Blog Posts

Cluster autoscaler is a popular tool for automatically adjusting the size of a Kubernetes cluster based on the current workload. It helps ensure that your applications have enough resources to run efficiently while minimizing costs by scaling down unused nodes. However, monitoring the cluster autoscaler is crucial to ensure that it is functioning correctly and that your applications are running smoothly.

KEDA is a tool that provides event-driven autoscaling for Kubernetes, allowing you to scale your applications based on external metrics. It uses the Kubernetes Horizontal Pod Autoscaler (HPA) to adjust the number of pods in a deployment based on metrics like CPU usage, memory usage, or custom metrics from external sources. It also supports scaling based on event sources like message queues, databases as a job and defines a new Custom Resource Definition (CRD) called ScaledJob to configure the scaling behavior. Monitoring KEDA effectively is crucial to ensure that your autoscaling policies are working as expected and that your applications are performing optimally.