fuse.js vs fuzzy-search vs fuzzyset
Fuzzy Search Libraries
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Fuzzy Search Libraries

Fuzzy search libraries in JavaScript provide algorithms to find matches in a dataset that are similar to a given input, even if they are not exact matches. This is useful for applications like autocomplete, search suggestions, and data validation, where user input may contain typos or variations. These libraries use techniques like Levenshtein distance, tokenization, and scoring to rank results based on their similarity to the input, allowing for more flexible and user-friendly search experiences. fuse.js is a powerful, lightweight fuzzy search library that allows for searching through arrays of objects with customizable scoring and matching algorithms. fuzzy-search is a simple and fast fuzzy search library that provides a straightforward API for searching through arrays with minimal configuration. fuzzyset is a unique fuzzy matching library that creates a set of strings and uses a scoring system based on the Levenshtein distance algorithm to find and rank similar strings, making it ideal for applications that require precise control over matching accuracy.

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Feature Comparison: fuse.js vs fuzzy-search vs fuzzyset

Matching Algorithm

  • fuse.js:

    fuse.js uses a combination of tokenization and weighted scoring to match input against a dataset. It allows for partial matches, supports multiple keys, and provides customizable scoring functions to prioritize certain matches over others.

  • fuzzy-search:

    fuzzy-search uses a simple fuzzy matching algorithm that allows for typos and slight variations in the input. It provides a straightforward implementation that prioritizes speed and efficiency while maintaining good accuracy in matching.

  • fuzzyset:

    fuzzyset uses the Levenshtein distance algorithm to calculate the similarity between strings. It creates a set of strings with associated scores, allowing for precise control over how matches are determined and ranked based on their similarity.

Data Structure

  • fuse.js:

    fuse.js is designed to work with arrays of objects, allowing you to specify which keys to search within each object. This makes it highly versatile for searching through complex data structures with multiple attributes.

  • fuzzy-search:

    fuzzy-search works with simple arrays of strings or objects. It provides a flexible API for searching through different types of data, but it does not require any specific structure, making it easy to integrate into various projects.

  • fuzzyset:

    fuzzyset is focused on working with a set of strings. It requires you to create a fuzzy set by adding strings to it, after which you can perform fuzzy searches and retrieve matches along with their similarity scores.

Customization

  • fuse.js:

    fuse.js offers extensive customization options, including the ability to define multiple search keys, set weights for different keys, customize the tokenization process, and define your own scoring functions. This makes it highly adaptable to different use cases and data structures.

  • fuzzy-search:

    fuzzy-search provides limited customization, focusing on simplicity and ease of use. It allows you to configure the fuzziness level and specify which keys to search, but it does not offer as much flexibility as fuse.js.

  • fuzzyset:

    fuzzyset allows for some customization in terms of how strings are added to the set and how matches are scored. However, it is more focused on providing a clear and efficient fuzzy matching process rather than offering extensive configuration options.

Performance

  • fuse.js:

    fuse.js is optimized for performance, but its complexity and the ability to handle large datasets with multiple keys can lead to slower search times compared to simpler algorithms. Performance can be improved by limiting the number of search keys and using weighted scoring to prioritize matches.

  • fuzzy-search:

    fuzzy-search is designed for fast fuzzy searching, making it suitable for applications where performance is critical. Its lightweight implementation ensures quick search times, even with larger datasets, while maintaining good accuracy.

  • fuzzyset:

    fuzzyset performance depends on the size of the string set and the complexity of the Levenshtein distance calculations. While it is efficient for smaller sets, performance may degrade with very large datasets due to the computational cost of calculating similarity scores.

Ease of Use: Code Examples

  • fuse.js:

    Fuzzy search with fuse.js

    import Fuse from 'fuse.js';

const data = [
  { name: 'Alice', age: 25 },
  { name: 'Bob', age: 30 },
  { name: 'Charlie', age: 35 },
];

const fuse = new Fuse(data, {
  keys: ['name'], // Specify the keys to search
  threshold: 0.3, // Set the fuzziness level
});

const result = fuse.search('Alic'); // Search for a fuzzy match
console.log(result);
  • fuzzy-search:

    Fuzzy search with fuzzy-search

    import { fuzzySearch } from 'fuzzy-search';

const data = ['apple', 'banana', 'orange', 'grape'];
const searchTerm = 'appl';
const results = fuzzySearch(data, searchTerm);
console.log(results); // ['apple', 'grape']
  • fuzzyset:

    Fuzzy search with fuzzyset

    import FuzzySet from 'fuzzyset';

const fuzzySet = FuzzySet();
fuzzySet.add('apple');
fuzzySet.add('banana');
fuzzySet.add('orange');

const results = fuzzySet.get('appl'); // Get fuzzy matches for 'appl'
console.log(results);

How to Choose: fuse.js vs fuzzy-search vs fuzzyset

  • fuse.js:

    Choose fuse.js if you need a feature-rich fuzzy search library that supports searching through complex data structures (like arrays of objects) with customizable scoring, multiple search keys, and advanced options like tokenization and weighting. It is ideal for applications that require high accuracy and flexibility in search results.

