I knew LRU caches conceptually but blanked for a second on why a plain hashmap isn't enough.
Start by clarifying the requirements: O(1) get and put, capacity limit, and eviction policy. Then explain that a combination of a hash map and a doubly linked list achieves O(1) for both operations. Finally, walk through the implementation details and discuss edge cases and potential optimizations.
Pro tip: Mention that you would use a doubly linked list to maintain access order and a hash map for O(1) lookups, and highlight that this is a common design used in real systems like Redis and Memcached. Also, discuss thread-safety if the cache is to be used in a concurrent environment.
Ask about expected capacity, concurrency needs, and whether the cache should be thread-safe. Confirm that both get and put must be O(1) and that the least recently used item is evicted when capacity is exceeded.
Select a hash map for O(1) key lookup and a doubly linked list to maintain the order of usage. The hash map stores key to node references, and the linked list keeps most recently used at the head and least recently used at the tail.
For get: if key exists, move the node to the head and return its value; else return -1. For put: if key exists, update value and move to head; else create a new node, add to head, and if capacity exceeded, remove the tail node and delete its key from the map.
Consider capacity 0 or 1, updating an existing key, and eviction when the cache is full. Also discuss how to handle null values or keys if applicable.
Confirm that both operations are O(1) time and O(capacity) space. Mention potential optimizations like using a sentinel head and tail to simplify list operations, or using a concurrent hash map and synchronized blocks for thread safety.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.