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Run Production-Grade Databases on Kubernetes
Backup and Recovery Solution for Kubernetes
Run Production-Grade Vault on Kubernetes
Secure HAProxy Ingress Controller for Kubernetes
Kubernetes Configuration Syncer
Kubernetes Authentication WebHook Server
KubeDB simplifies Provision, Upgrade, Scaling, Volume Expansion, Monitor, Backup, Restore for various Databases in Kubernetes on any Public & Private Cloud
A complete Kubernetes native disaster recovery solution for backup and restore your volumes and databases in Kubernetes on any public and private clouds.
KubeVault is a Git-Ops ready, production-grade solution for deploying and configuring Hashicorp's Vault on Kubernetes.
Secure HAProxy Ingress Controller for Kubernetes
Kubernetes Configuration Syncer
Kubernetes Authentication WebHook Server
New to KubeDB? Please start here.
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.
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
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.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
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.