I knew the answer here (hashmap plus doubly linked list) but I fumbled explaining WHY the doubly linked list specifically.
Start by clarifying the requirements: O(1) get and put, and LRU eviction. Then propose a combination of a hash map and a doubly linked list, explaining how each operation works. Finally, discuss trade-offs and potential optimizations.
Pro tip: Mention that the doubly linked list allows O(1) removal and insertion, and the hash map provides O(1) access to nodes. Also, note that using a singly linked list would require extra pointers or a different approach, so the doubly linked list is optimal.
Confirm that get and put must be O(1), and that the cache should evict the least recently used item when full.
Select a hash map for O(1) key lookup and a doubly linked list to maintain recency order.
Describe how get moves the accessed node to the front, and put adds or updates a node, evicting the tail if necessary.
Compare with alternatives like arrays or singly linked lists, highlighting why the combination is optimal for O(1) operations.
Mention handling of cache capacity, updating existing keys, and thread safety if relevant.
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Coding this out was fine until I forgot to handle the update case in put, where the key already exists and you need to move it to the front.
Clarify the requirements and constraints, then design an LRU cache using a hash map and a doubly linked list to achieve O(1) time for both get and put operations. Implement the class with careful handling of edge cases like updating existing keys and evicting the least recently used item when capacity is exceeded.
Pro tip: Mention that you would use a doubly linked list with dummy head and tail nodes to simplify edge cases, and discuss how you would test the implementation with scenarios like repeated gets and puts, capacity 1, and eviction order.
Ask about expected capacity range, concurrency needs, and whether the cache should be thread-safe. Confirm that get and put must run in O(1) time.
Explain that a hash map provides O(1) access to nodes, and a doubly linked list maintains usage order. The map stores key to node references, and the list orders nodes from most to least recently used.
Define a node with key, value, prev, and next pointers. Implement helper methods to add a node to the front (most recently used) and remove a node from anywhere in O(1).
For get: if key exists, move node to front and return value; else return -1. For 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.
Walk through examples like capacity 1, updating an existing key, and eviction order. Mention potential optimizations or variations (e.g., using LinkedHashMap in Java).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by briefly describing your LRU cache design, emphasizing the combination of a hash map and a doubly linked list. Then systematically analyze the time complexity for get and put operations, explaining why each is O(1), and conclude with the overall space complexity O(capacity).
Pro tip: Mention that the O(1) time complexity relies on the hash map providing direct access to nodes and the doubly linked list enabling O(1) removal and insertion. Also, note that space is O(capacity) because you store at most capacity entries, but each entry has overhead for the map and list pointers.
Explain that you use a hash map (dictionary) for O(1) key lookup and a doubly linked list to maintain access order, with the most recently used at the head and least recently used at the tail.
State that get(key) is O(1) on average: hash map lookup gives the node in constant time, then moving the node to the head involves updating a constant number of pointers.
Explain that put(key, value) is O(1) on average: if key exists, update value and move to head; if not, insert at head and if capacity exceeded, remove tail node and delete its key from the map—all constant time operations.
Conclude that space is O(capacity) because the cache stores at most capacity entries, each occupying constant space in both the hash map and the linked list.
Mention that the analysis assumes average-case hash map operations; worst-case could be O(n) if many collisions, but typically we consider average. Also note that capacity is fixed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the core operations (get and put) and the invariants that must be preserved under concurrency. Then compare coarse-grained locking, fine-grained locking, and concurrent data structures in terms of performance, scalability, and complexity, and recommend a solution based on the expected read/write ratio and contention level.
Pro tip: Mention that Java's ConcurrentHashMap doesn't support atomic get-and-update for LRU ordering, so you often need a separate lock or a concurrent linked list; also note that read-heavy workloads can benefit from read-write locks or lock striping.
Determine the expected read/write ratio, contention level, and latency requirements to guide the choice of synchronization strategy.
Describe using a single lock (e.g., synchronized or ReentrantLock) around all operations; simple but serializes all access and limits scalability.
Describe partitioning the cache (e.g., by key hash) with separate locks per partition, or using a lock per entry; improves concurrency but adds complexity and potential deadlocks.
Discuss using ConcurrentHashMap for the map and a concurrent linked list for recency order, but note that atomic updates across both structures require additional synchronization.
Weigh trade-offs: coarse-grained is simple but slow under contention; fine-grained scales better but is complex; concurrent structures offer good performance but may need careful design to maintain LRU semantics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I listed a few: two threads evicting different entries at the same capacity boundary, a get and a put racing on the same key where the linked list node gets moved while another thread is reading it.
Start by identifying the specific race conditions that can occur when multiple threads call get and put simultaneously, such as lost updates, dirty reads, and inconsistent reads. Then explain how your design prevents these issues using synchronization primitives, atomic operations, or lock-free techniques, and discuss the trade-offs between consistency and performance.
Pro tip: Demonstrate awareness of the performance implications of your chosen synchronization strategy, and mention how you would test for race conditions using stress tests or tools like ThreadSanitizer.
List the specific problems that can arise, such as lost updates when two puts interleave, dirty reads when a get sees a partially written value, and inconsistent reads when a get observes a mix of old and new values.
Describe the synchronization primitives or concurrency control techniques used, such as mutexes, read-write locks, atomic references, or lock-free algorithms like compare-and-swap.
Clarify the level of consistency provided (e.g., linearizability, sequential consistency) and how it meets the application's requirements.
Compare your approach with alternatives in terms of performance, scalability, and complexity, and justify your choices.
Explain how you would test for race conditions, such as using stress tests, formal verification, or dynamic analysis tools.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.