Write Prometheus metrics by using the Prometheus sidecar

The Managed Service for Prometheus sidecar for Cloud Run is the Google-recommended way to get Prometheus-style monitoring for Cloud Run services. If your Cloud Run service writes OTLP metrics, then you can use the OpenTelemetry sidecar. But for services that write Prometheus metrics, use the Managed Service for Prometheus sidecar described in this document.

This document describes how to do the following:

The sidecar uses Google Cloud Managed Service for Prometheus on the server side and a distribution of the OpenTelemetry Collector, custom-built for serverless workloads, on the client side. This custom distribution includes some changes to support Cloud Run. The collector performs a start-up scrape after 10 seconds and performs a shut-down scrape, no matter how short-lived the instance is. For maximum reliability, we recommend that Cloud Run services running the sidecar also use an always-allocated CPU; for more information, see CPU allocation (services). Some scrape attempts might fail if the CPU allocation is throttled due to a low number of queries per second (QPS). An always-allocated CPU remains available.

The sidecar relies on the Cloud Run multicontainer (sidecar) feature to run the collector as a sidecar container alongside your workload container. For more information about sidecars in Cloud Run, see Deploying multiple containers to a service (sidecars).

The Managed Service for Prometheus sidecar for Cloud Run introduces a new configuration, RunMonitoring, a subset of the Kubernetes PodMonitoring custom resource. Most users can use the default RunMonitoring configuration, but you can also create custom configurations. For more information about the default and custom RunMonitoring configurations, see Add the sidecar to a Cloud Run service.

Before you begin

This section describes the setup necessary to use this document.

Install and configure the gcloud CLI

Many of the steps described in this document use the Google Cloud CLI. For information about installing the gcloud CLI, see Managing Google Cloud CLI components.

When invoking gcloud commands, you must specify the identifier for your Google Cloud project. You can set a default project for the Google Cloud CLI and eliminate the need to enter it with every command. To set your project as the default, enter the following command:

gcloud config set project PROJECT_ID

You can also set a default region for your Cloud Run service. To set a default region, enter the following command:

gcloud config set run/region REGION

Enable APIs

You must enable the following APIs in your Google Cloud project:

  • Cloud Run Admin API: run.googleapis.com
  • Artifact Registry API: artifactregistry.googleapis.com
  • Cloud Monitoring API: monitoring.googleapis.com
  • Cloud Logging API: logging.googleapis.com
  • (Optional) If you create a custom RunMonitoring config, then you must enable the Secret Manager API: secretmanager.googleapis.com
  • (Optional) If you choose to run the sample app by using Cloud Build, then you must also enable the following APIs:
    • Cloud Build API: cloudbuild.googleapis.com.
    • Identity and Access Management API: iam.googleapis.com. For more information, see Build and run the sample app.

To see the APIs enabled in your project, run the following command:

gcloud services list

To enable an API that isn't enabled, run one of following commands:

gcloud services enable run.googleapis.com
gcloud services enable artifactregistry.googleapis.com
gcloud services enable secretmanager.googleapis.com
gcloud services enable monitoring.googleapis.com
gcloud services enable logging.googleapis.com
gcloud services enable cloudbuild.googleapis.com
gcloud services enable iam.googleapis.com

Service account for Cloud Run

By default, Cloud Run jobs and services use the Compute Engine default service account, PROJECT_NUMBER-compute@developer.gserviceaccount.com. This service account usually has the Identity and Access Management (IAM) roles necessary to write the metrics and logs described in this document:

  • roles/monitoring.metricWriter
  • roles/logging.logWriter

If you create a custom RunMonitoring config, then your service account must also have the following roles:

  • roles/secretmanager.admin
  • roles/secretmanager.secretAccessor

You can also configure a user-managed service account for Cloud Run. A user-managed service account must also have these roles. For more information about service accounts for Cloud Run, see Configuring Service Identity.

Configure and add the sidecar to a Cloud Run service

The Managed Service for Prometheus sidecar for Cloud Run introduces a new configuration, RunMonitoring, which is a subset of the Kubernetes PodMonitoring custom resource. The RunMonitoring configuration uses existing PodMonitoring options to support Cloud Run while eliminating some options that are specific to Kubernetes.

