New to KubeDB? Please start here.

Autoscaling the Compute Resource of an Elasticsearch Combined Cluster

This guide will show you how to use KubeDB to autoscale compute resources i.e. cpu and memory of an Elasticsearch combined cluster.

Before You Begin

At first, you need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster.
Install KubeDB Community, Enterprise and Autoscaler operator in your cluster following the steps here.
Install Metrics Server from here
You should be familiar with the following KubeDB concepts:

To keep everything isolated, we are going to use a separate namespace called demo throughout this tutorial.

$ kubectl create ns demo
namespace/demo created

Note: YAML files used in this tutorial are stored in this directory of kubedb/docs repository.

Autoscaling of a Combined Cluster

Here, we are going to deploy an Elasticsearch in combined cluster mode using a supported version by KubeDB operator. Then we are going to apply ElasticsearchAutoscaler to set up autoscaling.

Deploy Elasticsearch standalone

In this section, we are going to deploy an Elasticsearch combined cluster with ElasticsearchVersion xpack-8.11.1. Then, in the next section, we will set up autoscaling for this database using ElasticsearchAutoscaler CRD. Below is the YAML of the Elasticsearch CR that we are going to create,

apiVersion: kubedb.com/v1
kind: Elasticsearch
metadata:
  name: es-combined
  namespace: demo
spec:
  enableSSL: true
  version: xpack-8.2.3
  storageType: Durable
  replicas: 3
  storage:
    storageClassName: "standard"
    accessModes:
      - ReadWriteOnce
    resources:
      requests:
        storage: 1Gi
  podTemplate:
    spec:
      containers:
      - name: elasticsearch
        resources:
          requests:
            cpu: "500m"
          limits:
            cpu: "500m"
            memory: "1.2Gi"
  deletionPolicy: WipeOut

Let’s create the Elasticsearch CRO we have shown above,

$ kubectl create -f https://github.com/kubedb/docs/raw/v2024.12.18/docs/guides/elasticsearch/autoscaler/compute/combined/yamls/es-combined.yaml 
elasticsearch.kubedb.com/es-combined created

Now, wait until es-combined has status Ready. i.e,

$ kubectl get elasticsearch -n demo -w
NAME          VERSION       STATUS         AGE
es-combined   xpack-8.2.3   Provisioning   4s
es-combined   xpack-8.2.3   Provisioning   7s
....
....
es-combined   xpack-8.2.3   Ready          60s

Let’s check the Pod containers resources,

$ kubectl get pod -n demo es-combined-0 -o json | jq '.spec.containers[].resources'
{
  "limits": {
    "cpu": "500m",
    "memory": "1288490188800m"
  },
  "requests": {
    "cpu": "500m",
    "memory": "1288490188800m"
  }
}

Let’s check the Elasticsearch resources,

$ kubectl get elasticsearch -n demo es-combined -o json | jq '.spec.podTemplate.spec.containers[] | select(.name == "elasticsearch") | .resources'
{
  "limits": {
    "cpu": "500m",
    "memory": "1288490188800m"
  },
  "requests": {
    "cpu": "500m",
    "memory": "1288490188800m"
  }
}

You can see from the above outputs that the resources are the same as the ones we have assigned while deploying the Elasticsearch.

We are now ready to apply the ElasticsearchAutoscaler CRO to set up autoscaling for this database.

Compute Resource Autoscaling

Here, we are going to set up compute (ie. cpu and memory) autoscaling using an ElasticsearchAutoscaler Object.

