chaos-testing

Chaos testing validates distributed system resilience by injecting controlled faults into dependencies to uncover weaknesses. It defines steady-state metrics and hypotheses to confirm whether a system maintains normal behavior when components fail.

To run a chaos experiment, define a runbook containing the steady-state, hypothesis, and blast radius. Then execute targeted failure scenarios using open-source chaos tools within a production-like staging environment to observe system metrics.

Yes, this supports planning GameDay events by defining steady-state, hypotheses, tooling, and runbooks for fault injection. It helps teams uncover unknown failure modes in production-like staging environments during simulated disaster scenarios.

The best way to define the blast radius for a chaos experiment is to scope it within a runbook alongside steady-state metrics and hypotheses. This ensures injected faults remain controlled and limited to staging or specific system boundaries.

A production-like staging environment is recommended for fault injection to safely uncover failure modes without affecting live users. Chaos testing uses this controlled setting to validate resilience through targeted failure scenarios and observed metrics.

Distributed systems fail during chaos testing when injected faults expose hidden weaknesses in dependency connections or failure handling. Defining steady-state metrics beforehand helps identify exactly which component caused the system to deviate from normal behavior.