kienbui1995/chaos-engineering

Chaos Engineering

When to Use

When proactively testing system resilience by injecting controlled failures before they happen in production.

Core Jobs

1. Define the Steady State

Before injecting chaos, define what "normal" looks like:

• Key metrics: error rate, latency, throughput, business KPI
• These must remain within bounds during the experiment
• If they leave bounds: experiment failed → learn why

2. Hypothesize

"We believe the system can tolerate [failure] because [reason]." Examples:

• "We believe losing one DB replica won't affect P99 latency because we have read replicas with failover."
• "We believe a 500ms network delay between service A and B won't cause cascading failures because we have timeouts."

3. Design the Experiment

• Scope: start small (one instance, not all)
• Blast radius: what's the worst case if wrong?
• Duration: 5–30 minutes (enough to see impact)
• Rollback: how do you stop the experiment instantly?
• Approval: who needs to know before you run this?

Common experiments:

• Kill random pod/instance
• Inject network latency (50ms, 200ms, 1s)
• Fill disk to 95%
• CPU stress test (one node at 90%)
• DNS failure for dependency
• Terminate DB connection pool

4. Run and Observe

• Run during business hours (not 3am — you need to react)
• Watch the steady state metrics in real-time
• Stop immediately if unexpected impact
• Document what happened

5. Learn and Improve

• Did hypothesis hold? If yes: expand scope
• If no: fix the gap, don't run same experiment until fixed

Key Outputs

• Chaos experiment plan (hypothesis + scope + rollback)
• Steady state dashboard
• Experiment results report
• Remediation items

Anti-Patterns

• Running chaos in production before running in staging
• No rollback plan
• Chaos without observability (can't see what's happening)
• Running multiple experiments simultaneously