Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Microsoft SWE interview that came down to a classic LRU cache problem. Not the hardest thing on paper, but the O(1) constraint plus the follow-up discussion on implementation details made it more involved than I expected.

Questions Asked (1)

Design an LRU cache data structure with get and put operations, both running in O(1) average time. Walk through how you'd implement it.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I knew the answer going in but fumbled the explanation of why you need both a hashmap and a doubly-linked list.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: O(1) get and put, capacity limit, and eviction policy (least recently used). Then describe the combination of a hash map for O(1) access and a doubly linked list for O(1) insertion/deletion to maintain recency order. Walk through the implementation details, including edge cases and complexity analysis.

Pro tip: Mention that you would use a doubly linked list with sentinel head and tail nodes to simplify edge cases, and that you would consider thread-safety if the cache is shared across threads, possibly using a lock or a concurrent data structure.

1. Clarify requirements and constraints

Confirm that the cache has a fixed capacity, that get and put must be O(1) average time, and that the eviction policy is LRU. Ask about thread-safety requirements and whether keys/values can be null.

2. Choose data structures

Explain that a hash map provides O(1) access to cache nodes, and a doubly linked list maintains the order of usage, with the most recently used at the head and least recently used at the tail. This combination allows O(1) get, put, and eviction.

3. Define node structure and operations

Describe the node with key, value, prev, and next pointers. For get: if key exists, move node to head and return value; else return -1. For put: if key exists, update value and move to head; else create new node, add to head, and if capacity exceeded, remove tail node and delete from map.

4. Handle edge cases and complexity

Discuss edge cases like updating an existing key, evicting when capacity is 1, and using sentinel nodes to avoid null checks. Analyze time complexity: O(1) average for both operations due to hash map and linked list operations.

5. Consider extensions and trade-offs

Mention possible extensions like thread-safety (using locks or ConcurrentHashMap with synchronized list), or alternative implementations (e.g., using LinkedHashMap in Java). Discuss trade-offs between memory overhead and performance.

Key Points to Mention

Hash map for O(1) key lookup, mapping keys to nodes in the linked list.
Doubly linked list to maintain recency order, with O(1) insertion and deletion.
Sentinel head and tail nodes to simplify boundary conditions.
Eviction policy: remove the least recently used node (tail) when capacity is exceeded.
Time complexity: O(1) average for both get and put operations.
Thread-safety considerations if the cache is accessed concurrently.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.