My first instinct was a fixed window and I almost went with it before catching myself.
Start by clarifying requirements and edge cases, then propose a data structure that efficiently tracks per-user request timestamps within the rolling window. Implement the allow method using a queue or deque per user, and analyze time and space complexity.
Pro tip: Use a deque (double-ended queue) per user to store timestamps, allowing O(1) amortized operations for adding new requests and removing expired ones. This is more efficient than scanning a list or using a heap.
Ask about concurrency, memory constraints, and whether timestamps are monotonically increasing. Confirm that the window is rolling (sliding) and that limits are per-user.
Select a hash map to store per-user deques of timestamps. Each deque holds timestamps of allowed requests within the current window.
For a given user and timestamp, remove timestamps from the front of the deque that are older than timestamp - windowSize. If the deque size is less than maxRequests, add the timestamp and return true; else return false.
Explain that each request is added and removed at most once, giving O(1) amortized time per operation. Space is O(number of users * maxRequests) in the worst case.
Mention potential improvements like using a token bucket for smoother limiting, handling distributed systems with Redis, or using a circular buffer for fixed memory.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.