Everything I Wanted To Know About Kubernetes Autoscaling

A HorizontalPodAutoscaler (HPA) in Kubernetes automatically adjusts the number of Pods in a workload, such as a Deployment or StatefulSet, to match demand, primarily through horizontal scaling, which involves adding more Pods in response to increased load. Vertical scaling, which increases resources like memory or CPU for existing Pods, is less common. Multi-dimensional scaling, combining both horizontal and vertical strategies, is rare and complex. The text emphasizes the importance of understanding application limits and bottlenecks before implementing autoscaling, as CPU-based scaling isn't always suitable. Recommendations include load testing to identify resource bottlenecks in applications and considering architecture changes if necessary. The text describes Qovery's use of autoscaling, highlighting memory as a bottleneck and the implementation of a custom metric to scale based on the number of tasks executed in parallel. Additionally, it discusses strategies for enhancing pod boot time, such as using overprovisioning pods, and stresses the importance of knowing application limits and conducting regular load testing to ensure successful autoscaling.