From 3448531c7f3cd9592a3f62fdc790309186f19b69 Mon Sep 17 00:00:00 2001 From: Hannah Hunter Date: Wed, 2 Aug 2023 17:55:18 -0400 Subject: [PATCH 1/8] move observability Signed-off-by: Hannah Hunter --- .../en/operations/observability/_index.md | 14 ++ .../observability/health-checks/_index.md | 7 + .../observability/health-checks/app-health.md | 169 ++++++++++++++++ .../health-checks/sidecar-health.md | 101 ++++++++++ .../observability/logging/_index.md | 8 + .../observability/logging/fluentd.md | 189 ++++++++++++++++++ .../operations/observability/logging/logs.md | 133 ++++++++++++ .../observability/logging/newrelic.md | 78 ++++++++ .../observability/metrics/_index.md | 7 + .../observability/metrics/azure-monitor.md | 134 +++++++++++++ .../observability/metrics/grafana.md | 182 +++++++++++++++++ .../observability/metrics/metrics-overview.md | 109 ++++++++++ .../observability/metrics/newrelic.md | 43 ++++ .../observability/metrics/prometheus.md | 122 +++++++++++ .../observability/tracing/_index.md | 7 + .../observability/tracing/datadog.md | 55 +++++ .../observability/tracing/jaeger.md | 187 +++++++++++++++++ .../observability/tracing/newrelic.md | 114 +++++++++++ .../tracing/otel-collector/_index.md | 7 + .../open-telemetry-collector-appinsights.md | 72 +++++++ .../open-telemetry-collector.md | 74 +++++++ .../observability/tracing/setup-tracing.md | 80 ++++++++ .../observability/tracing/tracing-overview.md | 113 +++++++++++ .../tracing/w3c-tracing-overview.md | 90 +++++++++ .../observability/tracing/zipkin.md | 110 ++++++++++ 25 files changed, 2205 insertions(+) create mode 100644 daprdocs/content/en/operations/observability/_index.md create mode 100644 daprdocs/content/en/operations/observability/health-checks/_index.md create mode 100644 daprdocs/content/en/operations/observability/health-checks/app-health.md create mode 100644 daprdocs/content/en/operations/observability/health-checks/sidecar-health.md create mode 100644 daprdocs/content/en/operations/observability/logging/_index.md create mode 100644 daprdocs/content/en/operations/observability/logging/fluentd.md create mode 100644 daprdocs/content/en/operations/observability/logging/logs.md create mode 100644 daprdocs/content/en/operations/observability/logging/newrelic.md create mode 100644 daprdocs/content/en/operations/observability/metrics/_index.md create mode 100644 daprdocs/content/en/operations/observability/metrics/azure-monitor.md create mode 100644 daprdocs/content/en/operations/observability/metrics/grafana.md create mode 100644 daprdocs/content/en/operations/observability/metrics/metrics-overview.md create mode 100644 daprdocs/content/en/operations/observability/metrics/newrelic.md create mode 100644 daprdocs/content/en/operations/observability/metrics/prometheus.md create mode 100644 daprdocs/content/en/operations/observability/tracing/_index.md create mode 100644 daprdocs/content/en/operations/observability/tracing/datadog.md create mode 100644 daprdocs/content/en/operations/observability/tracing/jaeger.md create mode 100644 daprdocs/content/en/operations/observability/tracing/newrelic.md create mode 100644 daprdocs/content/en/operations/observability/tracing/otel-collector/_index.md create mode 100644 daprdocs/content/en/operations/observability/tracing/otel-collector/open-telemetry-collector-appinsights.md create mode 100644 daprdocs/content/en/operations/observability/tracing/otel-collector/open-telemetry-collector.md create mode 100644 daprdocs/content/en/operations/observability/tracing/setup-tracing.md create mode 100644 daprdocs/content/en/operations/observability/tracing/tracing-overview.md create mode 100644 daprdocs/content/en/operations/observability/tracing/w3c-tracing-overview.md create mode 100644 daprdocs/content/en/operations/observability/tracing/zipkin.md diff --git a/daprdocs/content/en/operations/observability/_index.md b/daprdocs/content/en/operations/observability/_index.md new file mode 100644 index 00000000000..26d1771d702 --- /dev/null +++ b/daprdocs/content/en/operations/observability/_index.md @@ -0,0 +1,14 @@ +--- +type: docs +title: "Observability" +linkTitle: "Observability" +weight: 60 +description: See and measure the message calls to components and between networked services +--- + +{{% alert title="More about Dapr Observability" color="primary" %}} + Learn more about how to use Dapr Observability Lock: + - Explore observability via any of the supporting [Dapr SDKs]({{< ref sdks >}}). + - Review the [Observability API reference documentation]({{< ref health_api.md >}}). + - Read the [general overview of the observability concept]({{< ref observability-concept >}}) in Dapr. +{{% /alert %}} diff --git a/daprdocs/content/en/operations/observability/health-checks/_index.md b/daprdocs/content/en/operations/observability/health-checks/_index.md new file mode 100644 index 00000000000..33f1e4bea03 --- /dev/null +++ b/daprdocs/content/en/operations/observability/health-checks/_index.md @@ -0,0 +1,7 @@ +--- +type: docs +title: "Health checks" +linkTitle: "Health checks" +weight: 100 +description: "How to setup health checks for the Dapr sidecar and your application" +--- \ No newline at end of file diff --git a/daprdocs/content/en/operations/observability/health-checks/app-health.md b/daprdocs/content/en/operations/observability/health-checks/app-health.md new file mode 100644 index 00000000000..193233dd17c --- /dev/null +++ b/daprdocs/content/en/operations/observability/health-checks/app-health.md @@ -0,0 +1,169 @@ +--- +type: docs +title: "App health checks" +linkTitle: "App health checks" +weight: 100 +description: Reacting to apps' health status changes +--- + +The app health checks feature allows probing for the health of your application and reacting to status changes. + +Applications can become unresponsive for a variety of reasons. For example, your application: +- Could be too busy to accept new work; +- Could have crashed; or +- Could be in a deadlock state. + +Sometimes the condition can be transitory, for example: +- If the app is just busy and will resume accepting new work eventually +- If the application is being restarted for whatever reason and is in its initialization phase + +App health checks are disabled by default. Once you enable app health checks, the Dapr runtime (sidecar) periodically polls your application via HTTP or gRPC calls. When it detects a failure in the app's health, Dapr stops accepting new work on behalf of the application by: + +- Unsubscribing from all pub/sub subscriptions +- Stopping all input bindings +- Short-circuiting all service-invocation requests, which terminate in the Dapr runtime and are not forwarded to the application + +These changes are meant to be temporary, and Dapr resumes normal operations once it detects that the application is responsive again. + +Diagram showing the app health feature. Running Dapr with app health enabled causes Dapr to periodically probe the app for its health. + +## App health checks vs platform-level health checks + +App health checks in Dapr are meant to be complementary to, and not replace, any platform-level health checks, like [liveness probes](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/) when running on Kubernetes. + +Platform-level health checks (or liveness probes) generally ensure that the application is running, and cause the platform to restart the application in case of failures. + +Unlike platform-level health checks, Dapr's app health checks focus on pausing work to an application that is currently unable to accept it, but is expected to be able to resume accepting work *eventually*. Goals include: + +- Not bringing more load to an application that is already overloaded. +- Do the "polite" thing by not taking messages from queues, bindings, or pub/sub brokers when Dapr knows the application won't be able to process them. + +In this regard, Dapr's app health checks are "softer", waiting for an application to be able to process work, rather than terminating the running process in a "hard" way. + +{{% alert title="Note" color="primary" %}} +For Kubernetes, a failing app health check won't remove a pod from service discovery: this remains the responsibility of the Kubernetes liveness probe, _not_ Dapr. +{{% /alert %}} + +## Configuring app health checks + +App health checks are disabled by default, but can be enabled with either: + +- The `--enable-app-health-check` CLI flag; or +- The `dapr.io/enable-app-health-check: true` annotation when running on Kubernetes. + +Adding this flag is both necessary and sufficient to enable app health checks with the default options. + +The full list of options are listed in this table: + +| CLI flags | Kubernetes deployment annotation | Description | Default value | +| ----------------------------- | ----------------------------------- | ----------- | ------------- | +| `--enable-app-health-check` | `dapr.io/enable-app-health-check` | Boolean that enables the health checks | Disabled | +| [`--app-health-check-path`]({{< ref "app-health.md#health-check-paths" >}}) | `dapr.io/app-health-check-path` | Path that Dapr invokes for health probes when the app channel is HTTP (this value is ignored if the app channel is using gRPC) | `/healthz` | +| [`--app-health-probe-interval`]({{< ref "app-health.md#intervals-timeouts-and-thresholds" >}}) | `dapr.io/app-health-probe-interval` | Number of *seconds* between each health probe | `5` | +| [`--app-health-probe-timeout`]({{< ref "app-health.md#intervals-timeouts-and-thresholds" >}}) | `dapr.io/app-health-probe-timeout` | Timeout in *milliseconds* for health probe requests | `500` | +| [`--app-health-threshold`]({{< ref "app-health.md#intervals-timeouts-and-thresholds" >}}) | `dapr.io/app-health-threshold` | Max number of consecutive failures before the app is considered unhealthy | `3` | + +> See the [full Dapr arguments and annotations reference]({{< ref arguments-annotations-overview >}}) for all options and how to enable them. + +Additionally, app health checks are impacted by the protocol used for the app channel, which is configured with the following flag or annotation: + +| CLI flag | Kubernetes deployment annotation | Description | Default value | +| ----------------------------- | ----------------------------------- | ----------- | ------------- | +| [`--app-protocol`]({{< ref "app-health.md#health-check-paths" >}}) | `dapr.io/app-protocol` | Protocol used for the app channel. supported values are `http`, `grpc`, `https`, `grpcs`, and `h2c` (HTTP/2 Cleartext). | `http` | + +{{% alert title="Note" color="primary" %}} +A low app health probe timeout value can classify an application as unhealthy if it experiences a sudden high load, causing the response time to degrade. If this happens, increase the `dapr.io/app-health-probe-timeout` value. +{{% /alert %}} + +### Health check paths + +#### HTTP +When using HTTP (including `http`, `https`, and `h2c`) for `app-protocol`, Dapr performs health probes by making an HTTP call to the path specified in `app-health-check-path`, which is `/health` by default. + +For your app to be considered healthy, the response must have an HTTP status code in the 200-299 range. Any other status code is considered a failure. Dapr is only concerned with the status code of the response, and ignores any response header or body. + +#### gRPC +When using gRPC for the app channel (`app-protocol` set to `grpc` or `grpcs`), Dapr invokes the method `/dapr.proto.runtime.v1.AppCallbackHealthCheck/HealthCheck` in your application. Most likely, you will use a Dapr SDK to implement the handler for this method. + +While responding to a health probe request, your app *may* decide to perform additional internal health checks to determine if it's ready to process work from the Dapr runtime. However, this is not required; it's a choice that depends on your application's needs. + +### Intervals, timeouts, and thresholds + +#### Intervals +By default, when app health checks are enabled, Dapr probes your application every 5 seconds. You can configure the interval, in seconds, with `app-health-probe-interval`. These probes happen regularly, regardless of whether your application is healthy or not. + +#### Timeouts +When the Dapr runtime (sidecar) is initially started, Dapr waits for a successful health probe before considering the app healthy. This means that pub/sub subscriptions, input bindings, and service invocation requests won't be enabled for your application until this first health check is complete and successful. + +Health probe requests are considered successful if the application sends a successful response (as explained above) within the timeout configured in `app-health-probe-timeout`. The default value is 500, corresponding to 500 milliseconds (half a second). + +#### Thresholds +Before Dapr considers an app to have entered an unhealthy state, it will wait for `app-health-threshold` consecutive failures, whose default value is 3. This default value means that your application must fail health probes 3 times *in a row* to be considered unhealthy. + +If you set the threshold to 1, any failure causes Dapr to assume your app is unhealthy and will stop delivering work to it. + +A threshold greater than 1 can help exclude transient failures due to external circumstances. The right value for your application depends on your requirements. + +Thresholds only apply to failures. A single successful response is enough for Dapr to consider your app to be healthy and resume normal operations. + +## Example + +{{< tabs "Self-Hosted (CLI)" Kubernetes >}} + +{{% codetab %}} + +Use the CLI flags with the `dapr run` command to enable app health checks: + +```sh +dapr run \ + --app-id my-app \ + --app-port 7001 \ + --app-protocol http \ + --enable-app-health-check \ + --app-health-check-path=/healthz \ + --app-health-probe-interval 3 \ + --app-health-probe-timeout 200 \ + --app-health-threshold 2 \ + -- \ + +``` + +{{% /codetab %}} + +{{% codetab %}} + +To enable app health checks in Kubernetes, add the relevant annotations to your Deployment: + +```yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + name: my-app + labels: + app: my-app +spec: + template: + metadata: + labels: + app: my-app + annotations: + dapr.io/enabled: "true" + dapr.io/app-id: "my-app" + dapr.io/app-port: "7001" + dapr.io/app-protocol: "http" + dapr.io/enable-app-health-check: "true" + dapr.io/app-health-check-path: "/healthz" + dapr.io/app-health-probe-interval: "3" + dapr.io/app-health-probe-timeout: "200" + dapr.io/app-health-threshold: "2" +``` + +{{% /codetab %}} + +{{< /tabs >}} + +## Demo + +Watch this video for an [overview of using app health checks](https://youtu.be/srczBuOsAkI?t=533): + + diff --git a/daprdocs/content/en/operations/observability/health-checks/sidecar-health.md b/daprdocs/content/en/operations/observability/health-checks/sidecar-health.md new file mode 100644 index 00000000000..b81efeef9b9 --- /dev/null +++ b/daprdocs/content/en/operations/observability/health-checks/sidecar-health.md @@ -0,0 +1,101 @@ +--- +type: docs +title: "Sidecar health" +linkTitle: "Sidecar health" +weight: 200 +description: Dapr sidecar health checks +--- + +Dapr provides a way to determine its health using an [HTTP `/healthz` endpoint]({{< ref health_api.md >}}). With this endpoint, the *daprd* process, or sidecar, can be: + +- Probed for its health +- Determined for readiness and liveness + +In this guide, you learn how the Dapr `/healthz` endpoint integrate with health probes from the application hosting platform (for example, Kubernetes). + +When deploying Dapr to a hosting platform like Kubernetes, the Dapr health endpoint is automatically configured for you. + +{{% alert title="Note" color="primary" %}} +Dapr actors also have a health API endpoint where Dapr probes the application for a response to a signal from Dapr that the actor application is healthy and running. See [actor health API]({{< ref "actors_api.md#health-check" >}}). +{{% /alert %}} + +## Health endpoint: Integration with Kubernetes + +Kubernetes uses *readiness* and *liveness* probes to determines the health of the container. + +### Liveness +The kubelet uses liveness probes to know when to restart a container. For example, liveness probes could catch a deadlock (a running application that is unable to make progress). Restarting a container in such a state can help to make the application more available despite having bugs. + +#### How to configure a liveness probe in Kubernetes + +In the pod configuration file, the liveness probe is added in the containers spec section as shown below: + +```yaml + livenessProbe: + httpGet: + path: /healthz + port: 8080 + initialDelaySeconds: 3 + periodSeconds: 3 +``` + +In the above example, the `periodSeconds` field specifies that the kubelet should perform a liveness probe every 3 seconds. The `initialDelaySeconds` field tells the kubelet that it should wait 3 seconds before performing the first probe. To perform a probe, the kubelet sends an HTTP GET request to the server that is running in the container and listening on port 8080. If the handler for the server's `/healthz` path returns a success code, the kubelet considers the container to be alive and healthy. If the handler returns a failure code, the kubelet kills the container and restarts it. + +Any HTTP status code between 200 and 399 indicates success; any other status code indicates failure. + +### Readiness +The kubelet uses readiness probes to know when a container is ready to start accepting traffic. A pod is considered ready when all of its containers are ready. One use of this readiness signal is to control which pods are used as backends for Kubernetes services. When a pod is not ready, it is removed from Kubernetes service load balancers. + +{{% alert title="Note" color="primary" %}} +The Dapr sidecar will be in ready state once the application is accessible on its configured port. The application cannot access the Dapr components during application start up/initialization. +{{% /alert %}} + +#### How to configure a readiness probe in Kubernetes + +Readiness probes are configured similarly to liveness probes. The only difference is that you use the `readinessProbe` field instead of the `livenessProbe` field: + +```yaml + readinessProbe: + httpGet: + path: /healthz + port: 8080 + initialDelaySeconds: 3 + periodSeconds: 3 +``` + +### Sidecar Injector + +When integrating with Kubernetes, the Dapr sidecar is injected with a Kubernetes probe configuration telling it to use the Dapr `healthz` endpoint. This is done by the "Sidecar Injector" system service. The integration with the kubelet is shown in the diagram below. + +Diagram of Dapr services interacting + +#### How the Dapr sidecar health endpoint is configured with Kubernetes + +As mentioned above, this configuration is done automatically by the Sidecar Injector service. This section describes the specific values that are set on the liveness and readiness probes. + +Dapr has its HTTP health endpoint `/v1.0/healthz` on port 3500. This can be used with Kubernetes for readiness and liveness probe. When the Dapr sidecar is injected, the readiness and liveness probes are configured in the pod configuration file with the following values: + +```yaml + livenessProbe: + httpGet: + path: v1.0/healthz + port: 3500 + initialDelaySeconds: 5 + periodSeconds: 10 + timeoutSeconds : 5 + failureThreshold : 3 + readinessProbe: + httpGet: + path: v1.0/healthz + port: 3500 + initialDelaySeconds: 5 + periodSeconds: 10 + timeoutSeconds : 5 + failureThreshold: 3 +``` + +## Related links + +- [Endpoint health API]({{< ref health_api.md >}}) +- [Actor health API]({{< ref "actors_api.md#health-check" >}}) +- [Kubernetes probe configuration parameters](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/) diff --git a/daprdocs/content/en/operations/observability/logging/_index.md b/daprdocs/content/en/operations/observability/logging/_index.md new file mode 100644 index 00000000000..444f4cc9c97 --- /dev/null +++ b/daprdocs/content/en/operations/observability/logging/_index.md @@ -0,0 +1,8 @@ +--- +type: docs +title: "Logging" +linkTitle: "Logging" +weight: 400 +description: "How to setup loggings for Dapr sidecar, and your application" +--- + diff --git a/daprdocs/content/en/operations/observability/logging/fluentd.md b/daprdocs/content/en/operations/observability/logging/fluentd.md new file mode 100644 index 00000000000..b456b8d953e --- /dev/null +++ b/daprdocs/content/en/operations/observability/logging/fluentd.md @@ -0,0 +1,189 @@ +--- +type: docs +title: "How-To: Set up Fluentd, Elastic search and Kibana in Kubernetes" +linkTitle: "FluentD" +weight: 2000 +description: "How to install Fluentd, Elastic Search, and Kibana to search logs in Kubernetes" +--- + +## Prerequisites + +- Kubernetes (> 1.14) +- [kubectl](https://kubernetes.io/docs/tasks/tools/) +- [Helm 3](https://helm.sh/) + +## Install Elastic search and Kibana + +1. Create a Kubernetes namespace for monitoring tools + + ```bash + kubectl create namespace dapr-monitoring + ``` + +2. Add the helm repo for Elastic Search + + ```bash + helm repo add elastic https://helm.elastic.co + helm repo update + ``` + +3. Install Elastic Search using Helm + + By default, the chart creates 3 replicas which must be on different nodes. If your cluster has fewer than 3 nodes, specify a smaller number of replicas. For example, this sets the number of replicas to 1: + + ```bash + helm install elasticsearch elastic/elasticsearch --version 7.17.3 -n dapr-monitoring --set replicas=1 + ``` + + Otherwise: + + ```bash + helm install elasticsearch elastic/elasticsearch --version 7.17.3 -n dapr-monitoring + ``` + + If you are using minikube or simply want to disable persistent volumes for development purposes, you can do so by using the following command: + + ```bash + helm install elasticsearch elastic/elasticsearch --version 7.17.3 -n dapr-monitoring --set persistence.enabled=false,replicas=1 + ``` + +4. Install Kibana + + ```bash + helm install kibana elastic/kibana --version 7.17.3 -n dapr-monitoring + ``` + +5. Ensure that Elastic Search and Kibana are running in your Kubernetes cluster + + ```bash + $ kubectl get pods -n dapr-monitoring + NAME READY STATUS RESTARTS AGE + elasticsearch-master-0 1/1 Running 0 6m58s + kibana-kibana-95bc54b89-zqdrk 1/1 Running 0 4m21s + ``` + +## Install Fluentd + +1. Install config map and Fluentd as a daemonset + + Download these config files: + - [fluentd-config-map.yaml](/docs/fluentd-config-map.yaml) + - [fluentd-dapr-with-rbac.yaml](/docs/fluentd-dapr-with-rbac.yaml) + + > Note: If you already have Fluentd running in your cluster, please enable the nested json parser so that it can parse JSON-formatted logs from Dapr. + + Apply the configurations to your cluster: + + ```bash + kubectl apply -f ./fluentd-config-map.yaml + kubectl apply -f ./fluentd-dapr-with-rbac.yaml + ``` + +2. Ensure that Fluentd is running as a daemonset. The number of FluentD instances should be the same as the number of cluster nodes. In the example below, there is only one node in the cluster: + + ```bash + $ kubectl get pods -n kube-system -w + NAME READY STATUS RESTARTS AGE + coredns-6955765f44-cxjxk 1/1 Running 0 4m41s + coredns-6955765f44-jlskv 1/1 Running 0 4m41s + etcd-m01 1/1 Running 0 4m48s + fluentd-sdrld 1/1 Running 0 14s + ``` + +## Install Dapr with JSON formatted logs + +1. Install Dapr with enabling JSON-formatted logs + + ```bash + helm repo add dapr https://dapr.github.io/helm-charts/ + helm repo update + helm install dapr dapr/dapr --namespace dapr-system --set global.logAsJson=true + ``` + +2. Enable JSON formatted log in Dapr sidecar + + Add the `dapr.io/log-as-json: "true"` annotation to your deployment yaml. For example: + + ```yaml + apiVersion: apps/v1 + kind: Deployment + metadata: + name: pythonapp + namespace: default + labels: + app: python + spec: + replicas: 1 + selector: + matchLabels: + app: python + template: + metadata: + labels: + app: python + annotations: + dapr.io/enabled: "true" + dapr.io/app-id: "pythonapp" + dapr.io/log-as-json: "true" + ... + ``` + +## Search logs + +> Note: Elastic Search takes a time to index the logs that Fluentd sends. + +1. Port-forward from localhost to `svc/kibana-kibana` + + ```bash + $ kubectl port-forward svc/kibana-kibana 5601 -n dapr-monitoring + Forwarding from 127.0.0.1:5601 -> 5601 + Forwarding from [::1]:5601 -> 5601 + Handling connection for 5601 + Handling connection for 5601 + ``` + +2. Browse to `http://localhost:5601` + +3. Expand the drop-down menu and click **Management → Stack Management** + + ![Stack Management item under Kibana Management menu options](/images/kibana-1.png) + +4. On the Stack Management page, select **Data → Index Management** and wait until `dapr-*` is indexed. + + ![Index Management view on Kibana Stack Management page](/images/kibana-2.png) + +5. Once `dapr-*` is indexed, click on **Kibana → Index Patterns** and then the **Create index pattern** button. + + ![Kibana create index pattern button](/images/kibana-3.png) + +6. Define a new index pattern by typing `dapr*` into the **Index Pattern name** field, then click the **Next step** button to continue. + + ![Kibana define an index pattern page](/images/kibana-4.png) + +7. Configure the primary time field to use with the new index pattern by selecting the `@timestamp` option from the **Time field** drop-down. Click the **Create index pattern** button to complete creation of the index pattern. + + ![Kibana configure settings page for creating an index pattern](/images/kibana-5.png) + +8. The newly created index pattern should be shown. Confirm that the fields of interest such as `scope`, `type`, `app_id`, `level`, etc. are being indexed by using the search box in the **Fields** tab. + + > Note: If you cannot find the indexed field, please wait. The time it takes to search across all indexed fields depends on the volume of data and size of the resource that the elastic search is running on. + + ![View of created Kibana index pattern](/images/kibana-6.png) + +9. To explore the indexed data, expand the drop-down menu and click **Analytics → Discover**. + + ![Discover item under Kibana Analytics menu options](/images/kibana-7.png) + +10. In the search box, type in a query string such as `scope:*` and click the **Refresh** button to view the results. + + > Note: This can take a long time. The time it takes to return all results depends on the volume of data and size of the resource that the elastic search is running on. + + ![Using the search box in the Kibana Analytics Discover page](/images/kibana-8.png) + +## References + +* [Fluentd for Kubernetes](https://docs.fluentd.org/v/0.12/articles/kubernetes-fluentd) +* [Elastic search helm chart](https://github.com/elastic/helm-charts/tree/master/elasticsearch) +* [Kibana helm chart](https://github.com/elastic/helm-charts/tree/master/kibana) +* [Kibana Query Language](https://www.elastic.co/guide/en/kibana/current/kuery-query.html) +* [Troubleshooting using Logs]({{< ref "logs-troubleshooting.md" >}}) diff --git a/daprdocs/content/en/operations/observability/logging/logs.md b/daprdocs/content/en/operations/observability/logging/logs.md new file mode 100644 index 00000000000..397588b6969 --- /dev/null +++ b/daprdocs/content/en/operations/observability/logging/logs.md @@ -0,0 +1,133 @@ +--- +type: docs +title: "Logs" +linkTitle: "Logs" +weight: 1000 +description: "Understand Dapr logging" +--- + +Dapr produces structured logs to stdout, either in plain-text or JSON-formatted. By default, all Dapr processes (runtime, or sidecar, and all control plane services) write logs to the console (stdout) in plain-text. To enable JSON-formatted logging, you need to add the `--log-as-json` command flag when running Dapr processes. + +{{% alert title="Note" color="primary" %}} +If you want to use a search engine such as Elastic Search or Azure Monitor to search the logs, it is strongly recommended to use JSON-formatted logs which the log collector and search engine can parse using the built-in JSON parser. +{{% /alert %}} + +## Log schema + +Dapr produces logs based on the following schema: + +| Field | Description | Example | +|-------|-------------------|---------| +| time | ISO8601 Timestamp | `2011-10-05T14:48:00.000Z` | +| level | Log Level (info/warn/debug/error) | `info` | +| type | Log Type | `log` | +| msg | Log Message | `hello dapr!