The part that tripped me up was the double-counting rule.
Start by clarifying the requirements and edge cases, then design a data structure that efficiently tracks request counts per IP and per (IP, hostname) within the sliding window. Implement the solution with careful attention to how blocked requests are counted and how the window slides, and finally test with sample data to verify correctness.
Pro tip: Mention that you would use a deque or circular buffer for each key to store timestamps, which allows O(1) amortized operations per request and avoids scanning the entire window. Also, discuss the trade-off between memory usage and precision when choosing the time granularity.
Ask about the time window size, whether it's fixed or configurable, how timestamps are formatted, and whether requests are processed in chronological order. Clarify if blocked requests should be counted in the total and if they affect future windows.
Choose a sliding window approach using deques for each IP and each (IP, hostname) pair to store timestamps of requests. Consider using a hash map to map keys to their deques, and a global counter for blocked requests.
For each request, remove timestamps older than the window from the relevant deques, then check if adding the current request would exceed either limit. If either limit is exceeded, increment the blocked counter; otherwise, add the timestamp to both deques.
Ensure that blocked requests are still recorded in the deques (or at least counted) so they affect future rate-limit decisions. This means even if a request is blocked, its timestamp should be added to the deques for the IP and (IP, hostname) to count toward future windows.
Walk through a small example manually or with code to verify that the sliding window logic works, especially at window boundaries. Discuss potential optimizations and trade-offs, such as memory usage vs. accuracy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.