Snapchat·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Snapchat SWE interview with a classic LRU cache design question. Pretty standard for this type of role but the O(1) constraint is where things get interesting if you haven't thought it through before.

Questions Asked (1)

Design and implement an LRU cache data structure with get and put operations, both running in O(1) average time complexity.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The get and put interface is straightforward but the O(1) requirement forces you toward a specific combo of a hashmap and a doubly linked list.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then propose a hash map combined with a doubly linked list to achieve O(1) operations. Walk through the design, implement the core methods, and discuss trade-offs and potential optimizations.

Pro tip: Mention that you would use a doubly linked list with sentinel head and tail nodes to simplify edge cases, and discuss how this design can be extended to handle concurrency or persistence if needed.

1. Clarify requirements

Ask about expected cache size, concurrency needs, and whether eviction policy is strictly LRU. Confirm that get and put must be O(1) average time.

2. Propose data structures

Explain that a hash map provides O(1) access to nodes, and a doubly linked list maintains recency order. Together they enable O(1) get and put.

3. Detail operations

Describe how get moves the accessed node to the front, and put inserts or updates a node, evicting the least recently used (tail) when capacity is exceeded.

4. Implement code

Write clean code for the LRU cache class, using sentinel nodes to simplify list manipulation. Handle edge cases like updating an existing key and evicting when full.

5. Analyze and discuss trade-offs

Confirm O(1) time and O(capacity) space. Discuss alternatives like using an ordered dictionary, and mention concurrency considerations if relevant.

Key Points to Mention

Hash map for O(1) key lookup, mapping keys to nodes in the linked list.
Doubly linked list to maintain access order, with most recently used at head and least recently used at tail.
Sentinel head and tail nodes to avoid null checks and simplify insertion/removal.
Eviction policy: when capacity is reached, remove the tail node (LRU) and its corresponding entry in the hash map.
Time complexity: O(1) average for both get and put; space complexity: O(capacity).
Potential extensions: thread safety using locks or concurrent data structures, and persistence or TTL support.

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