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Trie (Prefix Tree) Data Structure

Certification: Advanced Level Accuracy: 100% Submissions: 2 Points: 15

Write a program to implement a Trie (Prefix Tree) data structure for efficient word storage and retrieval.

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:

      

    • Insert "apple", search returns true.
      • 

    • Search "app" returns false.
      • 

    • startsWith("app") is true.
      • 

    • Insert "app", now search returns true.
      • 

    

    •
Example 2
    

  • Input:
    ["Trie", "insert", "insert", "search", "startsWith", "search"]
    [[], ["hello"], ["world"], ["world"], ["worl"], ["wor"]]
    • 

  • Output:
    [null, null, null, true, true, false]
    • 

  • Explanation:

      

    • Insert "hello" and "world".
      • 

    • search("world") returns true.
      • 

    • startsWith("worl") true, search("wor") false.
      • 

    

    •
Constraints
    

  • 1 ≤ word.length, prefix.length ≤ 2000
    • 

  • Only lowercase English letters allowed
    • 

  • At most 3 * 10^4 calls to insert/search/startsWith
    • 

  • Time Complexity: O(m)
    • 

  • Space Complexity: O(n * m)
    •
Trie TreeHCL TechnologiesApple
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Hint
Solution

Solution Hints

    
  
  • Use a tree-like data structure where each node represents a single character
    • 
  
  • Each node should contain links to child nodes, one for each possible character
    • 
  
  • Mark nodes that represent the end of a word
    • 
  
  • For startsWith(), you only need to check if the prefix path exists
    • 
  
  • For search(), check if the path exists AND if the last node is marked as the end of a word
    •

Steps to solve by this approach:

 Step 1: Create a TrieNode class that will serve as the building block for our Trie. Each node contains a hashmap of children and a boolean flag indicating if it's the end of a word.

 Step 2: Implement the Trie class with a root TrieNode.

 Step 3: For insertion, traverse the Trie character by character, creating new nodes as needed, and mark the final node as the end of a word.

 Step 4: For searching, traverse the Trie following the path of the word. Return true only if the final node exists and is marked as the end of a word.

 Step 5: For startsWith, traverse the Trie following the path of the prefix. Return true if the entire prefix path exists, regardless of whether it's marked as the end of a word.

Submitted Code :