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Cluster logging with OpenShift Serverless

About deploying cluster logging

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.

About deploying and configuring cluster logging

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.

Configuring and Tuning Cluster Logging

You can configure your cluster logging environment by modifying the ClusterLogging custom resource deployed in the openshift-logging project.

You can modify any of the following components upon install or after install:

Memory and CPU

You can adjust both the CPU and memory limits for each component by modifying the resources block with valid memory and CPU values:

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"
Elasticsearch storage

You can configure a persistent storage class and size for the Elasticsearch cluster using the storageClass name and size parameters. The Cluster Logging Operator creates a persistent volume claim (PVC) for each data node in the Elasticsearch cluster based on these parameters.

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

This example specifies each data node in the cluster will be bound to a PVC that requests "200G" of "gp2" storage. Each primary shard will be backed by a single replica.

Omitting the storage block results in a deployment that includes ephemeral storage only.

  spec:
    logStore:
      type: "elasticsearch"
      elasticsearch:
        nodeCount: 3
        storage: {}
Elasticsearch replication policy

You can set the policy that defines how Elasticsearch shards are replicated across data nodes in the cluster:

  • FullRedundancy. The shards for each index are fully replicated to every data node.

  • MultipleRedundancy. The shards for each index are spread over half of the data nodes.

  • SingleRedundancy. A single copy of each shard. Logs are always available and recoverable as long as at least two data nodes exist.

  • ZeroRedundancy. No copies of any shards. Logs may be unavailable (or lost) in the event a node is down or fails.

Curator schedule

You specify the schedule for Curator in the cron format.

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

Sample modified ClusterLogging custom resource

The following is an example of a ClusterLogging custom resource modified using the options previously described.

Sample modified ClusterLogging custom resource
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

Using cluster logging to find logs for Knative Serving components

Procedure

  1. To open the Kibana UI, the visualization tool for Elasticsearch, use the following command to get the Kibana route:

    $ oc -n openshift-logging get route kibana

  2. Use the route’s URL to navigate to the Kibana dashboard and log in.

  3. Ensure the index is set to .all. If the index is not set to .all, only the OpenShift system logs will be listed.

  4. You can filter the logs by using the knative-serving namespace. Enter kubernetes.namespace_name:knative-serving in the search box to filter results.

    Knative Serving uses structured logging by default. You can enable the parsing of these logs by customizing the cluster logging Fluentd settings. This makes the logs more searchable and enables filtering on the log level to quickly identify issues.

Using cluster logging to find logs for services deployed with Knative Serving

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.

Procedure

  1. Use the following command to find the URL to Kibana:

    $ oc -n cluster-logging get route kibana`

  2. Enter the URL in your browser to open the Kibana UI.

  3. Ensure the index is set to .all. If the index is not set to .all, only the OpenShift system logs will be listed.

  4. Filter the logs by using the Kubernetes namespace your service is deployed in. Add a filter to identify the service itself: kubernetes.namespace_name:default AND kubernetes.labels.serving_knative_dev\/service:{SERVICE_NAME}.

    You can also filter by using /configuration or /revision.

  5. You can narrow your search by using kubernetes.container_name:<user-container> to only display the logs generated by your application. Otherwise, you will see logs from the queue-proxy.

    Use JSON-based structured logging in your application to allow for the quick filtering of these logs in production environments.