This is the kind of question where you think you know it until you're actually in it.
Start by clarifying requirements: scale, accuracy, latency, and whether rate limiting is per user, IP, or API key. Then propose a distributed architecture using a centralized store like Redis with atomic operations, and discuss trade-offs between different algorithms and consistency models.
Pro tip: Emphasize that rate limiting is often about protecting backend services and ensuring fair usage; mention that you would monitor and adjust limits dynamically based on traffic patterns and business needs.
Ask about scale (requests per second, number of users), accuracy needs, latency constraints, and whether the system should be highly available or can tolerate slight inaccuracies.
Select an algorithm like token bucket, leaky bucket, fixed window, or sliding window, and justify based on requirements (e.g., token bucket for burst handling).
Propose using a centralized data store (e.g., Redis) with atomic operations (Lua scripts) to maintain counters across multiple servers, and discuss sharding or replication for scalability.
Discuss trade-offs between strong and eventual consistency, and how to handle failures (e.g., fallback to local rate limiting or fail-open/fail-closed).
Suggest optimizations like local caching, hierarchical rate limiting, and monitoring to adjust limits dynamically.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.