The format tripped me up more than the problem itself.
Start by clarifying requirements and constraints, then verbally describe the LRU cache design using a hash map and doubly linked list, explaining operations and complexity. Finally, craft a precise prompt for an AI assistant to generate the implementation, ensuring it includes all necessary details and edge cases.
Pro tip: Demonstrate your ability to collaborate with AI by writing a clear, unambiguous prompt that specifies the data structures, operations, and edge cases, and mention that you would review and test the generated code for correctness and efficiency.
Ask about expected operations (get, put), capacity constraints, thread safety, and performance requirements to ensure the design meets the interviewer's expectations.
Explain that an LRU cache can be implemented with a hash map for O(1) access and a doubly linked list to track usage order, with the most recently used at the head and least recently used at the tail.
Detail how get and put work: for get, move the accessed node to the head; for put, add new node to head, and if capacity exceeded, remove the tail node and update the hash map.
State that both get and put operations run in O(1) time and O(capacity) space, and discuss potential edge cases like updating an existing key or capacity of zero.
Formulate a detailed prompt for the AI assistant that includes the chosen data structures, method signatures, edge cases, and language preference, and specify that the code should be efficient and well-commented.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about a coarse-grained lock first since it's the obvious starting point, then moved to read-write locks.
Start by explaining the need for thread safety in an LRU cache and outline the basic operations (get and put). Then, describe how to make it thread-safe using different locking strategies, comparing coarse-grained, fine-grained, and lock-free approaches. Finally, discuss the trade-offs in terms of performance, complexity, and scalability.
Pro tip: Mention that the choice of locking strategy depends on the read/write ratio and contention level; for read-heavy workloads, a read-write lock or optimistic concurrency can be beneficial, while for write-heavy, finer-grained locks or lock-free may be better. Also, highlight that LinkedIn's scale often demands high concurrency, so consider sharding or partitioning the cache.
Confirm the expected operations (get, put), concurrency level, and performance goals. Assume a standard LRU cache with a doubly linked list and hash map.
Point out that concurrent get and put can cause data races, inconsistent state, and corruption of the linked list and hash map.
Describe coarse-grained locking (single mutex), fine-grained locking (per-bucket or per-node locks), and lock-free/optimistic approaches (e.g., using atomic operations and CAS).
Compare strategies: coarse-grained is simple but limits concurrency; fine-grained improves concurrency but adds complexity and deadlock risks; lock-free offers high scalability but is hard to implement correctly.
Suggest a suitable approach for a high-scale system like LinkedIn, such as sharded locks or read-write locks, and justify why.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.