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The question itself is fine, I knew what an LRU cache was.
Start by clarifying requirements and edge cases, then propose a hash map combined with a doubly linked list to achieve O(1) operations. Explain how the hash map provides direct access to nodes, while the linked list maintains usage order for eviction. Walk through the get and put logic, and discuss handling of edge cases like updating existing keys and capacity zero.
Pro tip: Mention that you would use a doubly linked list with dummy head and tail nodes to simplify insertion and removal logic, avoiding null checks. Also, discuss thread-safety considerations if the cache might be accessed concurrently, showing awareness of real-world usage.
Ask about expected capacity, key/value types, thread-safety needs, and whether the cache should be in-memory or distributed. Confirm that O(1) average time is required for both get and put.
Propose a hash map for O(1) key lookup and a doubly linked list to track usage order. Explain that the hash map stores key to node references, and the linked list maintains most-recently-used at one end and least-recently-used at the other.
Describe get: if key exists, move its node to the front (most recently used) and return value; else return -1. Describe put: if key exists, update value and move to front; else create a new node, add to front, and if capacity exceeded, remove the tail node and delete its key from the hash map.
Discuss capacity zero (always evict), updating existing keys, and ensuring dummy head/tail nodes simplify list operations. Also mention potential concurrency issues if applicable.
Confirm that both operations are O(1) average time due to hash map and linked list. Mention possible optimizations like using a custom node class or arrays for performance, and trade-offs with thread-safety.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.