Start by clarifying the requirements: fixed capacity, O(1) average time for get and put, and LRU eviction. Then propose a hash map combined with a doubly linked list, explaining how each operation maintains O(1) time. Walk through the implementation details, including edge cases and potential optimizations.
Pro tip: Mention that you would use a doubly linked list to track usage order and a hash map for O(1) access, and discuss how to handle thread safety if the cache might be accessed concurrently. This shows you think about real-world deployment beyond the basic algorithm.
Confirm the expected operations (get, put), capacity behavior, and any assumptions about key/value types or concurrency. Ask if the cache needs to be thread-safe.
Propose a hash map for O(1) key lookup and a doubly linked list to maintain recency order. Explain that the hash map stores references to list nodes.
Describe how get moves the accessed node to the front (most recently used) and returns the value. For put, insert or update the node, move it to the front, and if capacity is exceeded, remove the tail node (least recently used) and delete its key from the map.
Discuss updating an existing key, evicting when capacity is 1, and handling null values. Mention that the list and map must stay in sync.
State that both operations are O(1) average time due to hash map and constant-time list manipulations. Mention space complexity O(capacity). Optionally, discuss alternatives like using an ordered dictionary or a combination of data structures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the API and constraints, then design a data structure combining a frequency map and a doubly linked list per frequency to achieve O(1) operations. Explain how to maintain the minimum frequency pointer and handle tie-breaking with LRU order within each frequency list.
Pro tip: Mention that you can use a single doubly linked list for each frequency, and that the min_freq pointer only increases by 1 on eviction, which is key to O(1) amortized time. Also, discuss how to handle edge cases like updating frequency when a key is accessed.
Confirm the expected operations (get, put), capacity behavior, and tie-breaking rules. Ask about thread safety if relevant.
Propose using a hash map for key-to-node mapping, a hash map for frequency-to-doubly-linked-list, and a min_freq variable. Explain how each node stores key, value, and frequency.
Walk through get and put: on access, move node to next frequency list; on insert, add to freq=1 list; on eviction, remove LRU from min_freq list and update min_freq if needed.
Argue O(1) average time for all operations, and discuss edge cases like capacity 0, updating existing key, and tie-breaking correctness.
Compare with LRU and LFU variants, mention memory overhead, and consider if a heap-based approach could be acceptable but not O(1).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.