Horizontal Pod Autoscaling (HPA)

Automatically scaling the number of Pods in a deployment based on observed CPU/Memory utilization.

Edit Concept ✏️

Horizontal Pod Autoscaling (HPA)

The HPA automatically scales the number of Pods in a Deployment, ReplicaSet, or StatefulSet based on observed CPU utilization (or, with custom metrics support, on some other application-provided metrics).

1. How it Works

The HPA is implemented as a control loop. The Horizontal Pod Autoscaler controller periodically (default every 15s) queries the resource utilization against the metrics specified in each HPA definition.

  • It fetches metrics from the Metrics Server (or a custom metrics API).
  • It calculates the ratio between current utilization and desired utilization.
  • It updates the replicas field of the target workload.

2. The Formula

desiredReplicas = ceil[currentReplicas * ( currentMetricValue / desiredMetricValue )]

3. Key Components

  • Metrics Server: A cluster-wide aggregator of resource usage data.
  • Cool-down / Delay: To prevent “flapping” (rapidly scaling up and down), HPA has built-in delays (e.g., stabilizationWindowSeconds).

4. Requirements

For HPA to work based on CPU/Memory, you must define requests for those resources in your Pod specification. If there are no requests, the HPA won’t know what “100% utilization” means and will fail to scale.