What Is Chaos Engineering? Break Your System to Make It Stronger

How resilient is your system to unexpected failures? What happens if a server crashes? If the network slows down? Chaos Engineering creates controlled chaos in production to discover weaknesses before real failures occur.

What Is Chaos Engineering?

Chaos Engineering is the discipline of experimenting on distributed systems to build confidence in their ability to withstand turbulent conditions. Popularized by Netflix, its goal is to find problems before real outages do.

Core Principles

1. Define steady state — Describe normal behavior with measurable metrics
2. Simulate real-world events — Server crashes, network issues, disk full
3. Run in production — Staging doesn't fully represent reality
4. Minimize blast radius — Keep experiments small and controlled

The Chaos Process

1. Define steady state (CPU < 70%, latency < 200ms, error rate < 1%)
2. Form hypothesis ("If a pod crashes, traffic reroutes to others")
3. Design experiment (Randomly kill a pod)
4. Run and observe
5. Analyze results
6. Fix weaknesses
7. Repeat

Common Chaos Experiments

1. Instance Termination

kubectl delete pod $(kubectl get pods -l app=api -o name | shuf -n 1)

2. Network Latency

tc qdisc add dev eth0 root netem delay 200ms 50ms

3. CPU/Memory Stress

stress --cpu 4 --timeout 60s

4. Dependency Failure

Simulate a third-party service not responding.

5. DNS Failure

Simulate DNS resolution errors.

Netflix's Chaos Tools

| Tool | Purpose | |------|---------| | Chaos Monkey | Randomly kill instances | | Latency Monkey | Add network latency | | Chaos Kong | Disable an entire region |

Chaos Engineering Tools

| Tool | Platform | Highlights | |------|----------|-----------| | Litmus | Kubernetes | Open source, ChaosHub | | Chaos Monkey | AWS | Netflix classic | | Gremlin | Multi-platform | SaaS, enterprise | | Chaos Mesh | Kubernetes | CNCF, powerful UI | | Toxiproxy | Any | Network proxy simulation |

Game Day Guide

1. Prepare — Plan hypotheses and experiments in advance
2. Monitor — Set up dashboards before starting
3. Execute — Run experiments sequentially
4. Observe — Record system behavior
5. Retrospective — Share findings and define action items

Best Practices

1. Start small — Begin in staging, then move to production
2. Abort mechanism — Stop immediately if things go wrong
3. Observability required — Don't run chaos without monitoring
4. Inform the team — Surprise chaos becomes real chaos
5. Automate — Integrate experiments into CI/CD pipelines
6. Document findings — Record results from every experiment

Conclusion

Chaos Engineering replaces "I hope it works" with "we tested it, it works." Finding weaknesses through controlled experiments is far cheaper than waiting for real outages.

Learn resilience and chaos engineering on LabLudus.