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Amazon·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate
Jun 2026

Summary

Amazon SWE coding round, one question the whole time: build a HashMap from scratch with no standard library help. Straightforward on paper but the implementation details add up fast.

Questions Asked (1)

Q1

Design and implement a simplified HashMap from scratch, without using any built-in hash containers. It needs to support put, get, and remove operations with average O(1) time complexity.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

My first instinct was to reach for a big array indexed by key and just store values directly, which technically works given the key range is bounded up to a million.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline the core components: an array of buckets, a hash function, and collision resolution via chaining. Implement the class with put, get, and remove methods, ensuring average O(1) by resizing when the load factor exceeds a threshold. Discuss trade-offs and potential optimizations.

Pro tip: Mention that you would use a good hash function to distribute keys uniformly and consider using a prime number for the bucket array size to reduce collisions. Also, discuss how you would handle null keys and the importance of resizing to maintain O(1) average time.

1. Clarify Requirements

Ask about expected key/value types, null handling, thread safety, and performance constraints. Confirm that average O(1) is required and that built-in hash containers are not allowed.

2. Design the Data Structure

Choose an array of buckets (e.g., linked lists or dynamic arrays) for collision resolution. Define a hash function and a load factor threshold for resizing.

3. Implement Core Operations

Write put, get, and remove methods: compute hash, find bucket, handle collisions, and update size. Include resizing logic when load factor exceeds threshold.

4. Analyze Complexity and Trade-offs

Explain why average O(1) is achieved and discuss worst-case O(n) scenarios. Mention alternatives like open addressing and their trade-offs.

5. Test and Optimize

Walk through edge cases (empty map, collisions, resizing) and suggest potential optimizations like better hash functions or tree-based buckets for high collisions.

Key Points to Mention

  • Hash function design and its impact on distribution and collisions
  • Collision resolution strategies: separate chaining vs. open addressing
  • Load factor and resizing (rehashing) to maintain O(1) average time
  • Handling of null keys and values, and equality checks
  • Time and space complexity analysis, including worst-case scenarios
  • Trade-offs between different implementations (e.g., linked list vs. balanced tree for buckets)

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