvisor/client\" func main(){ client, err := client.NewClient(\"http://192.168.19.30:4194/\") //http://:/ } 4.2.1 client定义 cadvisor/client/client.go // Client represents the base URL for a cAdvisor client. type Client struct { baseUrl string } // NewClient returns a new v1.3 client with the specified base URL. func NewClient(url string) (*Client, error) { if !strings.HasSuffix(url, \"/\") { url += \"/\" } return &Client{ baseUrl: fmt.Sprintf(\"%sapi/v1.3/\", url), }, nil } 4.2.2. client方法 1）MachineInfo // MachineInfo returns the JSON machine information for this client. // A non-nil error result indicates a problem with obtaining // the JSON machine information data. func (self *Client) MachineInfo() (minfo *v1.MachineInfo, err error) { u := self.machineInfoUrl() ret := new(v1.MachineInfo) if err = self.httpGetJsonData(ret, nil, u, \"machine info\"); err != nil { return } minfo = ret return } 2）ContainerInfo // ContainerInfo returns the JSON container information for the specified // container and request. func (self *Client) ContainerInfo(name string, query *v1.ContainerInfoRequest) (cinfo *v1.ContainerInfo, err error) { u := self.containerInfoUrl(name) ret := new(v1.ContainerInfo) if err = self.httpGetJsonData(ret, query, u, fmt.Sprintf(\"container info for %q\", name)); err != nil { return } cinfo = ret return } 3）DockerContainer // Returns the JSON container information for the specified // Docker container and request. func (self *Client) DockerContainer(name string, query *v1.ContainerInfoRequest) (cinfo v1.ContainerInfo, err error) { u := self.dockerInfoUrl(name) ret := make(map[string]v1.ContainerInfo) if err = self.httpGetJsonData(&ret, query, u, fmt.Sprintf(\"Docker container info for %q\", name)); err != nil { return } if len(ret) != 1 { err = fmt.Errorf(\"expected to only receive 1 Docker container: %+v\", ret) return } for _, cont := range ret { cinfo = cont } return } 4）AllDockerContainers // Returns the JSON container information for all Docker containers. func (self *Client) AllDockerContainers(query *v1.ContainerInfoRequest) (cinfo []v1.ContainerInfo, err error) { u := self.dockerInfoUrl(\"/\") ret := make(map[string]v1.ContainerInfo) if err = self.httpGetJsonData(&ret, query, u, \"all Docker containers info\"); err != nil { return } cinfo = make([]v1.ContainerInfo, 0, len(ret)) for _, cont := range ret { cinfo = append(cinfo, cont) } return } Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"monitor/heapster-introduction.html":{"url":"monitor/heapster-introduction.html","title":"Heapster介绍","keywords":"","body":"1. heapster简介2. heapster部署与配置2.1. 注意事项2.2. 容器式部署2.3. 配置说明2.3.1. –source2.3.2. –sink3. Metrics4. Labels5. heapster API1. heapster简介 Heapster是容器集群监控和性能分析工具，天然的支持Kubernetes和CoreOS。 Kubernetes有个出名的监控agent—cAdvisor。在每个kubernetes Node上都会运行cAdvisor，它会收集本机以及容器的监控数据(cpu,memory,filesystem,network,uptime)。 2. heapster部署与配置 2.1. 注意事项 需同步部署机器和被采集机器的时间：ntpdate time.windows.com 加入定时任务，定期同步时间 crontab –e 30 5 * /usr/sbin/ntpdate time.windows.com //每天早晨5点半执行 2.2. 容器式部署 #拉取镜像 docker pull heapster:latest #运行容器 docker run -d -p 8082:8082 --net=host heapster:latest --source=kubernetes:http://:8080?inClusterConfig=false\\&useServiceAccount=false --sink=influxdb:http://:8086?db= 2.3. 配置说明 可以参考官方文档 2.3.1. –source –source: 指定数据获取源。这里指定kube-apiserver即可。 后缀参数： inClusterConfig: kubeletPort: 指定kubelet的使用端口，默认10255 kubeletHttps: 是否使用https去连接kubelets(默认：false) apiVersion: 指定K8S的apiversion insecure: 是否使用安全证书(默认：false) auth: 安全认证 useServiceAccount: 是否使用K8S的安全令牌 2.3.2. –sink –sink: 指定后端数据存储。这里指定influxdb数据库。 后缀参数： user: InfluxDB用户 pw: InfluxDB密码 db: 数据库名 secure: 安全连接到InfluxDB(默认：false) withfields： 使用InfluxDB fields(默认：false)。 3. Metrics 分类 Metric Name Description 备注 cpu cpu/limit CPU hard limit in millicores. CPU上限 cpu/node_capacity Cpu capacity of a node. Node节点的CPU容量 cpu/node_allocatable Cpu allocatable of a node. Node节点可分配的CPU cpu/node_reservation Share of cpu that is reserved on the node allocatable. cpu/node_utilization CPU utilization as a share of node allocatable. cpu/request CPU request (the guaranteed amount of resources) in millicores. cpu/usage Cumulative CPU usage on all cores. CPU总使用量 cpu/usage_rate CPU usage on all cores in millicores. filesystem filesystem/usage Total number of bytes consumed on a filesystem. 文件系统的使用量 filesystem/limit The total size of filesystem in bytes. 