The two edge cases are where people trip up.
Clarify the requirements and edge cases, then design a sliding-window rate limiter using a deque per IP to track request timestamps. Process the CSV log sequentially, updating each IP's window and counting blocked requests, and finally return the total count.
Pro tip: Use a monotonic queue (deque) for O(1) amortized operations and explicitly state that blocked requests are still added to the window. Also, discuss how you would handle large datasets or distributed scenarios to show system design awareness.
Confirm the exact window semantics (inclusive/exclusive), whether timestamps are sorted, and how to handle multiple IPs. Ask about the expected scale and if the solution should be memory-efficient.
Choose a sliding window approach using a hash map from IP to a deque of timestamps. Explain that the deque maintains timestamps within the last 60 seconds, and we evict old entries as we process each request.
Iterate through each request in the CSV, parse the IP and timestamp, and for each IP, remove timestamps older than 60 seconds from the deque. If the deque size is >= 50, increment the blocked count; otherwise, allow the request. In both cases, add the current timestamp to the deque.
After processing all requests, return the total number of blocked requests. Optionally, discuss how to handle ties or simultaneous requests.
State that the time complexity is O(N) where N is the number of requests, and space is O(M * W) where M is the number of IPs and W is the window size. Mention potential optimizations like using a circular buffer or approximate counting for large-scale systems.
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