The sliding window boundary being exclusive on both ends tripped me up at first.
Use a sliding window with a hash map from IP to a deque of timestamps, and for each request, evict timestamps older than T seconds, then check if the deque size exceeds the cap. If it does, count the request as blocked but still add its timestamp to the deque, since blocked requests count toward future windows. Process the log in chronological order to maintain correctness.
Pro tip: Clarify whether the log is strictly sorted and whether timestamps are unique; if not, sort or handle ties carefully. Also, mention that the deque approach gives O(1) amortized time per request and O(N) space, which is optimal for this problem.
Ask about the definition of 'previous T seconds' (inclusive/exclusive), whether the log is sorted, and if blocked requests should be counted in the window. Confirm the cap is per-IP and that the window slides with each request's timestamp.
Use a hash map to store per-IP deques of timestamps. The deque allows O(1) append and popleft for efficient sliding window maintenance.
For each request, get the deque for its IP, remove timestamps older than (current_time - T), then check if the deque size is >= cap. If so, increment blocked count; regardless, append the current timestamp to the deque.
After processing all requests, return the accumulated count of blocked requests.
Discuss time complexity O(N) and space O(N) in the worst case. Mention edge cases: multiple IPs, empty log, T=0, cap=0, and requests exactly at the boundary.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints, then propose a data structure that tracks both per-IP and per-(IP, host) counts, ensuring both are updated atomically. Explain how to evaluate both rules and combine their outcomes into a single blocked decision, and discuss trade-offs around concurrency, storage, and performance.
Pro tip: Mention that you would use a composite key like 'IP:host' for the per-(IP, host) counter, and emphasize the importance of atomic updates to avoid race conditions in a distributed environment.
Ask about the rate limiting algorithm (e.g., sliding window, token bucket), the expected scale, and whether the solution needs to be distributed. Confirm that both counters must be updated even if the request is blocked.
Propose using a hash map for per-IP counts and another for per-(IP, host) counts, with composite keys. Discuss using a time-based eviction strategy to prevent unbounded growth.
Outline the algorithm: increment both counters, then check if either exceeds its limit. If either does, block the request, but ensure the block is counted only once.
Explain how to handle concurrent requests, such as using locks, atomic operations, or a distributed store like Redis with Lua scripts to ensure both counters are updated atomically.
Talk about memory usage, latency, and scalability. Consider sharding, approximate counting, or separate services for rate limiting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.