Is Design Add and Search Words Data Structure easy or hard?

The problem is rated Medium on LeetCode. The difficulty comes from combining trie traversal with DFS to support wildcard characters. Understanding how to branch search paths efficiently is the key insight.

Design Add and Search Words Data Structure Python/Java solution

The standard solution uses a Trie class where each node contains a map or array of children and a boolean end-of-word flag. Python implementations typically use dictionaries for children, while Java often uses arrays or HashMaps. The search function performs DFS when encountering '.' to explore multiple branches.

How to solve Design Add and Search Words Data Structure in O(n)?

Insertion is O(m) using a trie because each character is processed once. Search is also close to O(m) when the pattern has no wildcards. The trie ensures you only traverse valid prefixes instead of scanning all stored words, which keeps operations proportional to word length rather than dictionary size.

What is the best approach for Design Add and Search Words Data Structure?

The trie-based approach with DFS is the most efficient and commonly expected solution. Words are stored in a prefix tree, and wildcard '.' characters trigger a DFS across child nodes. Adding a word takes O(m) time where m is the word length, and search is typically O(m) but may branch to O(26^w * m) in worst cases with many wildcards.

Is Design Add and Search Words Data Structure asked at Google/Amazon/Meta?

Trie design problems frequently appear in interviews at companies like Google, Amazon, and Meta because they test data structure design, recursion, and efficient string handling. Variations of this problem also appear in autocomplete systems and dictionary search features.

What data structure is used in Design Add and Search Words Data Structure?

The primary data structure is a Trie (prefix tree). Each node stores child pointers for characters and a flag marking the end of a word. During search, DFS is used when encountering the '.' wildcard to explore all valid character branches.

What is the time complexity of Design Add and Search Words Data Structure?

Using a trie, addWord runs in O(m) time where m is the length of the word. Search usually runs in O(m) for normal characters. If the pattern contains w wildcard '.' characters, the worst-case complexity becomes O(26^w * m) because each wildcard can branch into multiple child nodes.

Description Hints 1 Solution Video 19:50

211. Design Add and Search Words Data Structure

Medium48.9% acceptance13 companies

Design a data structure that supports adding new words and finding if a string matches any previously added string.

Implement the WordDictionary class:

WordDictionary() Initializes the object.
void addWord(word) Adds word to the data structure, it can be matched later.
bool search(word) Returns true if there is any string in the data structure that matches word or false otherwise. word may contain dots '.' where dots can be matched with any letter.

Example:

Input
["WordDictionary","addWord","addWord","addWord","search","search","search","search"]
[[],["bad"],["dad"],["mad"],["pad"],["bad"],[".ad"],["b.."]]
Output
[null,null,null,null,false,true,true,true]

Explanation
WordDictionary wordDictionary = new WordDictionary();
wordDictionary.addWord("bad");
wordDictionary.addWord("dad");
wordDictionary.addWord("mad");
wordDictionary.search("pad"); // return False
wordDictionary.search("bad"); // return True
wordDictionary.search(".ad"); // return True
wordDictionary.search("b.."); // return True

Constraints: