System Reliability: Scenario-Based Questions

Distributed systems are prone to partial failures, timeouts, and unpredictable latency. Designing for resilience means planning for failure and recovery — not avoiding it.

🛡️ Core Resilience Patterns

• Retries with Backoff: Retry failed calls with exponential delay
• Circuit Breakers: Stop sending traffic to broken services
• Bulkheads: Isolate resources (e.g., thread pools, containers) to limit blast radius
• Timeouts: Prevent hanging dependencies from stalling upstream systems

📦 System-Level Strategies

• Redundancy: Multi-zone or multi-region replicas
• Health Checks & Auto-Healing: Replace unhealthy instances
• Failover Routing: Route traffic to backup services

🧪 Test & Validation

• Inject failure via chaos engineering tools
• Define SLAs and SLOs with alerting on error budgets
• Run game days simulating cascading failures

✅ Best Practices

• Use idempotent APIs where retries are involved
• Track latency percentiles, not just averages
• Use observability tools to understand propagation of failures

🚫 Common Pitfalls

• Retry storms — retries amplifying traffic and causing overload
• No timeouts — callers hang indefinitely on bad dependencies
• Monolithic retry logic — hard to tune or observe per service

📌 Final Insight

Resilience is an architecture choice. By combining failure isolation, retries, circuit breakers, and observability, you build systems that degrade gracefully and recover quickly.