Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Microsoft SWE interview with a classic LRU cache design problem. Pretty standard for this type of role but the O(1) constraint is where it gets real and I fumbled explaining my data structure choices out loud.

Questions Asked (1)

Design an in-memory key-value cache with an LRU eviction policy, where both get and put operations must run in O(1) amortized time. Walk through the data structures you'd use and why.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I knew the answer involved a hashmap plus a doubly linked list but explaining WHY the doubly linked list specifically took me longer than it should have.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (capacity, thread-safety, eviction semantics) and then propose a hash map combined with a doubly linked list to achieve O(1) operations. Walk through the get and put logic, emphasizing how the linked list maintains recency order and how the hash map provides direct access to nodes.

Pro tip: Mention that you'd use a sentinel head and tail in the doubly linked list to eliminate edge cases and simplify the code, and discuss how this design extends to thread-safe or distributed caches.

1. Clarify Requirements

Ask about cache capacity, expected load, thread-safety needs, and eviction semantics (e.g., LRU vs. other policies). Confirm that O(1) amortized time is required for both get and put.

2. Choose Data Structures

Select a hash map for O(1) key lookup and a doubly linked list to track access order. Explain that the hash map stores key-to-node references, and the linked list nodes store key-value pairs.

3. Detail Operations

Describe get: if key exists, move node to front (most recently used) and return value; else return null. Describe put: if key exists, update value and move to front; else insert new node at front and if capacity exceeded, remove tail node and its hash map entry.

4. Analyze Complexity

Argue that each operation involves a constant number of hash map lookups and linked list pointer updates, yielding O(1) amortized time. Mention that resizing the hash map may cause occasional O(n) but amortizes to O(1).

5. Discuss Extensions and Trade-offs

Talk about thread-safety (e.g., using locks or concurrent data structures), memory overhead, and alternative eviction policies (LFU, FIFO). Mention real-world implementations like Redis or Memcached.

Key Points to Mention

Hash map provides O(1) average-case lookup, essential for fast access.
Doubly linked list allows O(1) removal and insertion at both ends, enabling recency updates.
Sentinel nodes (dummy head and tail) simplify edge cases and reduce conditional logic.
Capacity check and eviction: when size exceeds capacity, remove the least recently used node (tail) and delete its key from the hash map.
Amortized O(1) consideration: hash map resizing and potential collisions, but average case remains constant.
Thread-safety: discuss locking strategies (e.g., fine-grained locks or read-write locks) if concurrent access is required.

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