You can use the default RunMonitoring configuration, or you can create a custom configuration. The following sections describe both approaches. You don't need to do both. For more information about using default or custom configurations, select the corresponding tab.

Default configuration

Use the default RunMonitoring configuration

If you don't create a custom RunMonitoring configuration, then the sidecar collector synthesizes the following default configuration to scrape metrics from port 8080 at metrics path /metrics every 30 seconds:

apiVersion: monitoring.googleapis.com/v1beta
kind: RunMonitoring
metadata:
  name: run-gmp-sidecar
spec:
  endpoints:
  - port: 8080
    path: /metrics
    interval: 30s

Using the default configuration requires no additional setup beyond adding the sidecar to your Cloud Run service.

Add the sidecar to a Cloud Run service

To use the sidecar with the default RunMonitoring configuration, you need to modify your existing Cloud Run configuration to add the sidecar. To add the sidecar, do the following:

  • Add a container-dependency annotation that specifies the startup and shutdown order of the containers. In the following example, the sidecar container, named "collector", starts after and shuts down before the application container, named "app" in the example.
  • Create a container for the collector, named "collector" in the following example.

For example, add the lines preceded by the + (plus) character to your Cloud Run configuration, and then redeploy your service:

apiVersion: serving.knative.dev/v1
kind: Service
metadata:
  annotations:
    run.googleapis.com/launch-stage: ALPHA
    run.googleapis.com/cpu-throttling: 'false'
  name: my-cloud-run-service
spec:
  template:
    metadata:
      annotations:
        run.googleapis.com/execution-environment: gen2
+       run.googleapis.com/container-dependencies: '{"collector":["app"]}'
    spec:
      containers:
      - image: "REGION-docker.pkg.dev/PROJECT_ID/run-gmp/sample-app"
        name: app
        startupProbe:
          httpGet:
            path: /startup
            port: 8000
        livenessProbe:
          httpGet:
            path: /liveness
            port: 8000
        ports:
        - containerPort: 8000
+     - image: "us-docker.pkg.dev/cloud-ops-agents-artifacts/cloud-run-gmp-sidecar/cloud-run-gmp-sidecar:1.2.0"
+       name: collector

To redeploy the Cloud Run service with the configuration file run-service.yaml, run the following command:

gcloud run services replace run-service.yaml --region=REGION

Custom configuration

Create a custom RunMonitoring configuration

You can create a RunMonitoring configuration for a Cloud Run service if the default configuration is not sufficient. For example, you might create a configuration like the following:

apiVersion: monitoring.googleapis.com/v1beta
kind: RunMonitoring
metadata:
  name: my-custom-cloud-run-job
spec:
  endpoints:
  - port: 8080
    path: /metrics
    interval: 10s
    metricRelabeling:
    - action: replace
      sourceLabels:
      - __address__
      targetLabel: label_key
      replacement: label_value
  targetLabels:
    metadata:
    - service
    - revision

This configuration does the following:

  • Scrapes metrics from the port 8080 and uses the default metrics path /metrics.
  • Uses a 10-second scrape interval.
  • Uses relabelling to add the label label_key with the value label_value to every metric scraped.
  • Adds the service and revision metadata labels to every metric scraped.

For information on the available configuration options, see RunMonitoring spec: configuration options.

When you use a custom RunMonitoring configuration, you must perform the following additional configuration:

Store the configuration by using Secret Manager

To provide a custom RunMonitoring configuration, do the following:

  • Create a file that contains the custom configuration.
  • Create a Secret Manager secret that contains the custom configuration.

The following command creates a secret named mysecret from the custom-config.yaml file:

gcloud secrets create mysecret --data-file=custom-config.yaml

To pick up a change to the configuration after you modify the custom-config.yaml file, you must delete and recreate the secret, and then redeploy the Cloud Run service.

Updating the configuration in Secret Manager

If you want to update the custom RunMonitoring configuration at any time, you can add a new version of the existing secret to Secret Manager.

For example, to update mysecret with a new config stored in file custom-config-updated.yaml, you can run:

gcloud secrets versions add mysecret --data-file=custom-config-updated.yaml

The sidecar automatically reloads and applies the changes to its configuration.

