Manage compute resources

To help the Kubernetes scheduler correctly place Pods in available Kubernetes nodes and ensure quality of service (QoS), it is recommended to specify the CPU and memory requirements for objects managed by the operator (Elasticsearch, Kibana or APM Server). In Kubernetes, requests defines the minimum amount of resources that must be available for a Pod to be scheduled; limits defines the maximum amount of resources that a Pod is allowed to consume. For more information about how Kubernetes uses these concepts, see: Managing Compute Resources for Containers.

You can set compute resource constraints in the podTemplate of objects managed by the operator.

For Elasticsearch objects, make sure to consider the heap size when you set resource requirements. A good rule of thumb is to size it to half the size of RAM allocated to the Pod.

To minimize disruption caused by Pod evictions due to resource contention, you can run Elasticsearch pods at the "Guaranteed" QoS level by setting both requests and limits to the same value.

The value set for cpu requests directly impacts Elasticsearch node.processors setting. For example, with resources.requests.cpu: 1, Elasticsearch effectively relies on a single core, which may significantly limit performance. Consider setting a higher value that matches the desired number of cores Elasticsearch can use. You can also set your own value for node.processors in the Elasticsearch config.

Consider also that Kubernetes throttles containers exceeding the CPU limit defined in the limits section. Do not set this value too low or it would affect the performance of Elasticsearch, even if you have enough resources available in the Kubernetes cluster.

apiVersion: elasticsearch.k8s.elastic.co/v1
kind: Elasticsearch
metadata:
  name: quickstart
spec:
  version: 8.17.0
  nodeSets:
  - name: default
    count: 1
    podTemplate:
      spec:
        containers:
        - name: elasticsearch
          env:
          - name: ES_JAVA_OPTS
            value: -Xms2g -Xmx2g
          resources:
            requests:
              memory: 4Gi
              cpu: 8
            limits:
              memory: 4Gi

Kibana.

apiVersion: kibana.k8s.elastic.co/v1
kind: Kibana
metadata:
  name: quickstart
spec:
  version: 8.17.0
  podTemplate:
    spec:
      containers:
      - name: kibana
        env:
          - name: NODE_OPTIONS
            value: "--max-old-space-size=2048"
        resources:
          requests:
            memory: 1Gi
            cpu: 0.5
          limits:
            memory: 2.5Gi
            cpu: 2

APM Server.

apiVersion: apm.k8s.elastic.co/v1
kind: ApmServer
metadata:
  name: quickstart
spec:
  version: 8.17.0
  podTemplate:
    spec:
      containers:
      - name: apm-server
        resources:
          requests:
            memory: 1Gi
            cpu: 0.5
          limits:
            memory: 2Gi
            cpu: 2

Enterprise Search.

apiVersion: enterprisesearch.k8s.elastic.co/v1
kind: EnterpriseSearch
metadata:
  name: enterprise-search-quickstart
spec:
  version: 8.17.0
  podTemplate:
    spec:
      containers:
      - name: enterprise-search
        resources:
          requests:
            memory: 4Gi
            cpu: 1
          limits:
            memory: 4Gi
            cpu: 2
        env:
        - name: JAVA_OPTS
          value: -Xms3500m -Xmx3500m

For the container name, use apm-server, kibana or enterprise-search, respectively.

If resources is not defined in the specification of an object, then the operator applies a default memory limit to ensure that pods have enough resources to start correctly. As the operator cannot make assumptions about the available CPU resources in the cluster, no CPU limits will be set — resulting in the pods having the "Burstable" QoS class. Check if this is acceptable for your use case and follow the instructions in Set compute resources to configure appropriate limits.

If the Kubernetes cluster is configured with LimitRanges that enforce a minimum memory constraint, they could interfere with the operator defaults and cause object creation to fail.

For example, you might have a LimitRange that enforces a default and minimum memory limit on containers as follows:

apiVersion: v1
kind: LimitRange
metadata:
  name: default-mem-per-container
spec:
  limits:
  - min:
      memory: "3Gi"
    defaultRequest:
      memory: "3Gi"
    type: Container

With the above restriction in place, if you create an Elasticsearch object without defining the resources section, you will get the following error:

Cannot create pod elasticsearch-sample-es-ldbgj48c7r: pods "elasticsearch-sample-es-ldbgj48c7r" is forbidden: minimum memory usage per Container is 3Gi, but request is 2Gi

To avoid this, explicitly define the requests and limits mandated by your environment in the resource specification. It will prevent the operator from applying the built-in defaults.