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ElasticsearchAutoscaler

What is ElasticsearchAutoscaler

ElasticsearchAutoscaler is a Kubernetes Custom Resource Definitions (CRD). It provides a declarative configuration for autoscaling Elasticsearch compute resources and storage of database components in a Kubernetes native way.

ElasticsearchAutoscaler CRD Specifications

Like any official Kubernetes resource, a ElasticsearchAutoscaler has TypeMeta, ObjectMeta, Spec and Status sections.

Here, some sample ElasticsearchAutoscaler CROs for autoscaling different components of database is given below:

Sample ElasticsearchAutoscaler YAML for the Elasticsearch combined cluster:

apiVersion: autoscaling.kubedb.com/v1alpha1
kind: ElasticsearchAutoscaler
metadata:
  name: es-as
  namespace: demo
spec:
  databaseRef:
    name: es-combined
  compute:
    node:
      trigger: "On"
      podLifeTimeThreshold: 24h
      minAllowed:
        cpu: 250m
        memory: 350Mi
      maxAllowed:
        cpu: 1
        memory: 1Gi
      controlledResources: ["cpu", "memory"]
      containerControlledValues: "RequestsAndLimits"
      resourceDiffPercentage: 10
  storage:
    node:
      trigger: "On"
      usageThreshold: 60
      scalingThreshold: 50

Sample ElasticsearchAutoscaler YAML for the Elasticsearch topology cluster:

apiVersion: autoscaling.kubedb.com/v1alpha1
kind: ElasticsearchAutoscaler
metadata:
  name: mg-as-topology
  namespace: demo
spec:
  databaseRef:
    name: es-topology
  compute:
    topology:
      master:
        trigger: "On"
        podLifeTimeThreshold: 24h
        minAllowed:
          cpu: 250m
          memory: 350Mi
        maxAllowed:
          cpu: 1
          memory: 1Gi
        controlledResources: ["cpu", "memory"]
        containerControlledValues: "RequestsAndLimits"
        resourceDiffPercentage: 10
      data:
        trigger: "On"
        podLifeTimeThreshold: 24h
        minAllowed:
          cpu: 250m
          memory: 350Mi
        maxAllowed:
          cpu: 1
          memory: 1Gi
        controlledResources: ["cpu", "memory"]
        containerControlledValues: "RequestsAndLimits"
        resourceDiffPercentage: 10
      ingest:
        trigger: "On"
        podLifeTimeThreshold: 24h
        minAllowed:
          cpu: 250m
          memory: 350Mi
        maxAllowed:
          cpu: 1
          memory: 1Gi
        controlledResources: ["cpu", "memory"]
        containerControlledValues: "RequestsAndLimits"
        resourceDiffPercentage: 10
  storage:
    topology:
      data:
        trigger: "On"
        usageThreshold: 60
        scalingThreshold: 50

Here, we are going to describe the various sections of a ElasticsearchAutoscaler crd.

A ElasticsearchAutoscaler object has the following fields in the spec section.

spec.databaseRef

spec.databaseRef is a required field that point to the Elasticsearch object for which the autoscaling will be performed. This field consists of the following sub-field:

• spec.databaseRef.name : specifies the name of the Elasticsearch object.

spec.compute

spec.compute specifies the autoscaling configuration for the compute resources i.e. cpu and memory of the database components. This field consists of the following sub-field:

• spec.compute.node indicates the desired compute autoscaling configuration for a combined Elasticsearch cluster.
• spec.compute.topology indicates the desired compute autoscaling configuration for different type of nodes running in the Elasticsearch topology cluster mode.
  • topology.master indicates the desired compute autoscaling configuration for master nodes.
  • topology.data indicates the desired compute autoscaling configuration for data nodes.
  • topology.ingest indicates the desired compute autoscaling configuration for ingest nodes.

All of them has the following sub-fields:

• trigger indicates if compute autoscaling is enabled for this component of the database. If “On” then compute autoscaling is enabled. If “Off” then compute autoscaling is disabled.
• minAllowed specifies the minimal amount of resources that will be recommended, default is no minimum.
• maxAllowed specifies the maximum amount of resources that will be recommended, default is no maximum.
• controlledResources specifies which type of compute resources (cpu and memory) are allowed for autoscaling. Allowed values are “cpu” and “memory”.
• containerControlledValues specifies which resource values should be controlled. Allowed values are “RequestsAndLimits” and “RequestsOnly”.
• resourceDiffPercentage specifies the minimum resource difference between recommended value and the current value in percentage. If the difference percentage is greater than this value than autoscaling will be triggered.
• podLifeTimeThreshold specifies the minimum pod lifetime of at least one of the pods before triggering autoscaling.

spec.storage

spec.storage specifies the autoscaling configuration for the storage resources of the database components. This field consists of the following sub-field:

• spec.storage.node indicates the desired storage autoscaling configuration for a combined Elasticsearch cluster.
• spec.storage.topology indicates the desired storage autoscaling configuration for different type of nodes running in the Elasticsearch topology cluster mode.
  • topology.master indicates the desired storage autoscaling configuration for the master nodes.
  • topology.data indicates the desired storage autoscaling configuration for the data nodes.
  • topology.ingest indicates the desired storage autoscaling configuration for the ingest nodes.

All of them has the following sub-fields:

• trigger indicates if storage autoscaling is enabled for this component of the database. If “On” then storage autoscaling is enabled. If “Off” then storage autoscaling is disabled.
• usageThreshold indicates usage percentage threshold, if the current storage usage exceeds then storage autoscaling will be triggered.
• scalingThreshold indicates the percentage of the current storage that will be scaled.