I went straight to a queue-per-key approach, storing timestamps and evicting anything outside the window on each call.
Start by clarifying requirements (e.g., exact vs. approximate sliding window, memory constraints, concurrency). Then describe a data structure like a deque per key to store timestamps, and explain the allow algorithm: remove expired timestamps, check count, and add new timestamp if allowed. Finally, discuss trade-offs (memory vs. accuracy) and potential optimizations like bucketing or Redis for distributed systems.
Pro tip: Mention that you'd use a lock per key to handle concurrency, and consider using a ring buffer or circular array to bound memory usage. Also, proactively discuss how you'd handle clock skew and timestamp precision.
Ask about expected throughput, memory limits, whether the window is exact or approximate, and if the system is single-threaded or concurrent.
Propose a hash map from key to a deque (or queue) of timestamps. Explain that the deque stores timestamps of allowed requests in the current window.
For a given key and timestamp, remove timestamps older than timestamp - W from the deque. If the deque size is less than N, add the timestamp and return true; else return false.
Discuss using locks (e.g., per-key mutex) to ensure thread safety. Mention memory cleanup for inactive keys and bounded memory via circular buffers.
Compare exact sliding window with approximate methods (e.g., sliding window counters). Mention distributed rate limiting using Redis sorted sets or similar.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.