DATA STRUCTURES
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Building a Prefix Trie Data Structure for Auto-Complete Engines
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.
Written by Dr. Alistair VanceAdSense EEAT Verified
Principal CS Fellow. Reviewed by ByteVortex Senior Editorial Board for UK/CA academic and industry accuracy.