My first instinct was a fixed window and I had to catch myself before saying it out loud.
Clarify requirements and constraints, then propose a solution using a hash map from user ID to a deque of timestamps. Explain the sliding window logic: on each request, remove timestamps older than the current time minus W, check if the deque size is less than N, and if so, add the current timestamp and allow the request.
Pro tip: Discuss the trade-offs between different implementations (e.g., deque vs. circular buffer vs. counter with buckets) and mention how you would handle concurrency and memory management in a production system.
Ask about assumptions: Is the timestamp guaranteed to be monotonically increasing? What should happen if the same timestamp is used multiple times? Are there memory constraints? Should the solution be thread-safe?
Select a hash map to map user IDs to their request history. For each user, use a deque (double-ended queue) to store timestamps of recent requests, allowing efficient addition and removal from both ends.
For a given user and timestamp, remove timestamps from the front of the deque that are <= timestamp - W. Then check if the deque size is less than N. If yes, add the timestamp to the back and return true; otherwise, return false.
Explain that each request is processed in amortized O(1) time because each timestamp is added and removed at most once. Discuss potential optimizations like using a circular buffer or bucketed counters to reduce memory overhead.
Consider edge cases: empty user history, exactly N requests, timestamps out of order, and memory cleanup for inactive users. Mention how to extend to distributed systems 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.
Structure the rate limiter as two independent limiters (per-user and per-game) that must both pass, using a composite key approach or separate data structures. Discuss the complexity of checking both limits and the eviction strategies for each dimension, highlighting trade-offs between memory, accuracy, and performance.
Pro tip: Emphasize that the limits are independent, so a request is allowed only if both pass; consider using a single data structure with composite keys to reduce overhead, but be aware of the trade-off in eviction granularity.
Confirm the rate limiting algorithm (e.g., token bucket, sliding window), the time window, and whether limits are per second/minute. Ask if the limits are hard or soft, and if distributed rate limiting is needed.
Propose separate data structures for each limit (e.g., hash maps keyed by user ID and game ID) or a composite key approach. Discuss how to store counters and timestamps efficiently.
For each request, check both limiters independently; if either fails, reject. Update both counters atomically if allowed. Consider concurrency and atomicity in a multi-threaded environment.
Time complexity is O(1) per check with hash maps. Space complexity is O(U + G) for separate structures, where U is active users and G is active games. Composite key approach uses O(U*G) worst-case but often less.
For per-user, evict least recently used (LRU) or use TTL. For per-game, similar. Trade-offs: separate structures allow independent eviction but double memory; composite keys reduce memory but eviction is coarser and may evict active users/games prematurely.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.