How Trie Data Structure Is Implemented?

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Larry Thompson

The Trie data structure, also known as a prefix tree, is a tree-based data structure used for efficient retrieval of keys. It is particularly useful when dealing with problems that involve searching for words or prefixes in a large set of strings. In this article, we will explore how the Trie data structure is implemented.

What is a Trie?

A Trie is a tree-like data structure that stores strings. Each node in the Trie represents a character, and the edges represent the links between characters. The root of the Trie represents an empty string, and each path from the root to a leaf node forms a word.

Unlike other search data structures like arrays or hash tables, Tries provide efficient prefix-based lookups. They are commonly used in applications such as autocomplete systems and spell checkers.

Trie Implementation

To implement a Trie, we can define a class to represent each node in the tree. Each node can contain:

  • A boolean flag to indicate if it represents the end of a word.
  • An array or hashmap to store references to child nodes.

We can start with an empty root node and build the Trie by adding words one by one. When inserting a word into the Trie, we traverse through each character of the word and check if there is an existing child node representing that character. If not, we create one and link it to its parent node.


class TrieNode {
  boolean isEndOfWord;
  HashMap children;

  public TrieNode() {
    isEndOfWord = false;
    children = new HashMap<>();
  }
}

class Trie {
  private final TrieNode root;

  public Trie() {
    root = new TrieNode();
  }

  public void insert(String word) {
    TrieNode current = root;
    
    for (char c : word.toCharArray()) {
      current.children.putIfAbsent(c, new TrieNode());
      current = current.get(c);
    }
    
    current.isEndOfWord = true;
  }
}

// Usage example:
Trie trie = new Trie();
trie.insert("apple");
trie.insert("application");

Searching in a Trie

Searching in a Trie is quite similar to inserting. We start from the root node and traverse through each character of the search key. If at any point a character is not found in the child nodes, or we reach the end of the search key but the node doesn’t represent the end of a word, we conclude that the search key is not present in the Trie.


public boolean search(String word) {
  TrieNode current = root;
  
  for (char c : word.toCharArray()) {
    if (!current.containsKey(c)) {
      return false;
    }
    
    current = current.get(c);
  }
  
  return current.isEndOfWord;
}

The search operation has a time complexity proportional to the length of the search key. It can be significantly faster than other data structures when dealing with large sets of words.

Conclusion

The Trie data structure provides an efficient way to store and retrieve strings, especially when dealing with prefix-based searches. By using nodes to represent characters and linking them together, Tries allow for fast lookups and are widely used in various applications.

In this article, we explored how Tries are implemented using classes and methods. We learned about inserting words into a Trie, searching for words within it, and discussed their time complexities. Now that you understand the basics of Trie implementation, you can explore more advanced operations and optimizations to further enhance its functionality.

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