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Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Meta SWE interview with a single technical question on hash maps. Pretty standard stuff but worth knowing cold if you're going in.

Questions Asked (1)

Q1

How do hash maps work?

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Thought I had this but midway through explaining collision resolution I started second-guessing myself on open addressing vs chaining.

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AI HintsAI Generated

Suggested Approach

Start by defining a hash map as a key-value data structure that uses a hash function to map keys to buckets for average O(1) lookups. Then explain the core components (hash function, buckets, collision handling) and discuss trade-offs like load factor and resizing. Finally, mention real-world applications and how it compares to other data structures.

Pro tip: Emphasize the importance of a good hash function and how it affects performance; also mention that while average time is O(1), worst-case is O(n) due to collisions, showing you understand the nuances.

1. Define and Purpose

Define what a hash map is and its primary use case: efficient key-value storage and retrieval.

2. Core Components

Explain the hash function, buckets (or slots), and how keys are hashed to indices.

3. Collision Resolution

Describe common collision handling techniques like chaining (linked lists) and open addressing (linear probing, quadratic probing, double hashing).

4. Performance and Trade-offs

Discuss average vs worst-case time complexity, load factor, resizing (rehashing), and how these affect performance.

5. Real-world Applications

Give examples of where hash maps are used, such as database indexing, caching, and language dictionaries.

Key Points to Mention

  • Hash function: maps keys to indices, should be deterministic and uniform.
  • Collision resolution: chaining vs open addressing, and their trade-offs.
  • Time complexity: average O(1) for insert, delete, lookup; worst-case O(n).
  • Load factor and resizing: when to resize and how it affects performance.
  • Space complexity: O(n) and how it balances with time efficiency.
  • Comparison to other data structures: arrays, balanced trees (e.g., O(log n) operations).

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