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Bytedance·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Bytedance SWE interview that came down to a classic cache design problem. Nothing too exotic but the O(1) constraint is where people trip up if they haven't thought it through before.

Questions Asked (1)

Q1

Design and implement an LRU cache with a fixed capacity that supports get and put operations in O(1) average time, evicting the least recently used entry when the cache is full.

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

I knew the answer going in but still fumbled explaining why a doubly linked list specifically.

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AI HintsAI Generated

Suggested Approach

Start by clarifying 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 edge cases.

Pro tip: Mention that you would use a doubly linked list with sentinel nodes to simplify edge cases, and discuss thread-safety if the cache is used in a concurrent environment.

1. Clarify Requirements

Ask about capacity constraints, concurrency needs, and expected operation mix. Confirm that O(1) average time is required for both get and put.

2. Choose Data Structures

Propose a hash map for O(1) key lookup and a doubly linked list to track usage order. Explain how they work together to achieve O(1) operations.

3. Design the Algorithm

Detail the get and put logic: on get, move the accessed node to the front; on put, add or update the node and evict the least recently used (tail) if capacity is exceeded.

4. Implement Core Methods

Write clean code for get and put, using helper methods for adding to front and removing nodes. Handle edge cases like updating an existing key and evicting when full.

5. Analyze and Optimize

Discuss time and space complexity, potential optimizations (e.g., using an array-based linked list), and trade-offs (e.g., memory vs. speed).

Key Points to Mention

  • Hash map provides O(1) access to cache entries.
  • Doubly linked list maintains recency order with O(1) insertions and deletions.
  • Sentinel nodes (head and tail) simplify edge cases and avoid null checks.
  • Eviction policy: remove the least recently used node (tail) when capacity is exceeded.
  • Thread-safety considerations: use locks or concurrent data structures if needed.
  • Time complexity: O(1) average 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.