Vertical Scaling for Elasticsearch Clusters with KubeBlocks

This guide demonstrates how to vertically scale a Elasticsearch Cluster managed by KubeBlocks by adjusting compute resources (CPU and memory) while maintaining the same number of replicas.

Vertical scaling modifies compute resources (CPU and memory) for Elasticsearch instances while maintaining replica count. Key characteristics:

• Non-disruptive: When properly configured, maintains availability during scaling
• Granular: Adjust CPU, memory, or both independently
• Reversible: Scale up or down as needed

KubeBlocks ensures minimal impact during scaling operations by following a controlled, role-aware update strategy: Role-Aware Replicas (Primary/Secondary Replicas)

• Secondary replicas update first – Non-leader pods are upgraded to minimize disruption.
• Primary updates last – Only after all secondaries are healthy does the primary pod restart.
• Cluster state progresses from Updating → Running once all replicas are stable.

Role-Unaware Replicas (Ordinal-Based Scaling) If replicas have no defined roles, updates follow Kubernetes pod ordinal order:

• Highest ordinal first (e.g., pod-2 → pod-1 → pod-0) to ensure deterministic rollouts.

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.

Vertical Scale

Expected Workflow:

1. Pods are updated in pod ordinal order, from highest to lowest, (e.g., pod-2 → pod-1 → pod-0)
2. Cluster status transitions from Updating to Running

apiVersion: operations.kubeblocks.io/v1alpha1
kind: OpsRequest
metadata:
  name: es-multinode-vscale-ops
  namespace: demo
spec:
  clusterName: es-multinode
  type: VerticalScaling
  verticalScaling:
  - componentName: dit
    requests:
      cpu: '1'
      memory: 1Gi
    limits:
      cpu: '1'
      memory: 1Gi

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

NAME                      TYPE              CLUSTER        STATUS    PROGRESS   AGE
es-multinode-vscale-ops   VerticalScaling   es-multinode   Running   0/3        57s
es-multinode-vscale-ops   VerticalScaling   es-multinode   Running   1/3        60s
es-multinode-vscale-ops   VerticalScaling   es-multinode   Running   2/3        118s
es-multinode-vscale-ops   VerticalScaling   es-multinode   Running   3/3        2m51s
es-multinode-vscale-ops   VerticalScaling   es-multinode   Running   3/3        2m51s
es-multinode-vscale-ops   VerticalScaling   es-multinode   Succeed   3/3        2m51s

apiVersion: apps.kubeblocks.io/v1
kind: Cluster
spec:
  componentSpecs:
    - name: dit
      replicas: 3
      resources:
        requests:
          cpu: "1"       # Update the resources to your need.
          memory: "1Gi"  # Update the resources to your need.
        limits:
          cpu: "1"       # Update the resources to your need.
          memory: "1Gi"  # Update the resources to your need.
  ...

Best Practices & Considerations

Planning:

• Scale during maintenance windows or low-traffic periods
• Verify Kubernetes cluster has sufficient resources
• Check for any ongoing operations before starting

Execution:

• Maintain balanced CPU-to-Memory ratios
• Set identical requests/limits for guaranteed QoS

Post-Scaling:

• Monitor resource utilization and application performance
• Consider adjusting Elasticsearch parameters if needed

Verification

Verify the updated resources by inspecting the cluster configuration or Pod details:

kbcli cluster describe es-multinode -n demo

Expected Output:

Resources Allocation:
COMPONENT          INSTANCE-TEMPLATE   CPU(REQUEST/LIMIT)   MEMORY(REQUEST/LIMIT)   STORAGE-SIZE   STORAGE-CLASS
dit                                    1 / 1                1Gi / 1Gi               data:20Gi      <none>

Key Benefits of Vertical Scaling with KubeBlocks

• Seamless Scaling: Pods are recreated in a specific order to ensure minimal disruption.
• Dynamic Resource Adjustments: Easily scale CPU and memory based on workload requirements.
• Flexibility: Choose between OpsRequest for dynamic scaling or direct API updates for precise control.
• Improved Availability: The cluster remains operational during the scaling process, maintaining high availability.

Cleanup

To remove all created resources, delete the Elasticsearch Cluster along with its namespace:

kubectl delete cluster es-multinode -n demo
kubectl delete ns demo

Summary

In this guide, you learned how to:

1. Deploy a Elasticsearch Cluster managed by KubeBlocks.
2. Perform vertical scaling by increasing or decreasing resources for the elasticsearch component.
3. Use both OpsRequest and direct Cluster API updates to adjust resource allocations.

Vertical scaling is a powerful tool for optimizing resource utilization and adapting to changing workload demands, ensuring your Elasticsearch Cluster remains performant and resilient.