API Resilience & Performance: Scenario-Based Questions

Resilient APIs maintain functionality during failures, load spikes, or dependency issues. Rate limiting, timeouts, and graceful degradation are essential for reliability and client experience under stress.

🛡️ Rate Limiting Strategies

• Fixed Window: Limit X requests per minute — simple but burst-prone.
• Sliding Window: Smoother enforcement using rolling time intervals.
• Token Bucket: Allows bursty traffic within a refill rate.
• Leaky Bucket: Ensures consistent outflow regardless of spikes.
• Use API gateways (e.g., Kong, AWS API Gateway, Envoy) to enforce limits per IP/user/key.

⚙️ Graceful Degradation Techniques

• Circuit Breakers: Automatically disable failing services and prevent cascading failures.
• Fallbacks: Provide cached/stale responses or alternative services when primary fails.
• Timeouts: Limit max response time to prevent client hangs and wasted threads.
• Partial Responses: Return available data when full responses aren’t possible (e.g., missing a third-party dependency).

🧰 Tools and Libraries

• Resilience4j / Hystrix: Java-based fault-tolerance tools.
• Envoy, Istio: Service mesh with built-in retry, rate-limit, and circuit breaker support.
• API Gateway: AWS, GCP, and Azure all support per-method rate limits.

✅ Best Practices

• Expose HTTP headers for rate limit info (X-RateLimit-Remaining).
• Use retry with exponential backoff for transient failures.
• Log degraded behavior for analysis without alerting immediately.
• Test limits and fallback logic under load in staging.

🚫 Common Pitfalls

• Returning HTTP 500s for client-side throttling violations (should be 429).
• Retrying failed API calls without backoff — increases load under failure.
• Ignoring latency budgets in composite API calls.

📌 Real-World Insight

Resilient APIs treat failure as a feature — not a surprise. By combining smart throttling with fallback logic, they avoid downtime and deliver consistent performance even when dependencies falter.