---
title: '208. Implement Trie (Prefix Tree)'
description: A trie (pronounced as "try") or prefix tree is a tree data structure used to efficiently store and retrieve keys in a dataset of strings. There are various applications of this data structure, such as autocomplete and spellchecker
sidebar:
  label: 'Implement Trie (Prefix Tree)'
  badge: 'Medium'
---

Hash Table

### Example 1:
- Input: ``
- Output: ``
- Explanation: Input ["Trie", "insert", "search", "search", "startsWith", "insert", "search"] `[[]`, `["apple"]`, `["apple"]`, `["app"]`, `["app"]`, `["app"]`, ["app"]] Output [null, null, true, false, true, null, true] Explanation Trie trie = new Trie(); trie.insert("apple"); trie.search("apple");   // return True trie.search("app");     // return False trie.startsWith("app"); // return True trie.insert("app"); trie.search("app");     // return True

### Constraints:

- `1 <= word.length, prefix.length <= 2000`
- `word` and `prefix` consist only of lowercase English letters.
- At most 3 * 10^4 calls in total will be made to insert, search, and startsWith.

## 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 insert["insert(word)"]
    I1["cur = root"] --> I2{"more c in word?"}
    I2 -- yes --> I3{"c not in cur.children?"}
    I3 -- yes --> I4["cur.children[c] = TrieNode()"]
    I4 --> I5["cur = cur.children[c]"]
    I3 -- no --> I5
    I5 --> I2
    I2 -- no --> I6["cur.endOfWord = True"]
    I6 --> I7(["done"])
  end
  subgraph search["search(word)"]
    S1["cur = root"] --> S2{"more c in word?"}
    S2 -- yes --> S3{"c not in cur.children?"}
    S3 -- yes --> S4(["return False"])
    S3 -- no --> S5["cur = cur.children[c]"]
    S5 --> S2
    S2 -- no --> S6(["return cur.endOfWord — the path exists, but is it a whole word?"])
  end
  subgraph startsWith["startsWith(prefix)"]
    P1["cur = root"] --> P2{"more c in prefix?"}
    P2 -- yes --> P3{"c not in cur.children?"}
    P3 -- yes --> P4(["return False"])
    P3 -- no --> P5["cur = cur.children[c]"]
    P5 --> P2
    P2 -- no --> P6(["return True — every prefix char had a path"])
  end
  state --> insert
  state --> search
  state --> startsWith
```

## Solution

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


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

    def insert(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

    def search(self, word: str) -> bool:
        cur = self.root

        for c in word:
            if c not in cur.children:
                return False
            cur = cur.children[c]

        return cur.endOfWord

    def startsWith(self, prefix: str) -> bool:
        cur = self.root

        for c in prefix:
            if c not in cur.children:
                return False
            cur = cur.children[c]

        return True


# Your Trie object will be instantiated and called as such:
# obj = Trie()
# obj.insert(word)
# param_2 = obj.search(word)
# param_3 = obj.startsWith(prefix)
```

## Explanation

[Implement Trie (Prefix Tree) - Leetcode 208](https://www.youtube.com/watch?v=oobqoCJlHA0)