Trees for storing strings efficiently
Day 134 of 149
👉 Full deep-dive with code examples
The Autocomplete Analogy
When you type on your phone:
- Type "hel" → Suggestions: "hello", "help", "helicopter"
- Type "help" → Suggestions: "help", "helpful", "helpless"
The phone quickly finds all words starting with what you typed.
A Trie is the data structure that makes autocomplete super fast!
The Problem It Solves
Searching through all words is slow:
- Dictionary has 100,000 words
- Check if "hello" starts with "hel"... for each word?
- Very slow!
We need a smarter way to find words by their beginning.
How Tries Work
A Trie is a tree where:
- Each path from root spells a word
- Shared prefixes share paths
- Branching happens where words differ
(root)
/ \
c h
/ \
a e
/ \ \
r t l
| | |
[car][cat] l
|
o
|
[hello]
"car" and "cat" share "ca" path, then split.
Why It's Fast
To find "cat":
- Start at root
- Go to 'c' → found
- Go to 'a' → found
- Go to 't' → found, it's a word!
Only 3 steps, regardless of dictionary size!
Common Uses
- Autocomplete → Find all words starting with prefix
- Spell checking → Is this word in the dictionary?
- IP routing → Match network prefixes
- Predictive text → Suggest next words
Trie vs Hash Table
| Hash Table | Trie | |
|---|---|---|
| Find exact word | Fast | Fast |
| Find by prefix | Slow (check all) | Fast! |
| Memory | More compact | Uses more memory |
In One Sentence
A Trie is a tree structure that organizes strings by their characters, making it incredibly fast to find all words that start with a given prefix.
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