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Quick Start Guide

For a more detailed guide on how to use, compose, and work with SparkApplications, please refer to the User Guide. If you are running the Kubernetes Operator for Apache Spark on Google Kubernetes Engine and want to use Google Cloud Storage (GCS) and/or BigQuery for reading/writing data, also refer to the GCP guide. The Kubernetes Operator for Apache Spark will simply be referred to as the operator for the rest of this guide.

Table of Contents

Installation

To install the operator, use the Helm chart.

$ helm repo add incubator http://storage.googleapis.com/kubernetes-charts-incubator
$ helm install incubator/sparkoperator --namespace spark-operator --set sparkJobNamespace=default

Installing the chart will create a namespace spark-operator if it doesn't exist, and helm will set up RBAC for the operator to run in the namespace. It will also set up RBAC in the default namespace for driver pods of your Spark applications to be able to manipulate executor pods. In addition, the chart will create a Deployment in the namespace spark-operator. The chart's Spark Job Namespace is set to "" by default, in which case it will not set up RBAC. The chart by default does not enable Mutating Admission Webhook for Spark pod customization. When enabled, a webhook service and a secret storing the x509 certificate called spark-webhook-certs are created for that purpose. To install the operator with the mutating admission webhook on a Kubernetes cluster, install the chart with the flag enableWebhook=true:

$ helm install incubator/sparkoperator --namespace spark-operator --set enableWebhook=true

Due to a known issue in GKE, you will need to first grant yourself cluster-admin privileges before you can create custom roles and role bindings on a GKE cluster versioned 1.6 and up. Run the following command before installing the chart on GKE:

$ kubectl create clusterrolebinding <user>-cluster-admin-binding --clusterrole=cluster-admin --user=<user>@<domain>

Now you should see the operator running in the cluster by checking the status of the Helm release.

$ helm status <spark-operator-release-name>

Running the Examples

To run the Spark Pi example, run the following command:

$ kubectl apply -f examples/spark-pi.yaml

Note that spark-pi.yaml configures the driver pod to use the spark service account to communicate with the Kubernetes API server. You might need to replace it with the appropriate service account before submitting the job. If you installed the operator using the Helm chart and overrode sparkJobNamespace, the service account name ends with -spark and starts with the Helm release name. For example, if you would like to run your Spark jobs to run in a namespace called test-ns, first make sure it already exists, and then install the chart with the command:

$ helm install incubator/sparkoperator --namespace spark-operator --set sparkJobNamespace=test-ns

Then the chart will set up a service account for your Spark jobs to use in that namespace.

See the section on the Spark Job Namespace for details on the behavior of the default Spark Job Namespace.

Running the above command will create a SparkApplication object named spark-pi. Check the object by running the following command:

$ kubectl get sparkapplications spark-pi -o=yaml

This will show something similar to the following:

apiVersion: sparkoperator.k8s.io/v1beta2
kind: SparkApplication
metadata:
  ...
spec:
  deps: {}
  driver:
    coreLimit: 1200m
    cores: 1
    labels:
      version: 2.3.0
    memory: 512m
    serviceAccount: spark
  executor:
    cores: 1
    instances: 1
    labels:
      version: 2.3.0
    memory: 512m
  image: gcr.io/ynli-k8s/spark:v2.4.0
  mainApplicationFile: local:///opt/spark/examples/jars/spark-examples_2.11-2.3.0.jar
  mainClass: org.apache.spark.examples.SparkPi
  mode: cluster
  restartPolicy:
      type: OnFailure
      onFailureRetries: 3
      onFailureRetryInterval: 10
      onSubmissionFailureRetries: 5
      onSubmissionFailureRetryInterval: 20
  type: Scala
status:
  sparkApplicationId: spark-5f4ba921c85ff3f1cb04bef324f9154c9
  applicationState:
    state: COMPLETED
  completionTime: 2018-02-20T23:33:55Z
  driverInfo:
    podName: spark-pi-83ba921c85ff3f1cb04bef324f9154c9-driver
    webUIAddress: 35.192.234.248:31064
    webUIPort: 31064
    webUIServiceName: spark-pi-2402118027-ui-svc
    webUIIngressName: spark-pi-ui-ingress
    webUIIngressAddress: spark-pi.ingress.cluster.com
  executorState:
    spark-pi-83ba921c85ff3f1cb04bef324f9154c9-exec-1: COMPLETED
  LastSubmissionAttemptTime: 2018-02-20T23:32:27Z

To check events for the SparkApplication object, run the following command:

$ kubectl describe sparkapplication spark-pi

This will show the events similarly to the following:

Events:
  Type    Reason                      Age   From            Message
  ----    ------                      ----  ----            -------
  Normal  SparkApplicationAdded       5m    spark-operator  SparkApplication spark-pi was added, enqueued it for submission
  Normal  SparkApplicationTerminated  4m    spark-operator  SparkApplication spark-pi terminated with state: COMPLETED

The operator submits the Spark Pi example to run once it receives an event indicating the SparkApplication object was added.

