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Autoscaling the Compute Resource of a Sentinel
This guide will show you how to use KubeDB to autoscale compute resources i.e. cpu and memory of a Redis standalone database.
Before You Begin
At first, you need to have a Kubernetes cluster, and the
kubectlcommand-line tool must be configured to communicate with your cluster.Install
KubeDBProvisioner, Ops-manager and Autoscaler operator in your cluster following the steps here.Install
Metrics Serverfrom hereYou should be familiar with the following
KubeDBconcepts:
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 docs/examples/redis directory of kubedb/docs repository.
Autoscaling of Sentinel
Here, we are going to deploy a RedisSentinel instance using a supported version by KubeDB operator. Then we are going to apply RedisSentinelAutoscaler to set up autoscaling.
Deploy Redis standalone
In this section, we are going to deploy a RedisSentinel instance with version 6.2.14. Then, in the next section we will set up autoscaling for this database using RedisSentinelAutoscaler CRD. Below is the YAML of the RedisSentinel CR that we are going to create,
apiVersion: kubedb.com/v1
kind: RedisSentinel
metadata:
name: sen-demo
namespace: demo
spec:
version: "6.2.14"
storageType: Durable
replicas: 3
storage:
resources:
requests:
storage: 1Gi
podTemplate:
spec:
containers:
- name: redissentinel
resources:
requests:
cpu: "200m"
memory: "300Mi"
limits:
cpu: "200m"
memory: "300Mi"
deletionPolicy: WipeOut
Let’s create the RedisSentinel CRO we have shown above,
$ kubectl create -f https://github.com/kubedb/docs/raw/v2024.8.21/docs/examples/redis/autoscaling/compute/sentinel.yaml
redissentinel.kubedb.com/sen-demo created
Now, wait until sen-demo has status Ready. i.e,
$ kubectl get redissentinel -n demo
NAME VERSION STATUS AGE
sen-demo 6.2.14 Ready 86s
Let’s check the Pod containers resources,
$ kubectl get pod -n demo sen-demo-0 -o json | jq '.spec.containers[].resources'
{
"limits": {
"cpu": "200m",
"memory": "300Mi"
},
"requests": {
"cpu": "200m",
"memory": "300Mi"
}
}
Let’s check the RedisSentinel resources,
$ kubectl get redissentinel -n demo sen-demo -o json | jq '.spec.podTemplate.spec.containers[] | select(.name == "redissentinel") | .resources'
{
"limits": {
"cpu": "200m",
"memory": "300Mi"
},
"requests": {
"cpu": "200m",
"memory": "300Mi"
}
}
You can see from the above outputs that the resources are same as the one we have assigned while deploying the redissentinel.
We are now ready to apply the RedisSentinelAutoscaler CRO to set up autoscaling for this database.
Compute Resource Autoscaling
Here, we are going to set up compute (cpu and memory) autoscaling using a RedisSentinelAutoscaler Object.
Create RedisSentinelAutoscaler Object
In order to set up compute resource autoscaling for this standalone database, we have to create a RedisAutoscaler CRO with our desired configuration. Below is the YAML of the RedisAutoscaler object that we are going to create,
apiVersion: autoscaling.kubedb.com/v1alpha1
kind: RedisSentinelAutoscaler
metadata:
name: sen-as
namespace: demo
spec:
databaseRef:
name: sen-demo
opsRequestOptions:
timeout: 3m
apply: IfReady
compute:
sentinel:
trigger: "On"
podLifeTimeThreshold: 5m
resourceDiffPercentage: 20
minAllowed:
cpu: 400m
memory: 400Mi
maxAllowed:
cpu: 1
memory: 1Gi
controlledResources: ["cpu", "memory"]
containerControlledValues: "RequestsAndLimits"
Here,
spec.databaseRef.namespecifies that we are performing compute resource autoscaling onsen-demodatabase.spec.compute.standalone.triggerspecifies that compute resource autoscaling is enabled for this database.spec.compute.sentinel.podLifeTimeThresholdspecifies the minimum lifetime for at least one of the pod to initiate a vertical scaling. If the difference between current & recommended resource is less than ResourceDiffPercentage, Autoscaler Operator will ignore the updating.spec.compute.sentinel.minAllowedspecifies the minimum allowed resources for the database.spec.compute.sentinel.maxAllowedspecifies the maximum allowed resources for the database.spec.compute.sentinel.controlledResourcesspecifies the resources that are controlled by the autoscaler.spec.compute.sentinel.containerControlledValuesspecifies which resource values should be controlled. The default is “RequestsAndLimits”.spec.opsRequestOptionscontains the options to pass to the created OpsRequest. It has 2 fields. Know more about them here : timeout, apply.
If it was an InMemory database, we could also autoscaler the inMemory resources using Redis compute autoscaler, like below.
