My first instinct was to just iterate and accumulate, but I wasted a couple minutes trying to figure out whether to group by merchant first or provider first.
Clarify the problem requirements and edge cases, then design a streaming solution using hash maps to aggregate fees per merchant and revenue per provider. Discuss time/space complexity and potential optimizations for large-scale streams.
Pro tip: Mention that fees should be computed in the smallest currency unit (e.g., cents) to avoid floating-point precision issues, and consider how to handle late-arriving transactions in a streaming context.
Ask about input format, whether the stream is bounded or unbounded, and if there are constraints on memory or latency. Confirm the fee calculation formula and output format.
Use two hash maps: one mapping merchant ID to total fees, and another mapping provider ID to total revenue. Also store provider fee rules in a map for quick lookup.
For each transaction, look up the provider's fee rule, compute the fee (amount * percentage + flat fee), then update the merchant's total fees and the provider's total revenue.
Consider missing fee rules, negative amounts (refunds), and currency rounding. Decide whether to use integer arithmetic or decimal types.
State that time complexity is O(n) for n transactions, and space complexity is O(m + p) where m is number of merchants and p is number of providers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.