The core implementation isn't too bad once you realize you just need a running total per user updated on each transfer.
Clarify the data model and constraints first, then propose an efficient aggregation approach using a hash map to sum outgoing transfers per user, followed by sorting with a custom comparator for descending amount and ascending user ID. Discuss trade-offs between precomputation and on-demand computation, and analyze time/space complexity.
Pro tip: Mention that you would precompute and maintain a sorted structure or a min-heap for frequent topSpenders calls, and discuss how to handle ties and large n gracefully.
Ask about data scale, frequency of topSpenders calls, and whether transfers are immutable or can be updated. Confirm that only outgoing transfers count and that ties are broken by user ID ascending.
Propose iterating through all transfers to build a hash map mapping user ID to total outgoing amount. Discuss handling of users with no outgoing transfers (they should not appear).
Sort the aggregated list by total descending and user ID ascending, then take the first n. Alternatively, use a min-heap of size n for better efficiency when n is small.
Compare on-demand computation (O(T + U log U) per call) versus maintaining a sorted structure or heap with updates (O(log U) per transfer). Discuss space-time trade-offs and suitability for different call patterns.
Consider n larger than number of users, negative amounts (if allowed), and concurrent updates. Mention potential optimizations like caching or incremental updates.
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