The problem reads straightforward until you realize you need to handle skills that never appear in the logs at all.
Clarify the problem constraints (e.g., number of skills, number of queries, time range) and then design an efficient solution. Use a sliding window or binary search over sorted timestamps to count distinct skills with at least one request in each interval, then subtract from the total number of skills to get zero-request counts.
Pro tip: Mention that you would preprocess the request logs by grouping timestamps per skill and sorting them, enabling O(log n) per query. Also discuss trade-offs between offline sorting and online processing, and handle edge cases like empty intervals or skills with no requests.
Ask about the range of skill IDs, number of requests, number of queries, time window size, and whether timestamps are sorted. Confirm the definition of 'zero requests' (no request for that skill in the interval).
Group request timestamps by skill ID into sorted lists. Determine the total number of unique skills. Consider using a sliding window over time if queries are sorted, or binary search for each query.
For each query, compute the interval [queryTime - timeWindow, queryTime]. Count how many distinct skills have at least one timestamp in that interval. Subtract from total skills to get zero-request count.
If many queries, sort them and use a sliding window with a frequency map to maintain the count of active skills, updating as the window moves. Alternatively, use binary search per skill per query if queries are few.
Discuss time and space complexity. Handle edge cases: empty request list, skills with no requests, queries before any request, and overlapping intervals.
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