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The insert part was fine, standard Trie node with 26 children.
Start by clarifying the requirements: the dictionary supports inserting words and searching patterns where '.' matches any single lowercase letter. Then design a Trie where each node has an array of 26 children and a boolean flag for end-of-word, and implement search recursively: for a regular character, follow the corresponding child; for '.', recursively try all non-null children. Finally, discuss time and space complexity and potential optimizations like memory pooling or compressed tries.
Pro tip: Mention that the recursive search for '.' can be optimized by pruning branches early and that you can use a vector of child pointers instead of a fixed array to save memory for sparse nodes. Also, clarify that the solution should handle edge cases like empty strings and patterns with consecutive dots.
Confirm that words and patterns consist only of lowercase letters and '.', and that '.' matches exactly one character. Ask about expected word length, number of operations, and memory constraints.
Define a TrieNode with an array of 26 pointers (or a hash map) for children and a boolean isEndOfWord. Explain that this structure allows efficient prefix-based operations.
Traverse the Trie for each character in the word, creating nodes as needed, and mark the last node as end-of-word. This is O(L) time where L is word length.
Use a recursive helper function that takes a node and the pattern index. If the current pattern character is '.', recursively search all non-null children; otherwise, follow the specific child. Return true if the pattern is fully consumed and the node is end-of-word.
State that search time is O(26^d * L) in the worst case where d is the number of dots, but typically much faster. Mention space optimizations like using a map for children or a compressed trie (radix tree) if memory is a concern.
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