Started with a queue of timestamps, which works fine but I fumbled explaining the space complexity when hits pile up.
Start by clarifying requirements: hits are recorded at given timestamps, and getHits returns the count of hits in the last 300 seconds (inclusive of current timestamp). Then propose a solution using a queue or circular buffer to store timestamps, removing outdated hits on each call. Discuss time and space complexity, and consider optimizations for high throughput.
Pro tip: Mention that timestamps are monotonically increasing (a common assumption in such problems) and that you can use a circular array of size 300 for O(1) operations if timestamps are in seconds. This shows you think about practical constraints and efficiency.
Ask about timestamp granularity (seconds vs milliseconds), whether timestamps are monotonically increasing, and if multiple hits can occur at the same timestamp. Confirm the definition of 'last 300 seconds' (e.g., inclusive of current timestamp).
Select a queue (or deque) to store hit timestamps in chronological order, or a circular buffer if timestamps are in seconds and range is fixed. Explain why this structure supports efficient removal of outdated hits.
For hit(timestamp), append the timestamp to the queue. For getHits(timestamp), remove all timestamps from the front that are <= timestamp - 300, then return the queue size. Ensure operations are O(1) amortized.
Discuss time complexity: O(1) amortized per operation. Space complexity: O(number of hits in last 300 seconds). Compare with alternative approaches like using a hash map of counts per second, which uses O(300) space but may be less flexible.
Address high concurrency (e.g., using locks or thread-safe structures), memory usage under high hit rates, and edge cases like empty queue or timestamps far apart. Mention potential optimizations for distributed systems if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.