This is the kind of problem that looks straightforward until you start thinking about edge cases like duplicate transactions, partial matches, or timing windows.
Start by clarifying the data model and matching criteria, then propose an efficient algorithm using a hash map keyed on transaction identifiers. Discuss how to handle edge cases like missing or duplicate matches, and outline how this would integrate into Affirm's fraud detection pipeline.
Pro tip: Mention that in production, matching should be idempotent and support replay, and consider using a deterministic key like transaction ID plus timestamp to avoid false matches.
Ask about the structure of Fraud Events and transactions, what fields are available, and what constitutes a match (e.g., transaction ID, amount, timestamp). Confirm expected input size and performance requirements.
Propose using a hash map to index transactions by a unique key, then iterate through fraud events to find matches in O(n) time. Discuss alternative approaches like sorting and two-pointer if keys are not unique.
Address scenarios like missing transactions, duplicate fraud events, multiple matches, and out-of-order events. Decide on tie-breaking rules and how to log or flag unmatched events.
Explain how the function would fit into a larger system: batch vs. streaming, idempotency, retries, and how to scale with sharding or partitioning by transaction ID.
Outline unit tests for normal and edge cases, and discuss monitoring metrics like match rate and latency to ensure correctness in production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.