If you’re running Kubernetes you really should be using Horizontal Pod Autoscalers (HPAs) too. They work by continuously monitoring the resource usage of a deployment, replicaset, or other scalable resource, and automatically adjusting the number of replicas (pods) based on the defined scaling policies and metrics.

Here’s a step-by-step overview of how HPAs work:

  1. Metrics Collection: The HPA relies on metrics collected by the Kubernetes Metrics Server or a custom metrics solution like Prometheus. These metrics provide information about the current resource usage (CPU, memory, etc.) of the pods managed by the deployment or replicaset.

  2. Scaling Policy Definition: When creating an HPA, you define the scaling policy, which includes the target resource type (CPU or memory), a target utilization value (e.g., 50% CPU usage), and the minimum and maximum number of replicas allowed.

  3. Monitoring Loop: The HPA continuously monitors the resource usage of the pods managed by the deployment or replicaset. It compares the average resource usage across all pods against the target utilization defined in the scaling policy.

  4. Scaling Decisions: Based on the comparison between the actual resource usage and the target utilization, the HPA makes scaling decisions:
    • If the average resource usage exceeds the target utilization, the HPA will scale out by increasing the number of replicas (pods) to handle the increased load.
    • If the average resource usage falls below the target utilization, the HPA will scale in by decreasing the number of replicas to reduce resource consumption and costs.
  5. Scaling Up/Down: When a scaling decision is made, the HPA communicates with the Kubernetes API server to modify the desired replica count of the deployment or replicaset. The Kubernetes controller manager then ensures that the desired state is achieved by creating or terminating pods as needed.

  6. Stabilization Period: After a scaling event, the HPA waits for a stabilization period (controlled by the --horizontal-pod-autoscaler-upscale-delay and --horizontal-pod-autoscaler-downscale-delay flags) before re-evaluating the resource usage and making further scaling decisions. This prevents excessive scaling events due to temporary spikes or dips in resource usage.

  7. Scaling Limits: The HPA respects the minimum and maximum replica limits defined in the scaling policy. It will not scale below the minimum or above the maximum number of replicas, even if the resource usage suggests otherwise.

  8. Cooldown Period: After a failed scaling attempt (e.g., hitting the maximum replica limit), the HPA enters a cooldown period (controlled by the --horizontal-pod-autoscaler-cpu-initialization-period and --horizontal-pod-autoscaler-initial-readiness-delay flags) before attempting to scale again. This prevents thrashing and allows for stabilization.

HPAs provide an automated and dynamic way to scale applications in Kubernetes based on their actual resource usage, ensuring efficient resource utilization and meeting application performance requirements. However, it’s essential to carefully configure the scaling policies and monitor the HPA’s behavior to ensure optimal scaling decisions for your specific workloads. You can check out more details in the official documentation at

Written on March 22, 2024