I knew the answer involved a hash map and a doubly linked list but explaining why you need both at the same time, while also coding it up, is harder than it sounds.
Start by clarifying the requirements and constraints, then propose a design using a hash map and a doubly linked list to achieve O(1) operations. Explain how the hash map provides O(1) access to nodes, while the doubly linked list maintains the recency order for eviction. Finally, discuss potential trade-offs and optimizations.
Pro tip: Mention that you would use a doubly linked list with sentinel head and tail nodes to simplify edge cases, and consider thread-safety if the cache might be accessed concurrently.
Ask about expected cache size, concurrency requirements, and whether keys/values are generic types. Confirm that both get and put must be O(1).
Select a hash map for O(1) key lookup and a doubly linked list to track usage order. Explain that the hash map stores key-node pairs, and the list maintains most-recently-used at one end and least-recently-used at the other.
Detail how get moves the accessed node to the front (most recently used) and returns its value. For put, if the key exists, update value and move to front; if not, create a new node, add to front, and if capacity exceeded, remove the tail node and delete its key from the map.
Discuss handling of empty cache, updating existing keys, and eviction when full. Mention using sentinel nodes to avoid null checks and simplify list manipulation.
Confirm O(1) time for both operations and O(capacity) space. Discuss trade-offs such as memory overhead of the linked list and potential need for synchronization in multi-threaded environments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.