Trie (Prefix Tree)
Optimize prefix-based searches! Visualize how words are stored as a tree of characters. Key for autocomplete and spell checkers.
Trie (Prefix Tree)
Efficient String Storage! Store words by standardizing common prefixes.
Insert "cat"
Order the steps of inserting the word "cat" into a trie.
Tap a piece, then tap where it goes — or drag it. Tap the tray to take one back.
- 1Drop a step here
- 2Drop a step here
- 3Drop a step here
- 4Drop a step here
- 5Drop a step here
Test your knowledge
What is the Time Complexity of searching for a word of length L in a Trie?
Write the code
Implement Trie
The short version
Think of autocomplete on your phone. As you type 'A-P-P', it narrows down the massive dictionary to just the branch containing 'Apple', 'Appetizer', etc. efficient prefix lookup.
In more depth
The Prefix Tree
A Trie (pronounced "try") is a tree-like data structure used for efficiently storing and retrieving keys in a dataset of strings.
Why use it?
It excels at prefix-based searches. If you want to find all words starting with "App", a Hash Map can't help you easily, but a Trie can do it in O(L) time where L is the length of the prefix.
Structure:
Each node represents a character. Paths from the root to a node represent a string prefix.
When to reach for this
- Autocomplete / Typeahead suggestions
- Spell checker
- Longest Common Prefix
- Word Search strategies
Interview Questions
References & Further Reading
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