Application logging and monitoring

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This page shows how to configure a user cluster for Anthos clusters on VMware (GKE on-prem) so that custom logs and metrics from user applications are sent to Cloud Logging and Cloud Monitoring.

You can also use Google Cloud Managed Service for Prometheus to monitor your workloads. This is Google Cloud's fully managed storage and query service for Prometheus metrics. To use this feature, enable Managed Service for Prometheus and Cloud Logging by following the steps below.

Enabling Managed Service for Prometheus for user applications

The configuration for Managed Service for Prometheus is held in a Stackdriver object named stackdriver.

  1. Open the stackdriver object for editing:

    kubectl --kubeconfig=USER_CLUSTER_KUBECONFIG --namespace kube-system edit stackdriver stackdriver
    

    Replace USER_CLUSTER_KUBECONFIG with the path of your user cluster kubeconfig file.

  2. Under spec, set enableGMPForApplications to true:

      apiVersion: addons.sigs.k8s.io/v1alpha1
      kind: Stackdriver
      metadata:
        name: stackdriver
        namespace: kube-system
      spec:
        projectID: ...
        clusterName: ...
        clusterLocation: ...
        proxyConfigSecretName: ...
        enableGMPForApplications: true
        enableVPC: ...
        optimizedMetrics: true
    
  3. Close the edited file. This will begin running Google-managed Prometheus (GMP) components in the cluster.

  4. To check the components, run the following command:

    kubectl --kubeconfig=USER_CLUSTER_KUBECONFIG --namespace gmp-system get pods
    

    The output of this command is similar to the following:

     NAME                                 READY   STATUS    RESTARTS        AGE
     collector-abcde                      2/2     Running   1 (5d18h ago)   5d18h
     collector-fghij                      2/2     Running   1 (5d18h ago)   5d18h
     collector-klmno                      2/2     Running   1 (5d18h ago)   5d18h
     gmp-operator-68d49656fc-abcde        1/1     Running   0               5d18h
     rule-evaluator-7c686485fc-fghij      2/2     Running   1 (5d18h ago)   5d18h
    

Managed Service for Prometheus supports rule evaluation and alerting. To set up rule evaluation, see Rule evaluation.

Running an example application

In this section, you create an application that emits Prometheus metrics, and use Google-managed Prometheus to collect the metrics. For more information, see Google Cloud Managed Service for Prometheus.

Deploy the example application

  1. Create the gmp-test namespace for resources you create as part of the example application:

    kubectl --kubeconfig=USER_CLUSTER_KUBECONFIG create ns gmp-test
    
  2. The managed service provides a manifest for an example application that emits Prometheus metrics on its metrics port. The application uses three replicas.

    To deploy the example application, run the following command:

    kubectl --kubeconfig USER_CLUSTER_KUBECONFIG -n gmp-test apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/prometheus-engine/v0.4.1/examples/example-app.yaml
    

Configure a PodMonitoring resource

To ingest the metric data emitted by the example application, you use target scraping. The managed service uses PodMonitoring custom resources (CRs) to configure target scraping and metrics ingestion. You can convert your existing prometheus-operator resources to PodMonitoring CRs.

A PodMonitoring CR scrapes targets only in the namespace the CR is deployed in. To scrape targets in multiple namespaces, deploy the same PodMonitoring CR in each namespace. You can verify the PodMonitoring resource is installed in the intended namespace by running the following command:

 kubectl --kubeconfig USER_CLUSTER_KUBECONFIG get podmonitoring -A

For reference documentation about all the Managed Service for Prometheus CRs, see the prometheus-engine/doc/api reference.

The following manifest defines a PodMonitoring resource, prom-example, in the gmp-test namespace. The resource finds all Pods in the namespace that have the label app with the value prom-example. The matching Pods are scraped on a port named metrics, every 30 seconds, on the /metrics HTTP path.

apiVersion: monitoring.googleapis.com/v1
kind: PodMonitoring
metadata:
  name: prom-example
spec:
  selector:
    matchLabels:
      app: prom-example
  endpoints:
  - port: metrics
    interval: 30s

To apply this resource, run the following command:

kubectl --kubeconfig USER_CLUSTER_KUBECONFIG -n gmp-test apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/prometheus-engine/v0.4.1/examples/pod-monitoring.yaml

Managed Service for Prometheus is now scraping the matching Pods.

Query metrics data

The simplest way to verify that your Prometheus data is being exported is to use the PromQL-based Managed Service for Prometheus page in the Google Cloud console.

To view this page, do the following:

  1. In the Google Cloud console, go to Monitoring or use the following button:

    Go to Monitoring

  2. In the Monitoring navigation pane, click Managed Prometheus.

On the Managed Service for Prometheus page, you can use PromQL queries to retrieve and chart data collected with the managed service. This page can only query data collected by Managed Service for Prometheus.

