New to KubeDB? Please start here.
Autoscaling the Compute Resource of a Memcached Database
This guide will show you how to use KubeDB to autoscale compute resources i.e. cpu and memory of a Memcached 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/memcached directory of kubedb/docs repository.
Autoscaling of Memcached Database
Here, we are going to deploy a Memcached database using a supported version by KubeDB operator. Then we are going to apply MemcachedAutoscaler to set up autoscaling.
Deploy Memcached Database
In this section, we are going to deploy a Memcached database with version 1.6.22. Then, in the next section we will set up autoscaling for this database using MemcachedAutoscaler CRD. Below is the YAML of the Memcached CR that we are going to create:
apiVersion: kubedb.com/v1
kind: Memcached
metadata:
name: mc-autoscaler-compute
namespace: demo
spec:
replicas: 1
version: "1.6.22"
podTemplate:
spec:
containers:
- name: memcached
resources:
limits:
cpu: 100m
memory: 100Mi
requests:
cpu: 100m
memory: 100Mi
deletionPolicy: WipeOut
Let’s create the Memcached CRO we have shown above,
$ kubectl create -f https://github.com/kubedb/docs/raw/v2024.9.30/docs/examples/memcached/autoscaler/compute/mc-compute-autoscaler.yaml
Memcached.kubedb.com/mc-compute-autoscaler created
Now, wait until mc-compute-autoscaler has status Ready. i.e,
$ kubectl get mc -n demo
NAME VERSION STATUS AGE
mc-autoscaler-compute 1.6.22 Ready 2m
Let’s check the Pod containers resources,
$ kubectl get pod -n demo mc-autoscaler-compute-0 -o json | jq '.spec.containers[].resources'
{
"limits": {
"cpu": "100m",
"memory": "100Mi"
},
"requests": {
"cpu": "100m",
"memory": "100Mi"
}
}
Let’s check the Memcached resources,
$ kubectl get Memcached -n demo mc-autoscaler-compute -o json | jq '.spec.podTemplate.spec.containers[] | select(.name == "memcached") | .resources'
{
"limits": {
"cpu": "100m",
"memory": "100Mi"
},
"requests": {
"cpu": "100m",
"memory": "100Mi"
}
}
You can see from the above outputs that the resources are same as the one we have assigned while deploying the Memcached.
We are now ready to apply the MemcachedAutoscaler 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 MemcachedAutoscaler Object.
Create MemcachedAutoscaler Object
In order to set up compute resource autoscaling for this database, we have to create a MemcachedAutoscaler CRO with our desired configuration. Below is the YAML of the MemcachedAutoscaler object that we are going to create:
apiVersion: autoscaling.kubedb.com/v1alpha1
kind: MemcachedAutoscaler
metadata:
name: mc-autoscaler
namespace: demo
spec:
databaseRef:
name: mc-autoscaler-compute
opsRequestOptions:
timeout: 3m
apply: IfReady
compute:
memcached:
trigger: "On"
podLifeTimeThreshold: 1m
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 onmc-compute-autoscalerdatabase.spec.compute.memcached.triggerspecifies that compute resource autoscaling is enabled for this database.spec.compute.memcached.podLifeTimeThresholdspecifies the minimum lifetime for at least one of the pod to initiate a vertical scaling.spec.compute.memcached.resourceDiffPercentagespecifies 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.memcached.minAllowedspecifies the minimum allowed resources for the database.spec.compute.memcached.maxAllowedspecifies the maximum allowed resources for the database.spec.compute.memcached.controlledResourcesspecifies the resources that are controlled by the autoscaler.spec.compute.memcahced.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.
