STOP Please note: this EKS and Karpenter workshop version is now deprecated since the launch of Karpenter v1beta, and has been updated to a new home on AWS Workshop Studio here: Karpenter: Amazon EKS Best Practice and Cloud Cost Optimization.

This workshop remains here for reference to those who have used this workshop before, or those who want to reference this workshop for running Karpenter on version v1alpha5.

Implement AutoScaling with HPA and Karpenter

In this section, we will show patterns for scaling your worker nodes and applications deployments automatically. Automatic scaling in K8s comes in a few forms. In this section we will focus on the following three tasks

Monte Carlo Pi Approximation

  • Deploying a microservice application To illustrate application scaling using Horizontal Pod Autoscaler (HPA) and cluster scaling using Karpenter, we will deploy a microservice that generates CPU load. The microservice we will use is a trivial web service that uses a Monte Carlo method to approximate pi.

  • Horizontal Pod Autoscaler (HPA) scales the pods in a deployment or replica set. It is implemented as a K8s API resource and a controller. The controller manager queries the resource utilization against the metrics specified in each HorizontalPodAutoscaler definition. It obtains the metrics from either the resource metrics API (for per-pod resource metrics), or the custom metrics API (for all other metrics).

  • Karpenter By now, I’m sure you don’t need an introduction to Karpenter. You’ve seen it working and scaling it manually. In this section we will be showcasing how it works when in combination with HPA.