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Start by clearly defining the LRU cache requirements and the O(1) constraint, then describe the classic hash map + doubly linked list implementation. After that, discuss thread-safety by introducing locking strategies (e.g., coarse-grained vs. fine-grained) and trade-offs, and finally mention potential optimizations like lock striping or read-write locks.
Pro tip: Show awareness of real-world constraints: in a data science context, LRU caches often serve model predictions or feature stores, so emphasize how thread-safety impacts latency and throughput. Also, mention that Python's GIL doesn't guarantee atomicity for compound operations, so explicit locking is still needed.
Confirm that the cache has a fixed capacity, supports get and put in O(1), and evicts the least recently used item when full. Ask if thread-safety is required for read-heavy or write-heavy workloads.
Use a hash map for O(1) access to nodes and a doubly linked list to track recency order. Explain how get moves a node to the front and put inserts/updates and evicts from the tail.
Point out that concurrent get and put can cause race conditions, such as inconsistent list pointers or stale reads. Discuss the need for synchronization to maintain invariants.
Propose using a mutex (coarse-grained) for simplicity, or finer-grained locks (e.g., per-bucket or read-write locks) for better concurrency. Mention lock striping to reduce contention.
Compare performance impact: coarse locks are simple but limit concurrency; fine-grained locks improve throughput but add complexity. Mention alternatives like concurrent hash maps with approximate LRU or using existing thread-safe libraries.
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