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Horizontal Pod Autoscaler (HPA)

The Horizontal Pod Autoscaler (HPA) is a Kubernetes feature that automatically adjusts the number of pods in a deployment, replica set, or stateful set based on observed CPU/memory utilization or custom metrics. This guide provides an overview of HPA, its benefits, and how to configure it in a Kubernetes cluster.

🚧 Work in Progress

This page is currently under construction. Please check back later for detailed information about HPA setup and usage in Kubernetes.

Table of Contents​

Introduction​

The Horizontal Pod Autoscaler (HPA) is a Kubernetes controller that automatically scales the number of pods in a workload based on resource utilization or custom metrics. It helps ensure that your application can handle varying levels of traffic while optimizing resource usage.

Why Use HPA?​

  • Dynamic Scaling: Automatically adjusts the number of pods based on workload demand.
  • Resource Optimization: Ensures efficient use of cluster resources by scaling up or down as needed.
  • Improved Resilience: Helps maintain application performance during traffic spikes.

How HPA Works​

  1. Metrics Collection: HPA collects metrics such as CPU and memory usage from the Kubernetes Metrics Server or custom metrics providers.
  2. Scaling Decision: Based on the observed metrics and the target utilization defined in the HPA configuration, it calculates the desired number of pods.
  3. Scaling Action: HPA adjusts the number of pods in the workload to match the desired state.

Configuration​

To configure HPA, you need to define a HorizontalPodAutoscaler resource. Example configuration:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: my-app-hpa
  namespace: default
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app
  minReplicas: 2
  maxReplicas: 10
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 50

Best Practices​

  • Set Appropriate Limits: Define resource requests and limits for your pods to ensure accurate scaling.
  • Monitor Performance: Use monitoring tools to track the performance of your application and adjust HPA settings as needed.
  • Test Scaling Behavior: Simulate traffic spikes to test the scaling behavior of your application and ensure it meets performance requirements.
  • Use Custom Metrics: Consider using custom metrics for more granular control over scaling decisions.
  • Avoid Over-Scaling: Set reasonable limits on the maximum number of replicas to prevent resource exhaustion in the cluster.

Stay tuned for more detailed information on setting up and using the Horizontal Pod Autoscaler in Kubernetes!