Add the sidecar to a Cloud Run service

After you have created a Secret Manager secret for your RunMonitoring configuration, you must modify your Cloud Run configuration to do the following:

  • Add the Managed Service for Prometheus sidecar
    • Add a container-dependency annotation that specifies the startup and shutdown order of the containers. In the following example, the sidecar container, named "collector", starts after and shuts down before the application container, named "app" in the example.
    • Create a container for the collector, named "collector" in the following example.
  • Add the secret by mounting the secret at the location /etc/rungmp/config.yaml:
    • Add a secrets annotation that points to the secret holding your custom RunMonitoring configuration.
    • Create a volume, named "config" in the following example, for the secret that points to the file config.yaml.
    • Mount the volume as part of the collector image at the mount path /etc/rungmp.

For example, add the lines preceded by the + (plus) character to your Cloud Run configuration, and then redeploy your service:

apiVersion: serving.knative.dev/v1
kind: Service
metadata:
  annotations:
    run.googleapis.com/launch-stage: ALPHA
    run.googleapis.com/cpu-throttling: 'false'
  name: my-cloud-run-service
spec:
  template:
    metadata:
      annotations:
        run.googleapis.com/execution-environment: gen2
+       run.googleapis.com/container-dependencies: '{"collector":["app"]}'
+       run.googleapis.com/secrets: 'mysecret:projects/PROJECT_ID/secrets/mysecret'
    spec:
      containers:
      - image: "REGION-docker.pkg.dev/PROJECT_ID/run-gmp/sample-app"
        name: app
        startupProbe:
          httpGet:
            path: /startup
            port: 8000
        livenessProbe:
          httpGet:
            path: /liveness
            port: 8000
        ports:
        - containerPort: 8000
+     - image: "us-docker.pkg.dev/cloud-ops-agents-artifacts/cloud-run-gmp-sidecar/cloud-run-gmp-sidecar:1.2.0"
+       name: collector
+       volumeMounts:
+       - mountPath: /etc/rungmp/
+         name: config
+     volumes:
+     - name: config
+       secret:
+         items:
+         - key: latest
+           path: config.yaml
+         secretName: 'mysecret'

To redeploy the Cloud Run service with the configuration file run-service.yaml, run the following command:

gcloud run services replace run-service.yaml --region=REGION

RunMonitoring spec: configuration options

This section describes RunMonitoring configuration spec for the Managed Service for Prometheus sidecar. The following shows an example custom configuration:

apiVersion: monitoring.googleapis.com/v1beta
kind: RunMonitoring
metadata:
  name: my-custom-cloud-run-job
spec:
  endpoints:
  - port: 8080
    path: /metrics
    interval: 10s
    metricRelabeling:
    - action: replace
      sourceLabels:
      - __address__
      targetLabel: label_key
      replacement: label_value
  targetLabels:
    metadata:
    - service
    - revision

RunMonitoring

The RunMonitoring configuration monitors a Cloud Run service.

Field Description Scheme Required
metadata Metadata that all persisted resources must have. metav1.ObjectMeta false
spec Specification of the Cloud Run deployment selected for target discovery by Prometheus. RunMonitoringSpec true
RunMonitoringSpec

This spec describes how the RunMonitoring configuration monitors a Cloud Run service.

Field Description Scheme Required
endpoints Endpoints to scrape on the selected pods. []ScrapeEndpoint true
targetLabels Labels to add to the Prometheus target for discovered endpoints. The instance label is always set to the Cloud Run instance ID. RunTargetLabels false
limits Limits to apply at scrape time. *ScrapeLimits false
RunTargetLabels

The RunTargetLabels configuration lets you include Cloud Run labels from the discovered Prometheus targets.

Field Description Scheme Required
metadata Cloud Run metadata labels that are set on all scraped targets.

Permitted keys are instance, revision, service, and configuration.

If permitted keys are set, then the sidecar adds the corresponding value from the Cloud Run instance as metric labels to every metric: instanceID, revision_name, service_name, and configuration_name.

Defaults to all permitted keys being set.		 *[]string false

Build and run a sample app

This section describes how to run the Managed Service for Prometheus sidecar with a sample app. This section is optional. If you already have a Cloud Run service and want to deploy the sidecar with it, then see Add the Managed Service for Prometheus sidecar to a Cloud Run service.

