If you're tasked with performance testing Kubernetes workloads without much guidance, this episode offers clear, experience-based strategies that go beyond theory.
Stephan Schwarz, a DevOps engineer at iits-consulting, walks through his systematic approach to performance testing Kubernetes applications. He covers everything from defining what performance actually means, to the practical methodology of breaking individual pods to understand their limits, and navigating the complexities of Kubernetes-specific components that affect test results.
You will learn:
How to establish baseline performance metrics by systematically testing individual pods, disabling autoscaling features, and documenting each incremental change to understand real application limits
Why shared Kubernetes components skew results and how ingress controllers, service meshes, and monitoring stacks create testing challenges that require careful consideration of the entire request chain
Practical approaches to HPA configuration, including how to account for scaling latency, the time delays inherent in Kubernetes scaling operations, and planning for spare capacity based on your SLA requirements
The role of observability tools like OpenTelemetry in production environments where load testing isn't feasible, and how distributed tracing helps isolate performance bottlenecks across interdependent services
Sponsor
This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.
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