Create ElasticsearchAutoscaler Object

To set up compute resource autoscaling for this combined cluster, we have to create a ElasticsearchAutoscaler CRO with our desired configuration. Below is the YAML of the ElasticsearchAutoscaler object that we are going to create,

apiVersion: autoscaling.kubedb.com/v1alpha1
kind: ElasticsearchAutoscaler
metadata:
  name: es-combined-as
  namespace: demo
spec:
  databaseRef:
    name: es-combined
  compute:
    node:
      trigger: "On"
      podLifeTimeThreshold: 5m
      resourceDiffPercentage: 5
      minAllowed:
        cpu: 1
        memory: "2.1Gi"
      maxAllowed:
        cpu: 2
        memory: 3Gi
      controlledResources: ["cpu", "memory"]
      containerControlledValues: "RequestsAndLimits"

Here,

spec.databaseRef.name specifies that we are performing compute resource autoscaling on es-combined database.
spec.compute.node.trigger specifies that compute resource autoscaling is enabled for this cluster.
spec.compute.node.podLifeTimeThreshold specifies the minimum lifetime for at least one of the pod to initiate a vertical scaling.
spec.compute.node.minAllowed specifies the minimum allowed resources for the Elasticsearch node.
spec.compute.node.maxAllowed specifies the maximum allowed resources for the Elasticsearch node.
spec.compute.node.controlledResources specifies the resources that are controlled by the autoscaler.
spec.compute.node.resourceDiffPercentage specifies the minimum resource difference in percentage. The default is 10%. If the difference between current & recommended resource is less than ResourceDiffPercentage, Autoscaler Operator will ignore the updating.
spec.compute.node.containerControlledValues specifies which resource values should be controlled. The default is “RequestsAndLimits”.
- spec.opsRequestOptions contains the options to pass to the created OpsRequest. It has 2 fields. Know more about them here : timeout, apply.

Let’s create the ElasticsearchAutoscaler CR we have shown above,

$ kubectl apply -f https://github.com/kubedb/docs/raw/v2024.12.18/docs/guides/elasticsearch/autoscaler/compute/combined/yamls/es-auto-scaler.yaml
elasticsearchautoscaler.autoscaling.kubedb.com/es-combined-as created

Verify Autoscaling is set up successfully

Let’s check that the elasticsearchautoscaler resource is created successfully,

$kubectl get elasticsearchautoscaler -n demo
NAME             AGE
es-combined-as   14s

$ kubectl describe elasticsearchautoscaler -n demo  es-combined-as
Name:         es-combined-as
Namespace:    demo
Labels:       <none>
Annotations:  <none>
API Version:  autoscaling.kubedb.com/v1alpha1
Kind:         ElasticsearchAutoscaler
Metadata:
  Creation Timestamp:  2022-12-29T10:54:00Z
  Generation:          1
  Managed Fields:
    API Version:  autoscaling.kubedb.com/v1alpha1
    Fields Type:  FieldsV1
    fieldsV1:
      f:metadata:
        f:annotations:
          .:
          f:kubectl.kubernetes.io/last-applied-configuration:
      f:spec:
        .:
        f:compute:
          .:
          f:node:
            .:
            f:containerControlledValues:
            f:controlledResources:
            f:maxAllowed:
              .:
              f:cpu:
              f:memory:
            f:minAllowed:
