Chaos Engineering Basics: Breaking Things on Purpose to Build Resilience

Every chaos experiment follows a structure: 1. Define steady state — what does normal look like? (error rate <0.1%, p99 latency <200ms) 2. Hypothesize — 'if we kill one pod, the system will continue serving traffic within 30 seconds' 3. Inject failure — kill a pod, saturate a network link, inject latency 4. Observe — did steady state hold? 5. Fix weaknesses — if steady state broke, fix it before the next experiment

Never run experiments without a defined steady state and rollback plan.

Begin in a staging environment. Progress to production only after: - You have monitoring and alerting covering the experiment - You have a rollback mechanism tested and ready - You have agreed on an abort condition

Smallest possible first experiment: kill one replica of a stateless service with multiple replicas. Expected outcome: zero visible user impact.

- Pod/instance death — does your orchestrator restart it in time? - Network latency injection — add 500ms to a dependency; does your service degrade gracefully? - Dependency failure — take down a downstream service; does your circuit breaker open? - Resource exhaustion — fill disk to 90%; does your app handle this gracefully? - DNS failure — can your service survive a brief DNS resolution failure?

During every experiment, watch your AlertsDock monitors and your error rate metrics simultaneously. If any monitor goes red, abort the experiment immediately.

After each successful experiment, document what you proved works, and use that as your resilience baseline.

Once a quarter, run a team GameDay: simulate a real incident scenario (database failure, region outage, DDoS) and evaluate your detection time, response time, and resolution quality. This trains the human side of incident response.