The two-hashmap approach clicked for me eventually: one maps keys to nodes, the other maps frequency counts to a doubly linked list of nodes at that frequency.
Start by clarifying the requirements and edge cases, then propose a design using a combination of a hash map and a frequency-indexed doubly linked list to achieve O(1) operations. Walk through the data structures, explain how get and put work, and discuss how to handle eviction with tie-breaking by recency.
Pro tip: Mention that you can use a min-heap for O(log n) but emphasize that the doubly linked list approach is necessary for true O(1). Also, discuss how to handle concurrency if the cache needs to be thread-safe, as this shows depth.
Ask about expected cache size, concurrency needs, and whether O(1) is strictly required for both operations. Confirm tie-breaking rule: least frequently used, and among those, least recently used.
Propose using a hash map for key-to-node mapping, and a frequency map that maps frequency to a doubly linked list of nodes. Each node stores key, value, frequency, and pointers to prev/next.
For get: if key exists, update frequency and move node to appropriate frequency list, return value. For put: if key exists, update value and frequency; else insert new node with frequency 1, and if capacity exceeded, evict from lowest frequency list's tail (LRU).
Maintain a min frequency variable to quickly find the lowest frequency list. On eviction, remove the tail of that list (least recently used among least frequent). Update min frequency when lists become empty.
Explain that all operations are O(1) average due to hash map lookups and constant-time linked list operations. Discuss trade-offs: memory overhead vs. speed, and potential concurrency solutions like locks or lock-free structures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.