Clone the run-gmp-sidecar repository

To get the sample app and its configuration files, clone the run-gmp-sidecar repository by running the following command:

git clone https://github.com/GoogleCloudPlatform/run-gmp-sidecar/

After cloning the repository, change to the run-gmp-sidecar directory:

cd run-gmp-sidecar

Build and run the sample app

The sample app requires Docker or a similar container build system for Linux. You can build and run the sample app in either of the following ways:

  • Use Cloud Build to run without local Docker support. If you use Cloud Build, then you must also enable the Cloud Build API.
  • Build and run the sample app manually.

The following sections describe both approaches. You don't need to do both. For more information, select the tab for one of the approaches.

Use Cloud Build

Use Cloud Build

The run-gmp-sidecar repository includes configuration files for Cloud Build. These files bundle together the steps needed to build and deploy the sample app.

You can also manually perform the steps handled by Cloud Build. For more information, see Build and run manually.

Set up for Cloud Build

These configuration files require a Cloud Build service account and an Artifact Registry repository. The run-gmp-sidecar repository also includes a script, create-sa-and-ar.sh that does the following:

  • Creates a service account, run-gmp-sa@PROJECT_ID.iam.gserviceaccount.com.
  • Grants the following roles to the service account:
    • roles/iam.serviceAccountUser
    • roles/storage.objectViewer
    • roles/logging.logWriter
    • roles/artifactregistry.createOnPushWriter
    • roles/secretmanager.admin
    • roles/secretmanager.secretAccessor
    • roles/run.admin
  • Creates an Artifact Registry repository, run-gmp, for your container images.

To create the run-gmp-sa service account and run-gmp repository, run the following command:

./create-sa-and-ar.sh

It takes some time for the permissions to propagate, so we recommend waiting about 30 seconds before proceeding to the next step. Otherwise, you might see authorization errors.

Submit Cloud Build request

After you've set up the run-gmp-sa service account and run-gmp repository, you can start a Cloud Build job by submitting the Cloud Build configuration file. You can use the following gcloud builds submit command:

gcloud builds submit . --config=cloudbuild-simple.yaml --region=REGION

This command takes several minutes to run, but it almost immediately indicates you where you can find the Cloud Build build details. The message looks like the following:

Logs are available at [
https://console.cloud.google.com/cloud-build/builds/637860fb-7a14-46f2-861e-09c1dc4cea6b?project=PROJECT_NUMBER].

To watch the build progress, navigate to the returned URL in your browser. You can also view the sidecar logs in Cloud Logging.

Check the service URL

After the Cloud Build job ends, check the Cloud Run service endpoint URL by running the following command:

gcloud run services describe my-cloud-run-service --region=REGION --format="value(status.url)"

This command returns the service URL, which looks like the following:

https://my-cloud-run-service-abcdefghij-ue.a.run.app

Use the returned URL as the value of SERVICE_URL in the section Verify that your app is running.

Build and run manually

Build and run manually

You can manually perform the steps handled by Cloud Build, described in Use Cloud Build. The following sections describe how to manually perform the same tasks.

Set variables used by later steps

Several of the later steps use environment variables for common values. Set up these variables by using the following commands:

export GCP_PROJECT=PROJECT_ID
export REGION=REGION

Create a container image repository

Create an Artifact Registry repository for the container image by running the following command:

gcloud artifacts repositories create run-gmp \
    --repository-format=docker \
    --location=${REGION}

Build and push the sample app

This example uses Docker on Linux to build the sample app and push it to an Artifact Registry repository. You might need to adapt these commands if you are working in a Windows or macOS environment.

To build the sample app and push it to Artifact Registry, do the following:

  1. Authenticate your Docker client with the Google Cloud CLI:

    gcloud auth configure-docker ${REGION}-docker.pkg.dev

  2. Go to the simple-app directory in the run-gmp-sidecar repository: 

    pushd sample-apps/simple-app

  3. Build the simple-app app by running the following command:

    docker build -t ${REGION}-docker.pkg.dev/${GCP_PROJECT}/run-gmp/sample-app .

