How to Deploy Metrics Server on Vultr Kubernetes Engine

Introduction

Metrics server is a scalable cluster add-on for the Kubernetes built-in autoscaling pipelines. It is a critical element to scale pods in the Kubernetes cluster. It is designed to automatically scale up or down your application workloads using the Kubernetes Horizontal Pod Autoscaler.

Metrics server works by retrieving kubelet metrics from each worker node, collecting CPU and memory usage for each application workload then use the Kubernetes API server to expose CPU and memory usage metrics. Then, Horizontal Pod Autoscaler fetches CPU and memory usage metrics and scales up or down your application deployment pods based on metrics observation and target threshold.

Features

The main features of using Kubernetes metrics server are:

• Scale up and down resources automatically.
• Support up to 5,000 node clusters.
• Collect resource metrics such as CPU and memory utilization in horizontal autoscaling.
• Fewer resources requirement.
• Monitors cluster metrics that are not Kubernetes-specific.

This guide shows you how to install Metrics server on Vultr Kubernetes Engine.

Prerequisites

• Deploy a Kubernetes cluster at Vultr with at least three nodes.
• The kubectl CLI installed and configured in your local machine.
• Helm client installed in your local machine.

Add Helm Repo for Metrics Server

1. First, add the Helm repository for the Metrics server.

    # helm repo add metrics-server https://kubernetes-sigs.github.io/metrics-server
   

2. Next, update the Helm repository to the latest version.

    # helm repo update
   

3. Then, verify the added repository.

    # helm repo list

   Sample output.

    NAME              URL                                             
    metrics-server    https://kubernetes-sigs.github.io/metrics-server

4. After that, create a namespace for the Metrics server.

    # kubectl create ns metrics-server
   

5. Next, set default namespace to metrics-server.

    # kubectl config set-context $(kubectl config current-context) --namespace=metrics-server

Install Metrics Server

1. Before installing the Metrics server, obtain the default configuration value from the Helm repo and save it to the file metrics-server.values.

    # helm show values metrics-server/metrics-server > ~/metrics-server.values
   

2. Next, edit the file metrics-server.values.

    # nano metrics-server.values

   Change the desired configuration to file.

    replicas: 4
   
    defaultArgs:
    - --cert-dir=/tmp
    - --kubelet-preferred-address-types=InternalIP,ExternalIP,Hostname
    - --kubelet-use-node-status-port
    - --metric-resolution=15s
    - --kubelet-insecure-tls
   

3. Now, deploy the Metrics server to Kubernetes.

    # helm install metrics-server metrics-server/metrics-server -n  metrics-server --values ~/metrics-server.values

   Sample output.

    NAME: metrics-server
    LAST DEPLOYED: Fri Jan 13 10:02:19 2023
    NAMESPACE: metrics-server
    STATUS: deployed
    REVISION: 1
    TEST SUITE: None
    NOTES:
    ***********************************************************************
    * Metrics Server                                                      *
    ***********************************************************************
    Chart version: 3.8.3
    App version:   0.6.2
    Image tag:     k8s.gcr.io/metrics-server/metrics-server:v0.6.2
    ***********************************************************************
   

4. After the successful deployment, verify it using the following command.

    # helm ls -n metrics-server

   Sample output.

    NAME              NAMESPACE         REVISION    UPDATED                                    STATUS      CHART                   APP    VERSION
    metrics-server    metrics-server    1           2023-01-13 10:02:19.083957787 +0530 IST    deployed    metrics-server-3.8.3    0.6.2      
   

5. To verify other components, run.

    # kubectl get all -n metrics-server

   Sample output.

    NAME                                  READY   STATUS    RESTARTS   AGE
    pod/metrics-server-54c4d8c9df-6z25l   1/1     Running   0          31s
    pod/metrics-server-54c4d8c9df-7t5bn   1/1     Running   0          31s
    pod/metrics-server-54c4d8c9df-rjdt2   1/1     Running   0          31s
    pod/metrics-server-54c4d8c9df-tz54s   1/1     Running   0          31s
   
    NAME                     TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)   AGE
    service/metrics-server   ClusterIP   10.96.76.218   <none>        443/TCP   32s
   
    NAME                             READY   UP-TO-DATE   AVAILABLE   AGE
    deployment.apps/metrics-server   4/4     4            4           33s
   
    NAME                                        DESIRED   CURRENT   READY   AGE
    replicaset.apps/metrics-server-54c4d8c9df   4         4         4       33s

Verify Available Resource Metrics

The simple and easiest way to verify the metrics data using the kubectl top command. You can verify the resource usage at the cluster level and at the Pods level.

To verify the resource usage at the cluster level, run:

# kubectl top nodes  

This command will show you the resource usage for both CPU and memory on each worker node.

NAME                              CPU(cores)   CPU%   MEMORY(bytes)   MEMORY%   
metrics-kubernetes-226bb1c797fe   77m          9%     853Mi           50%       
metrics-kubernetes-7588a6dad02f   46m          5%     765Mi           45%       
metrics-kubernetes-cd72d509b5a3   46m          5%     736Mi           43%       

To verify the resource usage at the Pod level, run:

# kubectl top pods -n metrics-server

This command will show you the CPU usage in millicores and memory usage in Mebibytes.

NAMESPACE        NAME                                       CPU(cores)   MEMORY(bytes)   
metrics-server   metrics-server-54c4d8c9df-6z25l            4m           13Mi            
metrics-server   metrics-server-54c4d8c9df-7t5bn            2m           18Mi            
metrics-server   metrics-server-54c4d8c9df-rjdt2            3m           18Mi            
metrics-server   metrics-server-54c4d8c9df-tz54s            3m           18Mi            

Auto-Scale Pods Based on Resource Utilization

In this section, you will configure Horizontal Pod Autoscaling to grow and shrinks Pods automatically based on resource utilization.

