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Snapchat·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Snapchat coding interview, pretty much one big question about LRU cache design and implementation. Felt like a classic but they pushed hard on the internals.

Questions Asked (1)

Q1

Design and implement an LRU cache with get and put operations both running in O(1) average time. When capacity is hit, evict the least recently used entry. Walk through your data structure choices and explain how each operation keeps recency ordering correct.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I knew the answer going in (hash map plus doubly linked list, classic combo) but fumbled explaining WHY the doubly linked list specifically.

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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 get and put logic, emphasizing how the linked list maintains recency order and how eviction works when capacity is exceeded.

Pro tip: Mention that using a doubly linked list with sentinel head and tail nodes simplifies edge cases and avoids null checks, making the code cleaner and less error-prone.

1. Clarify Requirements

Confirm that the cache should support get and put in O(1) average time, and that when capacity is reached, the least recently used item is evicted. Ask about edge cases like updating an existing key.

2. Choose Data Structures

Propose a hash map for O(1) key lookup and a doubly linked list to maintain recency 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 the node to the front (most recently used) and return value; else return -1. Describe 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.

4. Explain Recency Maintenance

Emphasize that the doubly linked list keeps most recently used at the head and least recently used at the tail. Each access or insertion moves the node to the head, ensuring O(1) updates.

5. Analyze Complexity and Trade-offs

State that both operations are O(1) average time due to hash map and linked list operations. Discuss space complexity O(capacity) and potential trade-offs like using a singly linked list with extra pointers or alternative implementations.

Key Points to Mention

  • Hash map provides O(1) average time for key lookup.
  • Doubly linked list allows O(1) removal and insertion at both ends.
  • Sentinel nodes (dummy head and tail) simplify edge cases.
  • Recency order: most recently used at head, least recently used at tail.
  • Eviction: remove tail node and delete its key from hash map.
  • Time complexity: O(1) for both get and put; space complexity: O(capacity).

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