The following screenshot shows a chart that displays the up metric:

Managed Service for Prometheus chart for the Prometheus `up` metric.

If you collect a lot of data, you might want to filter exported metrics to keep costs down.

Enabling Cloud Logging for user applications

The configuration for Logging is held in a Stackdriver object named stackdriver.

  1. Open the stackdriver object for editing:

    kubectl --kubeconfig=USER_CLUSTER_KUBECONFIG --namespace kube-system edit stackdriver stackdriver
    

    Replace USER_CLUSTER_KUBECONFIG with the path of your user cluster kubeconfig file.

  2. Under spec, set enableCloudLoggingForApplications to true:

      apiVersion: addons.sigs.k8s.io/v1alpha1
      kind: Stackdriver
      metadata:
        name: stackdriver
        namespace: kube-system
      spec:
        projectID: ...
        clusterName: ...
        clusterLocation: ...
        proxyConfigSecretName: ...
        enableCloudLoggingForApplications: true
        enableVPC: ...
        optimizedMetrics: true
    
  3. Close the edited file.

Running an example application

In this section, you create an application that writes custom logs.

  1. Save the following Deployment manifest to a file named my-app.yaml.

    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: "monitoring-example"
      namespace: "default"
      labels:
        app: "monitoring-example"
    spec:
      replicas: 1
      selector:
        matchLabels:
          app: "monitoring-example"
      template:
        metadata:
          labels:
            app: "monitoring-example"
        spec:
          containers:
          - image: gcr.io/google-samples/prometheus-dummy-exporter:latest
            name: prometheus-example-exporter
            imagePullPolicy: Always
            command:
            - /bin/sh
            - -c
            - ./prometheus-dummy-exporter --metric-name=example_monitoring_up --metric-value=1 --port=9090
            resources:
              requests:
                cpu: 100m
    
  2. Create the Deployment:

    kubectl --kubeconfig USER_CLUSTER_KUBECONFIG apply -f my-app.yaml
    

Viewing application logs

Console

  1. Go to the Logs explorer in the Google Cloud console.

    Go to the Logs explorer

  2. Click Resource. Under ALL_RESOURCE_TYPES, select Kubernetes Container.

  3. Under CLUSTER_NAME, select the name of your user cluster.

  4. Under NAMESPACE_NAME, select default.

  5. Click Add and then click Run Query.

  6. Under Query results, you can see log entries from the monitoring-example Deployment. For example:

    {
      "textPayload": "2020/11/14 01:24:24 Starting to listen on :9090\n",
      "insertId": "1oa4vhg3qfxidt",
      "resource": {
        "type": "k8s_container",
        "labels": {
          "pod_name": "monitoring-example-7685d96496-xqfsf",
          "cluster_name": ...,
          "namespace_name": "default",
          "project_id": ...,
          "location": "us-west1",
          "container_name": "prometheus-example-exporter"
        }
      },
      "timestamp": "2020-11-14T01:24:24.358600252Z",
      "labels": {
        "k8s-pod/pod-template-hash": "7685d96496",
        "k8s-pod/app": "monitoring-example"
      },
      "logName": "projects/.../logs/stdout",
      "receiveTimestamp": "2020-11-14T01:24:39.562864735Z"
    }
    

gcloud

  1. Run this command:

    gcloud logging read 'resource.labels.project_id="PROJECT_ID" AND \
        resource.type="k8s_container" AND resource.labels.namespace_name="default"'
    

    Replace PROJECT_ID with the ID of your logging-monitoring project.

  2. In the output, you can see log entries from the monitoring-example Deployment. For example:

    insertId: 1oa4vhg3qfxidt
    labels:
      k8s-pod/app: monitoring-example
      k8s- pod/pod-template-hash: 7685d96496
    logName: projects/.../logs/stdout
    receiveTimestamp: '2020-11-14T01:24:39.562864735Z'
    resource:
      labels:
        cluster_name: ...
        container_name: prometheus-example-exporter
        location: us-west1
        namespace_name: default
        pod_name: monitoring-example-7685d96496-xqfsf
        project_id: ...
      type: k8s_container
    textPayload: |
      2020/11/14 01:24:24 Starting to listen on :9090
    timestamp: '2020-11-14T01:24:24.358600252Z'
    

Enabling Logging and Monitoring for user applications

This section describes how to enable Logging and Monitoring if you are not using Managed Service for Prometheus. The configuration for Logging and Monitoring is held in a Stackdriver object named stackdriver.

  1. Open the stackdriver object for editing:

    kubectl --kubeconfig=USER_CLUSTER_KUBECONFIG --namespace kube-system edit stackdriver stackdriver
    

    Replace USER_CLUSTER_KUBECONFIG with the path of your user cluster kubeconfig file.

