My first instinct was to just loop through everything and compare field by field, which works but I spent too long second-guessing whether they wanted something more clever.
Clarify the problem constraints first, then propose an efficient solution using hash maps to index merchants by each field value. Discuss trade-offs between preprocessing and on-the-fly computation, and handle edge cases like missing fields or duplicate matches.
Pro tip: Mention that email and phone should be normalized (e.g., lowercase, strip non-digits) to avoid false negatives, and consider using a union-find structure if the problem extends to transitive connections.
Ask about data size, whether fields can be empty, if normalization is needed, and if the result should be deduplicated. Confirm that 'share at least one field value' means exact match after normalization.
Propose using hash maps to index merchants by each field value (id, name, email, phone). This allows O(1) lookups per field for the target merchant.
Build the indexes by iterating through the list once. Then, for the target merchant, retrieve all merchants from each index and union the results, ensuring no duplicates.
Discuss time and space complexity: O(N) preprocessing and O(k) query time, where k is the number of matches. Compare with brute-force O(N) per query and mention when each is appropriate.
Address missing fields, normalization, and potential transitive matches. Suggest extensions like union-find for connected components if the problem evolves.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.