How does fuzzysearch performance compare to regex-based search?

Fuzzysearch is significantly faster than regex-based implementations, as shown in the performance chart in the README, because it uses an optimized algorithm for sequential character matching without regex overhead.

Can I use fuzzysearch for typo correction?

No, fuzzysearch requires characters to appear in order without approximation, so it won't handle typos or insertions/deletions. For typo correction, consider libraries with levenshtein distance.

How to integrate fuzzysearch into a React autocomplete component?

You can filter options in real-time by calling `fuzzysearch` on user input against your dataset. Check out the horsey demo linked in the README for a practical example of an autocomplete implementation.

Fuzzysearch vs fuse.js: what are the differences?

Fuzzysearch is faster and simpler, focusing on strict sequential matching with a tiny API, while fuse.js offers more features like scoring, typo tolerance, and configuration, making it better for complex searches but heavier.

Does fuzzysearch handle non-English characters or Unicode?

The README doesn't specify, but since it's basic character matching, it should work with Unicode if the JavaScript engine handles it, but you may need to normalize strings externally for consistency.

What are the limitations of fuzzysearch's matching algorithm?

It only matches characters in exact order without approximation, so it's not suitable for fuzzy searches that need to account for typos, transpositions, or provide similarity scores beyond a simple boolean result.

Open-Awesome

fuzzysearch

MITJavaScript

A tiny and blazing-fast fuzzy search implementation in JavaScript for flexible string matching.

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2.7k stars84 forks0 contributors

What is fuzzysearch?

Fuzzysearch is a tiny JavaScript library that performs fuzzy string matching, allowing flexible partial input matching for filtering data quickly. It solves the problem of efficiently matching user input against a dataset in real-time, such as in autocomplete components, without the computational cost of levenshtein distance algorithms.

Target Audience

JavaScript developers building autocomplete components, search interfaces, or data filtering features that require fast and lightweight fuzzy matching.

Value Proposition

Developers choose Fuzzysearch for its exceptional speed and minimal footprint, offering a simple yet highly optimized alternative to regex-based or complex distance-based fuzzy search implementations.

Overview

:crystal_ball: Tiny and blazing-fast fuzzy search in JavaScript

Use Cases

Best For

Building autocomplete components with real-time filtering
Implementing fast search interfaces in web applications
Filtering large datasets based on partial user input
Creating lightweight in-memory search utilities
Adding fuzzy matching to command-line tools or Node.js applications
Performance-critical applications where regex-based search is too slow

Not Ideal For

Projects requiring typo-tolerant search with levenshtein distance or edit distance calculations
Applications that need ranked or scored fuzzy matching results for better user experience
Complex search systems requiring configurable matching algorithms or multiple criteria

Pros & Cons

Pros

Blazing Fast Performance

The library significantly outperforms regex-based implementations, as evidenced by the performance chart in the README showing it's optimized for speed.

Tiny and Lightweight

With minimal dependencies and a small codebase, it's ideal for performance-critical applications where bundle size matters.

Simple and Intuitive API

It offers a single function `fuzzysearch(needle, haystack)` that returns a boolean, making it easy to integrate without a steep learning curve.

Optimized Algorithm

Uses an algorithm suggested by a V8 engineer, ensuring efficiency in JavaScript engines for real-time filtering.

Cons

No Approximation or Scoring

It doesn't implement levenshtein distance, so it can't handle typos or provide similarity scores, limiting its use for advanced fuzzy search scenarios.

Limited Customization

The API is fixed with no options for tuning matching behavior, such as case sensitivity or partial word matching, requiring external handling.

Sparse Documentation

The README is brief with only basic examples and a demo link, lacking comprehensive guides, API details, or troubleshooting resources.

Frequently Asked Questions

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