How does a HorizontalPodAutoscaler work?
A HorizontalPodAutoscaler (HPA) in Kubernetes is a tool that automatically adjusts the number of pod replicas in a deployment, replica set, or stateful set based on observed CPU utilization (or other select metrics). Here’s a simple breakdown of how it works:
- Monitoring Metrics: The HPA continuously monitors the resource usage (like CPU or memory) of the pods. This is typically done using the Kubernetes metrics server, which collects data from the nodes and pods.
- Defining Targets: You set a target resource utilization level. For example, you might want your pods to use an average of 50% of their allocated CPU.
- Calculating Desired Replicas: The HPA calculates the desired number of replicas based on the current resource usage and the target utilization. If the average usage is above the target, it will increase the number of replicas; if it’s below, it will decrease them.
- Scaling the Pods: Based on its calculations, the HPA adjusts the number of replicas in the deployment, adding or removing pods as needed to meet the target utilization.
How to Use Kubernetes Horizontal Pod Autoscaler?
The process of automatically scaling in and scaling out of resources is called Autoscaling. There are three different types of autoscalers in Kubernetes: cluster autoscalers, horizontal pod autoscalers, and vertical pod autoscalers. In this article, we’re going to see Horizontal Pod Autoscaler.
Application running workload can be scaled manually by changing the replicas field in the workload manifest file. Although manual scaling is okay for times when you can anticipate load spikes in advance or when the load changes gradually over long periods of time, requiring manual intervention to handle sudden, unpredictable traffic increases isn’t ideal.
To solve this problem, Kubernetes has a resource called Horizontal Pod Autoscaler that can monitor pods and scale them automatically as soon as it detects an increase in CPU or memory usage (Based on a defined metric). Horizontal Pod Autoscaling is the process of automatically scaling the number of pod replicas managed by a controller based on the usage of the defined metric, which is managed by the Horizontal Pod Autoscaler Kubernetes resource to match the demand.