9.3. Cluster logging with OpenShift Serverless

OpenShift Container Platform cluster administrators can deploy cluster logging using the OpenShift Container Platform web console or CLI to install the Elasticsearch Operator and Cluster Logging Operator. When the operators are installed, you create a ClusterLogging custom resource (CR) to schedule cluster logging pods and other resources necessary to support cluster logging. The operators are responsible for deploying, upgrading, and maintaining cluster logging.

The ClusterLogging CR defines a complete cluster logging environment that includes all the components of the logging stack to collect, store and visualize logs. The Cluster Logging Operator watches the Cluster Logging CR and adjusts the logging deployment accordingly.

Administrators and application developers can view the logs of the projects for which they have view access.

OpenShift Container Platform cluster logging is designed to be used with the default configuration, which is tuned for small to medium sized OpenShift Container Platform clusters.

The installation instructions that follow include a sample ClusterLogging custom resource (CR), which you can use to create a cluster logging instance and configure your cluster logging environment.

If you want to use the default cluster logging install, you can use the sample CR directly.

If you want to customize your deployment, make changes to the sample CR as needed. The following describes the configurations you can make when installing your cluster logging instance or modify after installation. See the Configuring sections for more information on working with each component, including modifications you can make outside of the ClusterLogging custom resource.

spec:
  logStore:
    elasticsearch:
      resources:
        limits:
          cpu:
          memory: 16Gi
        requests:
          cpu: 500m
          memory: 16Gi
      type: "elasticsearch"
  collection:
    logs:
      fluentd:
        resources:
          limits:
            cpu:
            memory:
          requests:
            cpu:
            memory:
        type: "fluentd"
  visualization:
    kibana:
      resources:
        limits:
          cpu:
          memory:
        requests:
          cpu:
          memory:
     type: kibana
  curation:
    curator:
      resources:
        limits:
          memory: 200Mi
        requests:
          cpu: 200m
          memory: 200Mi
      type: "curator"

  spec:
    logStore:
      type: "elasticsearch"
      elasticsearch:
        nodeCount: 3
        storage:
          storageClassName: "gp2"
          size: "200G"

  spec:
    logStore:
      type: "elasticsearch"
      elasticsearch:
        nodeCount: 3
        storage: {}

  spec:
    curation:
    type: "curator"
    resources:
    curator:
      schedule: "30 3 * * *"

apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
metadata:
  name: "instance"
  namespace: "openshift-logging"
spec:
  managementState: "Managed"
  logStore:
    type: "elasticsearch"
    retentionPolicy:
      application:
        maxAge: 1d
      infra:
        maxAge: 7d
      audit:
        maxAge: 7d
    elasticsearch:
      nodeCount: 3
      resources:
        limits:
          memory: 32Gi
        requests:
          cpu: 3
          memory: 32Gi
      storage: {}
      redundancyPolicy: "SingleRedundancy"
  visualization:
    type: "kibana"
    kibana:
      resources:
        limits:
          memory: 1Gi
        requests:
          cpu: 500m
          memory: 1Gi
      replicas: 1
  curation:
    type: "curator"
    curator:
      resources:
        limits:
          memory: 200Mi
        requests:
          cpu: 200m
          memory: 200Mi
      schedule: "*/5 * * * *"
  collection:
    logs:
      type: "fluentd"
      fluentd:
        resources:
          limits:
            memory: 1Gi
          requests:
            cpu: 200m
            memory: 1Gi

With OpenShift Cluster Logging, the logs that your applications write to the console are collected in Elasticsearch. The following procedure outlines how to apply these capabilities to applications deployed by using Knative Serving.