chaos-engineer

Start by defining a hypothesis about system behavior, verify steady state metrics like error rate and latency, then limit the blast radius to the smallest scope. Inject one failure at a time with automated rollback triggers, and document learnings after each experiment.

Create a ChaosEngine manifest targeting your deployment with the pod-delete experiment, setting TOTAL_CHAOS_DURATION, CHAOS_INTERVAL, and PODS_AFFECTED_PERC. Apply it with kubectl, then watch the ChaosResult verdict and patch engineState to stop for rollback.

Litmus Chaos uses ChaosEngine CRDs with a broad experiment hub for Kubernetes workloads, while Chaos Mesh offers fine-grained fault types like NetworkChaos, StressChaos, IOChaos, and DNSChaos with a built-in dashboard. Both integrate with kubectl and CI/CD pipelines.

Yes, but only with safety nets: cap the blast radius at a small traffic percentage, require feature flags or canary isolation, enforce automated rollback within 30 seconds, and verify steady state before injecting any failure. Start in staging and expand progressively.

Use Toxiproxy to create a proxy in front of a dependency and add a latency toxic with jitter, or use Chaos Mesh NetworkChaos for Kubernetes pods. For Docker containers, Pumba netem can inject delay, packet loss, and bandwidth limits.

A game day needs defined objectives, participant roles, timed failure scenarios with success criteria, a communication plan, and rollback procedures. Follow it with a debrief, a post-mortem report capturing metrics like time to detect and recover, and tracked action items.