Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Amazon SWE technical screen, one coding question, interviewer was pretty relaxed and collaborative about the whole thing.

Questions Asked (1)

Design and implement an LRU Cache.

Algorithms & Data StructuresSystem Design

Author's notes

The interviewer had a specific implementation in mind using a doubly linked list.

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Suggested Approach

Start by clarifying requirements (capacity, thread-safety, eviction policy) and then propose a design using a hash map and a doubly linked list to achieve O(1) get and put operations. Walk through the implementation details, including edge cases and potential optimizations, and discuss how you would test and scale the solution.

Pro tip: Demonstrate awareness of concurrency by mentioning thread-safe implementations (e.g., using locks or ConcurrentHashMap) and discuss trade-offs between different eviction policies (LRU vs LFU) in real-world systems like Amazon's caching layers.

1. Clarify Requirements

Ask about capacity, expected operations, thread-safety, and whether the cache should be distributed or in-memory. Confirm the eviction policy (LRU) and any constraints.

2. Propose Data Structures

Explain that a hash map provides O(1) access to cache entries, while a doubly linked list maintains the order of usage. Together they enable O(1) get and put.

3. Outline Operations

Describe how get moves the accessed item to the front (most recently used) and how put adds or updates an item, evicting the least recently used (tail) when capacity is exceeded.

4. Handle Edge Cases

Discuss handling of capacity 0 or 1, updating existing keys, and thread-safety if required. Mention potential use of locks or concurrent data structures.

5. Implement and Test

Write clean code for the LRU cache class, then walk through test cases including get/put sequences, eviction, and concurrency scenarios if applicable.

Key Points to Mention

O(1) time complexity for both get and put operations using hash map + doubly linked list.
Eviction of least recently used item when capacity is reached.
Thread-safety considerations and possible implementations (e.g., synchronized methods, ReentrantReadWriteLock, or ConcurrentHashMap with a custom eviction policy).
Trade-offs between different eviction policies (LRU, LFU, FIFO) and their use cases.
Handling of edge cases such as capacity 0, updating existing keys, and null values.
Potential optimizations like using a sentinel head/tail to simplify list operations.

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