The grouping part was fine, nested maps are pretty mechanical once you stop overthinking the key structure.
Clarify the requirements first: which dimensions and combinations, what top-K means (by sum of amount), and whether the stream is bounded or unbounded. Then propose a single-pass solution using hash maps for aggregates and a min-heap for top-K, discussing time/space complexity and trade-offs.
Pro tip: Mention that for unbounded streams, you'd need approximate algorithms like Count-Min Sketch or Space-Saving for top-K, and that you'd handle late data with windowing or watermarks. This shows you think beyond the basic coding problem.
Ask about the expected data volume, whether the stream is bounded, the exact dimensions and combinations needed, and the definition of top-K (e.g., by total amount). Confirm if the output should be updated continuously or at the end.
Use a dictionary mapping each dimension key (e.g., 'user_id', 'country', 'user_id+country') to a pair of count and sum. For top-K, maintain a min-heap of size K storing (total_amount, user_id) for each user.
For each record, update the aggregates for all relevant dimension keys and update the user's total amount in a separate map. Then adjust the top-K heap if the user's total exceeds the heap minimum.
Discuss time complexity O(N * D) where D is the number of dimension combinations, and space O(U + K) where U is the number of unique keys. Mention that for large cardinality, approximate methods may be needed.
Consider empty stream, ties in top-K, and late-arriving data. For unbounded streams, propose windowing or approximate algorithms like Count-Min Sketch for heavy hitters.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.