` | +| scope | Logging Scope | `dapr.runtime` | +| instance | Container Name | `dapr-pod-xxxxx` | +| app_id | Dapr App ID | `dapr-app` | +| ver | Dapr Runtime Version | `1.9.0` | + +API logging may add other structured fields, as described in the [documentation for API logging]({{< ref "api-logs-troubleshooting.md" >}}). + +## Plain text and JSON formatted logs + +* Plain-text log examples + +```bash +time="2022-11-01T17:08:48.303776-07:00" level=info msg="starting Dapr Runtime -- version 1.9.0 -- commit v1.9.0-g5dfcf2e" instance=dapr-pod-xxxx scope=dapr.runtime type=log ver=1.9.0 +time="2022-11-01T17:08:48.303913-07:00" level=info msg="log level set to: info" instance=dapr-pod-xxxx scope=dapr.runtime type=log ver=1.9.0 +``` + +* JSON-formatted log examples + +```json +{"instance":"dapr-pod-xxxx","level":"info","msg":"starting Dapr Runtime -- version 1.9.0 -- commit v1.9.0-g5dfcf2e","scope":"dapr.runtime","time":"2022-11-01T17:09:45.788005Z","type":"log","ver":"1.9.0"} +{"instance":"dapr-pod-xxxx","level":"info","msg":"log level set to: info","scope":"dapr.runtime","time":"2022-11-01T17:09:45.788075Z","type":"log","ver":"1.9.0"} +``` + +## Log formats + +Dapr supports printing either plain-text, the default, or JSON-formatted logs. + +To use JSON-formatted logs, you need to add additional configuration options when you install Dapr and when deploy your apps. The recommendation is to use JSON-formatted logs because most log collectors and search engines can parse JSON more easily with built-in parsers. + +## Enabling JSON logging with the Dapr CLI + +When using the Dapr CLI to run an application, pass the `--log-as-json` option to enable JSON-formatted logs, for example: + +```sh +dapr run \ + --app-id orderprocessing \ + --resources-path ./components/ \ + --log-as-json \ + -- python3 OrderProcessingService.py +``` + +## Enabling JSON logging in Kubernetes + +The following steps describe how to configure JSON-formatted logs for Kubernetes + +### Dapr control plane + +All services in the Dapr control plane (such as `operator`, `sentry`, etc) support a `--log-as-json` option to enable JSON-formatted logging. + +If you're deploying Dapr to Kubernetes using a Helm chart, you can enable JSON-formatted logs for Dapr system services by passing the `--set global.logAsJson=true` option; for example: + +```bash +helm upgrade --install dapr \ + dapr/dapr \ + --namespace dapr-system \ + --set global.logAsJson=true +``` + +### Enable JSON-formatted log for Dapr sidecars + +You can enable JSON-formatted logs in Dapr sidecars by adding the `dapr.io/log-as-json: "true"` annotation to the deployment, for example: + +```yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + name: pythonapp + labels: + app: python +spec: + selector: + matchLabels: + app: python + template: + metadata: + labels: + app: python + annotations: + dapr.io/enabled: "true" + dapr.io/app-id: "pythonapp" + # This enables JSON-formatted logging + dapr.io/log-as-json: "true" +... +``` + +## API Logging + +API logging enables you to see the API calls your application makes to the Dapr sidecar, to debug issues or monitor the behavior of your application. You can combine both Dapr API logging with Dapr log events. + +See [configure and view Dapr Logs]({{< ref "logs-troubleshooting.md" >}}) and [configure and view Dapr API Logs]({{< ref "api-logs-troubleshooting.md" >}}) for more information. + +## Log collectors + +If you run Dapr in a Kubernetes cluster, [Fluentd](https://www.fluentd.org/) is a popular container log collector. You can use Fluentd with a [JSON parser plugin](https://docs.fluentd.org/parser/json) to parse Dapr JSON-formatted logs. This [how-to]({{< ref fluentd.md >}}) shows how to configure Fluentd in your cluster. + +If you are using Azure Kubernetes Service, you can use the built-in agent to collect logs with Azure Monitor without needing to install Fluentd. + +## Search engines + +If you use [Fluentd](https://www.fluentd.org/), we recommend using Elastic Search and Kibana. This [how-to]({{< ref fluentd.md >}}) shows how to set up Elastic Search and Kibana in your Kubernetes cluster. + +If you are using the Azure Kubernetes Service, you can use [Azure Monitor for containers](https://docs.microsoft.com/azure/azure-monitor/insights/container-insights-overview) without installing any additional monitoring tools. Also read [How to enable Azure Monitor for containers](https://docs.microsoft.com/azure/azure-monitor/insights/container-insights-onboard) + +## References + +- [How-to: Set up Fleuntd, Elastic search, and Kibana]({{< ref fluentd.md >}}) +- [How-to: Set up Azure Monitor in Azure Kubernetes Service]({{< ref azure-monitor.md >}}) +- [Configure and view Dapr Logs]({{< ref "logs-troubleshooting.md" >}}) +- [Configure and view Dapr API Logs]({{< ref "api-logs-troubleshooting.md" >}}) diff --git a/daprdocs/content/en/operations/observability/logging/newrelic.md b/daprdocs/content/en/operations/observability/logging/newrelic.md new file mode 100644 index 00000000000..9261d09ab44 --- /dev/null +++ b/daprdocs/content/en/operations/observability/logging/newrelic.md @@ -0,0 +1,78 @@ +--- +type: docs +title: "How-To: Set-up New Relic for Dapr logging" +linkTitle: "New Relic" +weight: 3000 +description: "Set-up New Relic for Dapr logging" +--- + +## Prerequisites + +- Perpetually [free New Relic account](https://newrelic.com/signup?ref=dapr), 100 GB/month of free data ingest, 1 free full access user, unlimited free basic users + +## Background + +New Relic offers a [Fluent Bit](https://fluentbit.io/) output [plugin](https://github.com/newrelic/newrelic-fluent-bit-output) to easily forward your logs to [New Relic Logs](https://github.com/newrelic/newrelic-fluent-bit-output). This plugin is also provided in a standalone Docker image that can be installed in a Kubernetes cluster in the form of a DaemonSet, which we refer as the Kubernetes plugin. + +This document explains how to install it in your cluster, either using a Helm chart (recommended), or manually by applying Kubernetes manifests. + +## Installation + +### Install using the Helm chart (recommended) + +1. Install Helm following the official instructions. + +2. Add the New Relic official Helm chart repository following these instructions + +3. Run the following command to install the New Relic Logging Kubernetes plugin via Helm, replacing the placeholder value YOUR_LICENSE_KEY with your [New Relic license key](https://docs.newrelic.com/docs/accounts/accounts-billing/account-setup/new-relic-license-key/): + +- Helm 3 + ```bash + helm install newrelic-logging newrelic/newrelic-logging --set licenseKey=YOUR_LICENSE_KEY + ``` + +- Helm 2 + ```bash + helm install newrelic/newrelic-logging --name newrelic-logging --set licenseKey=YOUR_LICENSE_KEY + ``` + +For EU users, add `--set endpoint=https://log-api.eu.newrelic.com/log/v1 to any of the helm install commands above. + +By default, tailing is set to /var/log/containers/*.log. To change this setting, provide your preferred path by adding --set fluentBit.path=DESIRED_PATH to any of the helm install commands above. + +### Install the Kubernetes manifest + +1. Download the following 3 manifest files into your current working directory: + + ```bash + curl https://raw.githubusercontent.com/newrelic/helm-charts/master/charts/newrelic-logging/k8s/fluent-conf.yml > fluent-conf.yml + curl https://raw.githubusercontent.com/newrelic/helm-charts/master/charts/newrelic-logging/k8s/new-relic-fluent-plugin.yml > new-relic-fluent-plugin.yml + curl https://raw.githubusercontent.com/newrelic/helm-charts/master/charts/newrelic-logging/k8s/rbac.yml > rbac.yml + ``` + +2. In the downloaded new-relic-fluent-plugin.yml file, replace the placeholder value LICENSE_KEY with your New Relic license key. + + For EU users, replace the ENDPOINT environment variable to https://log-api.eu.newrelic.com/log/v1. + +3. Once the License key has been added, run the following command in your terminal or command-line interface: + ```bash + kubectl apply -f . + ``` + +4. [OPTIONAL] You can configure how the plugin parses the data by editing the parsers.conf section in the fluent-conf.yml file. For more information, see Fluent Bit's documentation on Parsers configuration. + + By default, tailing is set to /var/log/containers/*.log. To change this setting, replace the default path with your preferred path in the new-relic-fluent-plugin.yml file. + +## View Logs + +![Dapr Annotations](/images/nr-logging-1.png) + +![Search](/images/nr-logging-2.png) + +## Related Links/References + +* [New Relic Account Signup](https://newrelic.com/signup) +* [Telemetry Data Platform](https://newrelic.com/platform/telemetry-data-platform) +* [New Relic Logging](https://github.com/newrelic/helm-charts/tree/master/charts/newrelic-logging) +* [Types of New Relic API keys](https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/) +* [Alerts and Applied Intelligence](https://docs.newrelic.com/docs/alerts-applied-intelligence/overview/) diff --git a/daprdocs/content/en/operations/observability/metrics/_index.md b/daprdocs/content/en/operations/observability/metrics/_index.md new file mode 100644 index 00000000000..dd7496e7276 --- /dev/null +++ b/daprdocs/content/en/operations/observability/metrics/_index.md @@ -0,0 +1,7 @@ +--- +type: docs +title: "Metrics" +linkTitle: "Metrics" +weight: 300 +description: "How to view Dapr metrics" +--- diff --git a/daprdocs/content/en/operations/observability/metrics/azure-monitor.md b/daprdocs/content/en/operations/observability/metrics/azure-monitor.md new file mode 100644 index 00000000000..3011ef399b4 --- /dev/null +++ b/daprdocs/content/en/operations/observability/metrics/azure-monitor.md @@ -0,0 +1,134 @@ +--- +type: docs +title: "How-To: Set up Azure Monitor to search logs and collect metrics" +linkTitle: "Azure Monitor" +weight: 7000 +description: "Enable Dapr metrics and logs with Azure Monitor for Azure Kubernetes Service (AKS)" +--- + +## Prerequisites + +- [Azure Kubernetes Service](https://docs.microsoft.com/azure/aks/) +- [Enable Azure Monitor For containers in AKS](https://docs.microsoft.com/azure/azure-monitor/insights/container-insights-overview) +- [kubectl](https://kubernetes.io/docs/tasks/tools/) +- [Helm 3](https://helm.sh/) + +## Enable Prometheus metric scrape using config map + +1. Make sure that Azure Monitor Agents (AMA) are running. + + ```bash + $ kubectl get pods -n kube-system + NAME READY STATUS RESTARTS AGE + ... + ama-logs-48kpv 2/2 Running 0 2d13h + ama-logs-mx24c 2/2 Running 0 2d13h + ama-logs-rs-f9bbb9898-vbt6k 1/1 Running 0 30h + ama-logs-sm2mz 2/2 Running 0 2d13h + ama-logs-z7p4c 2/2 Running 0 2d13h + ... + ``` + +1. Apply config map to enable Prometheus metrics endpoint scrape. + + You can use [azm-config-map.yaml](/docs/azm-config-map.yaml) to enable Prometheus metrics endpoint scrape. + + If you installed Dapr to a different namespace, you need to change the `monitor_kubernetes_pod_namespaces` array values. For example: + + ```yaml + ... + prometheus-data-collection-settings: |- + [prometheus_data_collection_settings.cluster] + interval = "1m" + monitor_kubernetes_pods = true + monitor_kubernetes_pods_namespaces = ["dapr-system", "default"] + [prometheus_data_collection_settings.node] + interval = "1m" + ... + ``` + + Apply config map: + + ```bash + kubectl apply -f ./azm-config.map.yaml + ``` + +## Install Dapr with JSON formatted logs + +1. Install Dapr with enabling JSON-formatted logs. + + ```bash + helm install dapr dapr/dapr --namespace dapr-system --set global.logAsJson=true + ``` + +1. Enable JSON formatted log in Dapr sidecar and add Prometheus annotations. + + > Note: The Azure Monitor Agents (AMA) only sends the metrics if the Prometheus annotations are set. + + Add `dapr.io/log-as-json: "true"` annotation to your deployment yaml. + + Example: + + ```yaml + apiVersion: apps/v1 + kind: Deployment + metadata: + name: pythonapp + namespace: default + labels: + app: python + spec: + replicas: 1 + selector: + matchLabels: + app: python + template: + metadata: + labels: + app: python + annotations: + dapr.io/enabled: "true" + dapr.io/app-id: "pythonapp" + dapr.io/log-as-json: "true" + prometheus.io/scrape: "true" + prometheus.io/port: "9090" + prometheus.io/path: "/" + + ... + ``` + +## Search metrics and logs with Azure Monitor + +1. Go to Azure Monitor in the Azure portal. + +1. Search Dapr **Logs**. + + Here is an example query, to parse JSON formatted logs and query logs from Dapr system processes. + + ``` + ContainerLog + | extend parsed=parse_json(LogEntry) + | project Time=todatetime(parsed['time']), app_id=parsed['app_id'], scope=parsed['scope'],level=parsed['level'], msg=parsed['msg'], type=parsed['type'], ver=parsed['ver'], instance=parsed['instance'] + | where level != "" + | sort by Time + ``` + +1. Search **Metrics**. + + This query, queries `process_resident_memory_bytes` Prometheus metrics for Dapr system processes and renders timecharts. + + ``` + InsightsMetrics + | where Namespace == "prometheus" and Name == "process_resident_memory_bytes" + | extend tags=parse_json(Tags) + | project TimeGenerated, Name, Val, app=tostring(tags['app']) + | summarize memInBytes=percentile(Val, 99) by bin(TimeGenerated, 1m), app + | where app startswith "dapr-" + | render timechart + ``` + +## References + +- [Configure scraping of Prometheus metrics with Azure Monitor for containers](https://docs.microsoft.com/azure/azure-monitor/insights/container-insights-prometheus-integration) +- [Configure agent data collection for Azure Monitor for containers](https://docs.microsoft.com/azure/azure-monitor/insights/container-insights-agent-config) +- [Azure Monitor Query](https://docs.microsoft.com/azure/azure-monitor/log-query/query-language) diff --git a/daprdocs/content/en/operations/observability/metrics/grafana.md b/daprdocs/content/en/operations/observability/metrics/grafana.md new file mode 100644 index 00000000000..5d3949552ed --- /dev/null +++ b/daprdocs/content/en/operations/observability/metrics/grafana.md @@ -0,0 +1,182 @@ +--- +type: docs +title: "How-To: Observe metrics with Grafana" +linkTitle: "Grafana dashboards" +weight: 5000 +description: "How to view Dapr metrics in a Grafana dashboard." +--- + +## Available dashboards + +{{< tabs "System Service" "Sidecars" "Actors" >}} + +{{% codetab %}} +The `grafana-system-services-dashboard.json` template shows Dapr system component status, dapr-operator, dapr-sidecar-injector, dapr-sentry, and dapr-placement: + +Screenshot of the system service dashboard +{{% /codetab %}} + +{{% codetab %}} +The `grafana-sidecar-dashboard.json` template shows Dapr sidecar status, including sidecar health/resources, throughput/latency of HTTP and gRPC, Actor, mTLS, etc.: + +Screenshot of the sidecar dashboard +{{% /codetab %}} + +{{% codetab %}} +The `grafana-actor-dashboard.json` template shows Dapr Sidecar status, actor invocation throughput/latency, timer/reminder triggers, and turn-based concurrnecy: + +Screenshot of the actor dashboard +{{% /codetab %}} + +{{< /tabs >}} + +## Pre-requisites + +- [Setup Prometheus]({{}}) + +## Setup on Kubernetes + +### Install Grafana + +1. Add the Grafana Helm repo: + + ```bash + helm repo add grafana https://grafana.github.io/helm-charts + helm repo update + ``` + +1. Install the chart: + + ```bash + helm install grafana grafana/grafana -n dapr-monitoring + ``` + + {{% alert title="Note" color="primary" %}} + If you are Minikube user or want to disable persistent volume for development purpose, you can disable it by using the following command instead: + + ```bash + helm install grafana grafana/grafana -n dapr-monitoring --set persistence.enabled=false + ``` + {{% /alert %}} + + +1. Retrieve the admin password for Grafana login: + + ```bash + kubectl get secret --namespace dapr-monitoring grafana -o jsonpath="{.data.admin-password}" | base64 --decode ; echo + ``` + + You will get a password similar to `cj3m0OfBNx8SLzUlTx91dEECgzRlYJb60D2evof1%`. Remove the `%` character from the password to get `cj3m0OfBNx8SLzUlTx91dEECgzRlYJb60D2evof1` as the admin password. + +1. Validation Grafana is running in your cluster: + + ```bash + kubectl get pods -n dapr-monitoring + + NAME READY STATUS RESTARTS AGE + dapr-prom-kube-state-metrics-9849d6cc6-t94p8 1/1 Running 0 4m58s + dapr-prom-prometheus-alertmanager-749cc46f6-9b5t8 2/2 Running 0 4m58s + dapr-prom-prometheus-node-exporter-5jh8p 1/1 Running 0 4m58s + dapr-prom-prometheus-node-exporter-88gbg 1/1 Running 0 4m58s + dapr-prom-prometheus-node-exporter-bjp9f 1/1 Running 0 4m58s + dapr-prom-prometheus-pushgateway-688665d597-h4xx2 1/1 Running 0 4m58s + dapr-prom-prometheus-server-694fd8d7c-q5d59 2/2 Running 0 4m58s + grafana-c49889cff-x56vj 1/1 Running 0 5m10s + ``` + +### Configure Prometheus as data source +First you need to connect Prometheus as a data source to Grafana. + +1. Port-forward to svc/grafana: + + ```bash + kubectl port-forward svc/grafana 8080:80 -n dapr-monitoring + + Forwarding from 127.0.0.1:8080 -> 3000 + Forwarding from [::1]:8080 -> 3000 + Handling connection for 8080 + Handling connection for 8080 + ``` + +1. Open a browser to `http://localhost:8080` + +1. Login to Grafana + - Username = `admin` + - Password = Password from above + +1. Select `Configuration` and `Data Sources` + + Screenshot of the Grafana add Data Source menu + + +1. Add Prometheus as a data source. + + Screenshot of the Prometheus add Data Source + +1. Get your Prometheus HTTP URL + + The Prometheus HTTP URL follows the format `http://.` + + Start by getting the Prometheus server endpoint by running the following command: + + ```bash + kubectl get svc -n dapr-monitoring + + NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE + dapr-prom-kube-state-metrics ClusterIP 10.0.174.177 8080/TCP 7d9h + dapr-prom-prometheus-alertmanager ClusterIP 10.0.255.199 80/TCP 7d9h + dapr-prom-prometheus-node-exporter ClusterIP None 9100/TCP 7d9h + dapr-prom-prometheus-pushgateway ClusterIP 10.0.190.59 9091/TCP 7d9h + dapr-prom-prometheus-server ClusterIP 10.0.172.191 80/TCP 7d9h + elasticsearch-master ClusterIP 10.0.36.146 9200/TCP,9300/TCP 7d10h + elasticsearch-master-headless ClusterIP None 9200/TCP,9300/TCP 7d10h + grafana ClusterIP 10.0.15.229 80/TCP 5d5h + kibana-kibana ClusterIP 10.0.188.224 5601/TCP 7d10h + + ``` + + In this guide the server name is `dapr-prom-prometheus-server` and the namespace is `dapr-monitoring`, so the HTTP URL will be `http://dapr-prom-prometheus-server.dapr-monitoring`. + +1. Fill in the following settings: + + - Name: `Dapr` + - HTTP URL: `http://dapr-prom-prometheus-server.dapr-monitoring` + - Default: On + - Skip TLS Verify: On + - Necessary in order to save and test the configuration + + Screenshot of the Prometheus Data Source configuration + +1. Click `Save & Test` button to verify that the connection succeeded. + +## Import dashboards in Grafana + +1. In the upper left corner of the Grafana home screen, click the "+" option, then "Import". + + You can now import [Grafana dashboard templates](https://github.com/dapr/dapr/tree/master/grafana) from [release assets](https://github.com/dapr/dapr/releases) for your Dapr version: + + Screenshot of the Grafana dashboard upload option + +1. Find the dashboard that you imported and enjoy + + Screenshot of Dapr service dashboard + + {{% alert title="Tip" color="primary" %}} + Hover your mouse over the `i` in the corner to the description of each chart: + + Screenshot of the tooltip for graphs + {{% /alert %}} + +## References + +* [Dapr Observability]({{}}) +* [Prometheus Installation](https://github.com/prometheus-community/helm-charts) +* [Prometheus on Kubernetes](https://github.com/coreos/kube-prometheus) +* [Prometheus Query Language](https://prometheus.io/docs/prometheus/latest/querying/basics/) +* [Supported Dapr metrics](https://github.com/dapr/dapr/blob/master/docs/development/dapr-metrics.md) + +## Example + +
+ +
diff --git a/daprdocs/content/en/operations/observability/metrics/metrics-overview.md b/daprdocs/content/en/operations/observability/metrics/metrics-overview.md new file mode 100644 index 00000000000..0d30185dbb8 --- /dev/null +++ b/daprdocs/content/en/operations/observability/metrics/metrics-overview.md @@ -0,0 +1,109 @@ +--- +type: docs +title: "Configure metrics" +linkTitle: "Configure metrics" +weight: 4000 +description: "Enable or disable Dapr metrics " +--- + +By default, each Dapr system process emits Go runtime/process metrics and has their own [Dapr metrics](https://github.com/dapr/dapr/blob/master/docs/development/dapr-metrics.md). + +## Prometheus endpoint +The Dapr sidecars exposes a [Prometheus](https://prometheus.io/) metrics endpoint that you can scrape to gain a greater understanding of how Dapr is behaving. + +## Configuring metrics using the CLI + +The metrics application endpoint is enabled by default. You can disable it by passing the command line argument `--enable-metrics=false`. + +The default metrics port is `9090`. You can override this by passing the command line argument `--metrics-port` to Daprd. + +## Configuring metrics in Kubernetes +You can also enable/disable the metrics for a specific application by setting the `dapr.io/enable-metrics: "false"` annotation on your application deployment. With the metrics exporter disabled, `daprd` does not open the metrics listening port. + +The following Kubernetes deployment example shows how metrics are explicitly enabled with the port specified as "9090". + +```yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + name: nodeapp + labels: + app: node +spec: + replicas: 1 + selector: + matchLabels: + app: node + template: + metadata: + labels: + app: node + annotations: + dapr.io/enabled: "true" + dapr.io/app-id: "nodeapp" + dapr.io/app-port: "3000" + dapr.io/enable-metrics: "true" + dapr.io/metrics-port: "9090" + spec: + containers: + - name: node + image: dapriosamples/hello-k8s-node:latest + ports: + - containerPort: 3000 + imagePullPolicy: Always +``` + +## Configuring metrics using application configuration +You can also enable metrics via application configuration. To disable the metrics collection in the Dapr sidecars running in a specific namespace: + +- Use the `metrics` spec configuration. +- Set `enabled: false` to disable the metrics in the Dapr runtime. + +```yaml +apiVersion: dapr.io/v1alpha1 +kind: Configuration +metadata: + name: tracing + namespace: default +spec: + tracing: + samplingRate: "1" + metrics: + enabled: false +``` + +## High cardinality metrics + +Depending on your use case, some metrics emitted by Dapr might contain values that have a high cardinality. This might cause increased memory usage for the Dapr process/container and incur expensive egress costs in certain cloud environments. To mitigate this issue, you can set regular expressions for every metric exposed by the Dapr sidecar. [See a list of all Dapr metrics](https://github.com/dapr/dapr/blob/master/docs/development/dapr-metrics.md). + +The following example shows how to apply a regular expression for the label `method` in the metric `dapr_runtime_service_invocation_req_sent_total`: + +```yaml +apiVersion: dapr.io/v1alpha1 +kind: Configuration +metadata: + name: daprConfig +spec: + metric: + enabled: true + rules: + - name: dapr_runtime_service_invocation_req_sent_total + labels: + - name: method + regex: + "orders/": "orders/.+" +``` + +When this configuration is applied, a recorded metric with the `method` label of `orders/a746dhsk293972nz` will be replaced with `orders/`. + +### Watch the demo + +Watch [this video to walk through handling high cardinality metrics](https://youtu.be/pOT8teL6j_k?t=1524): + + + + +## References + +* [Howto: Run Prometheus locally]({{< ref prometheus.md >}}) +* [Howto: Set up Prometheus and Grafana for metrics]({{< ref grafana.md >}}) diff --git a/daprdocs/content/en/operations/observability/metrics/newrelic.md b/daprdocs/content/en/operations/observability/metrics/newrelic.md new file mode 100644 index 00000000000..92133d65bb1 --- /dev/null +++ b/daprdocs/content/en/operations/observability/metrics/newrelic.md @@ -0,0 +1,43 @@ +--- +type: docs +title: "How-To: Set-up New Relic to collect and analyze metrics" +linkTitle: "New Relic" +weight: 6000 +description: "Set-up New Relic for Dapr metrics" +--- + +## Prerequisites + +- Perpetually [free New Relic account](https://newrelic.com/signup?ref=dapr), 100 GB/month of free data ingest, 1 free full access user, unlimited free basic users + +## Background + +New Relic offers a Prometheus OpenMetrics Integration. + +This document explains how to install it in your cluster, either using a Helm chart (recommended). + +## Installation + +1. Install Helm following the official instructions. + +2. Add the New Relic official Helm chart repository following [these instructions](https://github.com/newrelic/helm-charts/blob/master/README.md#installing-charts) + +3. Run the following command to install the New Relic Logging Kubernetes plugin via Helm, replacing the placeholder value YOUR_LICENSE_KEY with your [New Relic license key](https://docs.newrelic.com/docs/accounts/accounts-billing/account-setup/new-relic-license-key): + + ```bash + helm install nri-prometheus newrelic/nri-prometheus --set licenseKey=YOUR_LICENSE_KEY + ``` + +## View Metrics + +![Dapr Metrics](/images/nr-metrics-1.png) + +![Dashboard](/images/nr-dashboard-dapr-metrics-1.png) + +## Related Links/References + +* [New Relic Account Signup](https://newrelic.com/signup) +* [Telemetry Data Platform](https://newrelic.com/platform/telemetry-data-platform) +* [New Relic Prometheus OpenMetrics Integration](https://github.com/newrelic/helm-charts/tree/master/charts/nri-prometheus) +* [Types of New Relic API keys](https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/) +* [Alerts and Applied Intelligence](https://docs.newrelic.com/docs/alerts-applied-intelligence/overview/) diff --git a/daprdocs/content/en/operations/observability/metrics/prometheus.md b/daprdocs/content/en/operations/observability/metrics/prometheus.md new file mode 100644 index 00000000000..3c787602f85 --- /dev/null +++ b/daprdocs/content/en/operations/observability/metrics/prometheus.md @@ -0,0 +1,122 @@ +--- +type: docs +title: "How-To: Observe metrics with Prometheus" +linkTitle: "Prometheus" +weight: 4000 +description: "Use Prometheus to collect time-series data relating to the execution of the Dapr runtime itself" +--- + +## Setup Prometheus Locally +To run Prometheus on your local machine, you can either [install and run it as a process](#install) or run it as a [Docker container](#Run-as-Container). + +### Install +{{% alert title="Note" color="warning" %}} +You don't need to install Prometheus if you plan to run it as a Docker container. Please refer to the [Container](#run-as-container) instructions. +{{% /alert %}} + +To install Prometheus, follow the steps outlined [here](https://prometheus.io/docs/prometheus/latest/getting_started/) for your OS. + +### Configure +Now you've installed Prometheus, you need to create a configuration. + +Below is an example Prometheus configuration, save this to a file i.e. `/tmp/prometheus.yml` or `C:\Temp\prometheus.yml` +```yaml +global: + scrape_interval: 15s # By default, scrape targets every 15 seconds. + +# A scrape configuration containing exactly one endpoint to scrape: +# Here it's Prometheus itself. +scrape_configs: + - job_name: 'dapr' + + # Override the global default and scrape targets from this job every 5 seconds. + scrape_interval: 5s + + static_configs: + - targets: ['localhost:9090'] # Replace with Dapr metrics port if not default +``` + +### Run as Process +Run Prometheus with your configuration to start it collecting metrics from the specified targets. +```bash +./prometheus --config.file=/tmp/prometheus.yml --web.listen-address=:8080 +``` +> We change the port so it doesn't conflict with Dapr's own metrics endpoint. + +If you are not currently running a Dapr application, the target will show as offline. In order to start +collecting metrics you must start Dapr with the metrics port matching the one provided as the target in the configuration. + +Once Prometheus is running, you'll be able to visit its dashboard by visiting `http://localhost:8080`. + +### Run as Container +To run Prometheus as a Docker container on your local machine, first ensure you have [Docker](https://docs.docker.com/install/) installed and running. + +Then you can run Prometheus as a Docker container using: +```bash +docker run \ + --net=host \ + -v /tmp/prometheus.yml:/etc/prometheus/prometheus.yml \ + prom/prometheus --config.file=/etc/prometheus/prometheus.yml --web.listen-address=:8080 +``` +`--net=host` ensures that the Prometheus instance will be able to connect to any Dapr instances running on the host machine. If you plan to run your Dapr apps in containers as well, you'll need to run them on a shared Docker network and update the configuration with the correct target address. + +Once Prometheus is running, you'll be able to visit its dashboard by visiting `http://localhost:8080`. + +## Setup Prometheus on Kubernetes + +### Prerequisites + +- Kubernetes (> 1.14) +- [kubectl](https://kubernetes.io/docs/tasks/tools/) +- [Helm 3](https://helm.sh/) + +### Install Prometheus + +1. First create namespace that can be used to deploy the Grafana and Prometheus monitoring tools + +```bash +kubectl create namespace dapr-monitoring +``` + +2. Install Prometheus + +```bash +helm repo add prometheus-community https://prometheus-community.github.io/helm-charts +helm repo update +helm install dapr-prom prometheus-community/prometheus -n dapr-monitoring +``` + +If you are Minikube user or want to disable persistent volume for development purposes, you can disable it by using the following command. + +```bash +helm install dapr-prom prometheus-community/prometheus -n dapr-monitoring + --set alertmanager.persistence.enabled=false --set pushgateway.persistentVolume.enabled=false --set server.persistentVolume.enabled=false +``` + +3. Validation + +Ensure Prometheus is running in your cluster. + +```bash +kubectl get pods -n dapr-monitoring + +NAME READY STATUS RESTARTS AGE +dapr-prom-kube-state-metrics-9849d6cc6-t94p8 1/1 Running 0 4m58s +dapr-prom-prometheus-alertmanager-749cc46f6-9b5t8 2/2 Running 0 4m58s +dapr-prom-prometheus-node-exporter-5jh8p 1/1 Running 0 4m58s +dapr-prom-prometheus-node-exporter-88gbg 1/1 Running 0 4m58s +dapr-prom-prometheus-node-exporter-bjp9f 1/1 Running 0 4m58s +dapr-prom-prometheus-pushgateway-688665d597-h4xx2 1/1 Running 0 4m58s +dapr-prom-prometheus-server-694fd8d7c-q5d59 2/2 Running 0 4m58s +``` + +## Example + +
+ +
+ +## References + +* [Prometheus Installation](https://github.com/prometheus-community/helm-charts) +* [Prometheus Query Language](https://prometheus.io/docs/prometheus/latest/querying/basics/) diff --git a/daprdocs/content/en/operations/observability/tracing/_index.md b/daprdocs/content/en/operations/observability/tracing/_index.md new file mode 100644 index 00000000000..f29b7ba7e10 --- /dev/null +++ b/daprdocs/content/en/operations/observability/tracing/_index.md @@ -0,0 +1,7 @@ +--- +type: docs +title: "Tracing" +linkTitle: "Tracing" +weight: 200 +description: Learn about tracing scenarios and how to use tracing for visibility in your application +--- \ No newline at end of file diff --git a/daprdocs/content/en/operations/observability/tracing/datadog.md b/daprdocs/content/en/operations/observability/tracing/datadog.md new file mode 100644 index 00000000000..3742cf408f0 --- /dev/null +++ b/daprdocs/content/en/operations/observability/tracing/datadog.md @@ -0,0 +1,55 @@ +--- +type: docs +title: "How-To: Set up Datadog for distributed tracing" +linkTitle: "Datadog" +weight: 5000 +description: "Set up Datadog for distributed tracing" +--- + +Dapr captures metrics and traces that can be sent directly to Datadog through the OpenTelemetry Collector Datadog exporter. + +## Configure Dapr tracing with the OpenTelemetry Collector and Datadog + +Using the OpenTelemetry Collector Datadog exporter, you can configure Dapr to create traces for each application in your Kubernetes cluster and collect them in Datadog. + +> Before you begin, [set up the OpenTelemetry Collector]({{< ref "open-telemetry-collector.md#setting-opentelemetry-collector" >}}). + +1. Add your Datadog API key to the `./deploy/opentelemetry-collector-generic-datadog.yaml` file in the `datadog` exporter configuration section: + ```yaml + data: + otel-collector-config: + ... + exporters: + ... + datadog: + api: + key: + ``` + +1. Apply the `opentelemetry-collector` configuration by running the following command. + + ```sh + kubectl apply -f ./deploy/open-telemetry-collector-generic-datadog.yaml + ``` + +1. Set up a Dapr configuration file that will turn on tracing and deploy a tracing exporter component that uses the OpenTelemetry Collector. + + ```sh + kubectl apply -f ./deploy/collector-config.yaml + +1. Apply the `appconfig` configuration by adding a `dapr.io/config` annotation to the container that you want to participate in the distributed tracing. + + ```yml + annotations: + dapr.io/config: "appconfig" + +1. Create and configure the application. Once running, telemetry data is sent to Datadog and visible in Datadog APM. + +Datadog APM showing telemetry data. + + +## Related Links/References + +* [Complete example of setting up Dapr on a Kubernetes cluster](https://github.com/ericmustin/quickstarts/tree/master/hello-kubernetes) +* [Datadog documentation about OpenTelemetry support](https://docs.datadoghq.com/opentelemetry/) +* [Datadog Application Performance Monitoring](https://docs.datadoghq.com/tracing/) \ No newline at end of file diff --git a/daprdocs/content/en/operations/observability/tracing/jaeger.md b/daprdocs/content/en/operations/observability/tracing/jaeger.md new file mode 100644 index 00000000000..7ef470a37fe --- /dev/null +++ b/daprdocs/content/en/operations/observability/tracing/jaeger.md @@ -0,0 +1,187 @@ +--- +type: docs +title: "How-To: Set up Jaeger for distributed tracing" +linkTitle: "Jaeger" +weight: 3000 +description: "Set up Jaeger for distributed tracing" +type: docs +--- + +Dapr supports the Zipkin protocol. Since Jaeger is compatible with Zipkin, the Zipkin protocol can be used to communication with Jaeger. + +## Configure self hosted mode + +### Setup + +The simplest way to start Jaeger is to use the pre-built all-in-one Jaeger image published to DockerHub: + +```bash +docker run -d --name jaeger \ + -e COLLECTOR_ZIPKIN_HOST_PORT=:9412 \ + -p 16686:16686 \ + -p 9412:9412 \ + jaegertracing/all-in-one:1.22 +``` + + +Next, create the following YAML files locally: + +* **config.yaml**: Note that because we are using the Zipkin protocol +to talk to Jaeger, we specify the `zipkin` section of tracing +configuration set the `endpointAddress` to address of the Jaeger +instance. + +```yaml +apiVersion: dapr.io/v1alpha1 +kind: Configuration +metadata: + name: tracing + namespace: default +spec: + tracing: + samplingRate: "1" + zipkin: + endpointAddress: "http://localhost:9412/api/v2/spans" +``` + +To launch the application referring to the new YAML file, you can use +`--config` option: + +```bash +dapr run --app-id mynode --app-port 3000 node app.js --config config.yaml +``` + +### Viewing Traces +To view traces, in your browser go to http://localhost:16686 to see the Jaeger UI. + +## Configure Kubernetes +The following steps shows you how to configure Dapr to send distributed tracing data to Jaeger running as a container in your Kubernetes cluster, how to view them. + +### Setup + +First create the following YAML file to install Jaeger, file name is `jaeger-operator.yaml` + +#### Development and test + +By default, the allInOne Jaeger image uses memory as the backend storage and it is not recommended to use this in a production environment. + +```yaml +apiVersion: jaegertracing.io/v1 +kind: "Jaeger" +metadata: + name: jaeger +spec: + strategy: allInOne + ingress: + enabled: false + allInOne: + image: jaegertracing/all-in-one:1.22 + options: + query: + base-path: /jaeger +``` + +#### Production +Jaeger uses Elasticsearch as the backend storage, and you can create a secret in k8s cluster to access Elasticsearch server with access control. + + +```shell +kubectl create secret generic jaeger-secret --from-literal=ES_PASSWORD='xxx' --from-literal=ES_USERNAME='xxx' -n ${NAMESPACE} +``` + +```yaml +apiVersion: jaegertracing.io/v1 +kind: "Jaeger" +metadata: + name: jaeger +spec: + strategy: production + query: + options: + log-level: info + query: + base-path: /jaeger + collector: + maxReplicas: 5 + resources: + limits: + cpu: 500m + memory: 516Mi + storage: + type: elasticsearch + esIndexCleaner: + enabled: false ## turn the job deployment on and off + numberOfDays: 7 ## number of days to wait before deleting a record + schedule: "55 23 * * *" ## cron expression for it to run + image: jaegertracing/jaeger-es-index-cleaner ## image of the job + secretName: jaeger-secret + options: + es: + server-urls: http://elasticsearch:9200 +``` + +The pictures are as follows, include Elasticsearch and Grafana tracing data: + +![jaeger-storage-es](/images/jaeger_storage_elasticsearch.png) + +![grafana](/images/jaeger_grafana.png) + + +Now, use the above YAML file to install Jaeger + +```bash +# Install Jaeger +helm repo add jaegertracing https://jaegertracing.github.io/helm-charts +helm install jaeger-operator jaegertracing/jaeger-operator +kubectl apply -f jaeger-operator.yaml + +# Wait for Jaeger to be up and running +kubectl wait deploy --selector app.kubernetes.io/name=jaeger --for=condition=available +``` + +Next, create the following YAML file locally: + +* **tracing.yaml** + +```yaml +apiVersion: dapr.io/v1alpha1 +kind: Configuration +metadata: + name: tracing + namespace: default +spec: + tracing: + samplingRate: "1" + zipkin: + endpointAddress: "http://jaeger-collector.default.svc.cluster.local:9411/api/v2/spans" +``` + +Finally, deploy the the Dapr component and configuration files: + +```bash +kubectl apply -f tracing.yaml +``` + +In order to enable this configuration for your Dapr sidecar, add the following annotation to your pod spec template: + +```yml +annotations: + dapr.io/config: "tracing" +``` + +That's it! Your Dapr sidecar is now configured for use with Jaeger. + +### Viewing Tracing Data + +To view traces, connect to the Jaeger Service and open the UI: + +```bash +kubectl port-forward svc/jaeger-query 16686 +``` + +In your browser, go to `http://localhost:16686` and you will see the Jaeger UI. + +![jaeger](/images/jaeger_ui.png) + +## References +- [Jaeger Getting Started](https://www.jaegertracing.io/docs/1.21/getting-started/#all-in-one) diff --git a/daprdocs/content/en/operations/observability/tracing/newrelic.md b/daprdocs/content/en/operations/observability/tracing/newrelic.md new file mode 100644 index 00000000000..183ab7e57d7 --- /dev/null +++ b/daprdocs/content/en/operations/observability/tracing/newrelic.md @@ -0,0 +1,114 @@ +--- +type: docs +title: "How-To: Set-up New Relic for distributed tracing" +linkTitle: "New Relic" +weight: 2000 +description: "Set-up New Relic for distributed tracing" +--- + +## Prerequisites + +- Perpetually [free New Relic account](https://newrelic.com/signup?ref=dapr), 100 GB/month of free data ingest, 1 free full access user, unlimited free basic users + +## Configure Dapr tracing + +Dapr natively captures metrics and traces that can be send directly to New Relic. The easiest way to export these is by configuring Dapr to send the traces to [New Relic's Trace API](https://docs.newrelic.com/docs/distributed-tracing/trace-api/report-zipkin-format-traces-trace-api/) using the Zipkin trace format. + +In order for the integration to send data to New Relic [Telemetry Data Platform](https://newrelic.com/platform/telemetry-data-platform), you need a [New Relic Insights Insert API key](https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/#insights-insert-key). + +```yaml +apiVersion: dapr.io/v1alpha1 +kind: Configuration +metadata: + name: appconfig + namespace: default +spec: + tracing: + samplingRate: "1" + zipkin: + endpointAddress: "https://trace-api.newrelic.com/trace/v1?Api-Key=&Data-Format=zipkin&Data-Format-Version=2" +``` + +### Viewing Traces + +New Relic Distributed Tracing overview +![New Relic Kubernetes Cluster Explorer App](/images/nr-distributed-tracing-overview.png) + +New Relic Distributed Tracing details +![New Relic Kubernetes Cluster Explorer App](/images/nr-distributed-tracing-detail.png) + +## (optional) New Relic Instrumentation + +In order for the integrations to send data to New Relic Telemetry Data Platform, you either need a [New Relic license key](https://docs.newrelic.com/docs/accounts/accounts-billing/account-setup/new-relic-license-key) or [New Relic Insights Insert API key](https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/#insights-insert-key). + +### OpenTelemetry instrumentation + +Leverage the different language specific OpenTelemetry implementations, for example [New Relic Telemetry SDK and OpenTelemetry support for .NET](https://github.com/newrelic/newrelic-telemetry-sdk-dotnet). In this case, use the [OpenTelemetry Trace Exporter](https://github.com/newrelic/newrelic-telemetry-sdk-dotnet/tree/main/src/NewRelic.OpenTelemetry). See example [here](https://github.com/harrykimpel/quickstarts/blob/master/distributed-calculator/csharp-otel/Startup.cs). + +### New Relic Language agent + +Similarly to the OpenTelemetry instrumentation, you can also leverage a New Relic language agent. As an example, the [New Relic agent instrumentation for .NET Core](https://docs.newrelic.com/docs/agents/net-agent/other-installation/install-net-agent-docker-container) is part of the Dockerfile. See example [here](https://github.com/harrykimpel/quickstarts/blob/master/distributed-calculator/csharp/Dockerfile). + +## (optional) Enable New Relic Kubernetes integration + +In case Dapr and your applications run in the context of a Kubernetes environment, you can enable additional metrics and logs. + +The easiest way to install the New Relic Kubernetes integration is to use the [automated installer](https://one.newrelic.com/launcher/nr1-core.settings?pane=eyJuZXJkbGV0SWQiOiJrOHMtY2x1c3Rlci1leHBsb3Jlci1uZXJkbGV0Lms4cy1zZXR1cCJ9) to generate a manifest. It bundles not just the integration DaemonSets, but also other New Relic Kubernetes configurations, like [Kubernetes events](https://docs.newrelic.com/docs/integrations/kubernetes-integration/kubernetes-events/install-kubernetes-events-integration), [Prometheus OpenMetrics](https://docs.newrelic.com/docs/integrations/prometheus-integrations/get-started/send-prometheus-metric-data-new-relic/), and [New Relic log monitoring](https://docs.newrelic.com/docs/logs/ui-data/use-logs-ui/). + +### New Relic Kubernetes Cluster Explorer + +The [New Relic Kubernetes Cluster Explorer](https://docs.newrelic.com/docs/integrations/kubernetes-integration/understand-use-data/kubernetes-cluster-explorer) provides a unique visualization of the entire data and deployments of the data collected by the Kubernetes integration. + +It is a good starting point to observe all your data and dig deeper into any performance issues or incidents happening inside of the application or microservices. + +![New Relic Kubernetes Cluster Explorer App](/images/nr-k8s-cluster-explorer-app.png) + +Automated correlation is part of the visualization capabilities of New Relic. + +### Pod-level details + +![New Relic K8s Pod Level Details](/images/nr-k8s-pod-level-details.png) + +### Logs in Context + +![New Relic K8s Logs In Context](/images/nr-k8s-logs-in-context.png) + +## New Relic Dashboards + +### Kubernetes Overview + +![New Relic Dashboard Kubernetes Overview](/images/nr-dashboard-k8s-overview.png) + +### Dapr System Services + +![New Relic Dashboard Dapr System Services](/images/nr-dashboard-dapr-system-services.png) + +### Dapr Metrics + +![New Relic Dashboard Dapr Metrics 1](/images/nr-dashboard-dapr-metrics-1.png) + +## New Relic Grafana integration + +New Relic teamed up with [Grafana Labs](https://grafana.com/) so you can use the [Telemetry Data Platform](https://newrelic.com/platform/telemetry-data-platform) as a data source for Prometheus metrics and see them in your existing dashboards, seamlessly tapping into the reliability, scale, and security provided by New Relic. + +[Grafana dashboard templates](https://github.com/dapr/dapr/blob/227028e7b76b7256618cd3236d70c1d4a4392c9a/grafana/README.md) to monitor Dapr system services and sidecars can easily be used without any changes. New Relic provides a [native endpoint for Prometheus metrics](https://docs.newrelic.com/docs/integrations/grafana-integrations/set-configure/configure-new-relic-prometheus-data-source-grafana) into Grafana. A datasource can easily be set-up: + +![New Relic Grafana Data Source](/images/nr-grafana-datasource.png) + +And the exact same dashboard templates from Dapr can be imported to visualize Dapr system services and sidecars. + +![New Relic Grafana Dashboard](/images/nr-grafana-dashboard.png) + +## New Relic Alerts + +All the data that is collected from Dapr, Kubernetes or any services that run on top of can be used to set-up alerts and notifications into the preferred channel of your choice. See [Alerts and Applied Intelligence](https://docs.newrelic.com/docs/alerts-applied-intelligence/overview/). + +## Related Links/References + +* [New Relic Account Signup](https://newrelic.com/signup) +* [Telemetry Data Platform](https://newrelic.com/platform/telemetry-data-platform) +* [Distributed Tracing](https://docs.newrelic.com/docs/distributed-tracing/concepts/introduction-distributed-tracing/) +* [New Relic Trace API](https://docs.newrelic.com/docs/distributed-tracing/trace-api/introduction-trace-api/) +* [Types of New Relic API keys](https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/) +* [New Relic OpenTelemetry User Experience](https://blog.newrelic.com/product-news/opentelemetry-user-experience/) +* [Alerts and Applied Intelligence](https://docs.newrelic.com/docs/alerts-applied-intelligence/overview/) diff --git a/daprdocs/content/en/operations/observability/tracing/otel-collector/_index.md b/daprdocs/content/en/operations/observability/tracing/otel-collector/_index.md new file mode 100644 index 00000000000..4f55a875020 --- /dev/null +++ b/daprdocs/content/en/operations/observability/tracing/otel-collector/_index.md @@ -0,0 +1,7 @@ +--- +type: docs +title: "Open Telemetry Collector" +linkTitle: "Open Telemetry Collector" +weight: 700 +description: "How to set up your observability tools to receive application traces" +--- diff --git a/daprdocs/content/en/operations/observability/tracing/otel-collector/open-telemetry-collector-appinsights.md b/daprdocs/content/en/operations/observability/tracing/otel-collector/open-telemetry-collector-appinsights.md new file mode 100644 index 00000000000..4fe2c95a720 --- /dev/null +++ b/daprdocs/content/en/operations/observability/tracing/otel-collector/open-telemetry-collector-appinsights.md @@ -0,0 +1,72 @@ +--- +type: docs +title: "Using OpenTelemetry Collector to collect traces to send to AppInsights" +linkTitle: "Using the OpenTelemetry for Azure AppInsights" +weight: 1000 +description: "How to push trace events to Azure Application Insights, using the OpenTelemetry Collector." +--- + +Dapr integrates with [OpenTelemetry Collector](https://github.com/open-telemetry/opentelemetry-collector) using the Zipkin API. This guide walks through an example using Dapr to push trace events to Azure Application Insights, using the OpenTelemetry Collector. + +## Requirements + +A installation of Dapr on Kubernetes. + +## How to configure distributed tracing with Application Insights + +### Setup Application Insights + +1. First, you'll need an Azure account. See instructions [here](https://azure.microsoft.com/free/) to apply for a **free** Azure account. +2. Follow instructions [here](https://docs.microsoft.com/azure/azure-monitor/app/create-new-resource) to create a new Application Insights resource. +3. Get the Application Insights Intrumentation key from your Application Insights page. + +### Run OpenTelemetry Collector to push to your Application Insights instance + +Install the OpenTelemetry Collector to your Kubernetes cluster to push events to your Application Insights instance + +1. Check out the file [open-telemetry-collector-appinsights.yaml](/docs/open-telemetry-collector/open-telemetry-collector-appinsights.yaml) and replace the `` placeholder with your Application Insights Instrumentation Key. + +2. Apply the configuration with `kubectl apply -f open-telemetry-collector-appinsights.yaml`. + +Next, set up both a Dapr configuration file to turn on tracing and deploy a tracing exporter component that uses the OpenTelemetry Collector. + +1. Create a collector-config.yaml file with this [content](/docs/open-telemetry-collector/collector-config.yaml) + +2. Apply the configuration with `kubectl apply -f collector-config.yaml`. + +### Deploy your app with tracing + +When running in Kubernetes mode, apply the `appconfig` configuration by adding a `dapr.io/config` annotation to the container that you want to participate in the distributed tracing, as shown in the following example: + +```yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + ... +spec: + ... + template: + metadata: + ... + annotations: + dapr.io/enabled: "true" + dapr.io/app-id: "MyApp" + dapr.io/app-port: "8080" + dapr.io/config: "appconfig" +``` + +Some of the quickstarts such as [distributed calculator](https://github.com/dapr/quickstarts/tree/master/tutorials/distributed-calculator) already configure these settings, so if you are using those no additional settings are needed. + +That's it! There's no need include any SDKs or instrument your application code. Dapr automatically handles the distributed tracing for you. + +> **NOTE**: You can register multiple tracing exporters at the same time, and the tracing logs are forwarded to all registered exporters. + +Deploy and run some applications. After a few minutes, you should see tracing logs appearing in your Application Insights resource. You can also use the **Application Map** to examine the topology of your services, as shown below: + +![Application map](/images/open-telemetry-app-insights.png) + +> **NOTE**: Only operations going through Dapr API exposed by Dapr sidecar (e.g. service invocation or event publishing) are displayed in Application Map topology. + +## Related links +* Try out the [observability quickstart](https://github.com/dapr/quickstarts/tree/master/tutorials/observability/README.md) +* How to set [tracing configuration options]({{< ref "configuration-overview.md#tracing" >}}) diff --git a/daprdocs/content/en/operations/observability/tracing/otel-collector/open-telemetry-collector.md b/daprdocs/content/en/operations/observability/tracing/otel-collector/open-telemetry-collector.md new file mode 100644 index 00000000000..4526683a52e --- /dev/null +++ b/daprdocs/content/en/operations/observability/tracing/otel-collector/open-telemetry-collector.md @@ -0,0 +1,74 @@ +--- +type: docs +title: "Using OpenTelemetry Collector to collect traces" +linkTitle: "Using the OpenTelemetry Collector" +weight: 900 +description: "How to use Dapr to push trace events through the OpenTelemetry Collector." +--- + +{{% alert title="Note" color="primary" %}} +Dapr directly writes traces using the OpenTelemetry (OTEL) protocol as the recommended method. For observability tools that support OTEL protocol, you do not need to use the OpenTelemetry Collector. + +Dapr can also write traces using the Zipkin protocol. Previous to supporting the OTEL protocol, combining the Zipkin protocol with the [OpenTelemetry Collector](https://github.com/open-telemetry/opentelemetry-collector) enabled you to send traces to observability tools such as AWS X-Ray, Google Cloud Operations Suite, and Azure AppInsights. This approach remains for reference purposes only. +{{% /alert %}} + +![Using OpenTelemetry Collect to integrate with many backend](/images/open-telemetry-collector.png) + +## Requirements + +1. A installation of Dapr on Kubernetes. + +2. You are already setting up your trace backends to receive traces. + +3. Check OpenTelemetry Collector exporters [here](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/exporter) and [here](https://github.com/open-telemetry/opentelemetry-collector/tree/main/exporter) to see if your trace backend is supported by the OpenTelemetry Collector. On those linked pages, find the exporter you want to use and read its doc to find out the parameters required. + +## Setting OpenTelemetry Collector + +### Run OpenTelemetry Collector to push to your trace backend + +1. Check out the file [open-telemetry-collector-generic.yaml](/docs/open-telemetry-collector/open-telemetry-collector-generic.yaml) and replace the section marked with `` with the correct settings for your trace exporter. Again, refer to the OpenTelemetry Collector links in the Prerequisites section to determine the correct settings. + +2. Apply the configuration with `kubectl apply -f open-telemetry-collector-generic.yaml`. + +## Set up Dapr to send trace to OpenTelemetry Collector + +### Turn on tracing in Dapr +Next, set up both a Dapr configuration file to turn on tracing and deploy a tracing exporter component that uses the OpenTelemetry Collector. + +1. Create a collector-config.yaml file with this [content](/docs/open-telemetry-collector/collector-config.yaml) + +2. Apply the configuration with `kubectl apply -f collector-config.yaml`. + +### Deploy your app with tracing + +When running in Kubernetes mode, apply the `appconfig` configuration by adding a `dapr.io/config` annotation to the container that you want to participate in the distributed tracing, as shown in the following example: + +```yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + ... +spec: + ... + template: + metadata: + ... + annotations: + dapr.io/enabled: "true" + dapr.io/app-id: "MyApp" + dapr.io/app-port: "8080" + dapr.io/config: "appconfig" +``` + +Some of the quickstarts such as [distributed calculator](https://github.com/dapr/quickstarts/tree/master/tutorials/distributed-calculator) already configure these settings, so if you are using those no additional settings are needed. + +That's it! There's no need include any SDKs or instrument your application code. Dapr automatically handles the distributed tracing for you. + +> **NOTE**: You can register multiple tracing exporters at the same time, and the tracing logs are forwarded to all registered exporters. + +Deploy and run some applications. Wait for the trace to propagate to your tracing backend and view them there. + +## Related links +* Try out the [observability quickstart](https://github.com/dapr/quickstarts/tree/master/tutorials/observability/README.md) +* How to set [tracing configuration options]({{< ref "configuration-overview.md#tracing" >}}) + diff --git a/daprdocs/content/en/operations/observability/tracing/setup-tracing.md b/daprdocs/content/en/operations/observability/tracing/setup-tracing.md new file mode 100644 index 00000000000..3ae01b27603 --- /dev/null +++ b/daprdocs/content/en/operations/observability/tracing/setup-tracing.md @@ -0,0 +1,80 @@ +--- +type: docs +title: "Configure Dapr to send distributed tracing data" +linkTitle: "Configure tracing" +weight: 30 +description: "Set up Dapr to send distributed tracing data" +--- + +{{% alert title="Note" color="primary" %}} +It is recommended to run Dapr with tracing enabled for any production scenario. You can configure Dapr to send tracing and telemetry data to many observability tools based on your environment, whether it is running in the cloud or on-premises. +{{% /alert %}} + + +## Configuration + +The `tracing` section under the `Configuration` spec contains the following properties: + +```yml +spec: + tracing: + samplingRate: "1" + otel: + endpointAddress: "https://..." + zipkin: + endpointAddress: "https://..." + +``` + +The following table lists the properties for tracing: + +| Property | Type | Description | +|--------------|--------|-------------| +| `samplingRate` | string | Set sampling rate for tracing to be enabled or disabled. +| `stdout` | bool | True write more verbose information to the traces +| `otel.endpointAddress` | string | Set the Open Telemetry (OTEL) server address. +| `otel.isSecure` | bool | Is the connection to the endpoint address encrypted. +| `otel.protocol` | string | Set to `http` or `grpc` protocol. +| `zipkin.endpointAddress` | string | Set the Zipkin server address. If this is used, you do not need to specify the `otel` section. + +To enable tracing, use a configuration file (in self hosted mode) or a Kubernetes configuration object (in Kubernetes mode). For example, the following configuration object changes the sample rate to 1 (every span is sampled), and sends trace using OTEL protocol to the OTEL server at localhost:4317 + +```yaml +apiVersion: dapr.io/v1alpha1 +kind: Configuration +metadata: + name: tracing +spec: + tracing: + samplingRate: "1" + otel: + endpointAddress: "localhost:4317" + isSecure: false + protocol: grpc +``` + +## Sampling rate + +Dapr uses probabilistic sampling. The sample rate defines the probability a tracing span will be sampled and can have a value between 0 and 1 (inclusive). The default sample rate is 0.0001 (i.e. 1 in 10,000 spans is sampled). + +Changing `samplingRate` to 0 disables tracing altogether. + +## Environment variables + +The OpenTelemetry (otel) endpoint can also be configured via an environment variables. The presence of the OTEL_EXPORTER_OTLP_ENDPOINT environment variable +turns on tracing for the sidecar. + +| Environment Variable | Description | +|----------------------|-------------| +| `OTEL_EXPORTER_OTLP_ENDPOINT` | Sets the Open Telemetry (OTEL) server address, turns on tracing | +| `OTEL_EXPORTER_OTLP_INSECURE` | Sets the connection to the endpoint as unencrypted (true/false) | +| `OTEL_EXPORTER_OTLP_PROTOCOL` | Transport protocol (`grpc`, `http/protobuf`, `http/json`) | + +## Next steps + +Learn how to set up tracing with one of the following tools: +- [OTEL Collector]({{< ref otel-collector >}}) +- [New Relic]({{< ref newrelic.md >}}) +- [Jaeger]({{< ref jaeger.md >}}) +- [Zipkin]({{< ref zipkin.md >}}) +- [Datadog]({{< ref datadog.md >}}) \ No newline at end of file diff --git a/daprdocs/content/en/operations/observability/tracing/tracing-overview.md b/daprdocs/content/en/operations/observability/tracing/tracing-overview.md new file mode 100644 index 00000000000..e7e08280751 --- /dev/null +++ b/daprdocs/content/en/operations/observability/tracing/tracing-overview.md @@ -0,0 +1,113 @@ +--- +type: docs +title: "Distributed tracing overview" +linkTitle: "Overview" +weight: 10 +description: "Overview on using tracing to get visibility into your application" +--- + +Dapr uses the Open Telemetry (OTEL) and Zipkin protocols for distributed traces. OTEL is the industry standard and is the recommended trace protocol to use. + +Most observability tools support OTEL, including: +- [Google Cloud Operations](https://cloud.google.com/products/operations) +- [New Relic](https://newrelic.com) +- [Azure Monitor](https://azure.microsoft.com/services/monitor/) +- [Datadog](https://www.datadoghq.com) +- Instana +- [Jaeger](https://www.jaegertracing.io/) +- [SignalFX](https://www.signalfx.com/) + +## Scenarios + +Tracing is used with service invocaton and pub/sub APIs. You can flow trace context between services that uses these APIs. There are two scenarios for how tracing is used: + + 1. Dapr generates the trace context and you propagate the trace context to another service. + 1. You generate the trace context and Dapr propagates the trace context to a service. + +### Scenario 1: Dapr generates trace context headers + +#### Propagating sequential service calls + +Dapr takes care of creating the trace headers. However, when there are more than two services, you're responsible for propagating the trace headers between them. Let's go through the scenarios with examples: + +##### Single service invocation call + +For example, `service A -> service B`. + +Dapr generates the trace headers in `service A`, which are then propagated from `service A` to `service B`. No further propagation is needed. + +##### Multiple sequential service invocation calls + +For example, `service A -> service B -> propagate trace headers to -> service C` and so on to further Dapr-enabled services. + +Dapr generates the trace headers at the beginning of the request in `service A`, which are then propagated to `service B`. You are now responsible for taking the headers and propagating them to `service C`, since this is specific to your application. + +In other words, if the app is calling to Dapr and wants to trace with an existing trace header (span), it must always propagate to Dapr (from `service B` to `service C`, in this example). Dapr always propagates trace spans to an application. + +{{% alert title="Note" color="primary" %}} +No helper methods are exposed in Dapr SDKs to propagate and retrieve trace