I started with a plain dict and felt good about it for about three minutes.
Start by clarifying requirements and edge cases, then propose a hash map for O(1) set/get and a sorting mechanism that preserves insertion order for ties. Implement get with optional sort_by, using a stable sort on the specified field, and discuss trade-offs between sorting on read vs. maintaining sorted indices.
Pro tip: Mention that Python's sorted() is stable, so you can sort by the key and rely on insertion order for ties—this avoids extra tie-breaking logic. Also, consider caching sorted results if get with the same sort_by is frequent.
Ask about expected data types, whether sort_by can be multiple fields, and how to handle missing keys (return None or raise exception). Confirm that overwrites should update the value and possibly the insertion order.
Use a hash map (dict) for O(1) set and get by key. For sorting, either sort on read or maintain a secondary index; discuss trade-offs.
For set, update the dict and track insertion order (e.g., using a counter or OrderedDict). For get with sort_by, retrieve all values, sort by the given field using a stable sort, and return the sorted list.
Ensure missing keys return a default or raise an error as specified. For multi-field records, sort_by should accept a field name or a tuple of fields. Stable ordering for equal sort values is naturally handled by stable sort.
Set is O(1) average. Get without sort is O(1). Get with sort is O(n log n) due to sorting, where n is number of records. Space is O(n) for storage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.