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Configuring CPU and memory limits for Logging components - Configuring your Logging <strong>deployment</strong> | Logging | OKD 4.8
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You can configure both the CPU and memory limits for each of the OpenShift Logging components as needed.

Configuring CPU and memory limits

The OpenShift Logging components allow for adjustments to both the CPU and memory limits.

Procedure
  1. Edit the ClusterLogging custom resource (CR) in the openshift-logging project:

    $ oc -n openshift-logging edit ClusterLogging instance
    apiVersion: "logging.openshift.io/v1"
    kind: "ClusterLogging"
    metadata:
      name: "instance"
      namespace: openshift-logging
    
    ...
    
    spec:
      managementState: "Managed"
      logStore:
        type: "elasticsearch"
        elasticsearch:
          nodeCount: 3
          resources: (1)
            limits:
              memory: 16Gi
            requests:
              cpu: 200m
              memory: 16Gi
          storage:
            storageClassName: "gp2"
            size: "200G"
          redundancyPolicy: "SingleRedundancy"
      visualization:
        type: "kibana"
        kibana:
          resources: (2)
            limits:
              memory: 1Gi
            requests:
              cpu: 500m
              memory: 1Gi
          proxy:
            resources: (2)
              limits:
                memory: 100Mi
              requests:
                cpu: 100m
                memory: 100Mi
          replicas: 2
      collection:
        logs:
          type: "fluentd"
          fluentd:
            resources: (3)
              limits:
                memory: 736Mi
              requests:
                cpu: 200m
                memory: 736Mi
    1 Specify the CPU and memory limits and requests for the log store as needed. For Elasticsearch, you must adjust both the request value and the limit value.
    2 Specify the CPU and memory limits and requests for the log visualizer as needed.
    3 Specify the CPU and memory limits and requests for the log collector as needed.