How to Deploy Metrics Server on Vultr Kubernetes Engine

Updated on February 1, 2023
How to Deploy Metrics Server on Vultr Kubernetes Engine header image


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.


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.


Add Helm Repo for Metrics Server

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

     # helm repo add 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                                             
  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
     - --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
     * Metrics Server                                                      *
     Chart version: 3.8.3
     App version:   0.6.2
     Image tag:
  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   <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
       name: sample-app
           run: sample-app
       replicas: 1
             run: sample-app
             - name: busybox
               image: busybox
                   cpu: 50m
                   cpu: 20m
               command: ["sh", "-c"]
                 - while [ 1 ]; do
                   echo "Test";
                   sleep 0.01;
  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
       name: my-app-hpa
         apiVersion: apps/v1
         kind: Deployment
         name: sample-app
       minReplicas: 2
       maxReplicas: 6
         - type: Resource
             name: cpu
               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.

     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
       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  
       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


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.