Classic problem, I've done this before, and yet I still fumbled the part where you have to keep the doubly linked list and the hashmap in sync on every operation.
Use a hash map for O(1) key lookup and a doubly linked list to maintain recency order, with the most recently used at the head and least recently used at the tail. On get, move the accessed node to the head; on put, insert or update at the head and evict the tail if capacity is exceeded.
Pro tip: Mention that you can use a sentinel head and tail to simplify edge cases, and clarify that O(1) is average for the hash map due to potential collisions. Also, discuss thread-safety if the cache might be accessed concurrently, as Lyft's services often require it.
Ask about capacity bounds, key/value types, thread-safety, and whether O(1) is strictly required. Confirm that eviction should remove the least recently used item when full.
Select a hash map for O(1) key access and a doubly linked list for O(1) insertion, deletion, and reordering. Explain that the list maintains recency order.
Detail get: if key exists, move node to head and return value; else return -1. Detail put: if key exists, update value and move to head; else create node, add to head, and if capacity exceeded, remove tail node and delete from map.
Use sentinel nodes to avoid null checks. Discuss handling capacity 0 or 1, and consider thread-safety with locks or concurrent data structures if needed.
State that both operations are O(1) average time and O(capacity) space. Walk through a small example to verify correctness, including eviction order.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements (e.g., thread safety, persistence, expected operations) and then implement a simple hash map-based store. For the second implementation, choose a contrasting approach like a log-structured store or a tree-based store, and compare them on performance, memory, and complexity. Conclude with trade-offs and when to use each.
Pro tip: Mention that real-world systems often combine approaches (e.g., LSM trees with in-memory memtables) and that the choice depends on workload characteristics like read/write ratio and latency requirements.
Ask about expected operations, data size, concurrency, persistence, and performance goals to scope the problem.
Code a simple hash map-based key-value store with put, get, and delete, discussing its O(1) average time complexity.
Choose a contrasting implementation, such as a balanced tree (e.g., red-black tree) or a log-structured store, and outline its structure.
Analyze both approaches on time complexity, memory usage, concurrency, persistence, and scalability.
Conclude with when each approach is preferable and mention hybrid solutions if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.