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apps/docs/content/trie/208-implement-trie-prefix-tree.mdx

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+---
+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'
+---
+
+<Badge variant="accent">Hash Table</Badge>
+
+### 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
+
+<YouTube id="oobqoCJlHA0" title="Implement Trie (Prefix Tree) - Leetcode 208" />