208. Implement Trie (Prefix Tree)

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LeetCode

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Problem

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.

Implement the Trie class:

• Trie() Initializes the trie object.
• void insert(String word) Inserts the string word into the trie.
• boolean search(String word) Returns true if the string word is in the trie (i.e., was inserted before), and false otherwise.
• boolean startsWith(String prefix) Returns true if there is a previously inserted string word that has the prefix prefix, and false otherwise.

Example 1:

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.

Code

class Trie {
    class TrieNode {
        TrieNode[] children;
        boolean isWord;
        String word;

        public TrieNode(){
            children = new TrieNode[26];
            isWord = false;
            word = "";
        }
    }

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

    }

    public void insert(String word) {
        TrieNode node = root;
        for(int i = 0; i < word.length(); i++){
            int index = word.charAt(i) - 'a';
            if(node.children[index] == null){
                node.children[index] = new TrieNode();
            }
            node = node.children[index];
        }
        node.isWord = true;
    }

    public boolean search(String word) {
        TrieNode node = root;
        for(int i = 0; i < word.length(); i++){
            int index = word.charAt(i) - 'a';
            if(node.children[index] == null) return false;
            node = node.children[index];
        }

        return node.isWord;
    }

    public boolean startsWith(String prefix) {
        TrieNode node = root;
        for(int i = 0; i < prefix.length(); i++){
            int index = prefix.charAt(i) - 'a';
            if(node.children[index] == null) return false;
            node = node.children[index];
        }
        return true;
    }
}

how to save trie to db

• Persistent storage
• Two options
  • Document store - like MongoDB
    • Serialize trie
  • key-value store
    • A trie can be represented in a hash table form
    • Every prefix in the trie is mapped to a value in a hash table
    • Data on each trie node is mapped to a value in a hash table