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Bytedance·Frontend Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026Remote

Summary

Bytedance frontend round that was heavier on algorithms than I expected. One coding problem, sliding window style, and it went okay but I definitely fumbled some of the edge case handling under pressure.

Questions Asked (1)

Q1

Given two strings, determine whether one string contains any permutation of the other as a contiguous substring.

Algorithms & Data Structures
Author's notes

I knew the sliding window approach going in, but fumbled the implementation of the match counter.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose a sliding window approach using frequency counts to achieve O(n) time. Explain the algorithm step-by-step, analyze time and space complexity, and discuss potential optimizations or alternative solutions.

Pro tip: Mention that you can optimize the sliding window by tracking the number of matched characters instead of comparing frequency arrays each time, reducing constant factors. Also, discuss how this problem relates to real-world frontend scenarios like detecting anagrams in user input or URL patterns.

1. Clarify the problem

Ask about constraints: string lengths, character set (ASCII/Unicode), case sensitivity, and whether empty strings are allowed. Confirm that 'permutation' means any rearrangement of characters.

2. Outline the approach

Propose a sliding window of length equal to the shorter string, maintaining frequency counts of characters in the window and comparing with the frequency of the shorter string.

3. Detail the algorithm

Initialize frequency arrays for the pattern and the first window. Slide the window one character at a time, updating counts and checking for a match. Use a variable to track matches for efficiency.

4. Analyze complexity

State that time complexity is O(n) where n is the length of the longer string, and space complexity is O(1) if the character set is fixed (e.g., 26 lowercase letters).

5. Discuss edge cases and optimizations

Handle cases where the shorter string is longer than the longer string, empty strings, and repeated characters. Mention possible optimizations like early exit or using a hash map for larger character sets.

Key Points to Mention

  • Sliding window technique for contiguous substrings
  • Frequency counting using arrays or hash maps
  • Time complexity O(n) and space complexity O(1) for fixed alphabet
  • Handling edge cases: empty strings, different lengths, case sensitivity
  • Optimization by tracking matched characters to avoid full array comparison
  • Relevance to frontend tasks like anagram detection or string pattern matching

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