context. You need to use HTTP/gRPC clients to propagate and retrieve trace headers through HTTP headers and gRPC metadata. +{{% /alert %}} + +##### Request is from external endpoint + +For example, `from a gateway service to a Dapr-enabled service A`. + +An external gateway ingress calls Dapr, which generates the trace headers and calls `service A`. `Service A` then calls `service B` and further Dapr-enabled services. + +You must propagate the headers from `service A` to `service B`. For example: `Ingress -> service A -> propagate trace headers -> service B`. This is similar to [case 2]({{< ref "tracing-overview.md#multiple-sequential-service-invocation-calls" >}}). + +##### Pub/sub messages + +Dapr generates the trace headers in the published message topic. These trace headers are propagated to any services listening on that topic. + +#### Propagating multiple different service calls + +In the following scenarios, Dapr does some of the work for you, with you then creating or propagating trace headers. + +##### Multiple service calls to different services from single service + +When you are calling multiple services from a single service, you need to propagate the trace headers. For example: + +``` +service A -> service B +[ .. some code logic ..] +service A -> service C +[ .. some code logic ..] +service A -> service D +[ .. some code logic ..] +``` + +In this case: +1. When `service A` first calls `service B`, Dapr generates the trace headers in `service A`. +1. The trace headers in `service A` are propagated to `service B`. +1. These trace headers are returned in the response from `service B` as part of response headers. +1. You then need to propagate the returned trace context to the next services, like `service C` and `service D`, as Dapr does not know you want to reuse the same header. + +### Scenario 2: You generate your own trace context headers from non-Daprized applications + +Generating your own trace context headers is more unusual and typically not required when calling Dapr. + +However, there are scenarios where you could specifically choose to add W3C trace headers into a service call. For example, you have an existing application that does not use Dapr. In this case, Dapr still propagates the trace context headers for you. + +If you decide to generate trace headers yourself, there are three ways this can be done: + +1. Standard OpenTelemetry SDK + + You can use the industry standard [OpenTelemetry SDKs](https://opentelemetry.io/docs/instrumentation/) to generate trace headers and pass these trace headers to a Dapr-enabled service. _This is the preferred method_. + +1. Vendor SDK + + You can use a vendor SDK that provides a way to generate W3C trace headers and pass them to a Dapr-enabled service. + +1. W3C trace context + + You can handcraft a trace context following [W3C trace context specifications](https://www.w3.org/TR/trace-context/) and pass them to a Dapr-enabled service. + + Read [the trace context overview]({{< ref w3c-tracing-overview >}}) for more background and examples on W3C trace context and headers. + +## Related Links + +- [Observability concepts]({{< ref observability-concept.md >}}) +- [W3C Trace Context for distributed tracing]({{< ref w3c-tracing-overview >}}) +- [W3C Trace Context specification](https://www.w3.org/TR/trace-context/) +- [Observability quickstart](https://github.com/dapr/quickstarts/tree/master/tutorials/observability) diff --git a/daprdocs/content/en/operations/observability/tracing/w3c-tracing-overview.md b/daprdocs/content/en/operations/observability/tracing/w3c-tracing-overview.md new file mode 100644 index 00000000000..52eccbef4d5 --- /dev/null +++ b/daprdocs/content/en/operations/observability/tracing/w3c-tracing-overview.md @@ -0,0 +1,90 @@ +--- +type: docs +title: "W3C trace context overview" +linkTitle: "W3C trace context" +weight: 20 +description: Background and scenarios for using W3C tracing context and headers with Dapr +--- + +Dapr uses the [Open Telemetry protocol](https://opentelemetry.io/), which in turn uses the [W3C trace context](https://www.w3.org/TR/trace-context/) for distributed tracing for both service invocation and pub/sub messaging. Dapr generates and propagates the trace context information, which can be sent to observability tools for visualization and querying. + +## Background + +Distributed tracing is a methodology implemented by tracing tools to follow, analyze, and debug a transaction across multiple software components. + +Typically, a distributed trace traverses more than one service, which requires it to be uniquely identifiable. **Trace context propagation** passes along this unique identification. + +In the past, trace context propagation was implemented individually by each different tracing vendor. In multi-vendor environments, this causes interoperability problems, such as: + +- Traces collected by different tracing vendors can't be correlated, as there is no shared unique identifier. +- Traces crossing boundaries between different tracing vendors can't be propagated, as there is no forwarded, uniformly agreed set of identification. +- Vendor-specific metadata might be dropped by intermediaries. +- Cloud platform vendors, intermediaries, and service providers cannot guarantee to support trace context propagation, as there is no standard to follow. + +Previously, most applications were monitored by a single tracing vendor and stayed within the boundaries of a single platform provider, so these problems didn't have a significant impact. + +Today, an increasing number of applications are distributed and leverage multiple middleware services and cloud platforms. This transformation of modern applications requires a distributed tracing context propagation standard. + +The [W3C trace context specification](https://www.w3.org/TR/trace-context/) defines a universally agreed-upon format for the exchange of trace context propagation data (referred to as trace context). Trace context solves the above problems by providing: + +- A unique identifier for individual traces and requests, allowing trace data of multiple providers to be linked together. +- An agreed-upon mechanism to forward vendor-specific trace data and avoid broken traces when multiple tracing tools participate in a single transaction. +- An industry standard that intermediaries, platforms, and hardware providers can support. + +This unified approach for propagating trace data improves visibility into the behavior of distributed applications, facilitating problem and performance analysis. + +## W3C trace context and headers format + +### W3C trace context + +Dapr uses the standard W3C trace context headers. + +- For HTTP requests, Dapr uses `traceparent` header. +- For gRPC requests, Dapr uses `grpc-trace-bin` header. + +When a request arrives without a trace ID, Dapr creates a new one. Otherwise, it passes the trace ID along the call chain. + +### W3C trace headers +These are the specific trace context headers that are generated and propagated by Dapr for HTTP and gRPC. + +{{< tabs "HTTP" "gRPC" >}} + +{{% codetab %}} + +Copy these headers when propagating a trace context header from an HTTP response to an HTTP request: + +**Traceparent header** + +The traceparent header represents the incoming request in a tracing system in a common format, understood by all vendors: + +``` +traceparent: 00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01 +``` + +[Learn more about the traceparent fields details](https://www.w3.org/TR/trace-context/#traceparent-header). + +**Tracestate header** + +The tracestate header includes the parent in a potentially vendor-specific format: + +``` +tracestate: congo=t61rcWkgMzE +``` + +[Learn more about the tracestate fields details](https://www.w3.org/TR/trace-context/#tracestate-header). + +{{% /codetab %}} + + + +{{% codetab %}} + +In the gRPC API calls, trace context is passed through `grpc-trace-bin` header. + +{{% /codetab %}} + +{{< /tabs >}} + +## Related Links +- [Learn more about distributed tracing in Dapr]({{< ref tracing-overview.md >}}) +- [W3C Trace Context specification](https://www.w3.org/TR/trace-context/) diff --git a/daprdocs/content/en/operations/observability/tracing/zipkin.md b/daprdocs/content/en/operations/observability/tracing/zipkin.md new file mode 100644 index 00000000000..23ecfd312f3 --- /dev/null +++ b/daprdocs/content/en/operations/observability/tracing/zipkin.md @@ -0,0 +1,110 @@ +--- +type: docs +title: "How-To: Set up Zipkin for distributed tracing" +linkTitle: "Zipkin" +weight: 4000 +description: "Set up Zipkin for distributed tracing" +type: docs +--- + +## Configure self hosted mode + +For self hosted mode, on running `dapr init`: + +1. The following YAML file is created by default in `$HOME/.dapr/config.yaml` (on Linux/Mac) or `%USERPROFILE%\.dapr\config.yaml` (on Windows) and it is referenced by default on `dapr run` calls unless otherwise overridden `: + +* config.yaml + +```yaml +apiVersion: dapr.io/v1alpha1 +kind: Configuration +metadata: + name: daprConfig + namespace: default +spec: + tracing: + samplingRate: "1" + zipkin: + endpointAddress: "http://localhost:9411/api/v2/spans" +``` + +2. The [openzipkin/zipkin](https://hub.docker.com/r/openzipkin/zipkin/) docker container is launched on running `dapr init` or it can be launched with the following code. + +Launch Zipkin using Docker: + +```bash +docker run -d -p 9411:9411 openzipkin/zipkin +``` + +3. The applications launched with `dapr run` by default reference the config file in `$HOME/.dapr/config.yaml` or `%USERPROFILE%\.dapr\config.yaml` and can be overridden with the Dapr CLI using the `--config` param: + +```bash +dapr run --app-id mynode --app-port 3000 node app.js +``` +### Viewing Traces +To view traces, in your browser go to http://localhost:9411 and you will see the Zipkin UI. + +## Configure Kubernetes + +The following steps shows you how to configure Dapr to send distributed tracing data to Zipkin running as a container in your Kubernetes cluster, and how to view them. + +### Setup + +First, deploy Zipkin: + +```bash +kubectl create deployment zipkin --image openzipkin/zipkin +``` + +Create a Kubernetes service for the Zipkin pod: + +```bash +kubectl expose deployment zipkin --type ClusterIP --port 9411 +``` + +Next, create the following YAML file locally: + +* tracing.yaml configuration + +```yaml +apiVersion: dapr.io/v1alpha1 +kind: Configuration +metadata: + name: tracing + namespace: default +spec: + tracing: + samplingRate: "1" + zipkin: + endpointAddress: "http://zipkin.default.svc.cluster.local:9411/api/v2/spans" +``` + +Now, deploy the the Dapr configuration file: + +```bash +kubectl apply -f tracing.yaml +``` + +In order to enable this configuration for your Dapr sidecar, add the following annotation to your pod spec template: + +```yml +annotations: + dapr.io/config: "tracing" +``` + +That's it! Your sidecar is now configured to send traces to Zipkin. + +### Viewing Tracing Data + +To view traces, connect to the Zipkin service and open the UI: + +```bash +kubectl port-forward svc/zipkin 9411:9411 +``` + +In your browser, go to `http://localhost:9411` and you will see the Zipkin UI. + +![zipkin](/images/zipkin_ui.png) + +## References +- [Zipkin for distributed tracing](https://zipkin.io/) From 70785f444da3499d22d836f30dee3657e4e57702 Mon Sep 17 00:00:00 2001 From: Hannah Hunter Date: Thu, 3 Aug 2023 17:15:20 -0400 Subject: [PATCH 2/8] updates per triage meeting Signed-off-by: Hannah Hunter --- .../en/concepts/building-blocks-concept.md | 9 +- .../content/en/concepts/components-concept.md | 2 +- .../en/concepts/observability-concept.md | 8 +- daprdocs/content/en/concepts/overview.md | 72 ++++--- .../building-blocks/observability/_index.md | 15 -- .../observability/develop-tracing/_index.md | 7 - .../develop-tracing/tracing-overview.md | 113 ----------- 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--- a/daprdocs/content/en/concepts/building-blocks-concept.md +++ b/daprdocs/content/en/concepts/building-blocks-concept.md @@ -6,15 +6,15 @@ weight: 200 description: "Modular best practices accessible over standard HTTP or gRPC APIs" --- -A [building block]({{< ref building-blocks >}}) is an HTTP or gRPC API that can be called from your code and uses one or more Dapr components. - -Building blocks address common challenges in building resilient, microservices applications and codify best practices and patterns. Dapr consists of a set of building blocks, with extensibility to add new building blocks. +A [building block]({{< ref building-blocks >}}) is an HTTP or gRPC API that can be called from your code and uses one or more Dapr components. Dapr consists of a set of building blocks, with extensibility to add new building blocks. Dapr's building blocks: +- Address common challenges in building resilient, microservices applications +- Codify best practices and patterns The diagram below shows how building blocks expose a public API that is called from your code, using components to implement the building blocks' capability. -The following are the building blocks provided by Dapr: +Dapr provides the following building blocks: @@ -25,7 +25,6 @@ The following are the building blocks provided by Dapr: | [**Publish and subscribe**]({{< ref "pubsub-overview.md" >}}) | `/v1.0/publish` `/v1.0/subscribe`| Pub/Sub is a loosely coupled messaging pattern where senders (or publishers) publish messages to a topic, to which subscribers subscribe. Dapr supports the pub/sub pattern between applications. | [**Bindings**]({{< ref "bindings-overview.md" >}}) | `/v1.0/bindings` | A binding provides a bi-directional connection to an external cloud/on-premise service or system. Dapr allows you to invoke the external service through the Dapr binding API, and it allows your application to be triggered by events sent by the connected service. | [**Actors**]({{< ref "actors-overview.md" >}}) | `/v1.0/actors` | An actor is an isolated, independent unit of compute and state with single-threaded execution. Dapr provides an actor implementation based on the virtual actor pattern which provides a single-threaded programming model and where actors are garbage collected when not in use. -| [**Observability**]({{< ref "observability-concept.md" >}}) | `N/A` | Dapr system components and runtime emit metrics, logs, and traces to debug, operate and monitor Dapr system services, components and user applications. | [**Secrets**]({{< ref "secrets-overview.md" >}}) | `/v1.0/secrets` | Dapr provides a secrets building block API and integrates with secret stores such as public cloud stores, local stores and Kubernetes to store the secrets. Services can call the secrets API to retrieve secrets, for example to get a connection string to a database. | [**Configuration**]({{< ref "configuration-api-overview.md" >}}) | `/v1.0/configuration` | The Configuration API enables you to retrieve and subscribe to application configuration items for supported configuration stores. This enables an application to retrieve specific configuration information, for example, at start up or when configuration changes are made in the store. | [**Distributed lock**]({{< ref "distributed-lock-api-overview.md" >}}) | `/v1.0-alpha1/lock` | The distributed lock API enables you to take a lock on a resource so that multiple instances of an application can access the resource without conflicts and provide consistency guarantees. diff --git a/daprdocs/content/en/concepts/components-concept.md b/daprdocs/content/en/concepts/components-concept.md index c54b1a82b7d..97885f90b0d 100644 --- a/daprdocs/content/en/concepts/components-concept.md +++ b/daprdocs/content/en/concepts/components-concept.md @@ -11,7 +11,7 @@ Dapr uses a modular design where functionality is delivered as a component. Each You can contribute implementations and extend Dapr's component interfaces capabilities via: - The [components-contrib repository](https://github.com/dapr/components-contrib) -- [Pluggable components]({{}}). +- [Pluggable components]({{< ref "components-concept.md#built-in-and-pluggable-components" >}}). A building block can use any combination of components. For example, the [actors]({{< ref "actors-overview.md" >}}) and the [state management]({{< ref "state-management-overview.md" >}}) building blocks both use [state components](https://github.com/dapr/components-contrib/tree/master/state). diff --git a/daprdocs/content/en/concepts/observability-concept.md b/daprdocs/content/en/concepts/observability-concept.md index 9de8295b931..11ab62fd9bc 100644 --- a/daprdocs/content/en/concepts/observability-concept.md +++ b/daprdocs/content/en/concepts/observability-concept.md @@ -27,7 +27,7 @@ Maintaining this instrumentation code, which is not part of the core logic of th ## Observability for your application with Dapr -When you leverage Dapr API building blocks to perform service-to-service calls and pub/sub messaging, Dapr offers an advantage with respect to [distributed tracing]({{< ref develop-tracing >}}). Since this inter-service communication flows through the Dapr runtime (or "sidecar"), Dapr is in a unique position to offload the burden of application-level instrumentation. +When you leverage Dapr API building blocks to perform service-to-service calls and pub/sub messaging, Dapr offers an advantage with respect to [distributed tracing]({{< ref tracing >}}). Since this inter-service communication flows through the Dapr runtime (or "sidecar"), Dapr is in a unique position to offload the burden of application-level instrumentation. ### Distributed tracing @@ -37,7 +37,7 @@ Dapr can be [configured to emit tracing data]({{< ref setup-tracing.md >}}) usin ### Automatic tracing context generation -Dapr uses [W3C tracing]({{< ref w3c-tracing-overview >}}) specification for tracing context, included as part Open Telemetry (OTEL), to generate and propagate the context header for the application or propagate user-provided context headers. This means that you get tracing by default with Dapr. +Dapr uses [W3C tracing]({{< ref tracing >}}) specification for tracing context, included as part Open Telemetry (OTEL), to generate and propagate the context header for the application or propagate user-provided context headers. This means that you get tracing by default with Dapr. ## Observability for the Dapr sidecar and control plane @@ -54,7 +54,7 @@ Dapr generates [logs]({{< ref logs.md >}}) to: - Provide visibility into sidecar operation - Help users identify issues and perform debugging -Log events contain warning, error, info, and debug messages produced by Dapr system services. You can also configure Dapr to send logs to collectors, such as Open Telemetry Collector, [Fluentd]({{< ref fluentd.md >}}), [New Relic]({{< ref "operations/monitoring/logging/newrelic.md" >}}), [Azure Monitor]({{< ref azure-monitor.md >}}), and other observability tools, so that logs can be searched and analyzed to provide insights. +Log events contain warning, error, info, and debug messages produced by Dapr system services. You can also configure Dapr to send logs to collectors, such as Open Telemetry Collector, [Fluentd]({{< ref fluentd.md >}}), [New Relic]({{< ref "operations/observability/logging/newrelic.md" >}}), [Azure Monitor]({{< ref azure-monitor.md >}}), and other observability tools, so that logs can be searched and analyzed to provide insights. ### Metrics @@ -70,5 +70,5 @@ Conversely, Dapr can be configured to probe for the [health of your application] ## Next steps -- [Learn more about observability in developing with Dapr]({{< ref develop-tracing >}}) +- [Learn more about observability in developing with Dapr]({{< ref tracing >}}) - [Learn more about observability in operating with Dapr]({{< ref tracing >}}) \ No newline at end of file diff --git a/daprdocs/content/en/concepts/overview.md b/daprdocs/content/en/concepts/overview.md index b7eac28ab49..232f8b8b71d 100644 --- a/daprdocs/content/en/concepts/overview.md +++ b/daprdocs/content/en/concepts/overview.md @@ -7,7 +7,7 @@ description: > Introduction to the Distributed Application Runtime --- -Dapr is a portable, event-driven runtime that makes it easy for any developer to build resilient, stateless and stateful applications that run on the cloud and edge and embraces the diversity of languages and developer frameworks. +Dapr is a portable, event-driven runtime that makes it easy for any developer to build resilient, stateless, and stateful applications that run on the cloud and edge and embraces the diversity of languages and developer frameworks.
@@ -15,23 +15,32 @@ Dapr is a portable, event-driven runtime that makes it easy for any developer to ## Any language, any framework, anywhere - + -Today we are experiencing a wave of cloud adoption. Developers are comfortable with web + database application architectures, for example classic 3-tier designs, but not with microservice application architectures which are inherently distributed. It’s hard to become a distributed systems expert, nor should you have to. Developers want to focus on business logic, while leaning on the platforms to imbue their applications with scale, resiliency, maintainability, elasticity and the other attributes of cloud-native architectures. +With the current wave of cloud adoption, web + database application architectures (such as classic 3-tier designs) are trending more toward microservice application architectures, which are inherently distributed. You shouldn't have to become a distributed systems expert just to create microservices applications. -This is where Dapr comes in. Dapr codifies the *best practices* for building microservice applications into open, independent APIs called building blocks, that enable you to build portable applications with the language and framework of your choice. Each building block is completely independent and you can use one, some, or all of them in your application. +This is where Dapr comes in. Dapr codifies the *best practices* for building microservice applications into open, independent APIs called [building blocks]({{< ref "#microservice-building-blocks-for-cloud-and-edge" >}}). Dapr's building blocks: +- Enable you to build portable applications using the language and framework of your choice. +- Are completely independent +- Have no limit to how many you use in your application -Using Dapr you can incrementally migrate your existing applications to a microservices architecture, thereby adopting cloud native patterns such scale out/in, resiliency and independent deployments. +Using Dapr, you can incrementally migrate your existing applications to a microservices architecture, thereby adopting cloud native patterns such scale out/in, resiliency, and independent deployments. -In addition, Dapr is platform agnostic, meaning you can run your applications locally, on any Kubernetes cluster, on virtual or physical machines and in other hosting environments that Dapr integrates with. This enables you to build microservice applications that can run on the cloud and edge. +Dapr is platform agnostic, meaning you can run your applications: +- Locally +- On any Kubernetes cluster +- On virtual or physical machines +- In other hosting environments that Dapr integrates with. + +This enables you to build microservice applications that can run on the cloud and edge. ## Microservice building blocks for cloud and edge - + -There are many considerations when architecting microservices applications. Dapr provides best practices for common capabilities when building microservice applications that developers can use in a standard way, and deploy to any environment. It does this by providing distributed system building blocks. +Dapr provides distributed system building blocks for you to build microservice applications in a standard way and to deploy to any environment. -Each of these building block APIs is independent, meaning that you can use one, some, or all of them in your application. The following building blocks are available: +Each of these building block APIs is independent, meaning that you can use any number of them in your application. | Building Block | Description | |----------------|-------------| @@ -40,13 +49,22 @@ Each of these building block APIs is independent, meaning that you can use one, | [**Publish and subscribe**]({{< ref "pubsub-overview.md" >}}) | Publishing events and subscribing to topics between services enables event-driven architectures to simplify horizontal scalability and make them resilient to failure. Dapr provides at-least-once message delivery guarantee, message TTL, consumer groups and other advance features. | [**Resource bindings**]({{< ref "bindings-overview.md" >}}) | Resource bindings with triggers builds further on event-driven architectures for scale and resiliency by receiving and sending events to and from any external source such as databases, queues, file systems, etc. | [**Actors**]({{< ref "actors-overview.md" >}}) | A pattern for stateful and stateless objects that makes concurrency simple, with method and state encapsulation. Dapr provides many capabilities in its actor runtime, including concurrency, state, and life-cycle management for actor activation/deactivation, and timers and reminders to wake up actors. -| [**Observability**]({{< ref "observability-concept.md" >}}) | Dapr emits metrics, logs, and traces to debug and monitor both Dapr and user applications. Dapr supports distributed tracing to easily diagnose and serve inter-service calls in production using the W3C Trace Context standard and Open Telemetry to send to different monitoring tools. | [**Secrets**]({{< ref "secrets-overview.md" >}}) | The secrets management API integrates with public cloud and local secret stores to retrieve the secrets for use in application code. | [**Configuration**]({{< ref "configuration-api-overview.md" >}}) | The configuration API enables you to retrieve and subscribe to application configuration items from configuration stores. | [**Distributed lock**]({{< ref "distributed-lock-api-overview.md" >}}) | The distributed lock API enables your application to acquire a lock for any resource that gives it exclusive access until either the lock is released by the application, or a lease timeout occurs. | [**Workflows**]({{< ref "workflow-overview.md" >}}) | The workflow API can be combined with other Dapr building blocks to define long running, persistent processes or data flows that span multiple microservices using Dapr workflows or workflow components. | [**Cryptography**]({{< ref "cryptography-overview.md" >}}) | The cryptography API provides an abstraction layer on top of security infrastructure such as key vaults. It contains APIs that allow you to perform cryptographic operations, such as encrypting and decrypting messages, without exposing keys to your applications. +### Cross-cutting APIs + +Alongside its building blocks, Dapr provides cross-cutting APIs that apply across all the build blocks you use. + +| Building Block | Description | +|----------------|-------------| +| [**Resiliency**]({{< ref "resiliency-concept.md" >}}) | Dapr provides the capability to define and apply fault tolerance resiliency policies via a resiliency spec. Supported specs define policies for resiliency patterns such as timeouts, retries/back-offs, and circuit breakers. +| [**Observability**]({{< ref "observability-concept.md" >}}) | Dapr emits metrics, logs, and traces to debug and monitor both Dapr and user applications. Dapr supports distributed tracing to easily diagnose and serve inter-service calls in production using the W3C Trace Context standard and Open Telemetry to send to different monitoring tools. +| [**Security**]({{< ref "security-concept.md" >}}) | Dapr supports in-transit encryption of communication between Dapr instances using the Dapr control plane, Sentry service. You can bring in your own certificates, or let Dapr automatically create and persist self-signed root and issuer certificates. + ## Sidecar architecture Dapr exposes its HTTP and gRPC APIs as a sidecar architecture, either as a container or as a process, not requiring the application code to include any Dapr runtime code. This makes integration with Dapr easy from other runtimes, as well as providing separation of the application logic for improved supportability. @@ -55,33 +73,41 @@ Dapr exposes its HTTP and gRPC APIs as a sidecar architecture, either as a conta ## Hosting environments -Dapr can be hosted in multiple environments, including self-hosted on a Windows/Linux/macOS machines for local development and on Kubernetes or clusters of physical or virtual machines in production. +Dapr can be hosted in multiple environments, including: +- Self-hosted on a Windows/Linux/macOS machine for local development +- On Kubernetes or clusters of physical or virtual machines in production ### Self-hosted local development -In [self-hosted mode]({{< ref self-hosted-overview.md >}}) Dapr runs as a separate sidecar process which your service code can call via HTTP or gRPC. Each running service has a Dapr runtime process (or sidecar) which is configured to use state stores, pub/sub, binding components and the other building blocks. +In [self-hosted mode]({{< ref self-hosted-overview.md >}}), Dapr runs as a separate sidecar process, which your service code can call via HTTP or gRPC. Each running service has a Dapr runtime process (or sidecar) configured to use state stores, pub/sub, binding components, and the other building blocks. -You can use the [Dapr CLI](https://github.com/dapr/cli#launch-dapr-and-your-app) to run a Dapr-enabled application on your local machine. The diagram below show Dapr's local development environment when configured with the CLI `init` command. Try this out with the [getting started samples]({{< ref getting-started >}}). +You can use the [Dapr CLI](https://github.com/dapr/cli#launch-dapr-and-your-app) to