How do you troubleshoot HPA custom metrics when it fails?

When troubleshooting Horizontal Pod Autoscaler (HPA) custom metrics failures, it's essential to follow a structured approach to identify and resolve the issues effectively. Here are the key steps to diagnose and fix HPA issues related to custom metrics.

Steps to Troubleshoot HPA Custom Metrics

1. Check HPA Configuration: Verify the configuration of the HPA, including the metric types, thresholds, and the target deployment or stateful set.
2. Inspect Metrics Server: Ensure that the metrics server is functioning correctly. Check its logs for any errors related to collecting or exposing metrics.
3. Validate Custom Metrics: Use the kubectl get --raw command to fetch metrics from the API server and confirm they are available and correctly formatted.
4. Debug the Metric Provider: If using a custom metrics provider, check its logs for errors and ensure it’s properly configured to communicate with the Kubernetes API.
5. Look for Resource Constraints: Ensure that your application’s resources are not constrained, causing the metrics to become stale or unavailable.
6. Review Event Logs: Check for any events related to scaling issues using kubectl describe hpa . This will provide insights into what is happening.
7. Test Scaling Manually: Manually adjust the replicas in the deployment and see if scaling takes effect, helping narrow down if the issue is with the HPA itself or the metrics.

Example: Fetching Custom Metrics

kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta1/"

By following these steps, you can identify and address issues with HPA custom metrics effectively and ensure your application scales as intended.