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Bytedance·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Bytedance SWE interview with a trie-based design question. Pretty standard algorithmic round but the wildcard search part trips people up if they haven't thought through the recursion carefully.

Questions Asked (1)

Q1

Design a data structure that supports adding words and searching for them, where the search pattern can include '.' as a wildcard that matches any single character.

Algorithms & Data StructuresSystem Design
Author's notes

I jumped straight to trie and felt good about addWord, but the search with '.' took me longer than I'd like to admit.

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AI HintsAI Generated

Suggested Approach

Use a trie (prefix tree) where each node represents a character and stores children in a hash map or array. For search with '.', recursively explore all children at that position. This gives O(1) add and O(26^m) worst-case search, but typically much faster.

Pro tip: Mention that you can optimize the wildcard search by pruning branches early and that using a hash map for children saves space when the alphabet is large. Also, clarify the trade-off between time and space upfront.

1. Clarify requirements and constraints

Ask about the character set (e.g., lowercase letters), maximum word length, and whether words can be added multiple times. This shows attention to detail and helps choose the right data structure.

2. Propose a trie as the core data structure

Explain that a trie efficiently stores words by sharing prefixes, and each node can have up to 26 children (or use a hash map). Adding a word is O(L) where L is the word length.

3. Design the search algorithm with wildcard support

For a normal character, follow the corresponding child. For '.', recursively search all existing children. Use DFS/backtracking to handle multiple wildcards.

4. Analyze time and space complexity

Add: O(L). Search: O(26^m) worst-case where m is the number of wildcards, but typically much less. Space: O(total characters) for the trie.

5. Discuss optimizations and edge cases

Mention pruning, using a hash map for sparse children, and handling empty strings or patterns. Also, consider iterative vs recursive search for stack depth.

Key Points to Mention

  • Trie (prefix tree) structure and its advantages for prefix-based operations
  • Recursive DFS for wildcard matching, exploring all children when encountering '.'
  • Time complexity: O(L) for add, O(26^m) worst-case for search with m wildcards
  • Space complexity: O(N * L) where N is number of words and L is average length
  • Using a hash map instead of an array for children to save space when alphabet is large
  • Edge cases: empty word, empty pattern, pattern with only wildcards, and duplicate words

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