Trie data structure is similar to a tree structure but optimised to deal with strings, it is commonly used in many applications that involve a lot of data and manipulation of strings such as spelling correction, auto-complete and text search. There is also a variation of the Trie data structure that became popular because of the Blockchain, which is the Merkle Patricia Trie, however in this article we will only explore the Trie data structure.

In a trie, each node represents a character or a string of characters, and the path from the root node to a leaf node represents a complete word. This design allows for a fast search and retrieval of data as each character in a word is stored in its own node, making it easy to find the node that represents a specific word.

How Trie Data Structures Work

Trie data structures work by creating a tree-like structure, where each node represents a character or a string of characters. The root node represents an empty string, and each child node represents a single character. As more characters are added to the trie, additional child nodes are created to represent the new characters.

For example, if we have a trie containing the words “apple”, “banana”, and “pear”, the root node would have three child nodes, one for each letter in the words “apple”, “banana”, and “pear”. Each of these child nodes would then have additional child nodes, representing the next character in the word. This process is repeated until all the characters in a word have been added to the trie.

When searching for a word in the trie, we simply follow the path from the root node to the leaf node that represents the word. This process is much faster than searching for a word in a traditional data structure such as a linked list or an array because we only need to search for the characters in the word, rather than searching through every element in the data structure.

Python Implementation

class TrieNode:
    def __init__(self):
        self.children = {}
        self.end_of_word = False
        
class Trie:
    def __init__(self):
        self.root = TrieNode()
        
    def insert(self, word):
        current = self.root
        for char in word:
            if char not in current.children:
                current.children[char] = TrieNode()
            current = current.children[char]
        current.end_of_word = True
        
    def search(self, word):
        current = self.root
        for char in word:
            if char not in current.children:
                return False
            current = current.children[char]
        return current.end_of_word

Conclusion

Tries are very similar to tree data structures but they are specialized to store and manipulate string(words) data. They scale well so it is a good alternative to deal with large amounts of data and it can be used on several different applications such as auto complete, text filters, search engines and pretty much anything related to words.