Is Implement Trie (Prefix Tree) easy or hard?

Implement Trie (Prefix Tree) is rated Medium because the idea is simple but requires careful data structure design. Candidates must correctly manage node creation, prefix traversal, and word termination flags while keeping operations efficient.

Implement Trie (Prefix Tree) Python/Java solution

In Python or Java, implement a TrieNode class containing children references and a boolean end-of-word flag. The Trie class manages insert, search, and startsWith by traversing nodes character by character. Each method processes the string sequentially, giving O(L) time complexity.

How to solve Implement Trie (Prefix Tree) in O(n)?

The problem is solved using a Trie where operations depend only on the length of the word being processed. By traversing characters sequentially and storing children pointers for each character, insert, search, and prefix checks all run in O(L) time. This avoids scanning all stored words, which would be much slower.

What is the best approach for Implement Trie (Prefix Tree)?

The standard solution uses a Trie with a fixed array of 26 children per node for lowercase English letters. Each operation (insert, search, startsWith) traverses characters one by one, giving O(L) time where L is the word length. Array indexing makes it faster than map-based implementations in most interview scenarios.

Is Implement Trie (Prefix Tree) asked at Google/Amazon/Meta?

Trie design questions frequently appear in interviews at companies like Google, Amazon, Meta, and Microsoft. Variations include autocomplete systems, word dictionaries with wildcards, and prefix-based search structures. Understanding Trie traversal and node design is commonly tested.

What data structure is used in Implement Trie (Prefix Tree)?

The core data structure is a Trie (prefix tree). Each node represents a character and stores references to child nodes plus a flag marking the end of a word. Implementations typically use either a fixed array of children or a hash map for dynamic branching.

What is the time complexity of Implement Trie (Prefix Tree)?

Each operation runs in O(L) time where L is the length of the input string. Insert creates nodes while traversing characters, search verifies a full path, and startsWith checks whether a prefix path exists. Space complexity is O(N * L) in the worst case for storing all inserted words.

Description Solution Video 18:56

208. Implement Trie (Prefix Tree)

Medium69.9% acceptance21 companies

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 * 10⁴ calls in total will be made to insert, search, and startsWith.