A Trie, also known as a Prefix Tree, is a tree-like data structure that stores a dynamic set of strings. It is commonly used for efficient retrieval of a key in a dataset of strings, such as autocomplete features and spell checkers.

What are the common operations on a Trie?

The main operations on a Trie are 'insert' (to add a word to the trie), 'search' (to check if a word exists), and 'startsWith' (to check if there is any word in the trie that starts with a given prefix).

Data Structures

Trie (Prefix Tree)

A tree-based data structure for efficient storage and retrieval of strings, perfect for applications like autocomplete and spell checkers.

Trie in Python

Here is a Python implementation of a Trie, including methods for inserting words, searching for full words, and checking for prefixes.

class TrieNode:
    def __init__(self):
        self.children = {}
        self.is_end_of_word = False

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

    def insert(self, word):
        node = self.root
        for char in word:
            if char not in node.children:
                node.children[char] = TrieNode()
            node = node.children[char]
        node.is_end_of_word = True

    def search(self, word):
        node = self.root
        for char in word:
            if char not in node.children:
                return False
            node = node.children[char]
        return node.is_end_of_word

    def startsWith(self, prefix):
        node = self.root
        for char in prefix:
            if char not in node.children:
                return False
            node = node.children[char]
        return True

AI Coach Hint: Tries are particularly powerful when you need to perform prefix-based searches. Consider how you could extend this implementation to return all words with a given prefix, a common follow-up question in interviews.