The interviewer basically handed me a lifeline by saying I could skip the linked list version, and I still froze.
Start by clarifying the requirements: LRU cache with get and put operations, O(1) time complexity, and capacity constraint. Since an array-based approach is acceptable, explain that you'll use a hash map for O(1) key lookup and an array to maintain recency order, moving accessed items to the end. Then, discuss the trade-offs of this approach versus a linked list, and implement the solution with careful handling of edge cases.
Pro tip: Mention that while an array-based approach is simpler to implement, it has O(n) time complexity for moving elements, which might be acceptable for small caches but not for large-scale systems. This shows you understand the trade-offs and can choose the right data structure based on context.
Ask about expected cache size, concurrency needs, and whether O(1) is strictly required. Confirm that an array-based approach is acceptable and discuss the implications.
Propose using a hash map (dictionary) for O(1) key lookup and an array (or list) to maintain recency order. Explain that the most recently used item will be at one end (e.g., end of array).
Outline get(key): if key exists, move it to the most recent position and return value; else return -1. For put(key, value): if key exists, update value and move to most recent; else add new item, and if capacity exceeded, evict least recently used (first element).
Write clean code with helper functions for moving an item to the most recent position and evicting the least recently used. Use the hash map to store key-index mappings for O(1) access to array positions.
State that get and put are O(1) for hash map lookup but O(n) for array shifting in the worst case. Discuss that a doubly linked list would give true O(1) but is more complex; array is acceptable for small caches or when simplicity is prioritized.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.