Start by clarifying the requirements and constraints (e.g., single-node vs distributed, accuracy vs performance, policy configurability) before proposing a design. Then present a clean interface and a concrete implementation (e.g., token bucket or sliding window) with clear trade-offs, and discuss how you'd test and scale it.
Pro tip: Explicitly call out the trade-off between strict accuracy and memory/performance, and mention that you'd start with a simple in-memory solution and only add distributed coordination (e.g., Redis) if needed—this shows you avoid over-engineering.
Ask about expected scale (QPS, number of keys), latency requirements, whether the limiter must be distributed, and what policies are needed (e.g., fixed window, sliding window, token bucket).
Specify the allow(key) method signature and how the policy (rate, burst, window) is configured, ensuring the API is simple and extensible.
Select a rate-limiting algorithm (e.g., token bucket for burstiness, sliding window log for accuracy) and implement it with appropriate data structures, considering thread safety.
Compare algorithms on accuracy, memory, and performance; explain how to handle distributed rate limiting (e.g., Redis with Lua scripts) and the consistency vs availability trade-off.
Describe unit tests for boundary conditions (exactly at limit, burst, key expiration) and how to handle cleanup of stale keys to prevent memory leaks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.