Use a hash map for O(1) key lookup and a doubly linked list to maintain usage order, with the most recently used at the head and least recently used at the tail. For get, move the accessed node to the head; for put, add or update the node at the head and evict the tail if capacity is exceeded.
Pro tip: Mention that you can implement the doubly linked list manually or use an ordered dictionary (like LinkedHashMap in Java or OrderedDict in Python) to simplify, but be prepared to explain the underlying mechanics. Also, discuss thread-safety considerations if the cache will be used in a concurrent environment.
Ask about expected capacity, concurrency needs, and whether keys/values are generic. Confirm that get and put must be O(1) and that eviction is based on least recent use.
Choose a hash map for O(1) access and a doubly linked list for O(1) insertion/deletion. Explain how they work together: map stores key -> node, list maintains 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 new node, add to head, and if size > capacity, remove tail and delete from map.
Write clean code with helper functions for add/remove node. Test with scenarios: empty cache, single item, eviction, updating existing key, and capacity 1.
Confirm O(1) time for both operations and O(capacity) space. Mention potential optimizations like using a sentinel head/tail to simplify edge cases or considering thread-safe variants.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the LRU cache operations and their thread-safety requirements, then propose a coarse-grained lock as a baseline and a more granular approach using separate locks for the hash map and the linked list. Compare the tradeoffs in terms of contention, complexity, and performance, and justify your choice based on expected workload.
Pro tip: Mention that you would first measure contention under realistic workloads before optimizing, and consider using existing thread-safe libraries (e.g., Java's ConcurrentHashMap and a concurrent linked list) to avoid reinventing the wheel.
Confirm the expected read/write ratio, concurrency level, and whether the cache must be strictly LRU or can be approximate. This sets the stage for choosing the right synchronization strategy.
Explain using a single mutex to protect all operations (get, put). This is simple and correct but serializes all accesses, causing high contention and poor scalability.
Propose separate locks for the hash map and the doubly linked list, or lock striping on the map. Detail how to avoid deadlocks by acquiring locks in a consistent order and handling concurrent evictions.
Discuss performance vs. complexity: coarse lock is easy but slow under high concurrency; fine-grained improves throughput but adds overhead, risk of deadlocks, and subtle bugs. Mention alternatives like lock-free or read-write locks.
Choose an approach based on the workload. For low contention, coarse lock suffices; for high contention, fine-grained or lock-free is better. Emphasize measuring and iterating.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the LFU eviction policy: evict the least frequently used item, breaking ties by least recently used. Then, explain how to extend the LRU cache design by adding a frequency tracking mechanism, such as a min-heap or a frequency map with doubly linked lists, and discuss the trade-offs in time and space complexity.
Pro tip: Mention that LFU is more complex than LRU and often requires a combination of data structures (e.g., frequency map + doubly linked lists) to achieve O(1) operations, and highlight that real-world systems sometimes use hybrid policies like LRU-K or LFU with aging to avoid cache pollution.
Define LFU: evict the item with the lowest access frequency; if multiple items have the same frequency, evict the least recently used among them (LRU tie-breaker).
To achieve O(1) operations, use a frequency map where each frequency points to a doubly linked list of items with that frequency, plus a min-frequency pointer to track the lowest frequency.
On eviction, remove the least recently used item from the list at the min-frequency. If that list becomes empty, increment the min-frequency pointer to the next non-empty frequency.
On access, move the item to the next higher frequency list and update the min-frequency if needed. On insertion, add the new item to the frequency-1 list and set min-frequency to 1 if necessary.
Compare LFU with LRU: LFU better retains frequently used items but can suffer from cache pollution and stale entries. Mention optimizations like frequency aging or using a heap for simpler but O(log n) operations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the cache's architecture and requirements (e.g., in-memory vs. distributed, consistency needs). Then propose a TTL mechanism, such as lazy expiration with periodic cleanup, and discuss trade-offs like memory overhead, eviction policies, and clock skew. Finally, mention how to handle edge cases like stale reads and thundering herd.
Pro tip: At Meta, scale and latency matter: emphasize that TTL should be configurable per key and that expiration should be probabilistic to avoid synchronized spikes. Also, consider using a hierarchical timer wheel for efficient expiration in large caches.
Ask about the cache type (in-memory, distributed), consistency requirements, expected scale, and whether TTL is per-key or global. This ensures your solution fits the context.
Decide between lazy expiration (check on access) and active expiration (background sweeper). Often a hybrid approach works best: lazy check plus periodic cleanup.
Store expiration timestamps alongside values. For active expiration, use a min-heap or timer wheel to efficiently find expired entries. For lazy, check timestamp on read and delete if expired.
Ensure thread-safe access to expiration data. Address clock skew in distributed systems by using a central time source or logical clocks. Prevent thundering herd by adding jitter to TTLs.
Compare memory overhead vs. CPU cost of active expiration. Consider eviction policies (LRU, LFU) interacting with TTL. Mention monitoring and tuning TTL values based on access patterns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.