Let’s create the MemcachedAutoscaler CR we have shown above,
$ kubectl apply -f https://github.com/kubedb/docs/raw/v2024.9.30/docs/examples/memcached/autoscaling/compute/mc-compute-autoscaler.yaml
Memcachedautoscaler.autoscaling.kubedb.com/rd-as created
Verify Autoscaling is set up successfully
Let’s check that the Memcachedautoscaler resource is created successfully,
$ kubectl get memcachedautoscaler -n demo
NAME AGE
mc-autoscaler 16m
$ kubectl describe memcachedautoscaler mc-autoscaler -n demo
Name: mc-autoscaler
Namespace: demo
Labels: <none>
Annotations: <none>
API Version: autoscaling.kubedb.com/v1alpha1
Kind: MemcachedAutoscaler
Metadata:
Creation Timestamp: 2024-09-10T12:55:35Z
Generation: 1
Owner References:
API Version: kubedb.com/v1
Block Owner Deletion: true
Controller: true
Kind: Memcached
Name: mc-autoscaler-compute
UID: 56a15163-0f8b-4f35-8cd9-ae9bd0976ea7
Resource Version: 105259
UID: 2ef29276-dc47-4b2d-8995-ad5114b419f3
Spec:
Compute:
Memcached:
Container Controlled Values: RequestsAndLimits
Controlled Resources:
cpu
memory
Max Allowed:
Cpu: 1
Memory: 1Gi
Min Allowed:
Cpu: 400m
Memory: 400Mi
Pod Life Time Threshold: 1m
Resource Diff Percentage: 20
Trigger: On
Database Ref:
Name: mc-autoscaler-compute
Ops Request Options:
Apply: IfReady
Timeout: 3m
Status:
Checkpoints:
Cpu Histogram:
Bucket Weights:
Index: 0
Weight: 10000
Reference Timestamp: 2024-09-10T13:10:00Z
Total Weight: 0.42972012872296605
First Sample Start: 2024-09-10T13:08:51Z
Last Sample Start: 2024-09-10T13:12:00Z
Last Update Time: 2024-09-10T13:12:04Z
Memory Histogram:
Reference Timestamp: 2024-09-10T13:15:00Z
Ref:
Container Name: memcached
Vpa Object Name: mc-autoscaler-compute
Total Samples Count: 4
Version: v3
Conditions:
Last Transition Time: 2024-09-10T13:10:04Z
Message: Successfully created MemcachedOpsRequest demo/mcops-mc-autoscaler-compute-p1usdl
Observed Generation: 1
Reason: CreateOpsRequest
Status: True
Type: CreateOpsRequest
Vpas:
Conditions:
Last Transition Time: 2024-09-10T13:09:04Z
Status: True
Type: RecommendationProvided
Recommendation:
Container Recommendations:
Container Name: memcached
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: mc-autoscaler-compute
Events: <none>
So, the Memcachedautoscaler 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 Memcachedopsrequest based on the recommendations, if the database pods are needed to scaled up or down.
Let’s watch the Memcachedopsrequest in the demo namespace to see if any Memcachedopsrequest object is created. After some time you’ll see that a Memcachedopsrequest will be created based on the recommendation.
$ watch kubectl get memcachedopsrequest -n demo
Every 2.0s: kubectl get memcachedopsrequest -n demo
NAME TYPE STATUS AGE
mcops-mc-autoscaler-compute-p1usdl VerticalScaling Progressing 10s
Let’s wait for the ops request to become successful.
$ watch kubectl get memcachedopsrequest -n demo
Every 2.0s: kubectl get memcachedopsrequest -n demo
NAME TYPE STATUS AGE
mcops-mc-autoscaler-compute-p1usdl VerticalScaling Successful 1m
We can see from the above output that the memcachedOpsRequest has succeeded.
$ kubectl get pod -n demo mc-autoscaler-compute-0 -o json | jq '.spec.containers[].resources'
{
"limits": {
"cpu": "400m",
"memory": "400Mi"
},
"requests": {
"cpu": "400m",
"memory": "400Mi"
}
}
$ kubectl get Memcached -n demo mc-autoscaler-compute -o json | jq '.spec.podTemplate.spec.containers[] | select(.name == "memcached") | .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 Memcached database.
Cleaning Up
To clean up the Kubernetes resources created by this tutorial, run:
$ kubectl patch -n demo mc/mc-autoscaler-compute -p '{"spec":{"deletionPolicy":"WipeOut"}}' --type="merge"
memcached.kubedb.com/mc-autoscaler-compute patched
$ kubectl delete mc -n demo mc-autoscaler-compute
memcached.kubedb.com "mc-autoscaler-compute" deleted
$ kubectl delete memcachedautoscaler -n demo mc-autoscaler
memcachedautoscaler.autoscaling.kubedb.com "mc-autoscaler" deleted