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Bloomberg·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jul 2026

Summary

Bloomberg SWE interview focused on LRU Cache, but the angle was more OOP design than raw algorithm grinding. Pretty chill experience overall, nothing too brutal.

Questions Asked (1)

Q1

Design and implement an LRU Cache from an object-oriented perspective.

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

The OOP framing was what made this interesting.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (capacity, thread-safety, eviction policy) and then describe an object-oriented design with separate classes for the cache, node, and eviction policy. Explain the standard O(1) implementation using a hash map and doubly linked list, and discuss trade-offs like memory overhead and concurrency.

Pro tip: Emphasize that you separate the eviction policy from the cache storage to follow the Single Responsibility Principle and allow future extensibility (e.g., LFU). Also, mention that you would use a sentinel head/tail to simplify edge cases in the linked list.

1. Clarify Requirements

Ask about expected capacity, thread-safety needs, and whether the cache should be generic. Confirm the eviction policy (LRU) and any performance constraints.

2. Define Core Classes

Identify main objects: Cache, Node (key-value pair with prev/next pointers), and EvictionPolicy interface. Explain how they interact.

3. Design Data Structures

Use a hash map for O(1) lookup and a doubly linked list for O(1) eviction and update. Describe how get and put operations manipulate both structures.

4. Handle Edge Cases and Concurrency

Discuss handling of null keys/values, capacity zero, and thread-safety (e.g., using locks or ConcurrentHashMap). Mention potential race conditions.

5. Analyze Trade-offs and Extensibility

Compare with alternative implementations (e.g., LinkedHashMap) and discuss time/space complexity. Explain how the design supports other policies.

Key Points to Mention

  • O(1) time complexity for get and put operations using hash map + doubly linked list.
  • Use of sentinel nodes (dummy head and tail) to simplify insertion and removal.
  • Thread-safety considerations: synchronization, read-write locks, or concurrent data structures.
  • Separation of concerns: cache storage vs. eviction policy for extensibility.
  • Memory overhead of storing pointers and potential optimizations.
  • Comparison with Java's LinkedHashMap (access-order) and its limitations.

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