Configuration

The operator is typically deployed and run using the Helm chart. However, users can still run it outside a Kubernetes cluster and make it talk to the Kubernetes API server of a cluster by specifying path to kubeconfig, which can be done using the -kubeconfig flag.

The operator uses multiple workers in the SparkApplication controller. The number of worker threads are controlled using command-line flag -controller-threads which has a default value of 10.

The operator enables cache resynchronization so periodically the informers used by the operator will re-list existing objects it manages and re-trigger resource events. The resynchronization interval in seconds can be configured using the flag -resync-interval, with a default value of 30 seconds.

By default, the operator will install the CustomResourceDefinitions for the custom resources it manages. This can be disabled by setting the flag -install-crds=false, in which case the CustomResourceDefinitions can be installed manually using kubectl apply -f manifest/spark-operator-crds.yaml.

The mutating admission webhook is an optional component and can be enabled or disabled using the -enable-webhook flag, which defaults to false.

By default, the operator will manage custom resource objects of the managed CRD types for the whole cluster. It can be configured to manage only the custom resource objects in a specific namespace with the flag -namespace=<namespace>

Upgrade

To upgrade the the operator, e.g., to use a newer version container image with a new tag, run the following command with updated parameters for the Helm release:

$ helm upgrade <YOUR-HELM-RELEASE-NAME> --set operatorImageName=org/image --set operatorVersion=newTag

Refer to the Helm documentation for more details on helm upgrade.

About the Spark Job Namespace

The Spark Job Namespace value defines the namespace(s) where SparkApplications can be deployed. The Helm chart value for the Spark Job Namespace is sparkJobNamespace, and its default value is "", as defined in the Helm chart's README. Note that in the Kubernetes apimachinery project, the constants NamespaceAll and NamespaceNone are both defined as the empty string. In this case, the empty string represents NamespaceAll. When set to "", the Spark Operator supports deploying SparkApplications to all namespaces. The Helm chart will create a service account in the namespace where the spark-operator is deployed, but Helm skips setting up the RBAC for driver pods of your SparkApplications to be able to manipulate executor pods. In order to successfully deploy SparkApplications, you will need to ensure the driver pod's service account meets the criteria described in the service accounts for driver pods section.

On the other hand, if you installed the operator using the Helm chart and overrode the sparkJobNamespace to some other, pre-existing namespace, the Helm chart will create the necessary service account and RBAC in the specified namespace.

The Spark Operator uses the Spark Job Namespace to identify and filter relevant events for the SparkApplication CRD. If you specify a namespace for Spark Jobs, and then submit a SparkApplication resource to another namespace, the Spark Operator will filter out the event, and the resource will not get deployed. If you don't specify a namespace, the Spark Operator will see SparkApplication events for all namespaces, and will deploy them to the namespace requested in the create call.

About the Service Account for Driver Pods

A Spark driver pod need a Kubernetes service account in the pod's namespace that has permissions to create, get, list, and delete executor pods, and create a Kubernetes headless service for the driver. The driver will fail and exit without the service account, unless the default service account in the pod's namespace has the needed permissions. To submit and run a SparkApplication in a namespace, please make sure there is a service account with the permissions in the namespace and set .spec.driver.serviceAccount to the name of the service account. Please refer to spark-rbac.yaml for an example RBAC setup that creates a driver service account named spark in the default namespace, with a RBAC role binding giving the service account the needed permissions.

Enable Metric Exporting to Prometheus

The operator exposes a set of metrics via the metric endpoint to be scraped by Prometheus. The Helm chart by default installs the operator with the additional flag to enable metrics (-enable-metrics=true) as well as other annotations used by Prometheus to scrape the metric endpoint. To install the operator without metrics enabled, pass the appropriate flag during helm install:

$ helm install incubator/sparkoperator --namespace spark-operator --set enableMetrics=false

If enabled, the operator generates the following metrics:

Spark Application Metrics

Metric Description
spark_app_submit_count Total number of SparkApplication submitted by the Operator.
spark_app_success_count Total number of SparkApplication which completed successfully.
spark_app_failure_count Total number of SparkApplication which failed to complete.
spark_app_running_count Total number of SparkApplication which are currently running.
spark_app_success_execution_time_microseconds Execution time for applications which succeeded.
spark_app_failure_execution_time_microseconds Execution time for applications which failed.
spark_app_executor_success_count Total number of Spark Executors which completed successfully.
spark_app_executor_failure_count Total number of Spark Executors which failed.
spark_app_executor_running_count Total number of Spark Executors which are currently running.

