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Using OpenShift <strong>logging</strong> with Serverless - Cluster <strong>logging</strong> | Observability | Red Hat OpenShift Serverless 1.30
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Using OpenShift logging with OpenShift Serverless

About deploying the logging subsystem for Red Hat OpenShift

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

The Clusterlogging CR defines a complete logging subsystem environment that includes all the components of the logging stack to collect, store and visualize logs. The Red Hat OpenShift logging Operator watches the logging subsystem 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 the logging subsystem for Red Hat OpenShift

The logging subsystem 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 logging subsystem instance and configure your logging subsystem environment.

If you want to use the default logging subsystem 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 OpenShift 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 the logging subsystem

You can configure your logging subsystem 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
Elasticsearch storage

You can configure a persistent storage class and size for the Elasticsearch cluster using the storageClass name and size parameters. The Red Hat OpenShift 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.

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:
          cpu: 200m
          memory: 16Gi
        requests:
          cpu: 200m
          memory: 16Gi
        storage:
          storageClassName: "gp2"
          size: "200G"
      redundancyPolicy: "SingleRedundancy"
  visualization:
    type: "kibana"
    kibana:
      resources:
        limits:
          memory: 1Gi
        requests:
          cpu: 500m
          memory: 1Gi
      replicas: 1
  collection:
    logs:
      type: "fluentd"
      fluentd:
        resources:
          limits:
            memory: 1Gi
          requests:
            cpu: 200m
            memory: 1Gi