run a Dapr-enabled application on your local machine. In the following diagram, Dapr's local development environment gets configured with the CLI `init` command. Try this out with the [getting started samples]({{< ref getting-started >}}). Architecture diagram of Dapr in self-hosted mode ### Kubernetes -Kubernetes can be used for either local development (for example with [minikube](https://minikube.sigs.k8s.io/docs/), [k3S](https://k3s.io/)) or in [production]({{< ref kubernetes >}}). In container hosting environments such as Kubernetes, Dapr runs as a sidecar container with the application container in the same pod. +Kubernetes can be used for either: +- Local development (for example, with [minikube](https://minikube.sigs.k8s.io/docs/) and [k3S](https://k3s.io/)), or +- In [production]({{< ref kubernetes >}}). + +In container hosting environments such as Kubernetes, Dapr runs as a sidecar container with the application container in the same pod. -Dapr has control plane services. The `dapr-sidecar-injector` and `dapr-operator` services provide first-class integration to launch Dapr as a sidecar container in the same pod as the service container and provide notifications of Dapr component updates provisioned in the cluster. +Dapr's `dapr-sidecar-injector` and `dapr-operator` control plane services provide first-class integration to: +- Launch Dapr as a sidecar container in the same pod as the service container +- Provide notifications of Dapr component updates provisioned in the cluster The `dapr-sentry` service is a certificate authority that enables mutual TLS between Dapr sidecar instances for secure data encryption, as well as providing identity via [Spiffe](https://spiffe.io/). For more information on the `Sentry` service, read the [security overview]({{< ref "security-concept.md#dapr-to-dapr-communication" >}}) -Deploying and running a Dapr-enabled application into your Kubernetes cluster is as simple as adding a few annotations to the deployment schemes. Visit the [Dapr on Kubernetes docs]({{< ref kubernetes >}}) +Deploying and running a Dapr-enabled application into your Kubernetes cluster is as simple as adding a few annotations to the deployment schemes. Visit the [Dapr on Kubernetes docs]({{< ref kubernetes >}}). Architecture diagram of Dapr in Kubernetes mode ### Clusters of physical or virtual machines -The Dapr control plane services can be deployed in High Availability (HA) mode to clusters of physical or virtual machines in production, for example, as shown in the diagram below. Here the Actor `Placement` and `Sentry` services are started on three different VMs to provide HA control plane. In order to provide name resolution using DNS for the applications running in the cluster, Dapr uses [Hashicorp Consul service]({{< ref setup-nr-consul >}}), also running in HA mode. +The Dapr control plane services can be deployed in High Availability (HA) mode to clusters of physical or virtual machines in production. In the diagram below, the Actor `Placement` and `Sentry` services are started on three different VMs to provide HA control plane. In order to provide name resolution using DNS for the applications running in the cluster, Dapr uses [Hashicorp Consul service]({{< ref setup-nr-consul >}}), also running in HA mode. Architecture diagram of Dapr control plane and Consul deployed to VMs in high availability mode @@ -91,17 +117,15 @@ Dapr offers a variety of SDKs and frameworks to make it easy to begin developing ### Dapr SDKs -To make using Dapr more natural for different languages, it also includes [language specific SDKs]({{}}) for: -- C++ +To make using Dapr more natural for different languages, it also includes [language specific SDKs]({{< ref sdks >}}) for: - Go - Java - JavaScript - .NET - PHP - Python -- Rust -These SDKs expose the functionality of the Dapr building blocks through a typed language API, rather than calling the http/gRPC API. This enables you to write a combination of stateless and stateful functions and actors all in the language of your choice. And because these SDKs share the Dapr runtime, you get cross-language actor and function support. +These SDKs expose the functionality of the Dapr building blocks through a typed language API, rather than calling the http/gRPC API. This enables you to write a combination of stateless and stateful functions and actors all in the language of your choice. Since these SDKs share the Dapr runtime, you get cross-language actor and function support. ### Developer frameworks @@ -120,7 +144,7 @@ Dapr can be used from any developer framework. Here are some that have been inte #### Integrations and extensions Visit the [integrations]({{< ref integrations >}}) page to learn about some of the first-class support Dapr has for various frameworks and external products, including: -- Public cloud services +- Public cloud services, like Azure and AWS - Visual Studio Code - GitHub @@ -128,6 +152,6 @@ Visit the [integrations]({{< ref integrations >}}) page to learn about some of t Dapr is designed for [operations]({{< ref operations >}}) and security. The Dapr sidecars, runtime, components, and configuration can all be managed and deployed easily and securely to match your organization's needs. -The [dashboard](https://github.com/dapr/dashboard), installed via the Dapr CLI, provides a web-based UI enabling you to see information, view logs and more for running Dapr applications. +The [dashboard](https://github.com/dapr/dashboard), installed via the Dapr CLI, provides a web-based UI enabling you to see information, view logs, and more for running Dapr applications. -The [monitoring tools support]({{< ref monitoring >}}) provides deeper visibility into the Dapr system services and side-cars and the [observability capabilities]({{}}) of Dapr provide insights into your application such as tracing and metrics. +Dapr supports [monitoring tools]({{< ref observability >}}) for deeper visibility into the Dapr system services and sidecars, while the [observability capabilities]({{< ref "observability-concept.md" >}}) of Dapr provide insights into your application, such as tracing and metrics. diff --git a/daprdocs/content/en/developing-applications/building-blocks/observability/_index.md b/daprdocs/content/en/developing-applications/building-blocks/observability/_index.md deleted file mode 100644 index 6b456a7d713..00000000000 --- a/daprdocs/content/en/developing-applications/building-blocks/observability/_index.md +++ /dev/null @@ -1,15 +0,0 @@ ---- -type: docs -title: "Observability" -linkTitle: "Observability" -weight: 60 -description: See and measure the message calls to components and between networked services ---- - -{{% alert title="More about Dapr Observability" color="primary" %}} - Learn more about how to use Dapr Observability Lock: - - Explore observability via any of the supporting [Dapr SDKs]({{< ref sdks >}}). - - Review the [Observability API reference documentation]({{< ref health_api.md >}}). - - Read the [general overview of the observability concept]({{< ref observability-concept >}}) in Dapr. - - Learn the [operations perspective and guidance on monitoring]({{< ref monitoring >}}). -{{% /alert %}} diff --git a/daprdocs/content/en/developing-applications/building-blocks/observability/develop-tracing/_index.md b/daprdocs/content/en/developing-applications/building-blocks/observability/develop-tracing/_index.md deleted file mode 100644 index bc0df410947..00000000000 --- a/daprdocs/content/en/developing-applications/building-blocks/observability/develop-tracing/_index.md +++ /dev/null @@ -1,7 +0,0 @@ ---- -type: docs -title: "Tracing" -linkTitle: "Tracing" -weight: 300 -description: Learn more about tracing scenarios and how to use tracing for visibility in your application ---- \ No newline at end of file diff --git a/daprdocs/content/en/developing-applications/building-blocks/observability/develop-tracing/tracing-overview.md b/daprdocs/content/en/developing-applications/building-blocks/observability/develop-tracing/tracing-overview.md deleted file mode 100644 index 9331a75f906..00000000000 --- a/daprdocs/content/en/developing-applications/building-blocks/observability/develop-tracing/tracing-overview.md +++ /dev/null @@ -1,113 +0,0 @@ ---- -type: docs -title: "Distributed tracing" -linkTitle: "Distributed tracing" -weight: 300 -description: "Use tracing to get visibility into your application" ---- - -Dapr uses the Open Telemetry (OTEL) and Zipkin protocols for distributed traces. OTEL is the industry standard and is the recommended trace protocol to use. - -Most observability tools support OTEL, including: -- [Google Cloud Operations](https://cloud.google.com/products/operations) -- [New Relic](https://newrelic.com) -- [Azure Monitor](https://azure.microsoft.com/services/monitor/) -- [Datadog](https://www.datadoghq.com) -- Instana -- [Jaeger](https://www.jaegertracing.io/) -- [SignalFX](https://www.signalfx.com/) - -## Scenarios - -Tracing is used with service invocaton and pub/sub APIs. You can flow trace context between services that uses these APIs. There are two scenarios for how tracing is used: - - 1. Dapr generates the trace context and you propagate the trace context to another service. - 1. You generate the trace context and Dapr propagates the trace context to a service. - -### Scenario 1: Dapr generates trace context headers - -#### Propagating sequential service calls - -Dapr takes care of creating the trace headers. However, when there are more than two services, you're responsible for propagating the trace headers between them. Let's go through the scenarios with examples: - -##### Single service invocation call - -For example, `service A -> service B`. - -Dapr generates the trace headers in `service A`, which are then propagated from `service A` to `service B`. No further propagation is needed. - -##### Multiple sequential service invocation calls - -For example, `service A -> service B -> propagate trace headers to -> service C` and so on to further Dapr-enabled services. - -Dapr generates the trace headers at the beginning of the request in `service A`, which are then propagated to `service B`. You are now responsible for taking the headers and propagating them to `service C`, since this is specific to your application. - -In other words, if the app is calling to Dapr and wants to trace with an existing trace header (span), it must always propagate to Dapr (from `service B` to `service C`, in this example). Dapr always propagates trace spans to an application. - -{{% alert title="Note" color="primary" %}} -No helper methods are exposed in Dapr SDKs to propagate and retrieve trace context. You need to use HTTP/gRPC clients to propagate and retrieve trace headers through HTTP headers and gRPC metadata. -{{% /alert %}} - -##### Request is from external endpoint - -For example, `from a gateway service to a Dapr-enabled service A`. - -An external gateway ingress calls Dapr, which generates the trace headers and calls `service A`. `Service A` then calls `service B` and further Dapr-enabled services. - -You must propagate the headers from `service A` to `service B`. For example: `Ingress -> service A -> propagate trace headers -> service B`. This is similar to [case 2]({{< ref "tracing-overview.md#multiple-sequential-service-invocation-calls" >}}). - -##### Pub/sub messages - -Dapr generates the trace headers in the published message topic. These trace headers are propagated to any services listening on that topic. - -#### Propagating multiple different service calls - -In the following scenarios, Dapr does some of the work for you, with you then creating or propagating trace headers. - -##### Multiple service calls to different services from single service - -When you are calling multiple services from a single service, you need to propagate the trace headers. For example: - -``` -service A -> service B -[ .. some code logic ..] -service A -> service C -[ .. some code logic ..] -service A -> service D -[ .. some code logic ..] -``` - -In this case: -1. When `service A` first calls `service B`, Dapr generates the trace headers in `service A`. -1. The trace headers in `service A` are propagated to `service B`. -1. These trace headers are returned in the response from `service B` as part of response headers. -1. You then need to propagate the returned trace context to the next services, like `service C` and `service D`, as Dapr does not know you want to reuse the same header. - -### Scenario 2: You generate your own trace context headers from non-Daprized applications - -Generating your own trace context headers is more unusual and typically not required when calling Dapr. - -However, there are scenarios where you could specifically choose to add W3C trace headers into a service call. For example, you have an existing application that does not use Dapr. In this case, Dapr still propagates the trace context headers for you. - -If you decide to generate trace headers yourself, there are three ways this can be done: - -1. Standard OpenTelemetry SDK - - You can use the industry standard [OpenTelemetry SDKs](https://opentelemetry.io/docs/instrumentation/) to generate trace headers and pass these trace headers to a Dapr-enabled service. _This is the preferred method_. - -1. Vendor SDK - - You can use a vendor SDK that provides a way to generate W3C trace headers and pass them to a Dapr-enabled service. - -1. W3C trace context - - You can handcraft a trace context following [W3C trace context specifications](https://www.w3.org/TR/trace-context/) and pass them to a Dapr-enabled service. - - Read [the trace context overview]({{< ref w3c-tracing-overview >}}) for more background and examples on W3C trace context and headers. - -## Related Links - -- [Observability concepts]({{< ref observability-concept.md >}}) -- [W3C Trace Context for distributed tracing]({{< ref w3c-tracing-overview >}}) -- [W3C Trace Context specification](https://www.w3.org/TR/trace-context/) -- [Observability quickstart](https://github.com/dapr/quickstarts/tree/master/tutorials/observability) diff --git a/daprdocs/content/en/developing-applications/building-blocks/observability/develop-tracing/w3c-tracing-overview.md b/daprdocs/content/en/developing-applications/building-blocks/observability/develop-tracing/w3c-tracing-overview.md deleted file mode 100644 index 53d315d6032..00000000000 --- a/daprdocs/content/en/developing-applications/building-blocks/observability/develop-tracing/w3c-tracing-overview.md +++ /dev/null @@ -1,91 +0,0 @@ ---- -type: docs -title: "W3C trace context" -linkTitle: "W3C trace context" -weight: 2000 -description: Background and scenarios for using W3C tracing with Dapr -type: docs ---- - -Dapr uses the [Open Telemetry protocol](https://opentelemetry.io/), which in turn uses the [W3C trace context](https://www.w3.org/TR/trace-context/) for distributed tracing for both service invocation and pub/sub messaging. Dapr generates and propagates the trace context information, which can be sent to observability tools for visualization and querying. - -## Background - -Distributed tracing is a methodology implemented by tracing tools to follow, analyze, and debug a transaction across multiple software components. - -Typically, a distributed trace traverses more than one service, which requires it to be uniquely identifiable. **Trace context propagation** passes along this unique identification. - -In the past, trace context propagation was implemented individually by each different tracing vendor. In multi-vendor environments, this causes interoperability problems, such as: - -- Traces collected by different tracing vendors can't be correlated, as there is no shared unique identifier. -- Traces crossing boundaries between different tracing vendors can't be propagated, as there is no forwarded, uniformly agreed set of identification. -- Vendor-specific metadata might be dropped by intermediaries. -- Cloud platform vendors, intermediaries, and service providers cannot guarantee to support trace context propagation, as there is no standard to follow. - -Previously, most applications were monitored by a single tracing vendor and stayed within the boundaries of a single platform provider, so these problems didn't have a significant impact. - -Today, an increasing number of applications are distributed and leverage multiple middleware services and cloud platforms. This transformation of modern applications requires a distributed tracing context propagation standard. - -The [W3C trace context specification](https://www.w3.org/TR/trace-context/) defines a universally agreed-upon format for the exchange of trace context propagation data (referred to as trace context). Trace context solves the above problems by providing: - -- A unique identifier for individual traces and requests, allowing trace data of multiple providers to be linked together. -- An agreed-upon mechanism to forward vendor-specific trace data and avoid broken traces when multiple tracing tools participate in a single transaction. -- An industry standard that intermediaries, platforms, and hardware providers can support. - -This unified approach for propagating trace data improves visibility into the behavior of distributed applications, facilitating problem and performance analysis. - -## W3C trace context and headers format - -### W3C trace context - -Dapr uses the standard W3C trace context headers. - -- For HTTP requests, Dapr uses `traceparent` header. -- For gRPC requests, Dapr uses `grpc-trace-bin` header. - -When a request arrives without a trace ID, Dapr creates a new one. Otherwise, it passes the trace ID along the call chain. - -### W3C trace headers -These are the specific trace context headers that are generated and propagated by Dapr for HTTP and gRPC. - -{{< tabs "HTTP" "gRPC" >}} - -{{% codetab %}} - -Copy these headers when propagating a trace context header from an HTTP response to an HTTP request: - -**Traceparent header** - -The traceparent header represents the incoming request in a tracing system in a common format, understood by all vendors: - -``` -traceparent: 00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01 -``` - -[Learn more about the traceparent fields details](https://www.w3.org/TR/trace-context/#traceparent-header). - -**Tracestate header** - -The tracestate header includes the parent in a potentially vendor-specific format: - -``` -tracestate: congo=t61rcWkgMzE -``` - -[Learn more about the tracestate fields details](https://www.w3.org/TR/trace-context/#tracestate-header). - -{{% /codetab %}} - - - -{{% codetab %}} - -In the gRPC API calls, trace context is passed through `grpc-trace-bin` header. - -{{% /codetab %}} - -{{< /tabs >}} - -## Related Links -- [Learn more about distributed tracing in Dapr]({{< ref tracing-overview.md >}}) -- [W3C Trace Context specification](https://www.w3.org/TR/trace-context/) diff --git a/daprdocs/content/en/developing-applications/building-blocks/service-invocation/service-invocation-overview.md b/daprdocs/content/en/developing-applications/building-blocks/service-invocation/service-invocation-overview.md index 4d2ce7a2c43..b9d2a9c8b5f 100644 --- a/daprdocs/content/en/developing-applications/building-blocks/service-invocation/service-invocation-overview.md +++ b/daprdocs/content/en/developing-applications/building-blocks/service-invocation/service-invocation-overview.md @@ -135,5 +135,5 @@ For quick testing, try using the Dapr CLI for service invocation: ## Next steps - Read the [service invocation API specification]({{< ref service_invocation_api.md >}}). This reference guide for service invocation describes how to invoke methods on other services. - Understand the [service invocation performance numbers]({{< ref perf-service-invocation.md >}}). -- Take a look at [observability]({{< ref monitoring.md >}}). Here you can dig into Dapr's monitoring tools like tracing, metrics and logging. +- Take a look at [observability]({{< ref observability >}}). Here you can dig into Dapr's monitoring tools like tracing, metrics and logging. - Read up on our [security practices]({{< ref security-concept.md >}}) around mTLS encryption, token authentication, and endpoint authorization. diff --git a/daprdocs/content/en/operations/hosting/kubernetes/kubernetes-production.md b/daprdocs/content/en/operations/hosting/kubernetes/kubernetes-production.md index e958ea8c34c..e5a4bcdd96c 100644 --- a/daprdocs/content/en/operations/hosting/kubernetes/kubernetes-production.md +++ b/daprdocs/content/en/operations/hosting/kubernetes/kubernetes-production.md @@ -226,7 +226,7 @@ To configure a tracing backend for Dapr visit [this]({{< ref "setup-tracing.md" For metrics, Dapr exposes a Prometheus endpoint listening on port 9090 which can be scraped by Prometheus. -To setup Prometheus, Grafana and other monitoring tools with Dapr, visit [this]({{< ref "monitoring" >}}) link. +To setup Prometheus, Grafana and other monitoring tools with Dapr, visit [this]({{< ref "observability" >}}) link. ## Injector watchdog diff --git a/daprdocs/content/en/operations/monitoring/_index.md b/daprdocs/content/en/operations/monitoring/_index.md deleted file mode 100644 index decdfcabcc2..00000000000 --- a/daprdocs/content/en/operations/monitoring/_index.md +++ /dev/null @@ -1,7 +0,0 @@ ---- -type: docs -title: "Observe your application with Dapr" -linkTitle: "Observability" -weight: 80 -description: "How to observe and gain insights into your application" ---- diff --git a/daprdocs/content/en/operations/monitoring/logging/_index.md b/daprdocs/content/en/operations/monitoring/logging/_index.md deleted file mode 100644 index 444f4cc9c97..00000000000 --- a/daprdocs/content/en/operations/monitoring/logging/_index.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -type: docs -title: "Logging" -linkTitle: "Logging" -weight: 400 -description: "How to setup loggings for Dapr sidecar, and your application" ---- - diff --git a/daprdocs/content/en/operations/monitoring/logging/fluentd.md b/daprdocs/content/en/operations/monitoring/logging/fluentd.md deleted file mode 100644 index b456b8d953e..00000000000 --- a/daprdocs/content/en/operations/monitoring/logging/fluentd.md +++ /dev/null @@ -1,189 +0,0 @@ ---- -type: docs -title: "How-To: Set up Fluentd, Elastic search and Kibana in Kubernetes" -linkTitle: "FluentD" -weight: 2000 -description: "How to install Fluentd, Elastic Search, and Kibana to search logs in Kubernetes" ---- - -## Prerequisites - -- Kubernetes (> 1.14) -- [kubectl](https://kubernetes.io/docs/tasks/tools/) -- [Helm 3](https://helm.sh/) - -## Install Elastic search and Kibana - -1. Create a Kubernetes namespace for monitoring tools - - ```bash - kubectl create namespace dapr-monitoring - ``` - -2. Add the helm repo for Elastic Search - - ```bash - helm repo add elastic https://helm.elastic.co - helm repo update - ``` - -3. Install Elastic Search using Helm - - By default, the chart creates 3 replicas which must be on different nodes. If your cluster has fewer than 3 nodes, specify a smaller number of replicas. For example, this sets the number of replicas to 1: - - ```bash - helm install elasticsearch elastic/elasticsearch --version 7.17.3 -n dapr-monitoring --set replicas=1 - ``` - - Otherwise: - - ```bash - helm install elasticsearch elastic/elasticsearch --version 7.17.3 -n dapr-monitoring - ``` - - If you are using minikube or simply want to disable persistent volumes for development purposes, you can do so by using the following command: - - ```bash - helm install elasticsearch elastic/elasticsearch --version 7.17.3 -n dapr-monitoring --set persistence.enabled=false,replicas=1 - ``` - -4. Install Kibana - - ```bash - helm install kibana elastic/kibana --version 7.17.3 -n dapr-monitoring - ``` - -5. Ensure that Elastic Search and Kibana are running in your Kubernetes cluster - - ```bash - $ kubectl get pods -n dapr-monitoring - NAME READY STATUS RESTARTS AGE - elasticsearch-master-0 1/1 Running 0 6m58s - kibana-kibana-95bc54b89-zqdrk 1/1 Running 0 4m21s - ``` - -## Install Fluentd - -1. Install config map and Fluentd as a daemonset - - Download these config files: - - [fluentd-config-map.yaml](/docs/fluentd-config-map.yaml) - - [fluentd-dapr-with-rbac.yaml](/docs/fluentd-dapr-with-rbac.yaml) - - > Note: If you already have Fluentd running in your cluster, please enable the nested json parser so that it can parse JSON-formatted logs from Dapr. - - Apply the configurations to your cluster: - - ```bash - kubectl apply -f ./fluentd-config-map.yaml - kubectl apply -f ./fluentd-dapr-with-rbac.yaml - ``` - -2. Ensure that Fluentd is running as a daemonset. The number of FluentD instances should be the same as the number of cluster nodes. In the example below, there is only one node in the cluster: - - ```bash - $ kubectl get pods -n kube-system -w - NAME READY STATUS RESTARTS AGE - coredns-6955765f44-cxjxk 1/1 Running 0 4m41s - coredns-6955765f44-jlskv 1/1 Running 0 4m41s - etcd-m01 1/1 Running 0 4m48s - fluentd-sdrld 1/1 Running 0 14s - ``` - -## Install Dapr with JSON formatted logs - -1. Install Dapr with enabling JSON-formatted logs - - ```bash - helm repo add dapr https://dapr.github.io/helm-charts/ - helm repo update - helm install dapr dapr/dapr --namespace dapr-system --set global.logAsJson=true - ``` - -2. Enable JSON formatted log in Dapr sidecar - - Add the `dapr.io/log-as-json: "true"` annotation to your deployment yaml. For example: - - ```yaml - apiVersion: apps/v1 - kind: Deployment - metadata: - name: pythonapp - namespace: default - labels: - app: python - spec: - replicas: 1 - selector: - matchLabels: - app: python - template: - metadata: - labels: - app: python - annotations: - dapr.io/enabled: "true" - dapr.io/app-id: "pythonapp" - dapr.io/log-as-json: "true" - ... - ``` - -## Search logs - -> Note: Elastic Search takes a time to index the logs that Fluentd sends. - -1. Port-forward from localhost to `svc/kibana-kibana` - - ```bash - $ kubectl port-forward svc/kibana-kibana 5601 -n dapr-monitoring - Forwarding from 127.0.0.1:5601 -> 5601 - Forwarding from [::1]:5601 -> 5601 - Handling connection for 5601 - Handling connection for 5601 - ``` - -2. Browse to `http://localhost:5601` - -3. Expand the drop-down menu and click **Management → Stack Management** - - ![Stack Management item under Kibana Management menu options](/images/kibana-1.png) - -4. On the Stack Management page, select **Data → Index Management** and wait until `dapr-*` is indexed. - - ![Index Management view on Kibana Stack Management page](/images/kibana-2.png) - -5. Once `dapr-*` is indexed, click on **Kibana → Index Patterns** and then the **Create index pattern** button. - - ![Kibana create index pattern button](/images/kibana-3.png) - -6. Define a new index pattern by typing `dapr*` into the **Index Pattern name** field, then click the **Next step** button to continue. - - ![Kibana define an index pattern page](/images/kibana-4.png) - -7. Configure the primary time field to use with the new index pattern by selecting the `@timestamp` option from the **Time field** drop-down. Click the **Create index pattern** button to complete creation of the index pattern. - - ![Kibana configure settings page for creating an index pattern](/images/kibana-5.png) - -8. The newly created index pattern should be shown. Confirm that the fields of interest such as `scope`, `type`, `app_id`, `level`, etc. are being indexed by using the search box in the **Fields** tab. - - > Note: If you cannot find the indexed field, please wait. The time it takes to search across all indexed fields depends on the volume of data and size of the resource that the elastic search is running on. - - ![View of created Kibana index pattern](/images/kibana-6.png) - -9. To explore the indexed data, expand the drop-down menu and click **Analytics → Discover**. - - ![Discover item under Kibana Analytics menu options](/images/kibana-7.png) - -10. In the search box, type in a query string such as `scope:*` and click the **Refresh** button to view the results. - - > Note: This can take a long time. The time it takes to return all results depends on the volume of data and size of the resource that the elastic search is running on. - - ![Using the search box in the Kibana Analytics Discover page](/images/kibana-8.png) - -## References - -* [Fluentd for Kubernetes](https://docs.fluentd.org/v/0.12/articles/kubernetes-fluentd) -* [Elastic search helm chart](https://github.com/elastic/helm-charts/tree/master/elasticsearch) -* [Kibana helm chart](https://github.com/elastic/helm-charts/tree/master/kibana) -* [Kibana Query Language](https://www.elastic.co/guide/en/kibana/current/kuery-query.html) -* [Troubleshooting using Logs]({{< ref "logs-troubleshooting.md" >}}) diff --git a/daprdocs/content/en/operations/monitoring/logging/logs.md b/daprdocs/content/en/operations/monitoring/logging/logs.md deleted file mode 100644 index 397588b6969..00000000000 --- a/daprdocs/content/en/operations/monitoring/logging/logs.md +++ /dev/null @@ -1,133 +0,0 @@ ---- -type: docs -title: "Logs" -linkTitle: "Logs" -weight: 1000 -description: "Understand Dapr logging" ---- - -Dapr produces structured logs to stdout, either in plain-text or JSON-formatted. By default, all Dapr processes (runtime, or sidecar, and all control plane services) write logs to the console (stdout) in plain-text. To enable JSON-formatted logging, you need to add the `--log-as-json` command flag when running Dapr processes. - -{{% alert title="Note" color="primary" %}} -If you want to use a search engine such as Elastic Search or Azure Monitor to search the logs, it is strongly recommended to use JSON-formatted logs which the log collector and search engine can parse using the built-in JSON parser. -{{% /alert %}} - -## Log schema - -Dapr produces logs based on the following schema: - -| Field | Description | Example | -|-------|-------------------|---------| -| time | ISO8601 Timestamp | `2011-10-05T14:48:00.000Z` | -| level | Log Level (info/warn/debug/error) | `info` | -| type | Log Type | `log` | -| msg | Log Message | `hello dapr!