My first instinct was to reach for a hashmap of queues, one queue per client storing timestamps, and just pop off anything older than 300 seconds on each call.
Clarify requirements (per-client tracking, rolling 5-minute window, O(1) amortized operations) and then design a solution using a hash map from client ID to a queue of timestamps. For hit, append the timestamp and evict expired entries; for getHits, evict expired entries and return the queue size. Discuss trade-offs like memory usage and potential optimizations.
Pro tip: Mention that the queue can be implemented as a circular buffer or a deque to achieve O(1) amortized operations, and highlight that space is proportional to the number of hits in the last 5 minutes, which is optimal.
Confirm that the rate limiter is per-client, uses a rolling 5-minute window, and that hit and getHits must be O(1) amortized. Ask about expected scale (number of clients, hit rate) and whether timestamps are monotonically increasing.
Use a hash map to map clientId to a queue (e.g., deque) of timestamps. The queue stores only timestamps within the last 5 minutes. This gives O(1) amortized insertion and deletion.
For hit: append timestamp to the client's queue, then remove timestamps older than timestamp - 300 seconds. For getHits: perform the same eviction and return the queue size. Both operations are O(1) amortized because each timestamp is added and removed at most once.
Time: O(1) amortized per operation. Space: O(total recent hits) across all clients. Discuss potential memory issues with many clients and possible optimizations like lazy eviction or using a sliding window counter with buckets.
Handle out-of-order timestamps (if not monotonic, use a different structure like a sorted list or bucket approach). Discuss thread safety, persistence, and distributed scenarios if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.