Work Queue Metrics

Metric Description
spark_application_controller_depth Current depth of workqueue
spark_application_controller_adds Total number of adds handled by workqueue
spark_application_controller_latency Latency for workqueue
spark_application_controller_work_duration How long processing an item from workqueue takes
spark_application_controller_retries Total number of retries handled by workqueue
spark_application_controller_unfinished_work_seconds Unfinished work in seconds
spark_application_controller_longest_running_processor_microseconds Longest running processor in microseconds

The following is a list of all the configurations the operators supports for metrics:

-enable-metrics=true
-metrics-port=10254
-metrics-endpoint=/metrics
-metrics-prefix=myServiceName
-metrics-label=label1Key
-metrics-label=label2Key

All configs except -enable-metrics are optional. If port and/or endpoint are specified, please ensure that the annotations prometheus.io/port, prometheus.io/path and containerPort in spark-operator-with-metrics.yaml are updated as well.

A note about metrics-labels: In Prometheus, every unique combination of key-value label pair represents a new time series, which can dramatically increase the amount of data stored. Hence labels should not be used to store dimensions with high cardinality with potentially a large or unbounded value range.

Additionally, these metrics are best-effort for the current operator run and will be reset on an operator restart. Also some of these metrics are generated by listening to pod state updates for the driver/executors and deleting the pods outside the operator might lead to incorrect metric values for some of these metrics.

Driver UI Access and Ingress

The operator, by default, makes the Spark UI accessible by creating a service of type ClusterIP which exposes the UI. This is only accessible from within the cluster. The operator also supports creating an Ingress for the UI. This can be turned on by setting the ingress-url-format command-line flag. The ingress-url-format should be a template like {{$appName}}.ingress.cluster.com and the operator will replace the {{$appName}} with the appropriate appName.

The operator also sets both WebUIAddress which is accessible from within the cluster as well as WebUIIngressAddress as part of the DriverInfo field of the SparkApplication.

About the Mutating Admission Webhook

The Kubernetes Operator for Apache Spark comes with an optional mutating admission webhook for customizing Spark driver and executor pods based on the specification in SparkApplication objects, e.g., mounting user-specified ConfigMaps and volumes, and setting pod affinity/anti-affinity, and adding tolerations.

The webhook requires a X509 certificate for TLS for pod admission requests and responses between the Kubernetes API server and the webhook server running inside the operator. For that, the certificate and key files must be accessible by the webhook server. The location of these certs is configurable and they will be reloaded on a configurable period. The Kubernetes Operator for Spark ships with a tool at hack/gencerts.sh for generating the CA and server certificate and putting the certificate and key files into a secret named spark-webhook-certs in the namespace spark-operator. This secret will be mounted into the operator pod.

Run the following command to create the secret with a certificate and key files using a batch Job, and install the operator Deployment with the mutating admission webhook:

$ kubectl apply -f manifest/spark-operator-with-webhook.yaml

This will create a Deployment named sparkoperator and a Service named spark-webhook for the webhook in namespace spark-operator.

If the operator is installed via the Helm chart using the default settings (i.e. with webhook enabled), the above steps are all automated for you.

Mutating Admission Webhooks on a private GKE cluster

If you are deploying the operator on a GKE cluster with the Private cluster setting enabled, and you wish to deploy the cluster with the Mutating Admission Webhook, then make sure to change the webhookPort to 443. Alternatively you can choose to allow connections to the default port (8080).

By default, firewall rules restrict your cluster master to only initiate TCP connections to your nodes on ports 443 (HTTPS) and 10250 (kubelet). For some Kubernetes features, you might need to add firewall rules to allow access on additional ports. For example, in Kubernetes 1.9 and older, kubectl top accesses heapster, which needs a firewall rule to allow TCP connections on port 8080. To grant such access, you can add firewall rules. From the docs

To install the operator with a custom port, pass the appropriate flag during helm install:

$ helm install incubator/sparkoperator  --set sparkJobNamespace=spark --set enableWebhook=true --set webhookPort=443