1. First, create a deployment to test Horizontal Pod Autoscaling.

    # nano deployment.yml

   Add the following configurations.

    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: sample-app
    spec:
      selector:
        matchLabels:
          run: sample-app
      replicas: 1
      template:
        metadata:
          labels:
            run: sample-app
        spec:
          containers:
            - name: busybox
              image: busybox
              resources:
                limits:
                  cpu: 50m
                requests:
                  cpu: 20m
              command: ["sh", "-c"]
              args:
                - while [ 1 ]; do
                  echo "Test";
                  sleep 0.01;
                  done
   

2. Apply the above manifest to the Kubernetes cluster.

    # kubectl apply -f deployment.yml
   

3. Verify the deployment.

    # kubectl get deployment

   Sample output.

    NAME             READY   UP-TO-DATE   AVAILABLE   AGE
    metrics-server   4/4     4            4           2m30s
    sample-app       1/1     1            1           25s

4. Next, create the Horizontal Pod Autoscaler CRD to configure Horizontal Pod Autoscaler (HPA) on your deployment

    # nano crd.yml

   Add the following configurations.

    apiVersion: autoscaling/v2
    kind: HorizontalPodAutoscaler
    metadata:
      name: my-app-hpa
    spec:
      scaleTargetRef:
        apiVersion: apps/v1
        kind: Deployment
        name: sample-app
      minReplicas: 2
      maxReplicas: 6
      metrics:
        - type: Resource
          resource:
            name: cpu
            target:
              type: Utilization
              averageUtilization: 50
   

   The above configuration will create a Horizontal Pod Autoscaler on the sample-app deployment with CPU max value set to 50 and scaling between 2-6 replicas.

5. Apply the manifest to the Kubernetes cluster.

    # kubectl apply -f crd.yml

   If you don't want to run Horizontal Pod Autoscaler using the above method then you can deploy it via the kubectl autoscale command.

    # kubectl autoscale deployment sample-app --cpu-percent=50 --min=2 --max=6
   

6. Now, verify the HPA creation using the following command.

    # kubectl get hpa

   You should the the current usage%/target usage% in the TARGET column.

    NAME         REFERENCE               TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
    my-app-hpa   Deployment/sample-app   30%/50%   2         6         2          7m19s
   

7. To see the detailed information of your HPA, run:

    # kubectl describe hpa my-app-hpa

   You should see the detail overview of your HPA deployment in the following output.

    Name:                                                  my-app-hpa
    Namespace:                                             metrics-server
    Labels:                                                <none>
    Annotations:                                           <none>
    CreationTimestamp:                                     Fri, 13 Jan 2023 10:06:40 +0530
    Reference:                                             Deployment/sample-app
    Metrics:                                               ( current / target )
      resource cpu on pods  (as a percentage of request):  22% (4m) / 50%
    Min replicas:                                          2
    Max replicas:                                          6
    Deployment pods:                                       2 current / 2 desired
    Conditions:
      Type            Status  Reason               Message
      ----            ------  ------               -------
      AbleToScale     True    ScaleDownStabilized  recent recommendations were higher than current one, applying the highest recent     recommendation
      ScalingActive   True    ValidMetricFound     the HPA was able to successfully calculate a replica count from cpu resource utilization (percentage of request)
      ScalingLimited  False   DesiredWithinRange   the desired count is within the acceptable range  
    Events:
      Type    Reason             Age   From                       Message
      ----    ------             ----  ----                       -------
      Normal  SuccessfulRescale  42s   horizontal-pod-autoscaler  New size: 2; reason: Current number of replicas below Spec.MinReplicas
   

8. Your sample-app is now autoscaled as per resource usage. You can verify it using the following command.

    # kubectl get deployment

   Sample output.

    NAME             READY   UP-TO-DATE   AVAILABLE   AGE
    metrics-server   4/4     4            4           12m
    sample-app       2/2     2            2           10m
   

9. You can also verify your Pods using the following command.

    # kubectl get pods

   Sample output.

    NAME                              READY   STATUS    RESTARTS   AGE
    metrics-server-54c4d8c9df-6z25l   1/1     Running   0          12m
    metrics-server-54c4d8c9df-7t5bn   1/1     Running   0          12m
    metrics-server-54c4d8c9df-rjdt2   1/1     Running   0          12m
    metrics-server-54c4d8c9df-tz54s   1/1     Running   0          12m
    sample-app-967cff66f-qjfq2        1/1     Running   0          10m
    sample-app-967cff66f-tnqcd        1/1     Running   0          103s

Upgrade Metrics Server

You can visit the metrics-server official release page to check the latest available versions.

To upgrade the Metrics server stack to the latest version, run the following command.

# helm upgrade metrics-server metrics-server/metrics-server --version "metrics-server-new-version" -n  metrics-server --values ~/metrics-server.values

You can check the helm upgrade command documentation for more information.

Uninstall Metrics Server

You can remove your complete Metrics server installation from your system using the helm uninstall command.

# helm uninstall metrics-server -n metrics-server

The above command will remove all metrics-server related components from your server. You will also need to delete the metrics-server namespace from your system. To delete the namespace, run the following command.

# kubectl delete ns metrics-server

Conclusion

You've finished deploying the Metrics server on the Vultr Kubernetes Engine. Metrics server is a powerful tool for monitoring Kubernetes autoscaling metrics based on CPU utilization or memory usage. For more information, check out the Metrics server official GitHub page.