I went with JSON over pickle pretty quickly because pickle felt like a trap for a production-adjacent context (arbitrary code execution, version issues).
Start by clarifying requirements and constraints, then design a simple class with a dictionary for storage and JSON-based serialization. Implement methods with robust error handling and type preservation, and write tests covering normal and edge cases. Discuss trade-offs and potential improvements.
Pro tip: Mention that while JSON is simple, it doesn't preserve all Python types (e.g., tuples become lists, sets aren't serializable). Propose using pickle for full type fidelity or a custom encoder/decoder, and discuss security implications of pickle.
Ask about expected data types, serialization format, performance needs, and error handling expectations. Confirm whether the store should be thread-safe or persistent.
Define the class interface: set(key, value), get(key), serialize(), deserialize(data). Choose an internal data structure (e.g., dict) and decide on serialization approach (e.g., JSON, pickle).
Write set and get with checks for missing keys (return None or raise KeyError). Implement serialize to convert the store to a string/bytes, and deserialize to reconstruct it, handling invalid input gracefully.
Use a serialization method that preserves types (e.g., pickle) or implement custom encoding for common types (e.g., datetime, set). Document limitations if using JSON.
Create tests for setting/getting values, serializing/deserializing, missing keys, and corrupt data. Verify round-trip fidelity for various types.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.