` | -| scope | Logging Scope | `dapr.runtime` | -| instance | Container Name | `dapr-pod-xxxxx` | -| app_id | Dapr App ID | `dapr-app` | -| ver | Dapr Runtime Version | `1.9.0` | - -API logging may add other structured fields, as described in the [documentation for API logging]({{< ref "api-logs-troubleshooting.md" >}}). - -## Plain text and JSON formatted logs - -* Plain-text log examples - -```bash -time="2022-11-01T17:08:48.303776-07:00" level=info msg="starting Dapr Runtime -- version 1.9.0 -- commit v1.9.0-g5dfcf2e" instance=dapr-pod-xxxx scope=dapr.runtime type=log ver=1.9.0 -time="2022-11-01T17:08:48.303913-07:00" level=info msg="log level set to: info" instance=dapr-pod-xxxx scope=dapr.runtime type=log ver=1.9.0 -``` - -* JSON-formatted log examples - -```json -{"instance":"dapr-pod-xxxx","level":"info","msg":"starting Dapr Runtime -- version 1.9.0 -- commit v1.9.0-g5dfcf2e","scope":"dapr.runtime","time":"2022-11-01T17:09:45.788005Z","type":"log","ver":"1.9.0"} -{"instance":"dapr-pod-xxxx","level":"info","msg":"log level set to: info","scope":"dapr.runtime","time":"2022-11-01T17:09:45.788075Z","type":"log","ver":"1.9.0"} -``` - -## Log formats - -Dapr supports printing either plain-text, the default, or JSON-formatted logs. - -To use JSON-formatted logs, you need to add additional configuration options when you install Dapr and when deploy your apps. The recommendation is to use JSON-formatted logs because most log collectors and search engines can parse JSON more easily with built-in parsers. - -## Enabling JSON logging with the Dapr CLI - -When using the Dapr CLI to run an application, pass the `--log-as-json` option to enable JSON-formatted logs, for example: - -```sh -dapr run \ - --app-id orderprocessing \ - --resources-path ./components/ \ - --log-as-json \ - -- python3 OrderProcessingService.py -``` - -## Enabling JSON logging in Kubernetes - -The following steps describe how to configure JSON-formatted logs for Kubernetes - -### Dapr control plane - -All services in the Dapr control plane (such as `operator`, `sentry`, etc) support a `--log-as-json` option to enable JSON-formatted logging. - -If you're deploying Dapr to Kubernetes using a Helm chart, you can enable JSON-formatted logs for Dapr system services by passing the `--set global.logAsJson=true` option; for example: - -```bash -helm upgrade --install dapr \ - dapr/dapr \ - --namespace dapr-system \ - --set global.logAsJson=true -``` - -### Enable JSON-formatted log for Dapr sidecars - -You can enable JSON-formatted logs in Dapr sidecars by adding the `dapr.io/log-as-json: "true"` annotation to the deployment, for example: - -```yaml -apiVersion: apps/v1 -kind: Deployment -metadata: - name: pythonapp - labels: - app: python -spec: - selector: - matchLabels: - app: python - template: - metadata: - labels: - app: python - annotations: - dapr.io/enabled: "true" - dapr.io/app-id: "pythonapp" - # This enables JSON-formatted logging - dapr.io/log-as-json: "true" -... -``` - -## API Logging - -API logging enables you to see the API calls your application makes to the Dapr sidecar, to debug issues or monitor the behavior of your application. You can combine both Dapr API logging with Dapr log events. - -See [configure and view Dapr Logs]({{< ref "logs-troubleshooting.md" >}}) and [configure and view Dapr API Logs]({{< ref "api-logs-troubleshooting.md" >}}) for more information. - -## Log collectors - -If you run Dapr in a Kubernetes cluster, [Fluentd](https://www.fluentd.org/) is a popular container log collector. You can use Fluentd with a [JSON parser plugin](https://docs.fluentd.org/parser/json) to parse Dapr JSON-formatted logs. This [how-to]({{< ref fluentd.md >}}) shows how to configure Fluentd in your cluster. - -If you are using Azure Kubernetes Service, you can use the built-in agent to collect logs with Azure Monitor without needing to install Fluentd. - -## Search engines - -If you use [Fluentd](https://www.fluentd.org/), we recommend using Elastic Search and Kibana. This [how-to]({{< ref fluentd.md >}}) shows how to set up Elastic Search and Kibana in your Kubernetes cluster. - -If you are using the Azure Kubernetes Service, you can use [Azure Monitor for containers](https://docs.microsoft.com/azure/azure-monitor/insights/container-insights-overview) without installing any additional monitoring tools. Also read [How to enable Azure Monitor for containers](https://docs.microsoft.com/azure/azure-monitor/insights/container-insights-onboard) - -## References - -- [How-to: Set up Fleuntd, Elastic search, and Kibana]({{< ref fluentd.md >}}) -- [How-to: Set up Azure Monitor in Azure Kubernetes Service]({{< ref azure-monitor.md >}}) -- [Configure and view Dapr Logs]({{< ref "logs-troubleshooting.md" >}}) -- [Configure and view Dapr API Logs]({{< ref "api-logs-troubleshooting.md" >}}) diff --git a/daprdocs/content/en/operations/monitoring/logging/newrelic.md b/daprdocs/content/en/operations/monitoring/logging/newrelic.md deleted file mode 100644 index 9261d09ab44..00000000000 --- a/daprdocs/content/en/operations/monitoring/logging/newrelic.md +++ /dev/null @@ -1,78 +0,0 @@ ---- -type: docs -title: "How-To: Set-up New Relic for Dapr logging" -linkTitle: "New Relic" -weight: 3000 -description: "Set-up New Relic for Dapr logging" ---- - -## Prerequisites - -- Perpetually [free New Relic account](https://newrelic.com/signup?ref=dapr), 100 GB/month of free data ingest, 1 free full access user, unlimited free basic users - -## Background - -New Relic offers a [Fluent Bit](https://fluentbit.io/) output [plugin](https://github.com/newrelic/newrelic-fluent-bit-output) to easily forward your logs to [New Relic Logs](https://github.com/newrelic/newrelic-fluent-bit-output). This plugin is also provided in a standalone Docker image that can be installed in a Kubernetes cluster in the form of a DaemonSet, which we refer as the Kubernetes plugin. - -This document explains how to install it in your cluster, either using a Helm chart (recommended), or manually by applying Kubernetes manifests. - -## Installation - -### Install using the Helm chart (recommended) - -1. Install Helm following the official instructions. - -2. Add the New Relic official Helm chart repository following these instructions - -3. Run the following command to install the New Relic Logging Kubernetes plugin via Helm, replacing the placeholder value YOUR_LICENSE_KEY with your [New Relic license key](https://docs.newrelic.com/docs/accounts/accounts-billing/account-setup/new-relic-license-key/): - -- Helm 3 - ```bash - helm install newrelic-logging newrelic/newrelic-logging --set licenseKey=YOUR_LICENSE_KEY - ``` - -- Helm 2 - ```bash - helm install newrelic/newrelic-logging --name newrelic-logging --set licenseKey=YOUR_LICENSE_KEY - ``` - -For EU users, add `--set endpoint=https://log-api.eu.newrelic.com/log/v1 to any of the helm install commands above. - -By default, tailing is set to /var/log/containers/*.log. To change this setting, provide your preferred path by adding --set fluentBit.path=DESIRED_PATH to any of the helm install commands above. - -### Install the Kubernetes manifest - -1. Download the following 3 manifest files into your current working directory: - - ```bash - curl https://raw.githubusercontent.com/newrelic/helm-charts/master/charts/newrelic-logging/k8s/fluent-conf.yml > fluent-conf.yml - curl https://raw.githubusercontent.com/newrelic/helm-charts/master/charts/newrelic-logging/k8s/new-relic-fluent-plugin.yml > new-relic-fluent-plugin.yml - curl https://raw.githubusercontent.com/newrelic/helm-charts/master/charts/newrelic-logging/k8s/rbac.yml > rbac.yml - ``` - -2. In the downloaded new-relic-fluent-plugin.yml file, replace the placeholder value LICENSE_KEY with your New Relic license key. - - For EU users, replace the ENDPOINT environment variable to https://log-api.eu.newrelic.com/log/v1. - -3. Once the License key has been added, run the following command in your terminal or command-line interface: - ```bash - kubectl apply -f . - ``` - -4. [OPTIONAL] You can configure how the plugin parses the data by editing the parsers.conf section in the fluent-conf.yml file. For more information, see Fluent Bit's documentation on Parsers configuration. - - By default, tailing is set to /var/log/containers/*.log. To change this setting, replace the default path with your preferred path in the new-relic-fluent-plugin.yml file. - -## View Logs - -![Dapr Annotations](/images/nr-logging-1.png) - -![Search](/images/nr-logging-2.png) - -## Related Links/References - -* [New Relic Account Signup](https://newrelic.com/signup) -* [Telemetry Data Platform](https://newrelic.com/platform/telemetry-data-platform) -* [New Relic Logging](https://github.com/newrelic/helm-charts/tree/master/charts/newrelic-logging) -* [Types of New Relic API keys](https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/) -* [Alerts and Applied Intelligence](https://docs.newrelic.com/docs/alerts-applied-intelligence/overview/) diff --git a/daprdocs/content/en/operations/monitoring/metrics/_index.md b/daprdocs/content/en/operations/monitoring/metrics/_index.md deleted file mode 100644 index 14e6885c851..00000000000 --- a/daprdocs/content/en/operations/monitoring/metrics/_index.md +++ /dev/null @@ -1,7 +0,0 @@ ---- -type: docs -title: "Metrics" -linkTitle: "View metrics" -weight: 300 -description: "How to view Dapr metrics" ---- diff --git a/daprdocs/content/en/operations/monitoring/metrics/azure-monitor.md b/daprdocs/content/en/operations/monitoring/metrics/azure-monitor.md deleted file mode 100644 index 3011ef399b4..00000000000 --- a/daprdocs/content/en/operations/monitoring/metrics/azure-monitor.md +++ /dev/null @@ -1,134 +0,0 @@ ---- -type: docs -title: "How-To: Set up Azure Monitor to search logs and collect metrics" -linkTitle: "Azure Monitor" -weight: 7000 -description: "Enable Dapr metrics and logs with Azure Monitor for Azure Kubernetes Service (AKS)" ---- - -## Prerequisites - -- [Azure Kubernetes Service](https://docs.microsoft.com/azure/aks/) -- [Enable Azure Monitor For containers in AKS](https://docs.microsoft.com/azure/azure-monitor/insights/container-insights-overview) -- [kubectl](https://kubernetes.io/docs/tasks/tools/) -- [Helm 3](https://helm.sh/) - -## Enable Prometheus metric scrape using config map - -1. Make sure that Azure Monitor Agents (AMA) are running. - - ```bash - $ kubectl get pods -n kube-system - NAME READY STATUS RESTARTS AGE - ... - ama-logs-48kpv 2/2 Running 0 2d13h - ama-logs-mx24c 2/2 Running 0 2d13h - ama-logs-rs-f9bbb9898-vbt6k 1/1 Running 0 30h - ama-logs-sm2mz 2/2 Running 0 2d13h - ama-logs-z7p4c 2/2 Running 0 2d13h - ... - ``` - -1. Apply config map to enable Prometheus metrics endpoint scrape. - - You can use [azm-config-map.yaml](/docs/azm-config-map.yaml) to enable Prometheus metrics endpoint scrape. - - If you installed Dapr to a different namespace, you need to change the `monitor_kubernetes_pod_namespaces` array values. For example: - - ```yaml - ... - prometheus-data-collection-settings: |- - [prometheus_data_collection_settings.cluster] - interval = "1m" - monitor_kubernetes_pods = true - monitor_kubernetes_pods_namespaces = ["dapr-system", "default"] - [prometheus_data_collection_settings.node] - interval = "1m" - ... - ``` - - Apply config map: - - ```bash - kubectl apply -f ./azm-config.map.yaml - ``` - -## Install Dapr with JSON formatted logs - -1. Install Dapr with enabling JSON-formatted logs. - - ```bash - helm install dapr dapr/dapr --namespace dapr-system --set global.logAsJson=true - ``` - -1. Enable JSON formatted log in Dapr sidecar and add Prometheus annotations. - - > Note: The Azure Monitor Agents (AMA) only sends the metrics if the Prometheus annotations are set. - - Add `dapr.io/log-as-json: "true"` annotation to your deployment yaml. - - Example: - - ```yaml - apiVersion: apps/v1 - kind: Deployment - metadata: - name: pythonapp - namespace: default - labels: - app: python - spec: - replicas: 1 - selector: - matchLabels: - app: python - template: - metadata: - labels: - app: python - annotations: - dapr.io/enabled: "true" - dapr.io/app-id: "pythonapp" - dapr.io/log-as-json: "true" - prometheus.io/scrape: "true" - prometheus.io/port: "9090" - prometheus.io/path: "/" - - ... - ``` - -## Search metrics and logs with Azure Monitor - -1. Go to Azure Monitor in the Azure portal. - -1. Search Dapr **Logs**. - - Here is an example query, to parse JSON formatted logs and query logs from Dapr system processes. - - ``` - ContainerLog - | extend parsed=parse_json(LogEntry) - | project Time=todatetime(parsed['time']), app_id=parsed['app_id'], scope=parsed['scope'],level=parsed['level'], msg=parsed['msg'], type=parsed['type'], ver=parsed['ver'], instance=parsed['instance'] - | where level != "" - | sort by Time - ``` - -1. Search **Metrics**. - - This query, queries `process_resident_memory_bytes` Prometheus metrics for Dapr system processes and renders timecharts. - - ``` - InsightsMetrics - | where Namespace == "prometheus" and Name == "process_resident_memory_bytes" - | extend tags=parse_json(Tags) - | project TimeGenerated, Name, Val, app=tostring(tags['app']) - | summarize memInBytes=percentile(Val, 99) by bin(TimeGenerated, 1m), app - | where app startswith "dapr-" - | render timechart - ``` - -## References - -- [Configure scraping of Prometheus metrics with Azure Monitor for containers](https://docs.microsoft.com/azure/azure-monitor/insights/container-insights-prometheus-integration) -- [Configure agent data collection for Azure Monitor for containers](https://docs.microsoft.com/azure/azure-monitor/insights/container-insights-agent-config) -- [Azure Monitor Query](https://docs.microsoft.com/azure/azure-monitor/log-query/query-language) diff --git a/daprdocs/content/en/operations/monitoring/metrics/grafana.md b/daprdocs/content/en/operations/monitoring/metrics/grafana.md deleted file mode 100644 index 5d3949552ed..00000000000 --- a/daprdocs/content/en/operations/monitoring/metrics/grafana.md +++ /dev/null @@ -1,182 +0,0 @@ ---- -type: docs -title: "How-To: Observe metrics with Grafana" -linkTitle: "Grafana dashboards" -weight: 5000 -description: "How to view Dapr metrics in a Grafana dashboard." ---- - -## Available dashboards - -{{< tabs "System Service" "Sidecars" "Actors" >}} - -{{% codetab %}} -The `grafana-system-services-dashboard.json` template shows Dapr system component status, dapr-operator, dapr-sidecar-injector, dapr-sentry, and dapr-placement: - -Screenshot of the system service dashboard -{{% /codetab %}} - -{{% codetab %}} -The `grafana-sidecar-dashboard.json` template shows Dapr sidecar status, including sidecar health/resources, throughput/latency of HTTP and gRPC, Actor, mTLS, etc.: - -Screenshot of the sidecar dashboard -{{% /codetab %}} - -{{% codetab %}} -The `grafana-actor-dashboard.json` template shows Dapr Sidecar status, actor invocation throughput/latency, timer/reminder triggers, and turn-based concurrnecy: - -Screenshot of the actor dashboard -{{% /codetab %}} - -{{< /tabs >}} - -## Pre-requisites - -- [Setup Prometheus]({{}}) - -## Setup on Kubernetes - -### Install Grafana - -1. Add the Grafana Helm repo: - - ```bash - helm repo add grafana https://grafana.github.io/helm-charts - helm repo update - ``` - -1. Install the chart: - - ```bash - helm install grafana grafana/grafana -n dapr-monitoring - ``` - - {{% alert title="Note" color="primary" %}} - If you are Minikube user or want to disable persistent volume for development purpose, you can disable it by using the following command instead: - - ```bash - helm install grafana grafana/grafana -n dapr-monitoring --set persistence.enabled=false - ``` - {{% /alert %}} - - -1. Retrieve the admin password for Grafana login: - - ```bash - kubectl get secret --namespace dapr-monitoring grafana -o jsonpath="{.data.admin-password}" | base64 --decode ; echo - ``` - - You will get a password similar to `cj3m0OfBNx8SLzUlTx91dEECgzRlYJb60D2evof1%`. Remove the `%` character from the password to get `cj3m0OfBNx8SLzUlTx91dEECgzRlYJb60D2evof1` as the admin password. - -1. Validation Grafana is running in your cluster: - - ```bash - kubectl get pods -n dapr-monitoring - - NAME READY STATUS RESTARTS AGE - dapr-prom-kube-state-metrics-9849d6cc6-t94p8 1/1 Running 0 4m58s - dapr-prom-prometheus-alertmanager-749cc46f6-9b5t8 2/2 Running 0 4m58s - dapr-prom-prometheus-node-exporter-5jh8p 1/1 Running 0 4m58s - dapr-prom-prometheus-node-exporter-88gbg 1/1 Running 0 4m58s - dapr-prom-prometheus-node-exporter-bjp9f 1/1 Running 0 4m58s - dapr-prom-prometheus-pushgateway-688665d597-h4xx2 1/1 Running 0 4m58s - dapr-prom-prometheus-server-694fd8d7c-q5d59 2/2 Running 0 4m58s - grafana-c49889cff-x56vj 1/1 Running 0 5m10s - ``` - -### Configure Prometheus as data source -First you need to connect Prometheus as a data source to Grafana. - -1. Port-forward to svc/grafana: - - ```bash - kubectl port-forward svc/grafana 8080:80 -n dapr-monitoring - - Forwarding from 127.0.0.1:8080 -> 3000 - Forwarding from [::1]:8080 -> 3000 - Handling connection for 8080 - Handling connection for 8080 - ``` - -1. Open a browser to `http://localhost:8080` - -1. Login to Grafana - - Username = `admin` - - Password = Password from above - -1. Select `Configuration` and `Data Sources` - - Screenshot of the Grafana add Data Source menu - - -1. Add Prometheus as a data source. - - Screenshot of the Prometheus add Data Source - -1. Get your Prometheus HTTP URL - - The Prometheus HTTP URL follows the format `http://.` - - Start by getting the Prometheus server endpoint by running the following command: - - ```bash - kubectl get svc -n dapr-monitoring - - NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE - dapr-prom-kube-state-metrics ClusterIP 10.0.174.177 8080/TCP 7d9h - dapr-prom-prometheus-alertmanager ClusterIP 10.0.255.199 80/TCP 7d9h - dapr-prom-prometheus-node-exporter ClusterIP None 9100/TCP 7d9h - dapr-prom-prometheus-pushgateway ClusterIP 10.0.190.59 9091/TCP 7d9h - dapr-prom-prometheus-server ClusterIP 10.0.172.191 80/TCP 7d9h - elasticsearch-master ClusterIP 10.0.36.146 9200/TCP,9300/TCP 7d10h - elasticsearch-master-headless ClusterIP None 9200/TCP,9300/TCP 7d10h - grafana ClusterIP 10.0.15.229 80/TCP 5d5h - kibana-kibana ClusterIP 10.0.188.224 5601/TCP 7d10h - - ``` - - In this guide the server name is `dapr-prom-prometheus-server` and the namespace is `dapr-monitoring`, so the HTTP URL will be `http://dapr-prom-prometheus-server.dapr-monitoring`. - -1. Fill in the following settings: - - - Name: `Dapr` - - HTTP URL: `http://dapr-prom-prometheus-server.dapr-monitoring` - - Default: On - - Skip TLS Verify: On - - Necessary in order to save and test the configuration - - Screenshot of the Prometheus Data Source configuration - -1. Click `Save & Test` button to verify that the connection succeeded. - -## Import dashboards in Grafana - -1. In the upper left corner of the Grafana home screen, click the "+" option, then "Import". - - You can now import [Grafana dashboard templates](https://github.com/dapr/dapr/tree/master/grafana) from [release assets](https://github.com/dapr/dapr/releases) for your Dapr version: - - Screenshot of the Grafana dashboard upload option - -1. Find the dashboard that you imported and enjoy - - Screenshot of Dapr service dashboard - - {{% alert title="Tip" color="primary" %}} - Hover your mouse over the `i` in the corner to the description of each chart: - - Screenshot of the tooltip for graphs - {{% /alert %}} - -## References - -* [Dapr Observability]({{}}) -* [Prometheus Installation](https://github.com/prometheus-community/helm-charts) -* [Prometheus on Kubernetes](https://github.com/coreos/kube-prometheus) -* [Prometheus Query Language](https://prometheus.io/docs/prometheus/latest/querying/basics/) -* [Supported Dapr metrics](https://github.com/dapr/dapr/blob/master/docs/development/dapr-metrics.md) - -## Example - -
- -
diff --git a/daprdocs/content/en/operations/monitoring/metrics/metrics-overview.md b/daprdocs/content/en/operations/monitoring/metrics/metrics-overview.md deleted file mode 100644 index 0d30185dbb8..00000000000 --- a/daprdocs/content/en/operations/monitoring/metrics/metrics-overview.md +++ /dev/null @@ -1,109 +0,0 @@ ---- -type: docs -title: "Configure metrics" -linkTitle: "Configure metrics" -weight: 4000 -description: "Enable or disable Dapr metrics " ---- - -By default, each Dapr system process emits Go runtime/process metrics and has their own [Dapr metrics](https://github.com/dapr/dapr/blob/master/docs/development/dapr-metrics.md). - -## Prometheus endpoint -The Dapr sidecars exposes a [Prometheus](https://prometheus.io/) metrics endpoint that you can scrape to gain a greater understanding of how Dapr is behaving. - -## Configuring metrics using the CLI - -The metrics application endpoint is enabled by default. You can disable it by passing the command line argument `--enable-metrics=false`. - -The default metrics port is `9090`. You can override this by passing the command line argument `--metrics-port` to Daprd. - -## Configuring metrics in Kubernetes -You can also enable/disable the metrics for a specific application by setting the `dapr.io/enable-metrics: "false"` annotation on your application deployment. With the metrics exporter disabled, `daprd` does not open the metrics listening port. - -The following Kubernetes deployment example shows how metrics are explicitly enabled with the port specified as "9090". - -```yaml -apiVersion: apps/v1 -kind: Deployment -metadata: - name: nodeapp - labels: - app: node -spec: - replicas: 1 - selector: - matchLabels: - app: node - template: - metadata: - labels: - app: node - annotations: - dapr.io/enabled: "true" - dapr.io/app-id: "nodeapp" - dapr.io/app-port: "3000" - dapr.io/enable-metrics: "true" - dapr.io/metrics-port: "9090" - spec: - containers: - - name: node - image: dapriosamples/hello-k8s-node:latest - ports: - - containerPort: 3000 - imagePullPolicy: Always -``` - -## Configuring metrics using application configuration -You can also enable metrics via application configuration. To disable the metrics collection in the Dapr sidecars running in a specific namespace: - -- Use the `metrics` spec configuration. -- Set `enabled: false` to disable the metrics in the Dapr runtime. - -```yaml -apiVersion: dapr.io/v1alpha1 -kind: Configuration -metadata: - name: tracing - namespace: default -spec: - tracing: - samplingRate: "1" - metrics: - enabled: false -``` - -## High cardinality metrics - -Depending on your use case, some metrics emitted by Dapr might contain values that have a high cardinality. This might cause increased memory usage for the Dapr process/container and incur expensive egress costs in certain cloud environments. To mitigate this issue, you can set regular expressions for every metric exposed by the Dapr sidecar. [See a list of all Dapr metrics](https://github.com/dapr/dapr/blob/master/docs/development/dapr-metrics.md). - -The following example shows how to apply a regular expression for the label `method` in the metric `dapr_runtime_service_invocation_req_sent_total`: - -```yaml -apiVersion: dapr.io/v1alpha1 -kind: Configuration -metadata: - name: daprConfig -spec: - metric: - enabled: true - rules: - - name: dapr_runtime_service_invocation_req_sent_total - labels: - - name: method - regex: - "orders/": "orders/.+" -``` - -When this configuration is applied, a recorded metric with the `method` label of `orders/a746dhsk293972nz` will be replaced with `orders/`. - -### Watch the demo - -Watch [this video to walk through handling high cardinality metrics](https://youtu.be/pOT8teL6j_k?t=1524): - - - - -## References - -* [Howto: Run Prometheus locally]({{< ref prometheus.md >}}) -* [Howto: Set up Prometheus and Grafana for metrics]({{< ref grafana.md >}}) diff --git a/daprdocs/content/en/operations/monitoring/metrics/newrelic.md b/daprdocs/content/en/operations/monitoring/metrics/newrelic.md deleted file mode 100644 index 92133d65bb1..00000000000 --- a/daprdocs/content/en/operations/monitoring/metrics/newrelic.md +++ /dev/null @@ -1,43 +0,0 @@ ---- -type: docs -title: "How-To: Set-up New Relic to collect and analyze metrics" -linkTitle: "New Relic" -weight: 6000 -description: "Set-up New Relic for Dapr metrics" ---- - -## Prerequisites - -- Perpetually [free New Relic account](https://newrelic.com/signup?ref=dapr), 100 GB/month of free data ingest, 1 free full access user, unlimited free basic users - -## Background - -New Relic offers a Prometheus OpenMetrics Integration. - -This document explains how to install it in your cluster, either using a Helm chart (recommended). - -## Installation - -1. Install Helm following the official instructions. - -2. Add the New Relic official Helm chart repository following [these instructions](https://github.com/newrelic/helm-charts/blob/master/README.md#installing-charts) - -3. Run the following command to install the New Relic Logging Kubernetes plugin via Helm, replacing the placeholder value YOUR_LICENSE_KEY with your [New Relic license key](https://docs.newrelic.com/docs/accounts/accounts-billing/account-setup/new-relic-license-key): - - ```bash - helm install nri-prometheus newrelic/nri-prometheus --set licenseKey=YOUR_LICENSE_KEY - ``` - -## View Metrics - -![Dapr Metrics](/images/nr-metrics-1.png) - -![Dashboard](/images/nr-dashboard-dapr-metrics-1.png) - -## Related Links/References - -* [New Relic Account Signup](https://newrelic.com/signup) -* [Telemetry Data Platform](https://newrelic.com/platform/telemetry-data-platform) -* [New Relic Prometheus OpenMetrics Integration](https://github.com/newrelic/helm-charts/tree/master/charts/nri-prometheus) -* [Types of New Relic API keys](https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/) -* [Alerts and Applied Intelligence](https://docs.newrelic.com/docs/alerts-applied-intelligence/overview/) diff --git a/daprdocs/content/en/operations/monitoring/metrics/prometheus.md b/daprdocs/content/en/operations/monitoring/metrics/prometheus.md deleted file mode 100644 index 3c787602f85..00000000000 --- a/daprdocs/content/en/operations/monitoring/metrics/prometheus.md +++ /dev/null @@ -1,122 +0,0 @@ ---- -type: docs -title: "How-To: Observe metrics with Prometheus" -linkTitle: "Prometheus" -weight: 4000 -description: "Use Prometheus to collect time-series data relating to the execution of the Dapr runtime itself" ---- - -## Setup Prometheus Locally -To run Prometheus on your local machine, you can either [install and run it as a process](#install) or run it as a [Docker container](#Run-as-Container). - -### Install -{{% alert title="Note" color="warning" %}} -You don't need to install Prometheus if you plan to run it as a Docker container. Please refer to the [Container](#run-as-container) instructions. -{{% /alert %}} - -To install Prometheus, follow the steps outlined [here](https://prometheus.io/docs/prometheus/latest/getting_started/) for your OS. - -### Configure -Now you've installed Prometheus, you need to create a configuration. - -Below is an example Prometheus configuration, save this to a file i.e. `/tmp/prometheus.yml` or `C:\Temp\prometheus.yml` -```yaml -global: - scrape_interval: 15s # By default, scrape targets every 15 seconds. - -# A scrape configuration containing exactly one endpoint to scrape: -# Here it's Prometheus itself. -scrape_configs: - - job_name: 'dapr' - - # Override the global default and scrape targets from this job every 5 seconds. - scrape_interval: 5s - - static_configs: - - targets: ['localhost:9090'] # Replace with Dapr metrics port if not default -``` - -### Run as Process -Run Prometheus with your configuration to start it collecting metrics from the specified targets. -```bash -./prometheus --config.file=/tmp/prometheus.yml --web.listen-address=:8080 -``` -> We change the port so it doesn't conflict with Dapr's own metrics endpoint. - -If you are not currently running a Dapr application, the target will show as offline. In order to start -collecting metrics you must start Dapr with the metrics port matching the one provided as the target in the configuration. - -Once Prometheus is running, you'll be able to visit its dashboard by visiting `http://localhost:8080`. - -### Run as Container -To run Prometheus as a Docker container on your local machine, first ensure you have [Docker](https://docs.docker.com/install/) installed and running. - -Then you can run Prometheus as a Docker container using: -```bash -docker run \ - --net=host \ - -v /tmp/prometheus.yml:/etc/prometheus/prometheus.yml \ - prom/prometheus --config.file=/etc/prometheus/prometheus.yml --web.listen-address=:8080 -``` -`--net=host` ensures that the Prometheus instance will be able to connect to any Dapr instances running on the host machine. If you plan to run your Dapr apps in containers as well, you'll need to run them on a shared Docker network and update the configuration with the correct target address. - -Once Prometheus is running, you'll be able to visit its dashboard by visiting `http://localhost:8080`. - -## Setup Prometheus on Kubernetes - -### Prerequisites - -- Kubernetes (> 1.14) -- [kubectl](https://kubernetes.io/docs/tasks/tools/) -- [Helm 3](https://helm.sh/) - -### Install Prometheus - -1. First create namespace that can be used to deploy the Grafana and Prometheus monitoring tools - -```bash -kubectl create namespace dapr-monitoring -``` - -2. Install Prometheus - -```bash -helm repo add prometheus-community https://prometheus-community.github.io/helm-charts -helm repo update -helm install dapr-prom prometheus-community/prometheus -n dapr-monitoring -``` - -If you are Minikube user or want to disable persistent volume for development purposes, you can disable it by using the following command. - -```bash -helm install dapr-prom prometheus-community/prometheus -n dapr-monitoring - --set alertmanager.persistence.enabled=false --set pushgateway.persistentVolume.enabled=false --set server.persistentVolume.enabled=false -``` - -3. Validation - -Ensure Prometheus is running in your cluster. - -```bash -kubectl get pods -n dapr-monitoring - -NAME READY STATUS RESTARTS AGE -dapr-prom-kube-state-metrics-9849d6cc6-t94p8 1/1 Running 0 4m58s -dapr-prom-prometheus-alertmanager-749cc46f6-9b5t8 2/2 Running 0 4m58s -dapr-prom-prometheus-node-exporter-5jh8p 1/1 Running 0 4m58s -dapr-prom-prometheus-node-exporter-88gbg 1/1 Running 0 4m58s -dapr-prom-prometheus-node-exporter-bjp9f 1/1 Running 0 4m58s -dapr-prom-prometheus-pushgateway-688665d597-h4xx2 1/1 Running 0 4m58s -dapr-prom-prometheus-server-694fd8d7c-q5d59 2/2 Running 0 4m58s -``` - -## Example - -