              .:
              f:cpu:
              f:memory:
            f:podLifeTimeThreshold:
            f:resourceDiffPercentage:
            f:trigger:
        f:databaseRef:
    Manager:      kubectl-client-side-apply
    Operation:    Update
    Time:         2022-12-29T10:54:00Z
    API Version:  autoscaling.kubedb.com/v1alpha1
    Fields Type:  FieldsV1
    fieldsV1:
      f:status:
        .:
        f:checkpoints:
        f:conditions:
        f:vpas:
    Manager:         kubedb-autoscaler
    Operation:       Update
    Subresource:     status
    Time:            2022-12-29T10:54:27Z
  Resource Version:  12469
  UID:               35640903-7aaf-46c6-9bc4-bd1771313e30
Spec:
  Compute:
    Node:
      Container Controlled Values:  RequestsAndLimits
      Controlled Resources:
        cpu
        memory
      Max Allowed:
        Cpu:     2
        Memory:  3Gi
      Min Allowed:
        Cpu:                     1
        Memory:                  2254857830400m
      Pod Life Time Threshold:   5m0s
      Resource Diff Percentage:  5
      Trigger:                   On
  Database Ref:
    Name:  es-combined
  Ops Request Options:
    Apply:  IfReady
Status:
  Checkpoints:
    Cpu Histogram:
      Bucket Weights:
        Index:              0
        Weight:             2849
        Index:              1
        Weight:             10000
        Index:              2
        Weight:             2856
        Index:              3
        Weight:             714
        Index:              5
        Weight:             714
        Index:              6
        Weight:             713
        Index:              7
        Weight:             714
        Index:              12
        Weight:             713
        Index:              21
        Weight:             713
        Index:              25
        Weight:             2138
      Reference Timestamp:  2022-12-29T00:00:00Z
      Total Weight:         4.257959878725071
    First Sample Start:     2022-12-29T10:54:03Z
    Last Sample Start:      2022-12-29T11:04:18Z
    Last Update Time:       2022-12-29T11:04:26Z
    Memory Histogram:
      Reference Timestamp:  2022-12-30T00:00:00Z
    Ref:
      Container Name:     elasticsearch
      Vpa Object Name:    es-combined
    Total Samples Count:  31
    Version:              v3
  Conditions:
    Last Transition Time:  2022-12-29T10:54:27Z
    Message:               Successfully created elasticsearchOpsRequest demo/esops-es-combined-ujb5hy
    Observed Generation:   1
    Reason:                CreateOpsRequest
    Status:                True
    Type:                  CreateOpsRequest
  Vpas:
    Conditions:
      Last Transition Time:  2022-12-29T10:54:26Z
      Status:                True
      Type:                  RecommendationProvided
    Recommendation:
      Container Recommendations:
        Container Name:  elasticsearch
        Lower Bound:
          Cpu:     1
          Memory:  2254857830400m
        Target:
          Cpu:     1
          Memory:  2254857830400m
        Uncapped Target:
          Cpu:     442m
          Memory:  1555165137
        Upper Bound:
          Cpu:     2
          Memory:  3Gi
    Vpa Name:      es-combined
Events:            <none>

