I went straight to regex and the interviewer seemed fine with that.
Start by clarifying the input formats and expected output, then outline a robust parsing strategy using regex to extract the invoice ID from the memo field. Discuss how to handle edge cases such as whitespace, empty memos, and malformed strings, and finally describe the lookup and result handling.
Pro tip: Mention that you would use a compiled regex pattern for efficiency and consider using a dictionary for O(1) invoice lookups, especially if the invoice list is large. Also, discuss the importance of logging or returning detailed error messages for debugging.
Ask about the exact format of the memo field, the structure of invoice and payment objects, and the expected output for not-found cases. Confirm whether multiple matches are possible and how to handle them.
Use a regular expression to extract the invoice ID from the memo, accounting for optional whitespace and case sensitivity. For example, r'Paying off:\s*(\S+)' to capture the ID.
Check for empty or None memos, malformed strings that don't match the pattern, and extra whitespace. Decide whether to return None, raise an exception, or return a custom not-found object.
Convert the invoice list into a dictionary keyed by invoice ID for O(1) lookup. If the list is small, a linear search may suffice, but mention the trade-off.
For each payment, return the matched invoice or a clear not-found indicator (e.g., None or a message). Consider returning a list of results corresponding to each payment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The fallback logic itself wasn't bad, sorting by due_date is straightforward.
First, clarify the matching logic and data structures, then outline a two-tier approach: primary prefix match, fallback to amount match with earliest due date tiebreaker. Finally, analyze time and space complexity, justifying choices and discussing trade-offs.
Pro tip: Demonstrate awareness of real-world data issues: mention that amounts might have floating-point precision issues and suggest using exact decimal comparison or a tolerance. Also, discuss how to handle multiple invoices with the same amount and due date (e.g., pick any or by invoice ID).
Restate the problem: primary matching by standardized prefix in memo; if absent, fallback to matching by payment amount; if multiple invoices share the amount, select the one with earliest due date; if none, return error. Ask clarifying questions about data types, error handling, and tie-breaking.
Propose using a hash map (dictionary) to index invoices by prefix for O(1) lookup. For fallback, build a map from amount to a list of invoices sorted by due date, or use a min-heap per amount. Outline the steps: check memo for prefix, if found return match; else extract amount, look up in amount map, pick earliest due date; else return error.
Preprocessing: building prefix map O(N) time, O(N) space; building amount map with sorting O(N log N) time, O(N) space. Query: O(1) for prefix match, O(1) for amount lookup if using sorted list and picking first, or O(log k) if using heap. Overall O(N log N) preprocessing, O(1) query. Justify why this is efficient.
Address edge cases: no prefix, no amount match, multiple matches with same amount and due date, floating-point precision, large datasets. Discuss trade-offs: sorting upfront vs. linear scan per query; memory vs. speed; handling dynamic updates.
Recap the solution, emphasizing correctness and efficiency. Mention potential optimizations or alternative approaches (e.g., using a database with indexes) and how you would test the solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.