KubeBlocks
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Overview
Quickstart

Operations

Lifecycle Management
Vertical Scaling
Horizontal Scaling
Volume Expansion
Manage Elasticsearch Services
Decommission Elasticsearch Replica

Monitoring

Observability for Elasticsearch Clusters

tpl

  1. Why Decommission Pods with KubeBlocks?
  2. Prerequisites
  3. Deploy a Elasticsearch Cluster
  4. Verifying the Deployment
  5. Decommission a Pod
    1. Monitor the Decommissioning Process
    2. Verify the Decommissioning
  6. Summary

Decommission a Specific Pod in KubeBlocks-Managed Elasticsearch Clusters

This guide explains how to decommission (take offline) specific Pods in Elasticsearch clusters managed by KubeBlocks. Decommissioning provides precise control over cluster resources while maintaining availability. Use this for workload rebalancing, node maintenance, or addressing failures.

Why Decommission Pods with KubeBlocks?

In traditional StatefulSet-based deployments, Kubernetes lacks the ability to decommission specific Pods. StatefulSets ensure the order and identity of Pods, and scaling down always removes the Pod with the highest ordinal number (e.g., scaling down from 3 replicas removes Pod-2 first). This limitation prevents precise control over which Pod to take offline, which can complicate maintenance, workload distribution, or failure handling.

KubeBlocks overcomes this limitation by enabling administrators to decommission specific Pods directly. This fine-grained control ensures high availability and allows better resource management without disrupting the entire cluster.

Prerequisites

    Before proceeding, ensure the following:

    • Environment Setup:
      • A Kubernetes cluster is up and running.
      • The kubectl CLI tool is configured to communicate with your cluster.
      • KubeBlocks CLI and KubeBlocks Operator are installed. Follow the installation instructions here.
    • Namespace Preparation: To keep resources isolated, create a dedicated namespace for this tutorial:
    kubectl create ns demo
    namespace/demo created
    

    Deploy a Elasticsearch Cluster

      KubeBlocks uses a declarative approach for managing Elasticsearch Clusters. Below is an example configuration for deploying a Elasticsearch Cluster with create a cluster with replicas for different roles.

      Apply the following YAML configuration to deploy the cluster:

      apiVersion: apps.kubeblocks.io/v1
      kind: Cluster
      metadata:
        name: es-multinode
        namespace: demo
      spec:
        terminationPolicy: Delete
        componentSpecs:
          - name: dit
            componentDef: elasticsearch-8
            serviceVersion: 8.8.2
            configs:
              - name: es-cm
                variables:
                  # use key `roles` to specify roles this component assume
                  roles: data,ingest,transform
            replicas: 3
            disableExporter: false
            resources:
              limits:
                cpu: "1"
                memory: "2Gi"
              requests:
                cpu: "1"
                memory: "2Gi"
            volumeClaimTemplates:
              - name: data
                spec:
                  accessModes:
                    - ReadWriteOnce
                  resources:
                    requests:
                      storage: 20Gi
          - name: master
            componentDef: elasticsearch-8
            serviceVersion: 8.8.2
            configs:
              - name: es-cm
                variables:
                  # use key `roles` to specify roles this component assume
                  roles: master
            replicas: 3
            disableExporter: false
            resources:
              limits:
                cpu: "1"
                memory: "2Gi"
              requests:
                cpu: "1"
                memory: "2Gi"
            volumeClaimTemplates:
              - name: data
                spec:
                  accessModes:
                    - ReadWriteOnce
                  resources:
                    requests:
                      storage: 20Gi
      

      Verifying the Deployment

        Monitor the cluster status until it transitions to the Running state:

        kubectl get cluster es-multinode -n demo -w
        

        Expected Output:

        NAME           CLUSTER-DEFINITION   TERMINATION-POLICY   STATUS     AGE
        es-multinode                        Delete               Creating   10s
        es-multinode                        Delete               Updating   41s
        es-multinode                        Delete               Running    42s
        

        Check the pod status and roles:

        kubectl get pods -l app.kubernetes.io/instance=es-multinode -n demo
        

        Expected Output:

        NAME                    READY   STATUS    RESTARTS   AGE
        es-multinode-dit-0      3/3     Running   0          6m21s
        es-multinode-dit-1      3/3     Running   0          6m21s
        es-multinode-dit-2      3/3     Running   0          6m21s
        es-multinode-master-0   3/3     Running   0          6m21s
        es-multinode-master-1   3/3     Running   0          6m21s
        es-multinode-master-2   3/3     Running   0          6m21s
        

        Once the cluster status becomes Running, your Elasticsearch cluster is ready for use.

        TIP

        If you are creating the cluster for the very first time, it may take some time to pull images before running.

        Decommission a Pod

        Expected Workflow:

        1. Replica specified in onlineInstancesToOffline is removed
        2. Pod terminates gracefully
        3. Cluster transitions from Updating to Running

        To decommission a specific Pod (e.g., 'es-multinode-dit-1'), you can use one of the following methods:

        Option 1: Using OpsRequest

        Create an OpsRequest to mark the Pod as offline:

        apiVersion: operations.kubeblocks.io/v1alpha1
        kind: OpsRequest
        metadata:
          name: es-multinode-decommission-ops
          namespace: demo
        spec:
          clusterName: es-multinode
          type: HorizontalScaling
          horizontalScaling:
          - componentName: dit
            scaleIn:
              onlineInstancesToOffline:
                - 'es-multinode-dit-1'  # Specifies the instance names that need to be taken offline
        

        Monitor the Decommissioning Process

        Check the progress of the decommissioning operation:

        kubectl get ops es-multinode-decommission-ops -n demo -w
        

        Example Output:

        NAME                            TYPE                CLUSTER        STATUS    PROGRESS   AGE
        es-multinode-decommission-ops   HorizontalScaling   es-multinode   Running   0/1        8s
        es-multinode-decommission-ops   HorizontalScaling   es-multinode   Running   1/1        31s
        es-multinode-decommission-ops   HorizontalScaling   es-multinode   Succeed   1/1        31s
        

        Option 2: Using Cluster API

        Alternatively, update the Cluster resource directly to decommission the Pod:

        apiVersion: apps.kubeblocks.io/v1
        kind: Cluster
        spec:
          componentSpecs:
            - name: dit
              replicas: 2       # explected replicas after decommission
              offlineInstances:
                - es-multinode-dit-1   # <----- Specify Pod to be decommissioned
         ...
        

        Verify the Decommissioning

        After applying the updated configuration, verify the remaining Pods in the cluster:

        kubectl get pods -n demo -l app.kubernetes.io/instance=es-multinode,apps.kubeblocks.io/component-name=dit
        

        Example Output:

        NAME                 READY   STATUS    RESTARTS   AGE
        es-multinode-dit-0   2/2     Running   0          24m
        es-multinode-dit-2   2/2     Running   0          2m1s
        

        Summary

        Key takeaways:

        • Traditional StatefulSets lack precise Pod removal control
        • KubeBlocks enables targeted Pod decommissioning
        • Two implementation methods: OpsRequest or Cluster API

        This provides granular cluster management while maintaining availability.

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