Monitoring Resource Usage for Kubernetes Pods

Monitoring Kubernetes pod resource usage is crucial for maintaining a healthy, efficient, and well-performing cluster.

By keeping an eye on resource utilization, you gain insights into workload requirements for the future, manage overused or underutilized resources effectively, and make informed choices when using tools like Horizontal Pod Autoscaler (HPA) or Vertical Pod Autoscaler (VPA).

kubectl provides a top command feature. Unlike the top command in Linux, this one just gives the CPU and memory utilization. But that’s all we need here, right?

You can get the resource usage of pods in the default namespace with:

kubectl top pods

And if you want to see resource utilization for pods in some other namespace, use:

kubectl top pods --namespace=demo-namespace

However, the above kubectl top commands won’t work if Metrics Server is not installed first.

Let me show you how it works by providing a hands-on example scenario.

I’ll create two pods: one in the default namespace and another in a newly created namespace. After setting up the pods, we’ll explore how to monitor resource usage for Kubernetes pods across both the default and custom namespaces.

Prerequisites for this scenario

Before getting started, make sure the following requirements are met:

• A running Kubernetes cluster (e.g., Minikube installed for local development)
• The kubectl command-line tool is configured to communicate with the cluster
• A shell environment, such as Bash or PowerShell

To monitor Kubernetes pod resource usage, I’ll first install the Metrics Server, create a Kubernetes pod, and then use the kubectl top command to check its resource utilization.

Install Metrics Server

Metrics Server is an essential add-on for Kubernetes that gathers resource metrics from the Kubelet running on each node in your cluster. It compiles this data and makes it available through the Kubernetes API server via the Metrics API. This allows you to easily access real-time resource usage stats using the kubectl top command.

To install the Metrics Server on Kubernetes, you have two options: using a YAML manifest file or deploying via a Helm chart.

In this guide, I am opting for installing the Metrics Server with the components.yaml manifest.

kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml

After installing the Metrics Server, you can confirm that it has been installed correctly and is in a ready state:

kubectl get deployment metrics-server -n kube-system

Output:

NAME             READY   UP-TO-DATE   AVAILABLE   AGE
metrics-server   1/1     1            1           4h9m

The Metrics Server add-on has now been successfully installed on the Kubernetes cluster.

Create Kubernetes Pods

To test the setup, I’ll deploy two Kubernetes pods, each running an Nginx and PHP container. One pod will be created in the default namespace, while the other will be set up in a new namespace called demo-namespace.

Here’s the YAML configuration to set up the namespace and define the two pods:

apiVersion: v1
kind: Namespace
metadata:
  name: demo-namespace
---
apiVersion: v1
kind: Pod
metadata:
  name: first-pod
  namespace: default
spec:
  containers:
  - name: nginx
    image: nginx:latest
  - name: php
    image: php:7.4-fpm
---
apiVersion: v1
kind: Pod
metadata:
  name: second-pod
  namespace: demo-namespace
spec:
  containers:
  - name: nginx
    image: nginx:latest
  - name: php
    image: php:7.4-fpm

Save the YAML configuration to a file called demo-pods.yml. After that, apply it to the resource using the following command:

kubectl apply -f demo-pods.yml

I’ve successfully launched two active Kubernetes pods, each operating within its own namespace.

Monitor Pods Resource Usage

To retrieve resource usage data for Kubernetes pods, you can utilize the built-in kubectl top command. This command provides essential metrics for each pod, including:

• CPU Usage (cores): The amount of CPU resources consumed by the pod, measured in CPU cores
• Memory Usage (bytes): The amount of memory consumed by the pod, measured in bytes

This data helps us to identify pods that are either over-utilizing or under-utilizing their allocated resources, which can later be used for capacity planning, performance optimization, and efficient autoscaling.

Now, let’s monitor the resource usage of pods in the default namespace:

kubectl top pods

Output

NAME        CPU(cores)   MEMORY(bytes)   
first-pod   1m           9Mi

The above output will appear once the Metrics Server has gathered enough resource data.

Next, track the resource usage of pods within a specific namespace by utilizing the --namespace flag:

kubectl top pods --namespace=demo-namespace

Output:

NAME         CPU(cores)   MEMORY(bytes)   
second-pod   1m           9Mi

Finally, to monitor resource utilization for pods across all namespaces in your cluster, simply use the –all-namespaces option:

kubectl top pods --all-namespaces

Output:

NAMESPACE              NAME                                                    CPU(cores)   MEMORY(bytes)
default                first-pod                                               1m           9Mi
other-namespace        second-pod                                              1m           9Mi
kubernetes-dashboard   kubernetes-dashboard-api-74bf7cb4bb-n9fmp               0m           10Mi
kubernetes-dashboard   kubernetes-dashboard-auth-65b56d647f-ngvrh              0m           8Mi

Conclusion

In this quick guide, you’ve learned how to utilize kubectl top to monitor resource usage for Kubernetes nodes and containers within specific pods.

We started by installing the Metrics Server and configuring Kubernetes pods across two different namespaces, and then learned how to track CPU and memory resource usage for pods in each namespace.

That provides a good enough base for you to get started and employ it in actual scenarios. Enjoy 😄

Vivek Y

FOSS enthusiast with extensive experience in system administration and a background in multiple industry-standard programming languages. Moreover, I enjoy writing technical content for leading publications in my golden hour.