Had actually reviewed this the night before, which made it worse in a way because I felt overconfident and just started coding without walking through my approach first.
Start by clarifying the requirements: operations (get, put), capacity, and tie-breaking policy. Then design a solution using a combination of a hash map and a frequency-based data structure (e.g., min-heap or doubly linked lists) to achieve O(1) time complexity. Discuss trade-offs between different implementations and handle edge cases like updating frequency on access and eviction when capacity is reached.
Pro tip: Mention that LFU can suffer from cache pollution where historically frequent items block new items; suggest using an aging mechanism or combining with LRU for better real-world performance. This shows awareness of practical limitations beyond textbook implementation.
Ask about expected operations (get, put), capacity constraints, and tie-breaking policy (e.g., LRU among same frequency). Confirm time complexity expectations.
Propose using a hash map for O(1) key lookup and a frequency map (e.g., min-heap or linked lists of frequency nodes) to track access counts. Explain how to maintain O(1) operations.
Detail the get and put methods: on get, increment frequency and update position; on put, insert new item or update existing, and evict least frequent item when capacity is exceeded.
Discuss tie-breaking (e.g., evict least recently used among least frequent), updating frequency on put for existing keys, and handling capacity 0 or 1.
Compare LFU with LRU, mention time/space complexity, and discuss potential optimizations like using a doubly linked list of frequency nodes for O(1) operations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.