The first part of this problem (just merchant ID and amount) came in an earlier round, so the timestamp rule was the new wrinkle here.
Clarify the input format and constraints, then propose an efficient algorithm that filters transactions by merchant ID and amount, checks the timestamp window, and selects the closest match. Discuss trade-offs between sorting, hashing, and early termination, and handle edge cases like multiple matches and null returns.
Pro tip: Mention that in a real payment system, you'd likely index transactions by merchant ID and amount to avoid scanning all transactions, and consider using a balanced BST or sorted list for efficient closest-match queries.
Ask about input sizes, whether transactions are sorted, if multiple matches are possible, and the definition of 'closest in time' (absolute difference). Confirm that W is inclusive and that timestamps are comparable.
Filter transactions where merchant ID and amount match the payment, and the absolute timestamp difference is ≤ W. Then select the transaction with the minimum absolute time difference.
If transactions are unsorted, a linear scan is O(n). For repeated queries, consider indexing by (merchant ID, amount) and using binary search on sorted timestamps to find the closest match in O(log n).
Return null if no matches. If multiple matches have the same minimal difference, define a tie-breaker (e.g., earlier timestamp). Ensure the window check uses absolute difference and handles boundary conditions.
Walk through examples: exact match, match at window boundary, no match, multiple matches. Discuss time and space complexity and potential improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.