Understanding Kubernetes: Part 30 Horizontal Pod Autoscaler (HPA)

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📢 If you’ve been following our Kubernetes series 2025, welcome back! For new readers, check out Part 29 Service Account

What is a Horizontal Pod Autoscaler (HPA)?

A Horizontal Pod Autoscaler (HPA) is a Kubernetes resource that automatically scales the number of pod replicas in a deployment, replica set, or stateful set based on CPU, memory, or custom metrics. It ensures optimal resource utilization and cost efficiency by adjusting the number of pods dynamically.

How HPA Works

HPA continuously monitors specified metrics (e.g., CPU or memory usage) and increases or decreases the number of pods to maintain a target threshold. The scaling decision is based on data collected from the Metrics Server or external monitoring systems like Prometheus.

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Use Cases

1. Auto-Scaling Based on CPU Usage

Ensuring that applications scale up when CPU load increases and scale down when demand drops.

2. Memory-Based Scaling

Optimizing resource usage by dynamically adjusting pod replicas based on memory consumption.

3. Scaling with Custom Metrics

Using external metrics (e.g., request rates, queue length) from Prometheus or an external API to trigger scaling.

4. Cost Optimization

Automatically reducing pod count during low traffic periods to save resources.

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HPA Syntax

A Horizontal Pod Autoscaler is created using the following YAML configuration:

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

Explanation:

• scaleTargetRef – Specifies the target deployment (webapp) to scale.

• minReplicas & maxReplicas – Define the range of pod replicas.

• metrics – Uses CPU utilization as the scaling metric with a target of 50% usage.

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Applying HPA

To apply the HPA configuration:

kubectl apply -f hpa.yaml

To check the status of the HPA:

kubectl get hpa

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Removing HPA

To delete an HPA:

kubectl delete hpa webapp-hpa

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Conclusion

The Horizontal Pod Autoscaler (HPA) is a powerful Kubernetes feature for dynamically adjusting workloads. It helps maintain application performance while optimizing resource usage.

In My Previous Role

As a Senior DevOps Engineer, I effectively utilized HPA for scaling workloads:

• CPU-Based Auto-Scaling: Implemented HPA to scale backend services based on CPU spikes during high traffic periods.

• Custom Metrics Scaling: Integrated HPA with Prometheus to scale pods based on request count and queue depth.

• Cost Optimization: Used HPA to reduce the number of running pods in low-demand periods, improving cloud cost efficiency.

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