How I Fixed Kubernetes Pods Not Scaling Automatically Due to Misconfigured HPA

Introduction:

In Kubernetes, Horizontal Pod Autoscaling (HPA) is a powerful feature that allows the system to automatically scale the number of pods in a deployment based on metrics like CPU or memory usage. It’s essential for handling dynamic workloads efficiently and ensuring that your application performs well under varying loads.

While working with Kubernetes on AWS EKS, I encountered an issue where the pods in my deployment weren't scaling automatically despite HPA being enabled. After some troubleshooting, I identified the misconfiguration that was preventing the autoscaling from working as expected.

In this post, I’ll walk you through the steps I took to resolve the issue and ensure that my Kubernetes pods scaled properly according to CPU usage.

The Issue:

I had deployed a service on AWS EKS, and everything seemed to be set up correctly. The deployment was configured with HPA, so I expected the system to scale the pods up and down based on the CPU usage.

However, I noticed that even when the load on the system increased, the number of pods did not scale up as anticipated. There was no change in the number of pods regardless of the CPU load, which indicated that the HPA was not functioning as expected.

What I Didn’t Immediately Notice:

At first, I assumed that the problem might lie in the HPA configuration itself or in the way I had defined the metrics for autoscaling. I checked the HPA resource, but it seemed fine at a glance. I also checked the CPU limits and requests for the pods, as well as the thresholds for scaling, but nothing seemed out of place.

It wasn’t until I dug deeper into the cluster that I realized the root cause: the Kubernetes metrics-server, which is responsible for collecting resource metrics (like CPU and memory usage) for the HPA, was missing from the cluster.

Troubleshooting Steps:

1. Checked HPA Configuration:

   The first step was to verify the HPA configuration. I ran the following command to inspect the HPA resource:

    kubectl get hpa
   

   This command showed me that the HPA was set up correctly, and it was configured to scale based on CPU usage. The scaling rules seemed appropriate, but it wasn’t clear why the pods weren’t scaling.

2. Checked Metrics Availability:

   After verifying the HPA configuration, I checked the metrics available in the cluster. To do this, I ran the following command to view the metrics:

    kubectl top pods
   

   This returned an error message saying that the metrics were not available. This was the first indication that there was an issue with the metrics-server, which is responsible for gathering CPU and memory metrics for the cluster.

3. Installed Metrics-Server:

   I realized that the metrics-server was missing from the cluster. The metrics-server is crucial for autoscaling because it provides the necessary resource usage data to HPA. Without it, HPA cannot make decisions about scaling.

   To resolve this, I installed the metrics-server using the following command:

    kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.5.0/components.yaml
   

   After installing the metrics-server, I verified that it was running correctly with:

    kubectl get deployment metrics-server -n kube-system
   

4. Verified Metrics Data:

   Once the metrics-server was up and running, I checked if metrics were being collected correctly. I ran the kubectl top pods command again, and this time, it returned the CPU and memory usage data for the pods, indicating that the metrics-server was functioning as expected.

5. Updated HPA Configuration (if needed):

   In some cases, you may need to update the HPA resource to ensure it’s pulling the correct metrics from the metrics-server. In my case, everything seemed to be set up correctly, but it’s always good to verify that the HPA is using the right metric source.

Solution:

1. Installed Metrics-Server:

   Installing the metrics-server was the key step in resolving the issue. Without the metrics-server, Kubernetes couldn’t collect the necessary CPU usage metrics for autoscaling.

2. Tested Scaling:

   After installing the metrics-server, I triggered a test by increasing the load on the application to simulate higher CPU usage. I then watched the HPA automatically scale the pods as the CPU usage increased, confirming that the autoscaling feature was now functioning correctly.

3. Monitored Performance:

   I continued to monitor the performance of the deployment over time, ensuring that the scaling behavior remained consistent and the application handled varying load efficiently.

Key Takeaways:

• Ensure Metrics-Server Is Installed: The metrics-server is an essential component for enabling HPA to scale your Kubernetes pods based on resource usage. Always make sure it’s installed and running in your cluster.

• Verify HPA and Metrics Integration: Even if HPA is configured correctly, it won’t be effective without proper metrics. Make sure that your HPA is pulling metrics from a functioning metrics-server.

• Test Scaling Under Load: After resolving issues, always test the auto scaling functionality by simulating load. This ensures that your system will scale as expected in real-world scenarios.

Conclusion:

Kubernetes Horizontal Pod Autoscaling is a critical feature for managing resources and ensuring that your applications can handle varying workloads. In my case, the issue was a simple misconfiguration due to the missing metrics-server, which was preventing the HPA from functioning correctly.

By installing the metrics-server and ensuring that it was properly integrated with HPA, I was able to get my deployment scaling automatically based on CPU usage. This experience reminded me of the importance of ensuring that all the required components are properly installed and configured in a Kubernetes cluster.

If you’ve run into similar issues with HPA or autoscaling, feel free to share your experiences or ask questions in the comments!