About configuring cluster logging - Configuring your cluster logging deployment | logging

About deploying and configuring cluster logging
- Configuring and Tuning Cluster logging
- Sample modified Cluster logging Custom Resource

After installing cluster logging into your OpenShift Container Platform cluster, you can make the following configurations.

You must set cluster logging to Unmanaged state before performing these configurations, unless otherwise noted. For more information, see Changing the cluster logging management state.

Operators in an unmanaged state are unsupported and the cluster administrator assumes full control of the individual component configurations and upgrades. For more information, see Support policy for unmanaged Operators.

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 Cluster logging Custom Resource (CR), which you can use to create a cluster logging instance and configure your cluster logging deployment.

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 Cluster logging Custom Resource.

Configuring and Tuning Cluster logging

You can configure your cluster logging environment by modifying the Cluster logging 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:
        requests:
          cpu: 1
          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 PersistentVolumeClaim 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 PersistentVolumeClaim 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 Cluster logging Custom Resource

The following is an example of a Cluster logging Custom Resource modified using the options previously described.

Sample modified Cluster logging Custom Resource

apiVersion: "logging.openshift.io/v1"
kind: "Clusterlogging"
metadata:
  name: "instance"
  namespace: "openshift-logging"
spec:
  managementState: "Managed"
  logStore:
    type: "elasticsearch"
    elasticsearch:
      nodeCount: 2
      resources:
        limits:
          memory: 2Gi
        requests:
          cpu: 200m
          memory: 2Gi
      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