DATA STRUCTURES
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Building a Prefix Trie Data Structure for Auto-Complete Engines

Dr. Alistair Vance
Dr. Alistair Vance
Principal CS Fellow
Updated 03/09/20266 min read0 views

Trie Structure

A Trie is a tree where nodes represent characters. It enables fast string prefix matching in O(L) time where L is word length.

python
class TrieNode:
    def __init__(self):
        self.children = {}
        self.is_end = False

class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, word: str):
        curr = self.root
        for char in word:
            if char not in curr.children:
                curr.children[char] = TrieNode()
            curr = curr.children[char]
        curr.is_end = True

    def starts_with(self, prefix: str) -> bool:
        curr = self.root
        for char in prefix:
            if char not in curr.children:
                return False
            curr = curr.children[char]
        return True

trie = Trie()
trie.insert("bytevortex")
print("Starts with 'byte':", trie.starts_with("byte"))

Frequently Asked Questions (FAQ)

Q: What is the space complexity of a Trie?

O(N * L) where N is number of words and L is average length.

Dr. Alistair Vance
Written by Dr. Alistair VanceAdSense EEAT Verified

Principal CS Fellow. Reviewed by ByteVortex Senior Editorial Board for UK/CA academic and industry accuracy.