How to Build a Trie Data Structure for Fast Autocomplete in Python

A developer building a side project encountered severe performance issues when implementing autocomplete using a naive loop that scanned an entire 100,000-word dictionary on every keystroke. The solution was a Trie, a tree-based data structure where each node represents a single character and shared prefixes reuse the same nodes, eliminating redundant scanning. Inserting words costs O(L) time across total characters, while prefix lookups require only O(p) steps for a prefix of length p. The article walks through a Python implementation covering node definition, word insertion, depth-first search collection, and a starts_with method that exits early when no matching prefix exists. Common pitfalls highlighted include forgetting to flag word-ending nodes and incorrectly mutating shared prefix strings during recursive traversal.