Tries

A trie (pronounced as “try”) or prefix tree is a tree data structure used for storing and searching a specific key from a set, usually a string set.

Using Trie, search complexities can be brought to O(n)(n = key length).

208. Implement Trie (Prefix Tree)

Properties

Below are some important properties of the Trie data structure¹:

• There is one root node in each Trie.
• Each node of a Trie represents a string and each edge represents a character.
• Every node consists of hashmaps or an array of pointers, with each index representing a character and a flag to indicate if any string ends at the current node.
• Trie data structure can contain any number of characters including alphabets, numbers, and special characters. But for this article, we will discuss strings with characters a-z. Therefore, only 26 pointers need for every node, where the 0th index represents ‘a’ and the 25th index represents ‘z’ characters. 我们仅讨论key为[a-z]的Trie.
• Each path from the root to any node represents a word or string.

Trie Data Structure

Data Structure

Every node of Trie consists of multiple branches. Each branch represents a possible character of keys:

An array of pointers inside every Trie node

Mark the last node of every key as the end of the word node. A Trie node field isEndOfWord is used to distinguish the node as the end of the word node.

public static class TrieNode{
        public TrieNode[] children;
        boolean isEndOfWord; // isEndOfWord is true if the node represents end of a word

        public TrieNode()
        {
            isEndOfWord = false;
            children = new TrieNode[ALPHABET_SIZE];
            for(TrieNode child: children)
                child = null;
        }
    }

Operations

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

public class Trie {

    // Alphabet size (# of symbols)
    static final int ALPHABET_SIZE = 26;

    public static class TrieNode{
			//...
    }

    private TrieNode root = new TrieNode();
    
    public void insert(String word) {
        __insert(word, root);
    }
    
    public boolean search(String word) {
        return __search(word, root);
    }
    
    public boolean startsWith(String prefix) {
        return __startsWith(prefix, root);
    }
}

Insert

每一个输入的word都对应一条path.

Insertion operation

使用递归的思路进行插入:

// If not present, inserts key into trie.
// If the key is prefix of trie node, just marks leaf node.
public void insert(String word) {
    __insert(word, root);
}

private void __insert(String word, TrieNode currentRoot) {
    if(word.length() == 0)
        return;

    int idx = word.charAt(0) - 'a';

    if(currentRoot.children[idx] == null) currentRoot.children[idx] = new TrieNode();
    if(word.length() == 1)
        currentRoot.children[idx].isEndOfWord = true;// mark last node as leaf

    __insert(word.substring(1), currentRoot.children[idx]);
}

Search

Searching in Trie

使用递归的思路进行查找. 如果当前字符匹配且该word只剩下这1个字符(currentRoot.children[idx] != null && word.length() == 1), 那么如果当前node的isEndOfWord==false, 则返回false; 如果isEndOfWord==true, 则返回true.

public boolean search(String word) {
    return __search(word, root);
}

// Returns true if key presents in trie, else false
private boolean __search(String word, TrieNode currentRoot) {
    if(word.length() == 0)//If input is a null string, we just return false.
        return false;

    int idx = word.charAt(0) - 'a';
    if(currentRoot.children[idx] == null) return false;
    else
    {
        if(word.length() == 1)
            return currentRoot.children[idx].isEndOfWord;
        else
            return __search(word.substring(1), currentRoot.children[idx]);
    }
}

StartsWith

和查找差不多, 但只要输入和某个路径的前面一部分匹配即可:

Trie trie = new Trie();
trie.insert("apple");
trie.search("apple");   // return True
trie.search("app");     // return False
trie.startsWith("app"); // return True

public boolean startsWith(String prefix) {
    return __startsWith(prefix, root);
}

private boolean __startsWith(String word, TrieNode currentRoot) {
    if(word.length() == 0)//If input is a null string, we just return false.
        return false;

    int idx = word.charAt(0) - 'a';
    if(currentRoot.children[idx] == null) return false;
    else
    {
        if(word.length() == 1)
            return true;
        else
            return __startsWith(word.substring(1), currentRoot.children[idx]);
    }
}

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1. https://www.geeksforgeeks.org/introduction-to-trie-data-structure-and-algorithm-tutorials/↩︎