Let’s create the RedisAutoscaler CR we have shown above,
$ kubectl apply -f https://github.com/kubedb/docs/raw/v2024.8.21/docs/examples/redis/compute/autoscaling/sen-as.yaml
redissentinelautoscaler.autoscaling.kubedb.com/sen-as created
Verify Autoscaling is set up successfully
Let’s check that the redisautoscaler resource is created successfully,
$ kubectl get redisautoscaler -n demo
NAME AGE
sen-as 102s
$ kubectl describe redissentinelautoscaler sen-as -n demo
Name: sen-as
Namespace: demo
Labels: <none>
Annotations: <none>
API Version: autoscaling.kubedb.com/v1alpha1
Kind: RedisSentinelAutoscaler
Metadata:
Creation Timestamp: 2023-02-09T11:14:18Z
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:sentinel:
.:
f:containerControlledValues:
f:controlledResources:
f:maxAllowed:
.:
f:cpu:
f:memory:
f:minAllowed:
.:
f:cpu:
f:memory:
f:podLifeTimeThreshold:
f:resourceDiffPercentage:
f:trigger:
f:databaseRef:
f:opsRequestOptions:
.:
f:apply:
f:timeout:
Manager: kubectl-client-side-apply
Operation: Update
Time: 2023-02-09T11:14:18Z
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: 2023-02-09T11:15:20Z
Resource Version: 845618
UID: 44da50a4-6e4f-49fa-b7e4-6c7f83c3e6c4
Spec:
Compute:
Sentinel:
Container Controlled Values: RequestsAndLimits
Controlled Resources:
cpu
memory
Max Allowed:
Cpu: 1
Memory: 1Gi
Min Allowed:
Cpu: 400m
Memory: 400Mi
Pod Life Time Threshold: 5m0s
Resource Diff Percentage: 20
Trigger: On
Database Ref:
Name: sen-demo
Ops Request Options:
Apply: IfReady
Timeout: 3m0s
Status:
Checkpoints:
Cpu Histogram:
Bucket Weights:
Index: 0
Weight: 10000
Reference Timestamp: 2023-02-09T00:00:00Z
Total Weight: 0.4150619553793766
First Sample Start: 2023-02-09T11:14:17Z
Last Sample Start: 2023-02-09T11:14:32Z
Last Update Time: 2023-02-09T11:14:35Z
Memory Histogram:
Reference Timestamp: 2023-02-10T00:00:00Z
Ref:
Container Name: redissentinel
Vpa Object Name: sen-demo
Total Samples Count: 3
Version: v3
Conditions:
Last Transition Time: 2023-02-09T11:15:20Z
Message: Successfully created RedisSentinelOpsRequest demo/rdsops-sen-demo-5emii6
Observed Generation: 1
Reason: CreateOpsRequest
Status: True
Type: CreateOpsRequest
Vpas:
Conditions:
Last Transition Time: 2023-02-09T11:14:35Z
Status: True
Type: RecommendationProvided
Recommendation:
Container Recommendations:
Container Name: redissentinel
Lower Bound:
Cpu: 400m
Memory: 400Mi
Target:
Cpu: 400m
Memory: 400Mi
Uncapped Target:
Cpu: 100m
Memory: 262144k
Upper Bound:
Cpu: 1
Memory: 1Gi
Vpa Name: sen-demo
Events: <none>
So, the redisautoscaler 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 redissentinelopsrequest based on the recommendations, if the database pods are needed to scaled up or down.
Let’s watch the redissentinelopsrequest in the demo namespace to see if any redissentinelopsrequest object is created. After some time you’ll see that a redissentinelopsrequest will be created based on the recommendation.
$ watch kubectl get redissentinelopsrequest -n demo
Every 2.0s: kubectl get redissentinelopsrequest -n demo
NAME TYPE STATUS AGE
rdsops-sen-demo-5emii6 VerticalScaling Progressing 10s
Let’s wait for the ops request to become successful.
$ watch kubectl get redissentinelopsrequest -n demo
Every 2.0s: kubectl get redissentinelopsrequest -n demo
NAME TYPE STATUS AGE
rdsops-sen-demo-5emii6 VerticalScaling Successfull 10s
We can see from the above output that the RedisSentinelOpsRequest has succeeded.
Now, we are going to verify from the Pod, and the Redis yaml whether the resources of the standalone database has updated to meet up the desired state, Let’s check,
$ kubectl get pod -n demo sen-demo-0 -o json | jq '.spec.containers[].resources'
{
"limits": {
"cpu": "400m",
"memory": "400Mi"
},
"requests": {
"cpu": "400m",
"memory": "400Mi"
}
}
$ kubectl get redis -n demo sen-demo -o json | jq '.spec.podTemplate.spec.containers[] | select(.name == "redissentinel") | .resources'
{
"limits": {
"cpu": "400m",
"memory": "400Mi"
},
"requests": {
"cpu": "400m",
"memory": "400Mi"
}
}
The above output verifies that we have successfully auto-scaled the resources of the Redis standalone database.
Cleaning Up
To clean up the Kubernetes resources created by this tutorial, run:
$ kubectl patch -n demo redissentinel/sen-demo -p '{"spec":{"deletionPolicy":"WipeOut"}}' --type="merge"
redissentinel.kubedb.com/sen-demo patched
$ kubectl delete redissentinel -n demo sen-demo
redissentinel.kubedb.com "sen-demo" deleted
$ kubectl delete redissentinelautoscaler -n demo sen-as
redissentinelautoscaler.autoscaling.kubedb.com "sen-as" deleted