leetcode/apps/docs/content/trie/211-design-add-and-search-words-data-structure.mdx

---
title: '211. Design Add and Search Words Data Structure'
description: Design a data structure that supports adding new words and finding if a string matches any previously added string
sidebar:
  label: 'Design Add and Search Words Data Structure'
  badge: 'Medium'
---

<Badge variant="accent">String</Badge>

### Constraints:

- `1 <= word.length <= 25`
- `word` in addWord consists of lowercase English letters.
- `word` in search consist of '.' or lowercase English letters.
- There will be at most 2 dots in `word` for search queries.
- At most 10^4 calls will be made to addWord and search.

## Approach

```mermaid
flowchart TD
  subgraph state["state — root TrieNode; every node holds a children dict and an endOfWord flag"]
    R["root"] --- C["... children[c] ..."] --- L["endOfWord = True — a word ends here"]
  end
  subgraph addWord["addWord(word)"]
    A1["cur = root"] --> A2{"more c in word?"}
    A2 -- yes --> A3{"c not in cur.children?"}
    A3 -- yes --> A4["cur.children[c] = TrieNode()"]
    A4 --> A5["cur = cur.children[c]"]
    A3 -- no --> A5
    A5 --> A2
    A2 -- no --> A6["cur.endOfWord = True"]
    A6 --> A7(["done"])
  end
  subgraph search["search(word)"]
    S1["dfs(0, root) — match from index 0 at the root"] --> S2["cur = root"]
    S2 --> S3{"more i in range(j, len(word))?"}
    S3 -- no --> S4(["return cur.endOfWord"])
    S3 -- yes --> S5["c = word[i]"]
    S5 --> S6{"c == '.'?"}
    S6 -- yes --> S7{"more child in cur.children.values()?"}
    S7 -- yes --> S8{"dfs(i + 1, child)? — try the rest of the word from this child"}
    S8 -- yes --> S9(["return True"])
    S8 -- no --> S7
    S7 -- no --> S10(["return False — no child matched the wildcard"])
    S6 -- no --> S11{"c not in cur.children?"}
    S11 -- yes --> S12(["return False"])
    S11 -- no --> S13["cur = cur.children[c]"]
    S13 --> S3
  end
  state --> addWord
  state --> search
```

## Solution

```py
class TrieNode:
    def __init__(self):
        self.children = {}
        self.endOfWord = False


class WordDictionary:
    def __init__(self):
        self.root = TrieNode()

    def addWord(self, word: str) -> None:
        cur = self.root
        for c in word:
            if c not in cur.children:
                cur.children[c] = TrieNode()
            cur = cur.children[c]
        cur.endOfWord = True

    # Leverage Recursion
    def search(self, word: str) -> bool:
        def dfs(j, root):
            cur = root
            for i in range(j, len(word)):
                c = word[i]

                if c == ".":
                    for child in cur.children.values():
                        if dfs(i + 1, child):
                            return True
                    return False
                else:
                    if c not in cur.children:
                        return False
                    cur = cur.children[c]
            return cur.endOfWord

        return dfs(0, self.root)


# Your WordDictionary object will be instantiated and called as such:
# obj = WordDictionary()
# obj.addWord(word)
# param_3 = obj.search(word)
```

## Explanation

<YouTube id="BTf05gs_8iU" title="Design Add and Search Words Data Structure - Leetcode 211 - Python" />