- -
- -## References - -* [Prometheus Installation](https://github.com/prometheus-community/helm-charts) -* [Prometheus Query Language](https://prometheus.io/docs/prometheus/latest/querying/basics/) diff --git a/daprdocs/content/en/operations/monitoring/tracing/_index.md b/daprdocs/content/en/operations/monitoring/tracing/_index.md deleted file mode 100644 index d9948ff4bfb..00000000000 --- a/daprdocs/content/en/operations/monitoring/tracing/_index.md +++ /dev/null @@ -1,7 +0,0 @@ ---- -type: docs -title: "Tracing" -linkTitle: "Tracing" -weight: 100 -description: "How to setup your observability tools to receive application traces" ---- diff --git a/daprdocs/content/en/operations/monitoring/tracing/datadog.md b/daprdocs/content/en/operations/monitoring/tracing/datadog.md deleted file mode 100644 index 3742cf408f0..00000000000 --- a/daprdocs/content/en/operations/monitoring/tracing/datadog.md +++ /dev/null @@ -1,55 +0,0 @@ ---- -type: docs -title: "How-To: Set up Datadog for distributed tracing" -linkTitle: "Datadog" -weight: 5000 -description: "Set up Datadog for distributed tracing" ---- - -Dapr captures metrics and traces that can be sent directly to Datadog through the OpenTelemetry Collector Datadog exporter. - -## Configure Dapr tracing with the OpenTelemetry Collector and Datadog - -Using the OpenTelemetry Collector Datadog exporter, you can configure Dapr to create traces for each application in your Kubernetes cluster and collect them in Datadog. - -> Before you begin, [set up the OpenTelemetry Collector]({{< ref "open-telemetry-collector.md#setting-opentelemetry-collector" >}}). - -1. Add your Datadog API key to the `./deploy/opentelemetry-collector-generic-datadog.yaml` file in the `datadog` exporter configuration section: - ```yaml - data: - otel-collector-config: - ... - exporters: - ... - datadog: - api: - key: - ``` - -1. Apply the `opentelemetry-collector` configuration by running the following command. - - ```sh - kubectl apply -f ./deploy/open-telemetry-collector-generic-datadog.yaml - ``` - -1. Set up a Dapr configuration file that will turn on tracing and deploy a tracing exporter component that uses the OpenTelemetry Collector. - - ```sh - kubectl apply -f ./deploy/collector-config.yaml - -1. Apply the `appconfig` configuration by adding a `dapr.io/config` annotation to the container that you want to participate in the distributed tracing. - - ```yml - annotations: - dapr.io/config: "appconfig" - -1. Create and configure the application. Once running, telemetry data is sent to Datadog and visible in Datadog APM. - -Datadog APM showing telemetry data. - - -## Related Links/References - -* [Complete example of setting up Dapr on a Kubernetes cluster](https://github.com/ericmustin/quickstarts/tree/master/hello-kubernetes) -* [Datadog documentation about OpenTelemetry support](https://docs.datadoghq.com/opentelemetry/) -* [Datadog Application Performance Monitoring](https://docs.datadoghq.com/tracing/) \ No newline at end of file diff --git a/daprdocs/content/en/operations/monitoring/tracing/jaeger.md b/daprdocs/content/en/operations/monitoring/tracing/jaeger.md deleted file mode 100644 index 7ef470a37fe..00000000000 --- a/daprdocs/content/en/operations/monitoring/tracing/jaeger.md +++ /dev/null @@ -1,187 +0,0 @@ ---- -type: docs -title: "How-To: Set up Jaeger for distributed tracing" -linkTitle: "Jaeger" -weight: 3000 -description: "Set up Jaeger for distributed tracing" -type: docs ---- - -Dapr supports the Zipkin protocol. Since Jaeger is compatible with Zipkin, the Zipkin protocol can be used to communication with Jaeger. - -## Configure self hosted mode - -### Setup - -The simplest way to start Jaeger is to use the pre-built all-in-one Jaeger image published to DockerHub: - -```bash -docker run -d --name jaeger \ - -e COLLECTOR_ZIPKIN_HOST_PORT=:9412 \ - -p 16686:16686 \ - -p 9412:9412 \ - jaegertracing/all-in-one:1.22 -``` - - -Next, create the following YAML files locally: - -* **config.yaml**: Note that because we are using the Zipkin protocol -to talk to Jaeger, we specify the `zipkin` section of tracing -configuration set the `endpointAddress` to address of the Jaeger -instance. - -```yaml -apiVersion: dapr.io/v1alpha1 -kind: Configuration -metadata: - name: tracing - namespace: default -spec: - tracing: - samplingRate: "1" - zipkin: - endpointAddress: "http://localhost:9412/api/v2/spans" -``` - -To launch the application referring to the new YAML file, you can use -`--config` option: - -```bash -dapr run --app-id mynode --app-port 3000 node app.js --config config.yaml -``` - -### Viewing Traces -To view traces, in your browser go to http://localhost:16686 to see the Jaeger UI. - -## Configure Kubernetes -The following steps shows you how to configure Dapr to send distributed tracing data to Jaeger running as a container in your Kubernetes cluster, how to view them. - -### Setup - -First create the following YAML file to install Jaeger, file name is `jaeger-operator.yaml` - -#### Development and test - -By default, the allInOne Jaeger image uses memory as the backend storage and it is not recommended to use this in a production environment. - -```yaml -apiVersion: jaegertracing.io/v1 -kind: "Jaeger" -metadata: - name: jaeger -spec: - strategy: allInOne - ingress: - enabled: false - allInOne: - image: jaegertracing/all-in-one:1.22 - options: - query: - base-path: /jaeger -``` - -#### Production -Jaeger uses Elasticsearch as the backend storage, and you can create a secret in k8s cluster to access Elasticsearch server with access control. - - -```shell -kubectl create secret generic jaeger-secret --from-literal=ES_PASSWORD='xxx' --from-literal=ES_USERNAME='xxx' -n ${NAMESPACE} -``` - -```yaml -apiVersion: jaegertracing.io/v1 -kind: "Jaeger" -metadata: - name: jaeger -spec: - strategy: production - query: - options: - log-level: info - query: - base-path: /jaeger - collector: - maxReplicas: 5 - resources: - limits: - cpu: 500m - memory: 516Mi - storage: - type: elasticsearch - esIndexCleaner: - enabled: false ## turn the job deployment on and off - numberOfDays: 7 ## number of days to wait before deleting a record - schedule: "55 23 * * *" ## cron expression for it to run - image: jaegertracing/jaeger-es-index-cleaner ## image of the job - secretName: jaeger-secret - options: - es: - server-urls: http://elasticsearch:9200 -``` - -The pictures are as follows, include Elasticsearch and Grafana tracing data: - -![jaeger-storage-es](/images/jaeger_storage_elasticsearch.png) - -![grafana](/images/jaeger_grafana.png) - - -Now, use the above YAML file to install Jaeger - -```bash -# Install Jaeger -helm repo add jaegertracing https://jaegertracing.github.io/helm-charts -helm install jaeger-operator jaegertracing/jaeger-operator -kubectl apply -f jaeger-operator.yaml - -# Wait for Jaeger to be up and running -kubectl wait deploy --selector app.kubernetes.io/name=jaeger --for=condition=available -``` - -Next, create the following YAML file locally: - -* **tracing.yaml** - -```yaml -apiVersion: dapr.io/v1alpha1 -kind: Configuration -metadata: - name: tracing - namespace: default -spec: - tracing: - samplingRate: "1" - zipkin: - endpointAddress: "http://jaeger-collector.default.svc.cluster.local:9411/api/v2/spans" -``` - -Finally, deploy the the Dapr component and configuration files: - -```bash -kubectl apply -f tracing.yaml -``` - -In order to enable this configuration for your Dapr sidecar, add the following annotation to your pod spec template: - -```yml -annotations: - dapr.io/config: "tracing" -``` - -That's it! Your Dapr sidecar is now configured for use with Jaeger. - -### Viewing Tracing Data - -To view traces, connect to the Jaeger Service and open the UI: - -```bash -kubectl port-forward svc/jaeger-query 16686 -``` - -In your browser, go to `http://localhost:16686` and you will see the Jaeger UI. - -![jaeger](/images/jaeger_ui.png) - -## References -- [Jaeger Getting Started](https://www.jaegertracing.io/docs/1.21/getting-started/#all-in-one) diff --git a/daprdocs/content/en/operations/monitoring/tracing/newrelic.md b/daprdocs/content/en/operations/monitoring/tracing/newrelic.md deleted file mode 100644 index 183ab7e57d7..00000000000 --- a/daprdocs/content/en/operations/monitoring/tracing/newrelic.md +++ /dev/null @@ -1,114 +0,0 @@ ---- -type: docs -title: "How-To: Set-up New Relic for distributed tracing" -linkTitle: "New Relic" -weight: 2000 -description: "Set-up New Relic for distributed tracing" ---- - -## Prerequisites - -- Perpetually [free New Relic account](https://newrelic.com/signup?ref=dapr), 100 GB/month of free data ingest, 1 free full access user, unlimited free basic users - -## Configure Dapr tracing - -Dapr natively captures metrics and traces that can be send directly to New Relic. The easiest way to export these is by configuring Dapr to send the traces to [New Relic's Trace API](https://docs.newrelic.com/docs/distributed-tracing/trace-api/report-zipkin-format-traces-trace-api/) using the Zipkin trace format. - -In order for the integration to send data to New Relic [Telemetry Data Platform](https://newrelic.com/platform/telemetry-data-platform), you need a [New Relic Insights Insert API key](https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/#insights-insert-key). - -```yaml -apiVersion: dapr.io/v1alpha1 -kind: Configuration -metadata: - name: appconfig - namespace: default -spec: - tracing: - samplingRate: "1" - zipkin: - endpointAddress: "https://trace-api.newrelic.com/trace/v1?Api-Key=&Data-Format=zipkin&Data-Format-Version=2" -``` - -### Viewing Traces - -New Relic Distributed Tracing overview -![New Relic Kubernetes Cluster Explorer App](/images/nr-distributed-tracing-overview.png) - -New Relic Distributed Tracing details -![New Relic Kubernetes Cluster Explorer App](/images/nr-distributed-tracing-detail.png) - -## (optional) New Relic Instrumentation - -In order for the integrations to send data to New Relic Telemetry Data Platform, you either need a [New Relic license key](https://docs.newrelic.com/docs/accounts/accounts-billing/account-setup/new-relic-license-key) or [New Relic Insights Insert API key](https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/#insights-insert-key). - -### OpenTelemetry instrumentation - -Leverage the different language specific OpenTelemetry implementations, for example [New Relic Telemetry SDK and OpenTelemetry support for .NET](https://github.com/newrelic/newrelic-telemetry-sdk-dotnet). In this case, use the [OpenTelemetry Trace Exporter](https://github.com/newrelic/newrelic-telemetry-sdk-dotnet/tree/main/src/NewRelic.OpenTelemetry). See example [here](https://github.com/harrykimpel/quickstarts/blob/master/distributed-calculator/csharp-otel/Startup.cs). - -### New Relic Language agent - -Similarly to the OpenTelemetry instrumentation, you can also leverage a New Relic language agent. As an example, the [New Relic agent instrumentation for .NET Core](https://docs.newrelic.com/docs/agents/net-agent/other-installation/install-net-agent-docker-container) is part of the Dockerfile. See example [here](https://github.com/harrykimpel/quickstarts/blob/master/distributed-calculator/csharp/Dockerfile). - -## (optional) Enable New Relic Kubernetes integration - -In case Dapr and your applications run in the context of a Kubernetes environment, you can enable additional metrics and logs. - -The easiest way to install the New Relic Kubernetes integration is to use the [automated installer](https://one.newrelic.com/launcher/nr1-core.settings?pane=eyJuZXJkbGV0SWQiOiJrOHMtY2x1c3Rlci1leHBsb3Jlci1uZXJkbGV0Lms4cy1zZXR1cCJ9) to generate a manifest. It bundles not just the integration DaemonSets, but also other New Relic Kubernetes configurations, like [Kubernetes events](https://docs.newrelic.com/docs/integrations/kubernetes-integration/kubernetes-events/install-kubernetes-events-integration), [Prometheus OpenMetrics](https://docs.newrelic.com/docs/integrations/prometheus-integrations/get-started/send-prometheus-metric-data-new-relic/), and [New Relic log monitoring](https://docs.newrelic.com/docs/logs/ui-data/use-logs-ui/). - -### New Relic Kubernetes Cluster Explorer - -The [New Relic Kubernetes Cluster Explorer](https://docs.newrelic.com/docs/integrations/kubernetes-integration/understand-use-data/kubernetes-cluster-explorer) provides a unique visualization of the entire data and deployments of the data collected by the Kubernetes integration. - -It is a good starting point to observe all your data and dig deeper into any performance issues or incidents happening inside of the application or microservices. - -![New Relic Kubernetes Cluster Explorer App](/images/nr-k8s-cluster-explorer-app.png) - -Automated correlation is part of the visualization capabilities of New Relic. - -### Pod-level details - -![New Relic K8s Pod Level Details](/images/nr-k8s-pod-level-details.png) - -### Logs in Context - -![New Relic K8s Logs In Context](/images/nr-k8s-logs-in-context.png) - -## New Relic Dashboards - -### Kubernetes Overview - -![New Relic Dashboard Kubernetes Overview](/images/nr-dashboard-k8s-overview.png) - -### Dapr System Services - -![New Relic Dashboard Dapr System Services](/images/nr-dashboard-dapr-system-services.png) - -### Dapr Metrics - -![New Relic Dashboard Dapr Metrics 1](/images/nr-dashboard-dapr-metrics-1.png) - -## New Relic Grafana integration - -New Relic teamed up with [Grafana Labs](https://grafana.com/) so you can use the [Telemetry Data Platform](https://newrelic.com/platform/telemetry-data-platform) as a data source for Prometheus metrics and see them in your existing dashboards, seamlessly tapping into the reliability, scale, and security provided by New Relic. - -[Grafana dashboard templates](https://github.com/dapr/dapr/blob/227028e7b76b7256618cd3236d70c1d4a4392c9a/grafana/README.md) to monitor Dapr system services and sidecars can easily be used without any changes. New Relic provides a [native endpoint for Prometheus metrics](https://docs.newrelic.com/docs/integrations/grafana-integrations/set-configure/configure-new-relic-prometheus-data-source-grafana) into Grafana. A datasource can easily be set-up: - -![New Relic Grafana Data Source](/images/nr-grafana-datasource.png) - -And the exact same dashboard templates from Dapr can be imported to visualize Dapr system services and sidecars. - -![New Relic Grafana Dashboard](/images/nr-grafana-dashboard.png) - -## New Relic Alerts - -All the data that is collected from Dapr, Kubernetes or any services that run on top of can be used to set-up alerts and notifications into the preferred channel of your choice. See [Alerts and Applied Intelligence](https://docs.newrelic.com/docs/alerts-applied-intelligence/overview/). - -## Related Links/References - -* [New Relic Account Signup](https://newrelic.com/signup) -* [Telemetry Data Platform](https://newrelic.com/platform/telemetry-data-platform) -* [Distributed Tracing](https://docs.newrelic.com/docs/distributed-tracing/concepts/introduction-distributed-tracing/) -* [New Relic Trace API](https://docs.newrelic.com/docs/distributed-tracing/trace-api/introduction-trace-api/) -* [Types of New Relic API keys](https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/) -* [New Relic OpenTelemetry User Experience](https://blog.newrelic.com/product-news/opentelemetry-user-experience/) -* [Alerts and Applied Intelligence](https://docs.newrelic.com/docs/alerts-applied-intelligence/overview/) diff --git a/daprdocs/content/en/operations/monitoring/tracing/otel-collector/_index.md b/daprdocs/content/en/operations/monitoring/tracing/otel-collector/_index.md deleted file mode 100644 index 4f55a875020..00000000000 --- a/daprdocs/content/en/operations/monitoring/tracing/otel-collector/_index.md +++ /dev/null @@ -1,7 +0,0 @@ ---- -type: docs -title: "Open Telemetry Collector" -linkTitle: "Open Telemetry Collector" -weight: 700 -description: "How to set up your observability tools to receive application traces" ---- diff --git a/daprdocs/content/en/operations/monitoring/tracing/otel-collector/open-telemetry-collector-appinsights.md b/daprdocs/content/en/operations/monitoring/tracing/otel-collector/open-telemetry-collector-appinsights.md deleted file mode 100644 index 4fe2c95a720..00000000000 --- a/daprdocs/content/en/operations/monitoring/tracing/otel-collector/open-telemetry-collector-appinsights.md +++ /dev/null @@ -1,72 +0,0 @@ ---- -type: docs -title: "Using OpenTelemetry Collector to collect traces to send to AppInsights" -linkTitle: "Using the OpenTelemetry for Azure AppInsights" -weight: 1000 -description: "How to push trace events to Azure Application Insights, using the OpenTelemetry Collector." ---- - -Dapr integrates with [OpenTelemetry Collector](https://github.com/open-telemetry/opentelemetry-collector) using the Zipkin API. This guide walks through an example using Dapr to push trace events to Azure Application Insights, using the OpenTelemetry Collector. - -## Requirements - -A installation of Dapr on Kubernetes. - -## How to configure distributed tracing with Application Insights - -### Setup Application Insights - -1. First, you'll need an Azure account. See instructions [here](https://azure.microsoft.com/free/) to apply for a **free** Azure account. -2. Follow instructions [here](https://docs.microsoft.com/azure/azure-monitor/app/create-new-resource) to create a new Application Insights resource. -3. Get the Application Insights Intrumentation key from your Application Insights page. - -### Run OpenTelemetry Collector to push to your Application Insights instance - -Install the OpenTelemetry Collector to your Kubernetes cluster to push events to your Application Insights instance - -1. Check out the file [open-telemetry-collector-appinsights.yaml](/docs/open-telemetry-collector/open-telemetry-collector-appinsights.yaml) and replace the `` placeholder with your Application Insights Instrumentation Key. - -2. Apply the configuration with `kubectl apply -f open-telemetry-collector-appinsights.yaml`. - -Next, set up both a Dapr configuration file to turn on tracing and deploy a tracing exporter component that uses the OpenTelemetry Collector. - -1. Create a collector-config.yaml file with this [content](/docs/open-telemetry-collector/collector-config.yaml) - -2. Apply the configuration with `kubectl apply -f collector-config.yaml`. - -### Deploy your app with tracing - -When running in Kubernetes mode, apply the `appconfig` configuration by adding a `dapr.io/config` annotation to the container that you want to participate in the distributed tracing, as shown in the following example: - -```yaml -apiVersion: apps/v1 -kind: Deployment -metadata: - ... -spec: - ... - template: - metadata: - ... - annotations: - dapr.io/enabled: "true" - dapr.io/app-id: "MyApp" - dapr.io/app-port: "8080" - dapr.io/config: "appconfig" -``` - -Some of the quickstarts such as [distributed calculator](https://github.com/dapr/quickstarts/tree/master/tutorials/distributed-calculator) already configure these settings, so if you are using those no additional settings are needed. - -That's it! There's no need include any SDKs or instrument your application code. Dapr automatically handles the distributed tracing for you. - -> **NOTE**: You can register multiple tracing exporters at the same time, and the tracing logs are forwarded to all registered exporters. - -Deploy and run some applications. After a few minutes, you should see tracing logs appearing in your Application Insights resource. You can also use the **Application Map** to examine the topology of your services, as shown below: - -![Application map](/images/open-telemetry-app-insights.png) - -> **NOTE**: Only operations going through Dapr API exposed by Dapr sidecar (e.g. service invocation or event publishing) are displayed in Application Map topology. - -## Related links -* Try out the [observability quickstart](https://github.com/dapr/quickstarts/tree/master/tutorials/observability/README.md) -* How to set [tracing configuration options]({{< ref "configuration-overview.md#tracing" >}}) diff --git a/daprdocs/content/en/operations/monitoring/tracing/otel-collector/open-telemetry-collector.md b/daprdocs/content/en/operations/monitoring/tracing/otel-collector/open-telemetry-collector.md deleted file mode 100644 index 4526683a52e..00000000000 --- a/daprdocs/content/en/operations/monitoring/tracing/otel-collector/open-telemetry-collector.md +++ /dev/null @@ -1,74 +0,0 @@ ---- -type: docs -title: "Using OpenTelemetry Collector to collect traces" -linkTitle: "Using the OpenTelemetry Collector" -weight: 900 -description: "How to use Dapr to push trace events through the OpenTelemetry Collector." ---- - -{{% alert title="Note" color="primary" %}} -Dapr directly writes traces using the OpenTelemetry (OTEL) protocol as the recommended method. For observability tools that support OTEL protocol, you do not need to use the OpenTelemetry Collector. - -Dapr can also write traces using the Zipkin protocol. Previous to supporting the OTEL protocol, combining the Zipkin protocol with the [OpenTelemetry Collector](https://github.com/open-telemetry/opentelemetry-collector) enabled you to send traces to observability tools such as AWS X-Ray, Google Cloud Operations Suite, and Azure AppInsights. This approach remains for reference purposes only. -{{% /alert %}} - -![Using OpenTelemetry Collect to integrate with many backend](/images/open-telemetry-collector.png) - -## Requirements - -1. A installation of Dapr on Kubernetes. - -2. You are already setting up your trace backends to receive traces. - -3. Check OpenTelemetry Collector exporters [here](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/exporter) and [here](https://github.com/open-telemetry/opentelemetry-collector/tree/main/exporter) to see if your trace backend is supported by the OpenTelemetry Collector. On those linked pages, find the exporter you want to use and read its doc to find out the parameters required. - -## Setting OpenTelemetry Collector - -### Run OpenTelemetry Collector to push to your trace backend - -1. Check out the file [open-telemetry-collector-generic.yaml](/docs/open-telemetry-collector/open-telemetry-collector-generic.yaml) and replace the section marked with `` with the correct settings for your trace exporter. Again, refer to the OpenTelemetry Collector links in the Prerequisites section to determine the correct settings. - -2. Apply the configuration with `kubectl apply -f open-telemetry-collector-generic.yaml`. - -## Set up Dapr to send trace to OpenTelemetry Collector - -### Turn on tracing in Dapr -Next, set up both a Dapr configuration file to turn on tracing and deploy a tracing exporter component that uses the OpenTelemetry Collector. - -1. Create a collector-config.yaml file with this [content](/docs/open-telemetry-collector/collector-config.yaml) - -2. Apply the configuration with `kubectl apply -f collector-config.yaml`. - -### Deploy your app with tracing - -When running in Kubernetes mode, apply the `appconfig` configuration by adding a `dapr.io/config` annotation to the container that you want to participate in the distributed tracing, as shown in the following example: - -```yaml -apiVersion: apps/v1 -kind: Deployment -metadata: - ... -spec: - ... - template: - metadata: - ... - annotations: - dapr.io/enabled: "true" - dapr.io/app-id: "MyApp" - dapr.io/app-port: "8080" - dapr.io/config: "appconfig" -``` - -Some of the quickstarts such as [distributed calculator](https://github.com/dapr/quickstarts/tree/master/tutorials/distributed-calculator) already configure these settings, so if you are using those no additional settings are needed. - -That's it! There's no need include any SDKs or instrument your application code. Dapr automatically handles the distributed tracing for you. - -> **NOTE**: You can register multiple tracing exporters at the same time, and the tracing logs are forwarded to all registered exporters. - -Deploy and run some applications. Wait for the trace to propagate to your tracing backend and view them there. - -## Related links -* Try out the [observability quickstart](https://github.com/dapr/quickstarts/tree/master/tutorials/observability/README.md) -* How to set [tracing configuration options]({{< ref "configuration-overview.md#tracing" >}}) - diff --git a/daprdocs/content/en/operations/monitoring/tracing/setup-tracing.md b/daprdocs/content/en/operations/monitoring/tracing/setup-tracing.md deleted file mode 100644 index 52147498cf8..00000000000 --- a/daprdocs/content/en/operations/monitoring/tracing/setup-tracing.md +++ /dev/null @@ -1,71 +0,0 @@ ---- -type: docs -title: "Configure Dapr to send distributed tracing data" -linkTitle: "Configure tracing" -weight: 100 -description: "Configure Dapr to send distributed tracing data" ---- - -It is recommended to run Dapr with tracing enabled for any production -scenario. You can configure Dapr to send tracing and telemetry data -to many observability tools based on your environment, whether it is running in -the cloud or on-premises. - -## Configuration - -The `tracing` section under the `Configuration` spec contains the following properties: - -```yml -spec: - tracing: - samplingRate: "1" - otel: - endpointAddress: "https://..." - zipkin: - endpointAddress: "https://..." - -``` - -The following table lists the properties for tracing: - -| Property | Type | Description | -|--------------|--------|-------------| -| `samplingRate` | string | Set sampling rate for tracing to be enabled or disabled. -| `stdout` | bool | True write more verbose information to the traces -| `otel.endpointAddress` | string | Set the Open Telemetry (OTEL) server address. -| `otel.isSecure` | bool | Is the connection to the endpoint address encrypted. -| `otel.protocol` | string | Set to `http` or `grpc` protocol. -| `zipkin.endpointAddress` | string | Set the Zipkin server address. If this is used, you do not need to specify the `otel` section. - -To enable tracing, use a configuration file (in self hosted mode) or a Kubernetes configuration object (in Kubernetes mode). For example, the following configuration object changes the sample rate to 1 (every span is sampled), and sends trace using OTEL protocol to the OTEL server at localhost:4317 - -```yaml -apiVersion: dapr.io/v1alpha1 -kind: Configuration -metadata: - name: tracing -spec: - tracing: - samplingRate: "1" - otel: - endpointAddress: "localhost:4317" - isSecure: false - protocol: grpc -``` - -## Sampling rate - -Dapr uses probabilistic sampling. The sample rate defines the probability a tracing span will be sampled and can have a value between 0 and 1 (inclusive). The default sample rate is 0.0001 (i.e. 1 in 10,000 spans is sampled). - -Changing `samplingRate` to 0 disables tracing altogether. - -## Environment variables - -The OpenTelemetry (otel) endpoint can also be configured via an environment variables. The presence of the OTEL_EXPORTER_OTLP_ENDPOINT environment variable -turns on tracing for the sidecar. - -| Environment Variable | Description | -|----------------------|-------------| -| `OTEL_EXPORTER_OTLP_ENDPOINT` | Sets the Open Telemetry (OTEL) server address, turns on tracing | -| `OTEL_EXPORTER_OTLP_INSECURE` | Sets the connection to the endpoint as unencrypted (true/false) | -| `OTEL_EXPORTER_OTLP_PROTOCOL` | Transport protocol (`grpc`, `http/protobuf`, `http/json`) | \ No newline at end of file diff --git a/daprdocs/content/en/operations/monitoring/tracing/zipkin.md b/daprdocs/content/en/operations/monitoring/tracing/zipkin.md deleted file mode 100644 index 23ecfd312f3..00000000000 --- a/daprdocs/content/en/operations/monitoring/tracing/zipkin.md +++ /dev/null @@ -1,110 +0,0 @@ ---- -type: docs -title: "How-To: Set up Zipkin for distributed tracing" -linkTitle: "Zipkin" -weight: 4000 -description: "Set up Zipkin for distributed tracing" -type: docs ---- - -## Configure self hosted mode - -For self hosted mode, on running `dapr init`: - -1. The following YAML file is created by default in `$HOME/.dapr/config.yaml` (on Linux/Mac) or `%USERPROFILE%\.dapr\config.yaml` (on Windows) and it is referenced by default on `dapr run` calls unless otherwise overridden `: - -* config.yaml - -```yaml -apiVersion: dapr.io/v1alpha1 -kind: Configuration -metadata: - name: daprConfig - namespace: default -spec: - tracing: - samplingRate: "1" - zipkin: - endpointAddress: "http://localhost:9411/api/v2/spans" -``` - -2. The [openzipkin/zipkin](https://hub.docker.com/r/openzipkin/zipkin/) docker container is launched on running `dapr init` or it can be launched with the following code. - -Launch Zipkin using Docker: - -```bash -docker run -d -p 9411:9411 openzipkin/zipkin -``` - -3. The applications launched with `dapr run` by default reference the config file in `$HOME/.dapr/config.yaml` or `%USERPROFILE%\.dapr\config.yaml` and can be overridden with the Dapr CLI using the `--config` param: - -```bash -dapr run --app-id mynode --app-port 3000 node app.js -``` -### Viewing Traces -To view traces, in your browser go to http://localhost:9411 and you will see the Zipkin UI. - -## Configure Kubernetes - -The following steps shows you how to configure Dapr to send distributed tracing data to Zipkin running as a container in your Kubernetes cluster, and how to view them. - -### Setup - -First, deploy Zipkin: - -```bash -kubectl create deployment zipkin --image openzipkin/zipkin -``` - -Create a Kubernetes service for the Zipkin pod: - -```bash -kubectl expose deployment zipkin --type ClusterIP --port 9411 -``` - -Next, create the following YAML file locally: - -* tracing.yaml configuration - -```yaml -apiVersion: dapr.io/v1alpha1 -kind: Configuration -metadata: - name: tracing - namespace: default -spec: - tracing: - samplingRate: "1" - zipkin: - endpointAddress: "http://zipkin.default.svc.cluster.local:9411/api/v2/spans" -``` - -Now, deploy the the Dapr configuration file: - -```bash -kubectl apply -f tracing.yaml -``` - -In order to enable this configuration for your Dapr sidecar, add the following annotation to your pod spec template: - -```yml -annotations: - dapr.io/config: "tracing" -``` - -That's it! Your sidecar is now configured to send traces to Zipkin. - -### Viewing Tracing Data - -To view traces, connect to the Zipkin service and open the UI: - -```bash -kubectl port-forward svc/zipkin 9411:9411 -``` - -In your browser, go to `http://localhost:9411` and you will see the Zipkin UI. - -![zipkin](/images/zipkin_ui.png) - -## References -- [Zipkin for distributed tracing](https://zipkin.io/) diff --git a/daprdocs/content/en/operations/observability/health-checks/app-health.md b/daprdocs/content/en/operations/observability/health-checks/app-health.md deleted file mode 100644 index 193233dd17c..00000000000 --- a/daprdocs/content/en/operations/observability/health-checks/app-health.md +++ /dev/null @@ -1,169 +0,0 @@ ---- -type: docs -title: "App health checks" -linkTitle: "App health checks" -weight: 100 -description: Reacting to apps' health status changes ---- - -The app health checks feature allows probing for the health of your application and reacting to status changes. - -Applications can become unresponsive for a variety of reasons. For example, your application: -- Could be too busy to accept new work; -- Could have crashed; or -- Could be in a deadlock state. - -Sometimes the condition can be transitory, for example: -- If the app is just busy and will resume accepting new work eventually -- If the application is being restarted for whatever reason and is in its initialization phase - -App health checks are disabled by default. Once you enable app health checks, the Dapr runtime (sidecar) periodically polls your application via HTTP or gRPC calls. When it detects a failure in the app's health, Dapr stops accepting new work on behalf of the application by: - -- Unsubscribing from all pub/sub subscriptions -- Stopping all input bindings -- Short-circuiting all service-invocation requests, which terminate in the Dapr runtime and are not forwarded to the application - -These changes are meant to be temporary, and Dapr resumes normal operations once it detects that the application is responsive again. - -Diagram showing the app health feature. Running Dapr with app health enabled causes Dapr to periodically probe the app for its health. - -## App health checks vs platform-level health checks - -App health checks in Dapr are meant to be complementary to, and not replace, any platform-level health checks, like [liveness probes](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/) when running on Kubernetes. - -Platform-level health checks (or liveness probes) generally ensure that the application is running, and cause the platform to restart the application in case of failures. - -Unlike platform-level health checks, Dapr's app health checks focus on pausing work to an application that is currently unable to accept it, but is expected to be able to resume accepting work *eventually*. Goals include: - -- Not bringing more load to an application that is already overloaded. -- Do the "polite" thing by not taking messages from queues, bindings, or pub/sub brokers when Dapr knows the application won't be able to process them. - -In this regard, Dapr's app health checks are "softer", waiting for an application to be able to process work, rather than terminating the running process in a "hard" way. - -{{% alert title="Note" color="primary" %}} -For Kubernetes, a failing app health check won't remove a pod from service discovery: this remains the responsibility of the Kubernetes liveness probe, _not_ Dapr. -{{% /alert %}} - -## Configuring app health checks - -App health checks are disabled by default, but can be enabled with either: - -- The `--enable-app-health-check` CLI flag; or -- The `dapr.io/enable-app-health-check: true` annotation when running on Kubernetes. - -Adding this flag is both necessary and sufficient to enable app health checks with the default options. - -The full list of options are listed in this table: - -| CLI flags | Kubernetes deployment annotation | Description | Default value | -| ----------------------------- | ----------------------------------- | ----------- | ------------- | -| `--enable-app-health-check` | `dapr.io/enable-app-health-check` | Boolean that enables the health checks | Disabled | -| [`--app-health-check-path`]({{< ref "app-health.md#health-check-paths" >}}) | `dapr.io/app-health-check-path` | Path that Dapr invokes for health probes when the app channel is HTTP (this value is ignored if the app channel is using gRPC) | `/healthz` | -| [`--app-health-probe-interval`]({{< ref "app-health.md#intervals-timeouts-and-thresholds" >}}) | `dapr.io/app-health-probe-interval` | Number of *seconds* between each health probe | `5` | -| [`--app-health-probe-timeout`]({{< ref "app-health.md#intervals-timeouts-and-thresholds" >}}) | `dapr.io/app-health-probe-timeout` | Timeout in *milliseconds* for health probe requests | `500` | -| [`--app-health-threshold`]({{< ref "app-health.md#intervals-timeouts-and-thresholds" >}}) | `dapr.io/app-health-threshold` | Max number of consecutive failures before the app is considered unhealthy | `3` | - -> See the [full Dapr arguments and annotations reference]({{< ref arguments-annotations-overview >}}) for all options and how to enable them. - -Additionally, app health checks are impacted by the protocol used for the app channel, which is configured with the following flag or annotation: - -| CLI flag | Kubernetes deployment annotation | Description | Default value | -| ----------------------------- | ----------------------------------- | ----------- | ------------- | -| [`--app-protocol`]({{< ref "app-health.md#health-check-paths" >}}) | `dapr.io/app-protocol` | Protocol used for the app channel. supported values are `http`, `grpc`, `https`, `grpcs`, and `h2c` (HTTP/2 Cleartext). | `http` | - -{{% alert title="Note" color="primary" %}} -A low app health probe timeout value can classify an application as unhealthy if it experiences a sudden high load, causing the response time to degrade. If this happens, increase the `dapr.io/app-health-probe-timeout` value. -{{% /alert %}} - -### Health check paths - -#### HTTP -When using HTTP (including `http`, `https`, and `h2c`) for `app-protocol`, Dapr performs health probes by making an HTTP call to the path specified in `app-health-check-path`, which is `/health` by default. - -For your app to be considered healthy, the response must have an HTTP status code in the 200-299 range. Any other status code is considered a failure. Dapr is only concerned with the status code of the response, and ignores any response header or body. - -#### gRPC -When using gRPC for the app channel (`app-protocol` set to `grpc` or `grpcs`), Dapr invokes the method `/dapr.proto.runtime.v1.AppCallbackHealthCheck/HealthCheck` in your application. Most likely, you will use a Dapr SDK to implement the handler for this method. - -While responding to a health probe request, your app *may* decide to perform additional internal health checks to determine if it's ready to process work from the Dapr runtime. However, this is not required; it's a choice that depends on your application's needs. - -### Intervals, timeouts, and thresholds - -#### Intervals -By default, when app health checks are enabled, Dapr probes your application every 5 seconds. You can configure the interval, in seconds, with `app-health-probe-interval`. These probes happen regularly, regardless of whether your application is healthy or not. - -#### Timeouts -When the Dapr runtime (sidecar) is initially started, Dapr waits for a successful health probe before considering the app healthy. This means that pub/sub subscriptions, input bindings, and service invocation requests won't be enabled for your application until this first health check is complete and successful. - -Health probe requests are considered successful if the application sends a successful response (as explained above) within the timeout configured in `app-health-probe-timeout`. The default value is 500, corresponding to 500 milliseconds (half a second). - -#### Thresholds -Before Dapr considers an app to have entered an unhealthy state, it will wait for `app-health-threshold` consecutive failures, whose default value is 3. This default value means that your application must fail health probes 3 times *in a row* to be considered unhealthy. - -If you set the threshold to 1, any failure causes Dapr to assume your app is unhealthy and will stop delivering work to it. - -A threshold greater than 1 can help exclude transient failures due to external circumstances. The right value for your application depends on your requirements. - -Thresholds only apply to failures. A single successful response is enough for Dapr to consider your app to be healthy and resume normal operations. - -## Example - -{{< tabs "Self-Hosted (CLI)" Kubernetes >}} - -{{% codetab %}} - -Use the CLI flags with the `dapr run` command to enable app health checks: - -```sh -dapr run \ - --app-id my-app \ - --app-port 7001 \ - --app-protocol http \ - --enable-app-health-check \ - --app-health-check-path=/healthz \ - --app-health-probe-interval 3 \ - --app-health-probe-timeout 200 \ - --app-health-threshold 2 \ - -- \ - -``` - -{{% /codetab %}} - -{{% codetab %}} - -To enable app health checks in Kubernetes, add the relevant annotations to your Deployment: - -```yaml -apiVersion: apps/v1 -kind: Deployment -metadata: - name: my-app - labels: - app: my-app -spec: - template: - metadata: - labels: - app: my-app - annotations: - dapr.io/enabled: "true" - dapr.io/app-id: "my-app" - dapr.io/app-port: "7001" - dapr.io/app-protocol: "http" - dapr.io/enable-app-health-check: "true" - dapr.io/app-health-check-path: "/healthz" - dapr.io/app-health-probe-interval: "3" - dapr.io/app-health-probe-timeout: "200" - dapr.io/app-health-threshold: "2" -``` - -{{% /codetab %}} - -{{< /tabs >}} - -## Demo - -Watch this video for an [overview of using app health checks](https://youtu.be/srczBuOsAkI?t=533): - - diff --git a/daprdocs/content/en/operations/observability/health-checks/sidecar-health.md b/daprdocs/content/en/operations/observability/health-checks/sidecar-health.md deleted file mode 100644 index b81efeef9b9..00000000000 --- a/daprdocs/content/en/operations/observability/health-checks/sidecar-health.md +++ /dev/null @@ -1,101 +0,0 @@ ---- -type: docs -title: "Sidecar health" -linkTitle: "Sidecar health" -weight: 200 -description: Dapr sidecar health checks ---- - -Dapr provides a way to determine its health using an [HTTP `/healthz` endpoint]({{< ref health_api.md >}}). With this endpoint, the *daprd* process, or sidecar, can be: - -- Probed for its health -- Determined for readiness and liveness - -In this guide, you learn how the Dapr `/healthz` endpoint integrate with health probes from the application hosting platform (for example, Kubernetes). - -When deploying Dapr to a hosting platform like Kubernetes, the Dapr health endpoint is automatically configured for you. - -{{% alert title="Note" color="primary" %}} -Dapr actors also have a health API endpoint where Dapr probes the application for a response to a signal from Dapr that the actor application is healthy and running. See [actor health API]({{< ref "actors_api.md#health-check" >}}). -{{% /alert %}} - -## Health endpoint: Integration with Kubernetes - -Kubernetes uses *readiness* and *liveness* probes to determines the health of the container. - -### Liveness -The kubelet uses liveness probes to know when to restart a container. For example, liveness probes could catch a deadlock (a running application that is unable to make progress). Restarting a container in such a state can help to make the application more available despite having bugs. - -#### How to configure a liveness probe in Kubernetes - -In the pod configuration file, the liveness probe is added in the containers spec section as shown below: - -```yaml - livenessProbe: - httpGet: - path: /healthz - port: 8080 - initialDelaySeconds: 3 - periodSeconds: 3 -``` - -In the above example, the `periodSeconds` field specifies that the kubelet should perform a liveness probe every 3 seconds. The `initialDelaySeconds` field tells the kubelet that it should wait 3 seconds before performing the first probe. To perform a probe, the kubelet sends an HTTP GET request to the server that is running in the container and listening on port 8080. If the handler for the server's `/healthz` path returns a success code, the kubelet considers the container to be alive and healthy. If the handler returns a failure code, the kubelet kills the container and restarts it. - -Any HTTP status code between 200 and 399 indicates success; any other status code indicates failure. - -### Readiness -The kubelet uses readiness probes to know when a container is ready to start accepting traffic. A pod is considered ready when all of its containers are ready. One use of this readiness signal is to control which pods are used as backends for Kubernetes services. When a pod is not ready, it is removed from Kubernetes service load balancers. - -{{% alert title="Note" color="primary" %}} -The Dapr sidecar will be in ready state once the application is accessible on its configured port. The application cannot access the Dapr components during application start up/initialization. -{{% /alert %}} - -#### How to configure a readiness probe in Kubernetes - -Readiness probes are configured similarly to liveness probes. The only difference is that you use the `readinessProbe` field instead of the `livenessProbe` field: - -```yaml - readinessProbe: - httpGet: - path: /healthz - port: 8080 - initialDelaySeconds: 3 - periodSeconds: 3 -``` - -### Sidecar Injector - -When integrating with Kubernetes, the Dapr sidecar is injected with a Kubernetes probe configuration telling it to use the Dapr `healthz` endpoint. This is done by the "Sidecar Injector" system service. The integration with the kubelet is shown in the diagram below. - -Diagram of Dapr services interacting - -#### How the Dapr sidecar health endpoint is configured with Kubernetes - -As mentioned above, this configuration is done automatically by the Sidecar Injector service. This section describes the specific values that are set on the liveness and readiness probes. - -Dapr has its HTTP health endpoint `/v1.0/healthz` on port 3500. This can be used with Kubernetes for readiness and liveness probe. When the Dapr sidecar is injected, the readiness and liveness probes are configured in the pod configuration file with the following values: - -```yaml - livenessProbe: - httpGet: - path: v1.0/healthz - port: 3500 - initialDelaySeconds: 5 - periodSeconds: 10 - timeoutSeconds : 5 - failureThreshold : 3 - readinessProbe: - httpGet: - path: v1.0/healthz - port: 3500 - initialDelaySeconds: 5 - periodSeconds: 10 - timeoutSeconds : 5 - failureThreshold: 3 -``` - -## Related links - -- [Endpoint health API]({{< ref health_api.md >}}) -- [Actor health API]({{< ref "actors_api.md#health-check" >}}) -- [Kubernetes probe configuration parameters](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/) diff --git a/daprdocs/content/en/operations/observability/tracing/tracing-overview.md b/daprdocs/content/en/operations/observability/tracing/tracing-overview.md index e7e08280751..24fed25a3cb 100644 --- a/daprdocs/content/en/operations/observability/tracing/tracing-overview.md +++ b/daprdocs/content/en/operations/observability/tracing/tracing-overview.md @@ -17,6 +17,10 @@ Most observability tools support OTEL, including: - [Jaeger](https://www.jaegertracing.io/) - [SignalFX](https://www.signalfx.com/) +The following diagram demonstrates how Dapr (using OTEL and Zipkin protocols) integrates with multiple observability tools. + +Distributed tracing with Dapr + ## Scenarios Tracing is used with service invocaton and pub/sub APIs. You can flow trace context between services that uses these APIs. There are two scenarios for how tracing is used: diff --git a/daprdocs/content/en/operations/observability/health-checks/_index.md b/daprdocs/content/en/operations/resiliency/health-checks/_index.md similarity index 92% rename from daprdocs/content/en/operations/observability/health-checks/_index.md rename to daprdocs/content/en/operations/resiliency/health-checks/_index.md index 33f1e4bea03..66730af2fa3 100644 --- a/daprdocs/content/en/operations/observability/health-checks/_index.md +++ b/daprdocs/content/en/operations/resiliency/health-checks/_index.md @@ -2,6 +2,6 @@ type: docs title: "Health checks" linkTitle: "Health checks" -weight: 100 +weight: 400 description: "How to setup health checks for the Dapr sidecar and your application" --- \ No newline at end of file diff --git a/daprdocs/content/en/developing-applications/building-blocks/observability/app-health.md b/daprdocs/content/en/operations/resiliency/health-checks/app-health.md similarity index 100% rename from daprdocs/content/en/developing-applications/building-blocks/observability/app-health.md rename to daprdocs/content/en/operations/resiliency/health-checks/app-health.md diff --git a/daprdocs/content/en/developing-applications/building-blocks/observability/sidecar-health.md b/daprdocs/content/en/operations/resiliency/health-checks/sidecar-health.md similarity index 100% rename from daprdocs/content/en/developing-applications/building-blocks/observability/sidecar-health.md rename to daprdocs/content/en/operations/resiliency/health-checks/sidecar-health.md diff --git a/daprdocs/content/en/operations/resiliency/policies.md b/daprdocs/content/en/operations/resiliency/policies.md index f87f4d9fb93..c287920ca4b 100644 --- a/daprdocs/content/en/operations/resiliency/policies.md +++ b/daprdocs/content/en/operations/resiliency/policies.md @@ -2,7 +2,7 @@ type: docs title: "Policies" linkTitle: "Policies" -weight: 4500 +weight: 200 description: "Configure resiliency policies for timeouts, retries, and circuit breakers" --- diff --git a/daprdocs/content/en/operations/resiliency/resiliency-overview.md b/daprdocs/content/en/operations/resiliency/resiliency-overview.md index bb6cdb502c8..8cfee4c6c42 100644 --- a/daprdocs/content/en/operations/resiliency/resiliency-overview.md +++ b/daprdocs/content/en/operations/resiliency/resiliency-overview.md @@ -2,7 +2,7 @@ type: docs title: "Overview" linkTitle: "Overview" -weight: 4500 +weight: 100 description: "Configure Dapr retries, timeouts, and circuit breakers" --- diff --git a/daprdocs/content/en/operations/resiliency/targets.md b/daprdocs/content/en/operations/resiliency/targets.md index ffb5ead96fb..70c3b859938 100644 --- a/daprdocs/content/en/operations/resiliency/targets.md +++ b/daprdocs/content/en/operations/resiliency/targets.md @@ -2,7 +2,7 @@ type: docs title: "Targets" linkTitle: "Targets" -weight: 4500 +weight: 300 description: "Apply resiliency policies to apps, components and actors" --- diff --git 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z;XzVbMH;(shO2zPJY1W-u#YpC_wP&TPyX9jI;}Bur}slNiU-VOr7{1VmY0P`)8!F; z0?}KxwPWHGZ*&SNs=qIb=Mj*mO7K)25tf^}V}+UiuRr=1j~@1v(hfF;Tl{;BnOauQ9;Xvy5 From 26e0fc6aa44ae59a0369795bc55964e77224dfa6 Mon Sep 17 00:00:00 2001 From: Hannah Hunter <94493363+hhunter-ms@users.noreply.github.com> Date: Fri, 4 Aug 2023 13:16:43 -0400 Subject: [PATCH 3/8] Update daprdocs/content/en/concepts/building-blocks-concept.md Co-authored-by: Mark Fussell Signed-off-by: Hannah Hunter <94493363+hhunter-ms@users.noreply.github.com> --- daprdocs/content/en/concepts/building-blocks-concept.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/daprdocs/content/en/concepts/building-blocks-concept.md b/daprdocs/content/en/concepts/building-blocks-concept.md index b714c89c6b7..4719626f3c6 100644 --- a/daprdocs/content/en/concepts/building-blocks-concept.md +++ b/daprdocs/content/en/concepts/building-blocks-concept.md @@ -6,7 +6,7 @@ weight: 200 description: "Modular best practices accessible over standard HTTP or gRPC APIs" --- -A [building block]({{< ref building-blocks >}}) is an HTTP or gRPC API that can be called from your code and uses one or more Dapr components. Dapr consists of a set of building blocks, with extensibility to add new building blocks. Dapr's building blocks: +A [building block]({{< ref building-blocks >}}) is an HTTP or gRPC API that can be called from your code and uses one or more Dapr components. Dapr consists of a set of API building blocks, with extensibility to add new building blocks. Dapr's building blocks: - Address common challenges in building resilient, microservices applications - Codify best practices and patterns From 9ef897dd2eba5b2cb4610f3a0147b0053adefd3b Mon Sep 17 00:00:00 2001 From: Hannah Hunter <94493363+hhunter-ms@users.noreply.github.com> Date: Fri, 4 Aug 2023 13:17:07 -0400 Subject: [PATCH 4/8] Update daprdocs/content/en/concepts/observability-concept.md Co-authored-by: Mark Fussell Signed-off-by: Hannah Hunter <94493363+hhunter-ms@users.noreply.github.com> --- daprdocs/content/en/concepts/observability-concept.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/daprdocs/content/en/concepts/observability-concept.md b/daprdocs/content/en/concepts/observability-concept.md index 11ab62fd9bc..44476c194dc 100644 --- a/daprdocs/content/en/concepts/observability-concept.md +++ b/daprdocs/content/en/concepts/observability-concept.md @@ -27,7 +27,7 @@ Maintaining this instrumentation code, which is not part of the core logic of th ## Observability for your application with Dapr -When you leverage Dapr API building blocks to perform service-to-service calls and pub/sub messaging, Dapr offers an advantage with respect to [distributed tracing]({{< ref tracing >}}). Since this inter-service communication flows through the Dapr runtime (or "sidecar"), Dapr is in a unique position to offload the burden of application-level instrumentation. +When you leverage Dapr API building blocks to perform service-to-service calls, pub/sub messaging, and other APIs, Dapr offers an advantage with respect to [distributed tracing]({{< ref tracing >}}). Since this inter-service communication flows through the Dapr runtime (or "sidecar"), Dapr is in a unique position to offload the burden of application-level instrumentation. ### Distributed tracing From 6608d59f577db094b38d45c3ab8ba6de6a6d169e Mon Sep 17 00:00:00 2001 From: Hannah Hunter <94493363+hhunter-ms@users.noreply.github.com> Date: Fri, 4 Aug 2023 13:17:17 -0400 Subject: [PATCH 5/8] Update daprdocs/content/en/concepts/observability-concept.md Co-authored-by: Mark Fussell Signed-off-by: Hannah Hunter <94493363+hhunter-ms@users.noreply.github.com> --- daprdocs/content/en/concepts/observability-concept.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/daprdocs/content/en/concepts/observability-concept.md b/daprdocs/content/en/concepts/observability-concept.md index 44476c194dc..8ddf10b04b9 100644 --- a/daprdocs/content/en/concepts/observability-concept.md +++ b/daprdocs/content/en/concepts/observability-concept.md @@ -37,7 +37,7 @@ Dapr can be [configured to emit tracing data]({{< ref setup-tracing.md >}}) usin ### Automatic tracing context generation -Dapr uses [W3C tracing]({{< ref tracing >}}) specification for tracing context, included as part Open Telemetry (OTEL), to generate and propagate the context header for the application or propagate user-provided context headers. This means that you get tracing by default with Dapr. +Dapr uses the [W3C tracing]({{< ref tracing >}}) specification for tracing context, included as part Open Telemetry (OTEL), to generate and propagate the context header for the application or propagate user-provided context headers. This means that you get tracing by default with Dapr. ## Observability for the Dapr sidecar and control plane From bc561a4bc63a4f68a80116186d70a5c3e2907c10 Mon Sep 17 00:00:00 2001 From: Hannah Hunter <94493363+hhunter-ms@users.noreply.github.com> Date: Fri, 4 Aug 2023 13:47:25 -0400 Subject: [PATCH 6/8] Update daprdocs/content/en/concepts/overview.md Co-authored-by: Mark Fussell Signed-off-by: Hannah Hunter <94493363+hhunter-ms@users.noreply.github.com> --- daprdocs/content/en/concepts/overview.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/daprdocs/content/en/concepts/overview.md b/daprdocs/content/en/concepts/overview.md index 232f8b8b71d..56624053722 100644 --- a/daprdocs/content/en/concepts/overview.md +++ b/daprdocs/content/en/concepts/overview.md @@ -107,7 +107,7 @@ Deploying and running a Dapr-enabled application into your Kubernetes cluster is ### Clusters of physical or virtual machines -The Dapr control plane services can be deployed in High Availability (HA) mode to clusters of physical or virtual machines in production. In the diagram below, the Actor `Placement` and `Sentry` services are started on three different VMs to provide HA control plane. In order to provide name resolution using DNS for the applications running in the cluster, Dapr uses [Hashicorp Consul service]({{< ref setup-nr-consul >}}), also running in HA mode. +The Dapr control plane services can be deployed in high availability (HA) mode to clusters of physical or virtual machines in production. In the diagram below, the Actor `Placement` and security `Sentry` services are started on three different VMs to provide HA control plane. In order to provide name resolution using DNS for the applications running in the cluster, Dapr uses [Hashicorp Consul service]({{< ref setup-nr-consul >}}), also running in HA mode. Architecture diagram of Dapr control plane and Consul deployed to VMs in high availability mode From 132627ec4c43f883da44f567ec163d5c4389e1d2 Mon Sep 17 00:00:00 2001 From: Hannah Hunter <94493363+hhunter-ms@users.noreply.github.com> Date: Fri, 4 Aug 2023 13:47:47 -0400 Subject: [PATCH 7/8] Update daprdocs/content/en/operations/resiliency/targets.md Co-authored-by: Mark Fussell Signed-off-by: Hannah Hunter <94493363+hhunter-ms@users.noreply.github.com> --- daprdocs/content/en/operations/resiliency/targets.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/daprdocs/content/en/operations/resiliency/targets.md b/daprdocs/content/en/operations/resiliency/targets.md index 70c3b859938..15090b2e0ba 100644 --- a/daprdocs/content/en/operations/resiliency/targets.md +++ b/daprdocs/content/en/operations/resiliency/targets.md @@ -3,7 +3,7 @@ type: docs title: "Targets" linkTitle: "Targets" weight: 300 -description: "Apply resiliency policies to apps, components and actors" +description: "Apply resiliency policies to targets including apps, components and actors" --- ### Targets From a1f7a78a928e8dc87bd7aeaaabcfbeb9f843e596 Mon Sep 17 00:00:00 2001 From: Hannah Hunter Date: Fri, 4 Aug 2023 13:50:13 -0400 Subject: [PATCH 8/8] updates from mark Signed-off-by: Hannah Hunter --- daprdocs/content/en/concepts/observability-concept.md | 2 +- daprdocs/content/en/operations/resiliency/policies.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/daprdocs/content/en/concepts/observability-concept.md b/daprdocs/content/en/concepts/observability-concept.md index 11ab62fd9bc..5ebda79a522 100644 --- a/daprdocs/content/en/concepts/observability-concept.md +++ b/daprdocs/content/en/concepts/observability-concept.md @@ -54,7 +54,7 @@ Dapr generates [logs]({{< ref logs.md >}}) to: - Provide visibility into sidecar operation - Help users identify issues and perform debugging -Log events contain warning, error, info, and debug messages produced by Dapr system services. You can also configure Dapr to send logs to collectors, such as Open Telemetry Collector, [Fluentd]({{< ref fluentd.md >}}), [New Relic]({{< ref "operations/observability/logging/newrelic.md" >}}), [Azure Monitor]({{< ref azure-monitor.md >}}), and other observability tools, so that logs can be searched and analyzed to provide insights. +Log events contain warning, error, info, and debug messages produced by Dapr system services. You can also configure Dapr to send logs to collectors, such as [Open Telemetry Collector]({{< ref otel-collector >}}), [Fluentd]({{< ref fluentd.md >}}), [New Relic]({{< ref "operations/observability/logging/newrelic.md" >}}), [Azure Monitor]({{< ref azure-monitor.md >}}), and other observability tools, so that logs can be searched and analyzed to provide insights. ### Metrics diff --git a/daprdocs/content/en/operations/resiliency/policies.md b/daprdocs/content/en/operations/resiliency/policies.md index c287920ca4b..db72dd78c5c 100644 --- a/daprdocs/content/en/operations/resiliency/policies.md +++ b/daprdocs/content/en/operations/resiliency/policies.md @@ -1,6 +1,6 @@ --- type: docs -title: "Policies" +title: "Resiliency policies" linkTitle: "Policies" weight: 200 description: "Configure resiliency policies for timeouts, retries, and circuit breakers"