Augustine's Notes

2025 Updates Horizontal Pod Autoscaler HPA

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2025 Updates Horizontal Pod Autoscaler HPA

Source: https://notes.kodekloud.com/docs/CKA-Certification-Course-Certified-Kubernetes-Administrator/Application-Lifecycle-Management/2025-Updates-Horizontal-Pod-Autoscaler-HPA/page

Summary: This article explores the Horizontal Pod Autoscaler in Kubernetes, detailing its automated scaling capabilities based on resource usage.

Key Notes

  • In this article, we explore the Horizontal Pod Autoscaler (HPA) in Kubernetes. HPA is a key feature that automates scaling of workloads based on resource usage, reducing the manual monitoring overhead for administrators.
  • Manual Horizontal Scaling
  • Imagine you are managing a Kubernetes cluster and need to ensure your application can handle traffic spikes. Consider an application where each pod requests 250 millicores (mCPU) and has a limit of 500 mCPU. Even under heavy load, a single pod will never exceed 500 mCPU.
  • To monitor the pod’s resource consumption manually, you might run:
  • $
  • When resource usage reaches a predefined threshold (e.g., 450 mCPU), you must manually scale the deployment:
  • —replicas=3
  • Below is a sample deployment configuration for this scenario:
  • apps/v1
  • “250m”
  • “500m”
  • Manually scaling pods requires continuous monitoring, which can be resource-intensive and error-prone during traffic surges.
  • Automated Scaling with Horizontal Pod Autoscaler
  • Kubernetes simplifies scaling with the Horizontal Pod Autoscaler. HPA monitors resource metrics—including CPU, memory, and custom metrics—using the metrics server. When usage exceeds a defined threshold, it automatically adjusts the number of pod replicas in deployments, stateful sets, or replica sets.
  • When CPU or memory usage is high, HPA scales up the number of pods; when usage drops, it scales them down to conserve system resources. HPA can even track multiple metrics concurrently.
  • Imperative Creation of an HPA
  • For an existing Nginx deployment, you can configure an HPA with the following command. This command sets the autoscaler to maintain CPU utilization at 50% with a replica count that can vary between 1 and 10:
  • —cpu-percent=50
  • —min=1
  • —max=10
  • Once executed, Kubernetes creates an HPA that continuously polls the metrics server based on the pod’s CPU limit (500 mCPU in this case). If CPU usage exceeds 50% of this limit, HPA automatically scales the deployment up or down. To check the status of your HPA, run:
  • This command displays details such as current CPU utilization against the threshold and the current number of running pods. If you need to remove the autoscaler later, simply run:
  • Declarative HPA Configuration
  • Alternatively, you can define the HPA using a declarative configuration file. The example below uses the autoscaling/v2 API:

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