文件系统的使用上限 filesystem/available The number of available bytes remaining in a the filesystem 可用的文件系统容量 filesystem/inodes The number of available inodes in a the filesystem filesystem/inodes_free The number of free inodes remaining in a the filesystem memory memory/limit Memory hard limit in bytes. 内存上限 memory/major_page_faults Number of major page faults. memory/major_page_faults_rate Number of major page faults per second. memory/node_capacity Memory capacity of a node. memory/node_allocatable Memory allocatable of a node. memory/node_reservation Share of memory that is reserved on the node allocatable. memory/node_utilization Memory utilization as a share of memory allocatable. memory/page_faults Number of page faults. memory/page_faults_rate Number of page faults per second. memory/request Memory request (the guaranteed amount of resources) in bytes. memory/usage Total memory usage. memory/cache Cache memory usage. memory/rss RSS memory usage. memory/working_set Total working set usage. Working set is the memory being used and not easily dropped by the kernel. network network/rx Cumulative number of bytes received over the network. network/rx_errors Cumulative number of errors while receiving over the network. network/rx_errors_rate Number of errors while receiving over the network per second. network/rx_rate Number of bytes received over the network per second. network/tx Cumulative number of bytes sent over the network network/tx_errors Cumulative number of errors while sending over the network network/tx_errors_rate Number of errors while sending over the network network/tx_rate Number of bytes sent over the network per second. uptime Number of milliseconds since the container was started. - 4. Labels Label Name Description pod_id Unique ID of a Pod pod_name User-provided name of a Pod pod_namespace The namespace of a Pod container_base_image Base image for the container container_name User-provided name of the container or full cgroup name for system containers host_id Cloud-provider specified or user specified Identifier of a node hostname Hostname where the container ran labels Comma-separated(Default) list of user-provided labels. Format is 'key:value' namespace_id UID of the namespace of a Pod resource_id A unique identifier used to differentiate multiple metrics of the same type. e.x. Fs partitions under filesystem/usage 5. heapster API 见官方文档：https://github.com/kubernetes/heapster/blob/master/docs/model.md Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "},"monitor/influxdb-introduction.html":{"url":"monitor/influxdb-introduction.html","title":"Influxdb介绍","keywords":"","body":"1. InfluxDB简介2. InfluxDB安装1）安装2）启动3）访问4）docker image方式安装3. InfluxDB的基本概念3.1. 与传统数据库中的名词做比较3.2. InfluxDB中独有的概念3.2.1. Point3.2.2. series4. InfluxDB的基本操作4.1. InfluxDB数据库操作4.2. InfluxDB数据表操作1. InfluxDB简介 InfluxDB是一个当下比较流行的时序数据库，InfluxDB使用 Go 语言编写，无需外部依赖，安装配置非常方便，适合构建大型分布式系统的监控系统。 主要特色功能： 1）基于时间序列，支持与时间有关的相关函数（如最大，最小，求和等） 2）可度量性：你可以实时对大量数据进行计算 3）基于事件：它支持任意的事件数据 2. InfluxDB安装 1）安装 wget https://dl.influxdata.com/influxdb/releases/influxdb-0.13.0.x86_64.rpm yum localinstall influxdb-0.13.0.armhf.rpm 2）启动 service influxdb start 3）访问 http://服务器IP:8083 4）docker image方式安装 docker pull influxdb docker run -d -p 8083:8083 -p 8086:8086 --expose 8090 --expose 8099 --volume=/opt/data/influxdb:/data --name influxsrv influxdb:latest 3. InfluxDB的基本概念 3.1. 与传统数据库中的名词做比较 influxDB中的名词 传统数据库中的概念 database 数据库 measurement 数据库中的表 points 表里面的一行数据 3.2. InfluxDB中独有的概念 3.2.1. Point Point由时间戳（time）、数据（field）、标签（tags）组成。 Point相当于传统数据库里的一行数据，如下表所示： Point属性 传统数据库中的概念 time 每个数据记录时间，是数据库中的主索引(会自动生成) fields 各种记录值（没有索引的属性）也就是记录的值：温度， 湿度 tags 各种有索引的属性：地区，海拔 3.2.2. series 所有在数据库中的数据，都需要通过图表来展示，而这个series表示这个表里面的数据，可以在图表上画成几条线：通过tags排列组合算出来 show series from cpu 4. InfluxDB的基本操作 InfluxDB提供三种操作方式： 1）客户端命令行方式 2）HTTP API接口 3）各语言API库 4.1. InfluxDB数据库操作 操作 命令 显示数据库 show databases 创建数据库 create database db_name 删除数据库 drop database db_name 使用某个数据库 use db_name 4.2. InfluxDB数据表操作 操作 命令 说明 显示所有表 SHOW MEASUREMENTS 创建数据表 insert table_name,hostname=server01 value=442221834240i 1435362189575692182 其中 disk_free 就是表名，hostname是索引，value=xx是记录值，记录值可以有多个，最后是指定的时间 删除数据表 drop measurement table_name 查看表内容 select * from table_name 查看series show series from table_name series表示这个表里面的数据，可以在图表上画成几条线，series主要通过tags排列组合算出来 Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at 2024-07-04 09:41:20 "}} \ No newline at end of file diff --git a/setup/installer/_index.html b/setup/installer/_index.html index 8d6f8b85..4fec222b 100644 --- a/setup/installer/_index.html +++ b/setup/installer/_index.html @@ -3001,7 +3001,7 @@

