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 map of children characters and a boolean flag indicating if a word ends at this node.
For the add operation, we traverse the tree character by character. If a child doesn't exist, we create it. We mark the final node as an end-of-word marker. This takes linear time relative to the word length.
The search function is more complex due to the wildcard '.'. I will use a Depth-First Search approach. When encountering a regular character, we simply move to the corresponding child if it exists. However, when we hit a '.', we must branch out and recursively check every possible child node in the current level. The recursion continues until we reach the end of the query string. If we successfully land on a node marked as an end-of-word, we return true. If any recursive path fails, we backtrack.
Regarding complexity, adding a word of length L is O(L). Searching is trickier; in the worst case without wildcards, it is O(L). With wildcards, we might explore up to 26 branches per character, leading to O(26^L * L) in the absolute worst case, though typically much faster as invalid paths prune quickly. This approach aligns well with Netflix's need for flexible yet performant search capabilities across large datasets.
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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