I started with the obvious OrderedDict approach and got the basic LRU logic down fine.
Start by clarifying the requirements and edge cases, then outline a design using an OrderedDict for O(1) LRU operations and a robust key normalization function to handle unhashable arguments. Implement the decorator with a wrapper that manages cache lookup, eviction, and exposes clear() and cache_info() methods, and finally discuss trade-offs and potential optimizations.
Pro tip: Mention that you can use functools.lru_cache as a reference but note its limitations (e.g., no support for unhashable arguments) to show depth. Also, emphasize that the key normalization should be deterministic and handle nested structures to avoid subtle bugs.
Ask clarifying questions about expected argument types, thread safety, and whether the cache should be per-function or global. Discuss how to handle unhashable arguments like lists and dicts.
Explain that you'll normalize arguments into a hashable key, e.g., by converting lists to tuples, dicts to sorted tuples of items, and using a custom serialization for other unhashable types. Ensure that semantically equivalent calls (e.g., f(1,2) and f(1, b=2)) produce the same key.
Use collections.OrderedDict to store key-value pairs and track recency. On access, move the key to the end; on insertion, if size exceeds maxsize, pop the first item. Wrap the function to compute the key, check the cache, and call the original function on miss.
Attach methods to the wrapper: clear() empties the cache and resets statistics; cache_info() returns a named tuple with hits, misses, maxsize, and current size. Update statistics on each cache hit or miss.
Talk about the overhead of key normalization, potential memory usage, and thread safety. Mention alternatives like using a doubly linked list with a dict for O(1) operations without OrderedDict, and consider using functools.lru_cache for hashable-only cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by outlining a JSON-based serialization strategy that encodes keys and values into a JSON-safe format, such as using tagged tuples or base64 for non-serializable objects. Then explain how to handle non-serializable return values by either converting them to a serializable representation or storing a fallback marker. Finally, discuss versioning by including a version field in the saved JSON and implementing migration logic on load.
Pro tip: Mention that you would use a custom JSON encoder to handle non-serializable types gracefully, and always include a version number to enable backward-compatible schema evolution.
Define a JSON structure that includes a version field, and represents cache entries as a list of objects with encoded keys and values. Use a consistent encoding scheme, such as tagging types or using base64 for binary data.
Convert keys and values into JSON-serializable forms. For simple types, use native JSON; for complex or non-serializable types, use a custom encoder that outputs a tagged representation (e.g., {"__type__": "datetime", "value": "..."}).
For values that cannot be serialized, either store a placeholder (e.g., null) and skip them on load, or attempt to serialize a meaningful subset. Document the behavior and ensure the cache remains functional.
In save(), serialize the cache to JSON with the version field and write to file. In load(), read the JSON, check the version, and deserialize using the reverse of the encoding scheme, applying any necessary migrations.
Include a version number in the saved file. On load, if the version is older, apply migration functions to update the data structure before deserializing. If newer, raise an error or attempt best-effort loading.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.