    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    https://kind.sigs.k8s.io/docs/user/quick-start/
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    部署和访问 Kubernetes 仪表板（Dashboard） | Kubernetes
    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    https://kubernetes.io/docs/tasks/tools/install-minikube/

    https://kubernetes.io/docs/tasks/tools/install-kubectl/

    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    安装扩展（Addon） | Kubernetes
    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    https://github.com/kubernetes-incubator/external-storage/tree/master/ceph/cephfs/deploy
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    https://kubernetes.io/docs/tasks/administer-cluster/change-pv-reclaim-policy/
    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    https://kubernetes.io/docs/concepts/storage/storage-classes/
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    https://pi-ops.oss-cn-hangzhou.aliyuncs.com/scripts/cgroupfs-mount.sh | bash

    2、重启设备即可解决

    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
    @@ -3090,7 +3090,7 @@

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    具体操作，在拉取镜像失败的节点上登录该镜像仓库，认证信息会更新到$HOME/.docker/config.json文件中。将该文件拷贝到/var/lib/kubelet/config.json中。

    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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    kubectl patch pvc {PVC_NAME} -p '{"metadata":{"finalizers":null}}'
    Copyright © www.huweihuang.com all right reserved，powered by GitbookUpdated at -2024-07-04 09:37:01 +2024-07-04 09:41:20
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