Application logging and monitoring

This page shows how to configure a user cluster for Google Distributed Cloud 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.gke.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 PromQL queries in the Metrics Explorer in the Google Cloud console.

To run a PromQL query, do the following:

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

    Go to Monitoring

  2. In the navigation pane, select Metrics Explorer.

  3. Use Prometheus Query Language (PromQL) to specify the data to display on the chart:

    1. In the toolbar of the Select a metric pane, select Code Editor.

    2. Select PromQL in the Language toggle. The language toggle is at the bottom of the Code Editor pane.

    3. Enter your query into the query editor. For example, to chart the average number of seconds CPUs spent in each mode over the past hour, use the following query:

      avg(rate(kubernetes_io:anthos_container_cpu_usage_seconds_total
{monitored_resource="k8s_node"}[1h]))

    For more information about using PromQL, see PromQL in Cloud Monitoring.

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

Managed Service for Prometheus chart for the Prometheus `anthos_container_cpu_usage_seconds_total` 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.gke.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'

Filter application logs

Application log filtering can reduce application logging billing and network traffic from the cluster to Cloud Logging. Starting with Google Distributed Cloud release 1.15.0, when enableCloudLoggingForApplications is set to true, you can filter application logs by the following criteria:

  • Pod labels (podLabelSelectors)
  • Namespaces (namespaces)
  • Regular expressions for log content (contentRegexes)

Google Distributed Cloud sends only the filter results to Cloud Logging.

Define application log filters

The configuration for Logging is specified 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 to your user cluster kubeconfig file.

  2. Add an appLogFilter section to the spec:

      apiVersion: addons.gke.io/v1alpha1
  kind: Stackdriver
  metadata:
    name: stackdriver
    namespace: kube-system
  spec:
    enableCloudLoggingForApplications: true
    projectID: ...
    clusterName: ...
    clusterLocation: ...
    appLogFilter:
      keepLogRules:
      - namespaces:
        - prod
        ruleName: include-prod-logs
      dropLogRules:
      - podLabelSelectors:
        - disableGCPLogging=yes
        ruleName: drop-logs

  3. Save and close the edited file.

  4. (Optional) If you're using podLabelSelectors, restart the stackdriver-log-forwarder DaemonSet to effect your changes as soon as possible:

    kubectl --kubeconfig USER_CLUSTER_KUBECONFIG --namespace kube-system \
    rollout restart daemonset stackdriver-log-forwarder

    Normally, podLabelSelectors are effective after 10 minutes. Restarting the DaemonSet stackdriver-log-forwarder makes the changes take effect more quickly.

Example: Include ERROR or WARN logs in prod namespace only

The following example illustrates an application log filter works. You define a filter that uses a namespace (prod), a regular expression (.*(ERROR|WARN).*), and a Pod label (disableGCPLogging=yes). Then, to verify that the filter works, you run a Pod in the prod namespace to test these filter conditions.

To define and test an application log filter:

  1. Specify an application log filter in the Stackdriver object:

    In the following appLogFilter example, only ERROR or WARN logs in the prod namespace are kept. Any logs for Pods with the label disableGCPLogging=yes are dropped:

    apiVersion: addons.gke.io/v1alpha1
kind: Stackdriver
metadata:
  name: stackdriver
  namespace: kube-system
spec:
  ...
  appLogFilter:
    keepLogRules:
    - namespaces:
      - prod
      contentRegexes:
      - ".*(ERROR|WARN).*"
      ruleName: include-prod-logs
    dropLogRules:
    - podLabelSelectors:
      - disableGCPLogging=yes # kubectl label pods pod disableGCPLogging=yes
      ruleName: drop-logs
...

  2. Deploy a Pod in the prod namespace and run a script that generates ERROR and INFO log entries:

    kubectl --kubeconfig USER_CLUSTER_KUBECONFIG run pod1 \
    --image gcr.io/cloud-marketplace-containers/google/debian10:latest \
    --namespace prod --restart Never --command -- \
    /bin/sh -c "while true; do echo 'ERROR is 404\\nINFO is not 404' && sleep 1; done"

    The filtered logs should contain the ERROR entries only, not the INFO entries.

  3. Add the label disableGCPLogging=yes to the Pod:

    kubectl --kubeconfig USER_CLUSTER_KUBECONFIG label pods pod1 \
    --namespace prod disableGCPLogging=yes

    The filtered log should no longer contain any entries for the pod1 Pod.

Application log filter API definition

The definition for the application log filter is declared within the stackdriver custom resource definition.

To get the stackdriver custom resource definition, run the following command:

kubectl --kubeconfig USER_CLUSTER_KUBECONFIG get crd stackdrivers.addons.gke.io \
    --namespace kube-system -o yaml

Enabling Logging and Monitoring for user applications (Legacy)

Note, it's strongly recommended to use the above enableGMPForApplications and enableCloudLoggingForApplications to enable monitoring and logging for user applications.

The following steps still work but aren't recommended. Please read this known issue before using the following steps.

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.gke.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 Pod manifest, or add the same annotation to the spec.template section in Deployment or DaemonSet manifest in order to be passed to their Pods.

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 or Kubernetes Container.

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

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