Kubernetes Autoscaling: HPA with Native CPU and Memory Metrics

Managing application scalability in Kubernetes does not always require complex third-party tools. By leveraging the built-in HorizontalPodAutoscaler (HPA) with native CPU and memory metrics, you can keep your deployments highly available and cost-efficient with minimal setup. This text-only course guides you through the core mechanics of Kubernetes resource management and autoscaling, transitioning you from manual replica management to configuring automated, responsive scaling policies using the default Metrics Server. What you'll learn: - Understand the foundational concepts of pod resource requests, limits, and the Kubernetes Metrics Server. - Configure HorizontalPodAutoscalers using declarative YAML manifests for CPU and memory utilization thresholds. - Apply scaling policies to manage scale-up sensitivity and scale-down stabilization windows. - Diagnose and troubleshoot common HPA issues, such as missing metrics or misconfigured resource limits. - Compare native resource-based scaling with custom metrics architectures to make informed infrastructure decisions. You will start with the fundamental definitions of cluster resource allocation before moving on to step-by-step written guides for writing, applying, and testing HPA configurations. The course concludes with practical troubleshooting scenarios and modern best practices for production autoscaling. This course is designed for junior DevOps engineers, system administrators, and software developers who are new to Kubernetes scaling and want a straightforward approach to resource management. No advanced cluster administration experience is required. Start reading today to simplify your Kubernetes deployment scaling strategy.