The basic Trie structure came to me pretty fast.
Start by clarifying the requirements: the Trie should support adding words and searching for words where '.' matches any single character. Then, design the Trie with a node structure containing children (e.g., array or hashmap) and a boolean flag for word endings. For search with wildcards, implement a recursive DFS that branches when encountering a '.'.
Pro tip: Mention that using a hashmap for children can save space when the alphabet is large or sparse, but an array offers faster access for fixed small alphabets. Also, discuss the trade-off between recursion depth and iterative approaches for wildcard search.
Ask about the character set (e.g., lowercase letters), maximum word length, and whether the wildcard '.' can appear in add operations. Confirm that search should return true if any word matches the pattern.
Define a TrieNode class with a children data structure (array of size 26 or hashmap) and a boolean isEndOfWord. Explain your choice based on expected alphabet size and memory considerations.
Iterate through each character of the word, creating new nodes as needed, and mark the final node as end of word. This is straightforward and O(L) time.
Use recursive DFS: for a normal character, follow the corresponding child; for '.', recursively try all existing children. Return true if any path leads to a node marked as end of word at the end of the pattern.
Time complexity for search is O(26^M) in worst case (M = number of dots), but typically much less. Space is O(total characters). Mention possible optimizations like pruning or caching.
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