Reliability Engineering: Scenario-Based Questions

Chaos engineering is the practice of intentionally introducing failures to test how systems respond under stress. It helps teams validate reliability, failover, and alerting in real-world scenarios before actual incidents occur.

🎯 Goals of Chaos Engineering

• Identify system weaknesses and single points of failure.
• Validate resilience mechanisms (e.g., retry logic, circuit breakers).
• Ensure incident response and alerting work as intended.

⚙️ Safe Execution Strategy

• Start in staging or shadow environments that mirror production.
• Define hypotheses before experiments: “If service X is unavailable, Y should recover.”
• Use gradual blast radius: inject faults in small, scoped services first.
• Roll back automatically on instability or metric thresholds breach.

🧪 Types of Chaos Experiments

• Service kill: terminate a pod, container, or EC2 instance.
• Latency injection: simulate network delays.
• Resource exhaustion: spike CPU, memory, or disk usage.
• Dependency failure: simulate DB, cache, or API unavailability.

🔧 Tools

• Gremlin: Enterprise-grade chaos platform with rich controls.
• Chaos Mesh: Kubernetes-native fault injection.
• LitmusChaos: Open source framework for cloud-native chaos experiments.
• Netflix’s Chaos Monkey: Terminates random instances in production to test fault tolerance.

✅ Best Practices

• Run chaos as part of regular testing or SRE rituals.
• Get stakeholder approval before production-level experiments.
• Establish rollback plans and observability coverage first.

🚫 Common Mistakes

• Running chaos in unstable environments with no guardrails.
• Skipping hypothesis definition and learning goals.
• Causing cascading failures due to poor blast radius control.

📌 Real-World Insight

Chaos engineering helps companies like Netflix, Slack, and LinkedIn harden their systems. The key is to test before failure happens, so your systems — and people — are ready when it does.