← Meta Interview Insights

Meta·Software Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

Meta production engineer interview with a coding-style question around text processing and frequency counting. Nothing too flashy but the follow-up discussion on memory constraints is where things got real.

Questions Asked (1)

Q1

Given a path to a text file (or a word stream), return the top N most frequent words in descending order of frequency, with ties broken alphabetically.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

Started with a Counter and full sort which works fine for small inputs, but they pushed back pretty fast asking what happens when the file is 50GB.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: file size, memory constraints, definition of a word, and expected N. Then propose a solution using a hash map to count frequencies, followed by sorting or a heap to extract the top N, discussing trade-offs between time and space complexity.

Pro tip: Mention that for very large files that don't fit in memory, you can use an external sort or a distributed approach like MapReduce, showing awareness of scalability beyond the basic algorithm.

1. Clarify Requirements

Ask about input size, memory limits, definition of a word (case sensitivity, punctuation), and whether N is known. This ensures you design the right solution.

2. Design Core Algorithm

Propose using a hash map to count word frequencies, then either sort all entries or use a min-heap of size N to find the top N. Discuss time and space complexity.

3. Handle Ties and Ordering

Explain how to break ties alphabetically: when frequencies are equal, compare words lexicographically. Ensure the final output is sorted by frequency descending, then alphabetically.

4. Optimize for Scale

If the file is huge, discuss streaming the file line by line, using external sorting, or a distributed approach like MapReduce. Mention memory-efficient data structures.

5. Test and Validate

Walk through edge cases: empty file, all words unique, N larger than unique words, punctuation handling. Suggest unit tests and performance benchmarks.

Key Points to Mention

  • Time complexity: O(W) for counting, O(U log U) for sorting or O(U log N) with heap, where W is total words and U is unique words.
  • Space complexity: O(U) for the hash map, which can be large; discuss memory constraints and possible optimizations.
  • Tie-breaking: sort by frequency descending, then alphabetically ascending; use a custom comparator.
  • Streaming approach: read file line by line to avoid loading entire file into memory.
  • Scalability: for massive data, consider MapReduce or external sorting; mention partitioning by word hash.
  • Edge cases: punctuation, case sensitivity, Unicode, and N larger than unique words.

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