Design Searchable Dictionary (Trie + DFS)
Design a data structure that supports adding words and searching for words. The search query can contain the wild-card character '.' that matches any letter.
Why Interviewers Ask This
Netflix evaluates candidates on scalable data structure design and recursive problem-solving. This specific question tests your ability to implement a Trie for efficient prefix storage while handling complex wildcard logic via Depth-First Search. It assesses whether you can balance time complexity against memory usage, a critical skill for their high-throughput streaming recommendation engines.
How to Answer This Question
1. Clarify requirements: Confirm if the dictionary is case-sensitive and what the maximum word length is. 2. Propose the Data Structure: Immediately suggest a Trie (Prefix Tree) where each node represents a character, with a boolean flag marking end-of-word nodes. 3. Define Add Logic: Explain how inserting a word involves traversing or creating child nodes for each character in O(L) time. 4. Design Search Strategy: Detail that standard search follows the path, but wildcards '.' require branching. For '.', iterate through all 26 children recursively. 5. Implement DFS: Describe a recursive helper function that tracks current index and returns true if a leaf node with end-of-word flag is reached. 6. Analyze Complexity: Discuss that insertion is O(M), while search is O(26^W * M) worst-case due to branching, where W is word length. 7. Optimize: Mention pruning branches that cannot lead to valid words to improve average performance.
Key Points to Cover
- Explicitly choosing a Trie over a Hash Map for prefix-based operations
- Correctly implementing backtracking logic for wildcard character expansion
- Clearly distinguishing between standard traversal and recursive branching scenarios
- Providing accurate time complexity analysis considering the exponential nature of wildcards
- Demonstrating clean code structure with clear separation of add and search logic
Sample Answer
To solve this efficiently, I would implement a Trie data structure. Unlike a hash map, a Trie allows us to store prefixes compactly and handle pattern matching naturally. First, we define a TrieNode class containing a ma…
Common Mistakes to Avoid
- Attempting to use simple regex matching on a stored list which results in poor O(N*L) performance
- Failing to handle the base case where the recursion reaches the end of the search pattern
- Ignoring the distinction between a node existing and a node being a valid end-of-word marker
- Not explaining why the Trie is chosen despite higher memory overhead compared to other structures
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