  4. Push the image for the built app by running the following command: 

    docker push ${REGION}-docker.pkg.dev/${GCP_PROJECT}/run-gmp/sample-app

  5. Return to the run-gmp-sidecar directory:

    popd

Configure the Cloud Run service

The run-service-simple.yaml file in the run-gmp-sidecar repository defines a multicontainer Cloud Run service that uses the sample app and collector images that you built in previous steps. The run-service-simple.yaml file contains the following Service specification:

apiVersion: serving.knative.dev/v1
kind: Service
metadata:
  annotations:
    run.googleapis.com/launch-stage: ALPHA
    run.googleapis.com/cpu-throttling: 'false'
  name: my-cloud-run-service
spec:
  template:
    metadata:
      annotations:
        run.googleapis.com/execution-environment: gen2
        run.googleapis.com/container-dependencies: '{"collector":["app"]}'
    spec:
      containers:
      - image: "%SAMPLE_APP_IMAGE%"
        name: app
        startupProbe:
          httpGet:
            path: /startup
            port: 8000
        livenessProbe:
          httpGet:
            path: /liveness
            port: 8000
        ports:
        - containerPort: 8000
      - image: "us-docker.pkg.dev/cloud-ops-agents-artifacts/cloud-run-gmp-sidecar/cloud-run-gmp-sidecar:1.2.0"
        name: collector

This file contains a placeholder value for the images you've built in previous steps, so you must update the placeholders with the actual values for your Google Cloud project.

Replace the %SAMPLE_APP_IMAGE% placeholder by running the following command:

sed -i s@%SAMPLE_APP_IMAGE%@REGION-docker.pkg.dev/${GCP_PROJECT}/run-gmp/sample-app@g run-service-simple.yaml

Deploy the Cloud Run service

After you've updated the run-service-simple.yaml file with your values, you can create and deploy the Cloud Run service by running the following command:

gcloud run services replace run-service-simple.yaml --region=REGION

This command returns the service URL, which looks like the following:

https://my-cloud-run-service-abcdefghij-ue.a.run.app

Use the returned URL as the value of SERVICE_URL in the section Verify that your app is running.

Allow unauthenticated HTTP access

Before you can make a request to the service URL, you need to change the Cloud Run service policy to accept unauthenticated HTTP access. The policy.yaml file in the run-gmp-sidecar repository contains the necessary change.

To change the service policy, run the following command:

gcloud run services set-iam-policy my-cloud-run-service policy.yaml --region=REGION

Verify that your app is running

To verify that the Cloud Run service has correctly run the sample app, use the curl utility to access the service's metrics endpoint.

  1. Set the SERVICE_URL environment variable to the Cloud Run service URL from the previous step:

    export SERVICE_URL=SERVICE_URL

  2. Send a request to the service URL by running the following command:

    curl $SERVICE_URL

If your app was successfully started, you see the following response:

User request received!

View app metrics in Metrics Explorer

The sample app container writes the following metrics:

  • foo_metric: The current time as a floating-point value (gauge).
  • bar_metric: The current time as a floating-point value (counter).

To view these metrics in Metrics Explorer, do the following:

  1. In the Google Cloud console, go to the  Metrics explorer page:

    Go to Metrics explorer

    If you use the search bar to find this page, then select the result whose subheading is Monitoring.

  2. In the toolbar of the query-builder pane, select the button whose name is either  MQL or  PromQL.

  3. Verify that PromQL is selected in the Language toggle. The language toggle is in the same toolbar that lets you format your query.

  4. Enter the following query into the editor pane:

    foo_metric

  5. Click Add query.

  6. In the toolbar of the query-builder pane, select the button whose name is either  MQL or  PromQL.

  7. Verify that PromQL is selected in the Language toggle. The language toggle is in the same toolbar that lets you format your query.

  8. Enter the following query into the second editor pane:

    bar_metric

  9. Click Run query.

  10. To see details about the metrics, in the toggle labeled Chart Table Both, select Both.

Clean up

When you are finished experimenting with the sample app, you can use the clean-up-cloud-run.sh script in the run-gmp-sidecar repository to delete the following resources you might have created for the sample:

  • The Cloud Run service.
  • The Artifact Registry repository.
  • The service account created for Cloud Build.

Deleting these resources ensures that you don't incur costs after running the sample.

