chaos-engineering

Inject controlled failures into Kubernetes clusters by targeting microservices with instance termination, latency injection, or network partitioning. This validates resilience and uncovers hidden failure modes using defined blast radius and abort conditions.

Chaos engineering discovers hidden weaknesses in distributed systems by intentionally injecting controlled failures. You need failure injection to prove systems remain available and recover automatically under real-world fault conditions.

Safely run chaos experiments by defining blast radius, opting in specific targets, and scheduling during business hours with blackout dates. Opt-in safety controls minimize customer impact while ensuring reproducible testing.

Validate service failover by injecting cross-AZ network partitions to test if microservices fail over to replicas within acceptable latency and error-rate bounds. Capture baseline metrics and abort conditions to guide remediation.

Chaos engineering works with observability by capturing baseline metrics, defining abort conditions, and producing experiment reports. This observability-driven analysis guides remediation and design improvements for distributed systems.

Limitations include needing strict blast radius control and blackout dates to prevent widespread customer impact. Experiments require opt-in targets and defined abort conditions to safely terminate if metrics exceed acceptable bounds.