Trie Pattern: LeetCode Guide & Problems

Prefix tree for efficient string search, autocomplete, and word matching.

✓ Pattern Solutions & Guides Practice Mode Enabled

5 Problem 0 Easy 4 Medium 1 Hard

Overview

A Trie (prefix tree) stores strings character by character, enabling O(m) search where m is the word length. It is the go-to structure for prefix queries and word dictionaries.

When to Use

Implement Trie (insert/search/startsWith)
Word search II
Design add/search words
Replace words
Autocomplete

Common Mistakes

Forgetting the isEnd flag on the last character
Memory overhead — each node has 26 children pointers
Using string concatenation in DFS — pass index instead

Complexity

Time

O(m) per operation where m is word length

Space

O(ALPHABET_SIZE × m × n)

class TrieNode {
    Map<Character, TrieNode> children = new HashMap<>();
    boolean isEnd = false;
}

class Trie {
    private final TrieNode root = new TrieNode();

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

    public boolean search(String word) {
        TrieNode node = root;
        for (char ch : word.toCharArray()) {
            if (!node.children.containsKey(ch)) return false;
            node = node.children.get(ch);
        }
        return node.isEnd;
    }
}

Problems (5)

Hide completed

Problem	Difficulty	Companies	Freq	Solution
Implement Trie (Prefix Tree) #Design #Trie	Medium	AmazonGoogle +2	High	Solution
Design Add and Search Words Data Structure #Design #Trie #DFS	Medium	FacebookBloomberg	Medium	Solution
Word Search II #Array #String #Backtracking	Hard	AmazonMicrosoft +1	Medium	Solution
Longest Word in Dictionary #Array #Hash Table #String	Medium	Google	Low	Solution
Replace Words #Array #Hash Table #String	Medium	FacebookAmazon	Medium	Solution

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Mastering the Trie Pattern for Coding Interviews

1. Mental Model & Visual Intuition

The Trie pattern is designed to eliminate redundant work and reduce search spaces. Rather than evaluating every possible state using a naive brute-force approach, this pattern relies on an invariant that allows you to safely discard subproblems that cannot lead to an optimal answer. When encountering an interview question, look for core triggers such as monotonicity, contiguous constraints, optimal substructure, or overlapping subproblems.

2. Algorithmic Invariants & Step-by-Step Execution

To implement this pattern flawlessly under interview conditions, structure your thought process into three discrete steps:

State Initialization: Define your boundaries, pointers, or memoization tables clearly before entering loops.
State Transition: Update tracking variables incrementally as new elements enter or leave your active evaluation window.
Feasibility Check: When constraints are violated, systematically shrink or adjust your state until the valid invariant is restored.

3. Time and Space Complexity Trade-Offs

In top-tier technical interviews at Google, Meta, and Amazon, arriving at a working solution is only half the battle. You must explicitly justify why your approach achieves optimal Big-O bounds. For Trie, the target time complexity is typically O(m) per operation where m is word length while maintaining an auxiliary space complexity of O(ALPHABET_SIZE × m × n). Always discuss whether an in-place modification or two-pass traversal can save memory.

4. Common Interview Edge Cases to Test

Before informing your interviewer that you have completed coding, trace your logic against these standard high-failure test cases:

Empty inputs, single-element collections, or all-identical values.
Inputs with negative numbers, duplicates, or extreme values that could cause integer overflow.
Odd versus even length inputs when dealing with pointer convergence or tree midpoints.

Tries solve prefix-matching and autocomplete in O(L) time, beating hash maps for prefix queries. Ful…