To clean up the sample app, run the clean-up-cloud-run.sh script:

./clean-up-cloud-run.sh

View self metrics in Metrics Explorer

The Managed Service for Prometheus sidecar reports the following metrics about itself to Cloud Monitoring:

  • agent_uptime: The uptime of the sidecar collector (counter).
  • agent_memory_usage: The memory being consumed by the sidecar collector (gauge).
  • agent_api_request_count: The number of API requests from the sidecar collector (counter).
  • agent_monitoring_point_count: The number of metric points written to Cloud Monitoring by the sidecar collector (counter).

To view the sidecar's self metrics in Metrics Explorer, do the following:

  1. In the Google Cloud console, go to the  Metrics explorer page:

    Go to Metrics explorer

    If you use the search bar to find this page, then select the result whose subheading is Monitoring.

  2. In the toolbar of the query-builder pane, select the button whose name is either  MQL or  PromQL.

  3. Verify that PromQL is selected in the Language toggle. The language toggle is in the same toolbar that lets you format your query.

  4. Enter the name of the metric you want to query into the editor pane, for example:

    agent_api_request_count

  5. Click Run query.

  6. To see details about the metrics, in the toggle labeled Chart Table Both, select Both.

View self logs in Logs Explorer

The Managed Service for Prometheus sidecar writes logs to Cloud Logging. The sidecar writes logs against the Logging monitored resource type cloud_run_revision.

To view the sidecar's logs in Logs Explorer, do the following:

  1. In the Google Cloud console, go to the Logs Explorer page:

    Go to Logs Explorer

    If you use the search bar to find this page, then select the result whose subheading is Logging.

  2. Select the Cloud Run Revision resource type, or enter the following query and click Run query:

    resource.type="cloud_run_revision"

About the collected metrics

When the metrics emitted by the sidecar are ingested into Cloud Monitoring, the metrics are written against the Cloud Monitoring prometheus_target monitored resource type. This resource type is used for both application metrics and the sidecar's self metrics.

When writing the metrics, the sidecar sets the resource labels as follows:

  • project_id: The ID of the Google Cloud project in which the container is running.
  • location: The Google Cloud region in which the container is running.
  • cluster: The value __run__.
  • namespace: The name of the Cloud Run service being run; comes from the K_SERVICE environment variable.
  • job: Name from the RunMonitoring configuration; defaults to run-gmp-sidecar.
  • instance: The value faas.ID:PORT of the local workload from which the container is configured to pull metrics. The faas.ID value is the instance ID of the Cloud Run instance.

The sidecar also adds the following metric labels:

  • instanceId: The ID of the Cloud Run instance.
  • service_name: The name of the Cloud Run service being run.
  • revision_name: The name of the Cloud Run revision being run.
  • configuration_name: The name of the Cloud Run configuration being run.

These metric labels are all added by default. If you use a custom RunMonitoring configuration, you can omit the service_name, revision_name, and configuration_name labels by using the targetLabels option in the RunMonitoring spec. You can also use a custom configuration to relabel the values of the service_name, revision_name, and configuration_name labels.

All other labels that appear with ingested metrics come from your metric. If a user-defined label conflicts with one of the system-provided labels, the user-defined label is prefixed with the string exported_. For example, a user-specified label namespace="playground" conflicts with the system-defined namespace label, so the user label appears as exported_namespace="playground".

Metric type

When the metrics emitted by the sidecar are ingested into Cloud Monitoring, the metrics are written as prometheus.googleapis.com metrics, and the type of the Prometheus metric is appended to the end of the name. For example, the sample app emits a Prometheus gauge metric named foo_metric. In Cloud Monitoring, the metric is stored as the metric type prometheus.googleapis.com/foo_metric/gauge.

When querying the metric with PromQL, you can use the Prometheus name, as illustrated in View app metrics in Metrics Explorer and View self metrics in Metrics Explorer. When using tools like the query builder or Monitoring Query Language (MQL) in Metrics Explorer, the Cloud Monitoring metric type is relevant.

Billing

Metrics emitted by the sidecar are ingested into Cloud Monitoring with the prefix prometheus.googleapis.com. Metrics with this prefix are charged by the number of samples ingested. For more information about billing and Managed Service for Prometheus, see Billing.