API Gateway & Rate Limiter

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design an API gateway that handles routing, authentication, rate limiting, and request transformation for 50 microservices.
• •Design for a peak load target around 80,000 RPS (including burst headroom).
• •Backend microservices: ~50
• •Peak requests/second: ~100,000
• •Gateway latency overhead: < 10 ms
• •Active API keys: ~50,000
• •Rate limit accuracy: > 99%

2) Capacity Planning Method

• •Convert traffic and growth constraints into request rate, storage growth, and concurrency budgets.
• •Keep at least 2-3x safety margin per tier (ingress, compute, storage, async workers).
• •Reserve explicit latency budgets per hop so p95 can be defended in review.

3) Architecture Decisions

• •API Design: Standardize API boundaries, idempotency keys, pagination, and error contracts first.
• •Rate Limiting: Enforce token/sliding-window limits at ingress and for sensitive internal APIs.
• •Caching: Put cache on hot read paths first and pick cache-aside or write-through explicitly.
• •Auth: Centralize identity verification and keep authorization checks close to domain resources.
• •Monitoring: Instrument golden signals (latency, traffic, errors, saturation) per tier and per tenant/domain.

4) Reliability and Failure Strategy

• •Apply strict input validation and backward-compatible versioning.
• •Return deterministic 429 behavior with clear retry headers.
• •Bound staleness with TTL + invalidation hooks for critical entities.
• •Use short-lived tokens and secure key rotation workflows.
• •Alert on user-impact SLOs, not only infrastructure metrics.

5) Validation Plan

• •Run one peak-load test, one dependency-degradation test, and one failover test.
• •Verify idempotency for all retried writes and async consumers.
• •Track user-facing SLOs first: p95 latency, error rate, and successful throughput.

6) Trade-offs to Call Out in Interviews

• •API Design: Rich APIs improve developer speed but can create long-term compatibility burden.
• •Rate Limiting: Aggressive limits protect the system but can hurt legitimate burst traffic.
• •Caching: Higher hit rate cuts latency/cost, but stale data and invalidation bugs become primary risks.
• •Auth: Central auth simplifies policy, but makes auth service availability/security critical.
• •Monitoring: Deep observability speeds incident response but raises ingestion and tooling costs.

Practical Notes

• •Rate limiting: use Redis with a Lua script for atomic increment-and-check. Sliding window log or token bucket gives smoother rate limiting than fixed windows.
• •The gateway itself must be stateless and horizontally scalable - run multiple instances behind a L4 load balancer.
• •Circuit breaker: track error rates per backend in-memory. After tripping, periodically allow a single request through to check recovery (half-open state).

Why This Solution Works

Request path: The solution keeps ingress, service logic, and stateful dependencies separated so each layer can scale independently.

Reference flow: Web Clients -> Load Balancer -> API Gateway -> Rate Limiter -> API Service -> Auth Service -> Redis Cache -> Monitoring

Design strengths

• •Cache sits on the read path to absorb repeated queries and keep DB pressure stable.
• •Monitoring and logs are wired in from day one for rapid incident triage.
• •Security controls are enforced at ingress to protect downstream capacity.

Interview defense

• •This design makes bottlenecks explicit (ingress, core compute, persistence, async workers).
• •It supports progressive scaling without re-architecting the core request path.
• •It keeps correctness-sensitive state changes in durable systems while offloading background work asynchronously.