This one has apparently made the rounds on forums quite a bit, so I'd seen some version of it before.
Start by clarifying the invoice schema and matching criteria, then design a modular pipeline: parse, normalize, and match. Discuss trade-offs between batch and streaming, and how to handle edge cases like missing fields or duplicate invoices.
Pro tip: Proactively mention idempotency and data consistency—Stripe values reliability, so showing you think about retries and duplicate payments sets you apart.
Ask about invoice format (JSON, CSV, API), required fields, matching rules (exact, fuzzy, date ranges), and expected scale. Confirm if matching is one-to-one or many-to-many.
Define a normalized invoice object with fields like id, amount, currency, date, customer, and status. Consider using a hash map or index for efficient lookups.
Write a parser that extracts fields and validates types (e.g., amount as decimal, date as ISO). Handle malformed records gracefully with error logging.
Apply criteria using a two-pointer or hash-based approach. For multiple parts, build incrementally: first exact match, then fuzzy match, then aggregate.
Write unit tests for edge cases (missing fields, duplicates, currency mismatch). Discuss time/space complexity and potential optimizations like indexing or parallel processing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.