So, the elasticsearchautoscaler resource is created successfully.

you can see in the Status.VPAs.Recommendation section, that recommendation has been generated for our database. Our autoscaler operator continuously watches the recommendation generated and creates an elasticsearchopsrequest based on the recommendations, if the database pods are needed to scaled up or down.

Let’s watch the elasticsearchopsrequest in the demo namespace to see if any elasticsearchopsrequest object is created. After some time you’ll see that an elasticsearchopsrequest will be created based on the recommendation.

$  kubectl get elasticsearchopsrequest -n demo
NAME                       TYPE              STATUS       AGE
esops-es-combined-ujb5hy   VerticalScaling   Progessing   1m

Let’s wait for the opsRequest to become successful.

$  kubectl get elasticsearchopsrequest -n demo
NAME                       TYPE              STATUS       AGE
esops-es-combined-ujb5hy   VerticalScaling   Successful   1m

We can see from the above output that the ElasticsearchOpsRequest has succeeded. If we describe the ElasticsearchOpsRequest we will get an overview of the steps that were followed to scale the database.

$ kubectl describe elasticsearchopsrequest -n demo esops-es-combined-ujb5hy
Name:         esops-es-combined-ujb5hy
Namespace:    demo
Labels:       <none>
Annotations:  <none>
API Version:  ops.kubedb.com/v1alpha1
Kind:         ElasticsearchOpsRequest
Metadata:
  Creation Timestamp:  2022-12-29T10:54:27Z
  Generation:          1
  Managed Fields:
    API Version:  ops.kubedb.com/v1alpha1
    Fields Type:  FieldsV1
    fieldsV1:
      f:metadata:
        f:ownerReferences:
          .:
          k:{"uid":"35640903-7aaf-46c6-9bc4-bd1771313e30"}:
      f:spec:
        .:
        f:apply:
        f:databaseRef:
        f:type:
        f:verticalScaling:
          .:
          f:node:
            .:
            f:limits:
              .:
              f:cpu:
              f:memory:
            f:requests:
              .:
              f:cpu:
              f:memory:
    Manager:      kubedb-autoscaler
    Operation:    Update
    Time:         2022-12-29T10:54:27Z
    API Version:  ops.kubedb.com/v1alpha1
    Fields Type:  FieldsV1
    fieldsV1:
      f:status:
        .:
        f:conditions:
        f:observedGeneration:
        f:phase:
    Manager:      kubedb-ops-manager
    Operation:    Update
    Subresource:  status
    Time:         2022-12-29T10:54:27Z
  Owner References:
    API Version:           autoscaling.kubedb.com/v1alpha1
    Block Owner Deletion:  true
    Controller:            true
    Kind:                  ElasticsearchAutoscaler
    Name:                  es-combined-as
    UID:                   35640903-7aaf-46c6-9bc4-bd1771313e30
  Resource Version:        11992
  UID:                     4aa5295f-0702-45ac-9ae8-3cb496b0e740
Spec:
  Apply:  IfReady
  Database Ref:
    Name:  es-combined
  Type:    VerticalScaling
  Vertical Scaling:
    Node:
      Limits:
        Cpu:     1
        Memory:  2254857830400m
      Requests:
        Cpu:     1
        Memory:  2254857830400m
Status:
  Conditions:
    Last Transition Time:  2022-12-29T10:54:27Z
    Message:               Elasticsearch ops request is vertically scaling the nodes
    Observed Generation:   1
    Reason:                VerticalScaling
    Status:                True
    Type:                  VerticalScaling
    Last Transition Time:  2022-12-29T10:54:39Z
    Message:               successfully reconciled the Elasticsearch resources
    Observed Generation:   1
    Reason:                Reconciled
    Status:                True
    Type:                  Reconciled
    Last Transition Time:  2022-12-29T10:58:39Z
    Message:               Successfully restarted all nodes
    Observed Generation:   1
    Reason:                RestartNodes
    Status:                True
    Type:                  RestartNodes
    Last Transition Time:  2022-12-29T10:58:44Z
    Message:               successfully updated Elasticsearch CR
    Observed Generation:   1
    Reason:                UpdateElasticsearchCR
    Status:                True
    Type:                  UpdateElasticsearchCR
    Last Transition Time:  2022-12-29T10:58:45Z
    Message:               Successfully completed the modification process.
    Observed Generation:   1
    Reason:                Successful
    Status:                True
    Type:                  Successful
  Observed Generation:     1
  Phase:                   Successful
Events:
  Type    Reason                 Age    From                         Message
  ----    ------                 ----   ----                         -------
  Normal  PauseDatabase          8m25s  KubeDB Ops-manager Operator  Pausing Elasticsearch demo/es-combined
  Normal  Reconciled             8m13s  KubeDB Ops-manager Operator  successfully reconciled the Elasticsearch resources
  Normal  RestartNodes           4m13s  KubeDB Ops-manager Operator  Successfully restarted all nodes
  Normal  UpdateElasticsearchCR  4m7s   KubeDB Ops-manager Operator  successfully updated Elasticsearch CR
  Normal  ResumeDatabase         4m7s   KubeDB Ops-manager Operator  Resuming Elasticsearch demo/es-combined
  Normal  Successful             4m7s   KubeDB Ops-manager Operator  Successfully Updated Database

Now, we are going to verify from the Pod, and the Elasticsearch YAML whether the resources of the standalone database has updated to meet up the desired state, Let’s check,

$ kubectl get pod -n demo es-combined-0 -o json | jq '.spec.containers[].resources'
{
  "limits": {
    "cpu": "500m",
    "memory": "1288490188800m"
  },
  "requests": {
    "cpu": "500m",
    "memory": "1288490188800m"
  }
}

$ kubectl get elasticsearch -n demo es-combined -o json | jq '.spec.podTemplate.spec.containers[] | select(.name == "elasticsearch") | .resources'
{
  "limits": {
    "cpu": "1",
    "memory": "2254857830400m"
  },
  "requests": {
    "cpu": "1",
    "memory": "2254857830400m"
  }
}

The above output verifies that we have successfully auto-scaled the resources of the Elasticsearch standalone database.

Cleaning Up

To clean up the Kubernetes resources created by this tutorial, run:

$ kubectl delete es -n demo es-combined 
$ kubectl delete elasticsearchautoscaler -n demo es-combined-as
$ kubectl delete ns demo

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