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Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Apr 2026Remote

Summary

Meta SWE coding round, one question about computing mutual friends in a graph. Pretty algorithmic but leaned more on clean implementation and test coverage than raw complexity.

Questions Asked (1)

Q1

Given a User class with an id and a list of friends, implement a function that returns the mutual friends of two users, with no duplicates, and write tests covering edge cases like empty lists and duplicate entries in friend lists.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The core logic is straightforward, intersection by id, but the test coverage requirement is where they actually spend time with you.

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AI HintsAI Generated

Suggested Approach

Clarify assumptions about the User class and friend list properties, then choose an efficient algorithm using hash sets to find mutual friends in O(n+m) time. Implement the function with deduplication, and write comprehensive tests covering edge cases like empty lists, duplicate entries, and no mutual friends.

Pro tip: Mention that using sets automatically handles duplicates and provides O(1) lookups, but if memory is a concern, sorting and two-pointer approach can be used. Also, discuss the trade-off between modifying input lists versus creating new sets.

1. Clarify requirements and assumptions

Ask if friend lists can contain duplicates, if order matters, and if the function should handle null inputs. Confirm the expected time and space complexity.

2. Choose data structures and algorithm

Decide to use hash sets for O(1) lookups and automatic deduplication. Consider converting the smaller friend list to a set for memory efficiency.

3. Implement the function

Write code that creates a set from one user's friends, then iterates through the other's friends, collecting those present in the set. Return the result as a list or set.

4. Write tests for edge cases

Include tests for empty friend lists, duplicate entries, no mutual friends, all mutual friends, and null inputs if applicable. Use assertions to verify correctness.

5. Analyze complexity and discuss trade-offs

Explain that the solution runs in O(n+m) time and O(min(n,m)) space. Mention alternative approaches like sorting and two-pointer if memory is constrained.

Key Points to Mention

  • Use of hash sets for O(1) lookups and automatic deduplication
  • Time complexity O(n+m) and space complexity O(min(n,m))
  • Handling edge cases: empty lists, duplicates, no mutual friends
  • Testing strategy: unit tests with assertions for each edge case
  • Trade-offs: set vs sorting approach, memory vs time
  • Assumptions about input: null safety, immutability of friend lists

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