  2. Under spec, set enableStackdriverForApplications to true:

      apiVersion: addons.sigs.k8s.io/v1alpha1
      kind: Stackdriver
      metadata:
        name: stackdriver
        namespace: kube-system
      spec:
        projectID: ...
        clusterName: ...
        clusterLocation: ...
        proxyConfigSecretName: ...
        enableStackdriverForApplications: true
        enableVPC: ...
        optimizedMetrics: true
    
  3. Close the edited file.

Annotating workloads

To enable the collection of custom metrics from an application, add the prometheus.io/scrape: "true" annotation to the application's Service or Deployment manifest.

To avoid garbage collection of metrics, we recommend setting the metrics scrape interval to one minute.

Running an example application

In this section, you create an application that writes custom logs and exposes a custom metric.

  1. Save the following Service and Deployment manifests to a file named my-app.yaml. Notice that the Service has the annotation prometheus.io/scrape: "true":

    kind: Service
    apiVersion: v1
    metadata:
      name: "monitoring-example"
      namespace: "default"
      annotations:
        prometheus.io/scrape: "true"
    spec:
      selector:
        app: "monitoring-example"
      ports:
        - name: http
          port: 9090
    ---
    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: "monitoring-example"
      namespace: "default"
      labels:
        app: "monitoring-example"
    spec:
      replicas: 1
      selector:
        matchLabels:
          app: "monitoring-example"
      template:
        metadata:
          labels:
            app: "monitoring-example"
        spec:
          containers:
          - image: gcr.io/google-samples/prometheus-dummy-exporter:latest
            name: prometheus-example-exporter
            imagePullPolicy: Always
            command:
            - /bin/sh
            - -c
            - ./prometheus-dummy-exporter --metric-name=example_monitoring_up --metric-value=1 --port=9090
            resources:
              requests:
                cpu: 100m
    
  2. Create the Deployment and the Service:

    kubectl --kubeconfig USER_CLUSTER_KUBECONFIG apply -f my-app.yaml
    

Viewing application logs

Console

  1. Go to the Logs explorer in the Google Cloud console.

    Go to the Logs explorer

  2. Click Resource. Under ALL_RESOURCE_TYPES, select Kubernetes Container.

  3. Under CLUSTER_NAME, select the name of your user cluster.

  4. Under NAMESPACE_NAME, select default.

  5. Click Add and then click Run Query.

  6. Under Query results, you can see log entries from the monitoring-example Deployment. For example:

    {
      "textPayload": "2020/11/14 01:24:24 Starting to listen on :9090\n",
      "insertId": "1oa4vhg3qfxidt",
      "resource": {
        "type": "k8s_container",
        "labels": {
          "pod_name": "monitoring-example-7685d96496-xqfsf",
          "cluster_name": ...,
          "namespace_name": "default",
          "project_id": ...,
          "location": "us-west1",
          "container_name": "prometheus-example-exporter"
        }
      },
      "timestamp": "2020-11-14T01:24:24.358600252Z",
      "labels": {
        "k8s-pod/pod-template-hash": "7685d96496",
        "k8s-pod/app": "monitoring-example"
      },
      "logName": "projects/.../logs/stdout",
      "receiveTimestamp": "2020-11-14T01:24:39.562864735Z"
    }
    

gcloud

  1. Run this command:

    gcloud logging read 'resource.labels.project_id="PROJECT_ID" AND \
        resource.type="k8s_container" AND resource.labels.namespace_name="default"'
    

    Replace PROJECT_ID with the ID of your logging-monitoring project.

  2. In the output, you can see log entries from the monitoring-example Deployment. For example:

    insertId: 1oa4vhg3qfxidt
    labels:
      k8s-pod/app: monitoring-example
      k8s- pod/pod-template-hash: 7685d96496
    logName: projects/.../logs/stdout
    receiveTimestamp: '2020-11-14T01:24:39.562864735Z'
    resource:
      labels:
        cluster_name: ...
        container_name: prometheus-example-exporter
        location: us-west1
        namespace_name: default
        pod_name: monitoring-example-7685d96496-xqfsf
        project_id: ...
      type: k8s_container
    textPayload: |
      2020/11/14 01:24:24 Starting to listen on :9090
    timestamp: '2020-11-14T01:24:24.358600252Z'
    

Viewing application metrics in the Google Cloud console

Your example application exposes a custom metric named example_monitoring_up. You can view the values of that metric in the Google Cloud console.

  1. Go to the Metrics explorer in the Google Cloud console.

    Go to the Metrics explorer

  2. For Resource type, select Kubernetes Pod.

  3. For metric, select external/prometheus/example_monitoring_up.

  4. In the chart, you can see that example_monitoring_up has a repeated value of 1.