Start by clarifying requirements: scale, latency, accuracy vs. availability, and multi-region needs. Then propose a distributed architecture using a fast in-memory store like Redis with atomic operations (e.g., Lua scripts) for per-key counters, and discuss trade-offs between algorithms (token bucket vs. sliding window) and consistency models. Finally, cover failure modes, observability, and dynamic configuration.
Pro tip: Emphasize that rate limiting is a trade-off between accuracy and availability—sometimes it's acceptable to over-limit or under-limit during failures. Also, mention that returning 429 with Retry-After and rate limit headers improves client experience and reduces retries.
Ask about scale (QPS, number of keys), latency targets, accuracy requirements, multi-region deployment, and whether limits are per endpoint, user tier, etc. This scopes the design.
Select an algorithm like token bucket (allows bursts) or sliding window (smoother) based on requirements. Discuss pros and cons, and how to implement it atomically in a distributed store.
Use a centralized store like Redis with Lua scripts for atomicity, or a distributed approach like gossip with local counters. Address consistency, replication, and failure handling.
Define response behavior: 429 status, Retry-After header, and rate limit headers. Explain how to dynamically update limits per endpoint or customer tier without redeploying.
Discuss sharding, caching, fallback strategies (e.g., local rate limiting if Redis is down), and monitoring metrics like limit hits and latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.