Incident Overview
A platform team runs a fictional, mid-sized Kubernetes cluster (version 1.28) hosting a checkout service behind a Horizontal Pod Autoscaler (HPA). At 14:02 the HPA scales the deployment from 6 to 18 replicas in four minutes.
The on-call engineer opens the CPU dashboard and finds node and pod CPU utilisation comfortably under 30%, well below the 70% target the HPA is meant to react to. The HPA status shows it is reacting to a custom external metric, not CPU, so the scale-up looks unjustified on the CPU graphs alone.
Investigation Options
Review the available operational moves and select the best immediate action.
Delete the HPA object and let Kubernetes recreate default scaling behaviour.
Manually scale the deployment down to match observed CPU usage and pause the HPA while investigating the metrics pipeline.
Restart the metrics-server pod immediately to force fresh metric collection.
Compare the custom metrics adapter's scrape interval against the HPA sync period, correct the mismatch, and re-verify metric freshness before trusting scale decisions again.