I knew this one cold so I jumped straight to hashmap plus doubly-linked list.
Start by clarifying the requirements: O(1) average time for get and put, and LRU eviction. Then propose a combination of a hash map for O(1) access and a doubly linked list to maintain recency order, explaining how they work together. Walk through the get and put operations, including edge cases like updating existing keys and evicting when at capacity.
Pro tip: Mention that this is the classic LRU cache problem and that the same design is used in real systems like Redis and CPU caches. Also, discuss thread-safety considerations if the interviewer seems interested in production-level details.
Confirm that get and put must be O(1) average time, and that eviction is least-recently-used. Ask about capacity, concurrency, and whether keys/values are generic.
Select a hash map for O(1) key lookup and a doubly linked list to track usage order. Explain that the hash map stores key -> node references, and the list maintains most-recently-used at one end and least-recently-used at the other.
Describe get: if key exists, move its node to the front (most-recently-used) and return value; else return null. Describe put: if key exists, update value and move to front; else create new node, add to front, and if capacity exceeded, remove the tail node and delete its key from the map.
Discuss edge cases: capacity 0 or 1, updating existing key, and eviction when full. Analyze time complexity: O(1) average for both operations due to hash map and constant-time list manipulations.
Mention possible extensions: thread-safe implementation using locks or concurrent data structures, or using a combination of LinkedHashMap in Java (which provides LRU via accessOrder). Also, discuss memory overhead and potential optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints of the LRU cache (e.g., read/write ratio, expected throughput, latency targets). Then systematically compare the three approaches—single mutex, lock-free, and sharded locking—by analyzing their trade-offs in terms of performance, complexity, and correctness. Conclude with a recommendation based on the specific use case, and mention hybrid or adaptive strategies.
Pro tip: Emphasize that the choice depends on workload characteristics; for example, a single mutex is simple but can bottleneck under high contention, while lock-free approaches are complex and may not be worth it unless extreme performance is needed. Show that you consider maintainability and correctness, not just raw speed.
Ask about expected read/write ratio, throughput, latency, and consistency requirements. This determines which synchronization approach is appropriate.
Discuss how a single mutex serializes all operations, ensuring correctness but potentially causing contention and limiting scalability. Mention that it's simple to implement and often sufficient for low to moderate concurrency.
Explain that lock-free typically uses atomic operations and careful memory management (e.g., hazard pointers, RCU) to avoid locks. It offers high scalability but is complex, error-prone, and may not guarantee wait-freedom.
Describe partitioning the cache into shards, each with its own lock, reducing contention. Discuss trade-offs: increased memory overhead, potential for uneven load, and complexity in maintaining global LRU order.
Based on the requirements, recommend an approach (e.g., sharded locking for high concurrency with acceptable complexity) and mention possible optimizations like read-write locks or adaptive sharding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: TTL semantics (per-entry vs global), expiration behavior (lazy vs active), and concurrency needs. Then propose a design that combines a hash map for O(1) access, a doubly linked list for LRU ordering, and a min-heap or timing wheel for efficient TTL expiration. Discuss trade-offs and implementation details, including how to handle expired entries during get/put operations and background cleanup.
Pro tip: Mention that you would use lazy expiration on access combined with a background thread for proactive cleanup to avoid memory bloat, and highlight the importance of thread-safety with fine-grained locking or lock-free structures for high concurrency.
Ask about TTL granularity (per-entry or global), expiration policy (lazy vs active), and concurrency requirements. Confirm expected operations (get, put, delete) and performance goals.
Use a hash map for O(1) key lookup, a doubly linked list for LRU ordering, and an auxiliary structure (min-heap or timing wheel) to track expiration times efficiently.
Implement lazy expiration: on get/put, check if the entry is expired and remove it if so. Also, run a background thread that periodically scans and evicts expired entries to free memory.
Ensure thread-safety using locks (e.g., per-bucket locks or read-write locks) or lock-free techniques. Discuss trade-offs between simplicity and scalability.
Compare min-heap vs timing wheel for expiration tracking. Discuss memory overhead, time complexity, and potential improvements like hierarchical timing wheels for large-scale systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.