Apple·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jul 2026

Summary

Apple SWE interview, one coding question that looks straightforward until you're actually in it. No libraries, just raw implementation.

Questions Asked (1)

Implement a hashmap from scratch without using any built-in libraries or data structure utilities.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went with an array of linked lists for collision handling, which is the obvious route, but I fumbled explaining the load factor and when to resize.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., key/value types, expected operations, performance constraints) and then outline the design: an array of buckets with a hash function and collision resolution (chaining or open addressing). Implement core methods (put, get, remove) with resizing to maintain load factor, and analyze time/space trade-offs.

Pro tip: Discuss how you would handle collisions and resizing, and mention that you'd test edge cases like null keys, high load factors, and concurrent access (if applicable). This shows you think about robustness and real-world usage.

1. Clarify Requirements

Ask about key/value types, expected operations (put, get, remove), performance goals, and any constraints (e.g., thread safety). This ensures you design the right solution.

2. Design the Data Structure

Choose an array of buckets (e.g., linked lists or arrays) and a hash function. Decide on collision resolution (chaining vs. open addressing) and initial capacity.

3. Implement Core Operations

Code put, get, and remove. For chaining, traverse the bucket's list; for open addressing, probe until found or empty. Handle updates and deletions carefully.

4. Handle Resizing

Track load factor (entries/buckets). When it exceeds a threshold (e.g., 0.75), double the capacity and rehash all existing entries to maintain O(1) average time.

5. Analyze and Optimize

Discuss time/space complexity, potential improvements (e.g., better hash functions, tree-based buckets), and trade-offs between chaining and open addressing.

Key Points to Mention

Hash function design: uniform distribution, handling negative keys, and using bitwise operations for speed.
Collision resolution: chaining (linked lists) vs. open addressing (linear/quadratic probing, double hashing) and their trade-offs.
Load factor and resizing: threshold selection, rehashing cost, and amortized O(1) operations.
Time complexity: average O(1) for put/get/remove, worst-case O(n) with poor hash function or high collisions.
Edge cases: null keys, duplicate keys, deletion in open addressing (tombstones), and concurrent modification.
Comparison with built-in libraries: why they use similar techniques and what additional features they offer (e.g., thread safety, iteration order).

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