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How Kubernetes HPA Decides Which Pod to Terminate When Scaling Down
- When resource demands increase, Kubernetes Horizontal Pod Autoscaler (HPA) adds pods to the deployment to meet the workload, and scales down by reducing the number of running pods when the demand drops.
- HPA doesn’t directly terminate pods; it adjusts the replica count, and the replicaset controller handles the actual pod termination decisions.
- By default, Kubernetes tends to terminate the newest pods first during scaling down, but factors like scheduling constraints, pod health, and disruption budgets can influence termination decisions.
- Kubernetes considers pod health and readiness during termination, prioritizing the removal of unhealthy pods to maintain deployment stability.
- Pod Disruption Budgets (PDBs) limit the number of pods that can be disrupted during scaling down, preventing too many critical pods from being terminated simultaneously.
- Custom rules like affinity and taints can indirectly affect termination decisions, but standard Kubernetes behavior is to prioritize removing pods that are easier to reschedule.
- Kubernetes sends a SIGTERM signal to pods selected for termination, giving them time to shut down gracefully according to the terminationGracePeriodSeconds specified in the pod specification.
- Understanding how HPA, replicaset controller, and termination processes interact helps predict and manage scaling in Kubernetes deployments effectively.
- HPA continuously monitors resource usage metrics and adjusts the replica count based on workload changes, with the replicaset controller making decisions on pod terminations.
- Kubernetes defaults to removing the newest pods first during scaling down, checking pod health status before finalizing termination decisions.
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