  • fuzzy-search:

    Choose fuzzy-search if you want a lightweight and fast fuzzy search solution with a simple API. It is best for projects where performance is critical and you need a no-frills library that provides good fuzzy matching without a lot of configuration or overhead.

  • fuzzyset:

    Choose fuzzyset if you need a library that focuses on fuzzy matching with a scoring system based on Levenshtein distance. It is suitable for applications that require precise control over matching accuracy and want to work with a set of strings rather than searching through complex data structures.

Similar Npm Packages to fuse.js

fuse.js is a powerful, lightweight fuzzy-search library that allows developers to perform fuzzy searching on large datasets. It provides a simple and efficient way to search through arrays of objects, returning results that match the search query even when there are minor typos or variations in the input. This makes it an excellent choice for applications that require user-friendly search functionality, such as autocomplete features or search bars.

While fuse.js is a popular choice for fuzzy searching, there are several alternatives available in the ecosystem. Here are a few noteworthy options:

  • elasticlunr is a lightweight full-text search library that is inspired by Elasticsearch. It allows you to create an index of documents and perform searches with various query capabilities. Elasticlunr is particularly useful for applications that require more structured search capabilities, such as filtering and scoring, while still being easy to set up and use. If you need a more robust search solution with features like tokenization and stemming, elasticlunr is worth considering.
  • fuzzy-search is a simple and straightforward fuzzy search library that allows you to search through arrays of strings or objects. It provides a basic implementation of fuzzy searching without the additional features that come with more complex libraries. If you are looking for a minimalistic solution for fuzzy searching that is easy to integrate and use, fuzzy-search is a great option.
  • fuzzysearch is another lightweight library that provides a simple way to perform fuzzy searches on strings. It focuses on matching substrings within a larger string, making it suitable for applications that require basic fuzzy matching capabilities. If your needs are simple and you want a no-frills approach to fuzzy searching, fuzzysearch is a suitable choice.
  • fuzzysort is a fast and efficient fuzzy search library that is designed for performance. It allows you to search through large datasets quickly and returns results based on the relevance of the matches. Fuzzysort is particularly useful for applications that require real-time search capabilities, such as live search features in user interfaces. If performance is a critical factor for your application, fuzzysort is an excellent alternative.

To see how fuse.js compares with elasticlunr, fuzzy-search, fuzzysearch, and fuzzysort, check out the comparison: Comparing elasticlunr vs fuse.js vs fuzzy-search vs fuzzysearch vs fuzzysort.

Similar Npm Packages to fuzzyset

fuzzyset is a JavaScript library designed for fuzzy string matching. It allows developers to perform approximate string matching, making it useful for applications that require searching and matching strings with minor variations or typos. This can be particularly helpful in search functionalities, autocomplete features, or any scenario where user input may not perfectly match the expected values. The library uses a set-based approach to efficiently handle fuzzy matching, providing a simple API for developers to integrate into their applications.

While fuzzyset is a solid choice for fuzzy string matching, there are other libraries that offer similar functionalities. Here are a few alternatives:

  • fuse.js is a powerful, lightweight fuzzy-search library that provides a flexible and efficient way to perform fuzzy searching on large datasets. It allows developers to search through arrays of objects and provides options for configuring the search behavior, such as specifying which fields to search and adjusting the sensitivity of the matching algorithm. Fuse.js is particularly well-suited for applications that require advanced searching capabilities, as it can handle complex queries and return results quickly, even with large datasets.
  • fuzzy-search is another library that focuses on providing a simple and efficient way to perform fuzzy searches. It is lightweight and straightforward, making it easy to integrate into projects without adding unnecessary complexity. Fuzzy-search is ideal for smaller applications or projects where a minimalistic approach to fuzzy searching is preferred. It allows developers to quickly implement fuzzy search functionality without the overhead of more complex libraries.

To see how fuzzyset compares with fuse.js and fuzzy-search, check out the comparison: Comparing fuzzyset vs fuse.js vs fuzzy-search.

README for fuse.js

Fuse.js

Node.js CI Version Downloads code style: prettier Contributors License

Fuse.js is a lightweight, zero-dependency fuzzy-search library written in TypeScript. It works in the browser and on the server, and is designed for searching small-to-medium datasets on the client side where you can't rely on a dedicated search backend.

✨ What's New: Token Search

Multi-word fuzzy search with relevance ranking. Type "javascrpt paterns" and find "JavaScript Patterns" — typo tolerance, multiple words, and smart ranking all at once.

const fuse = new Fuse(docs, {
  useTokenSearch: true,
  keys: ['title', 'author', 'description']
})

fuse.search('javascrpt paterns')
// → [{ item: { title: 'JavaScript Patterns', ... } }]

See Token Search below for details.

Installation

npm install fuse.js

yarn add fuse.js

Or include directly via CDN:

<script src="https://cdn.jsdelivr.net/npm/fuse.js/dist/fuse.min.mjs"></script>

Quick Start

import Fuse from 'fuse.js'

const books = [
  { title: "Old Man's War", author: 'John Scalzi' },
  { title: 'The Lock Artist', author: 'Steve Hamilton' },
  { title: 'HTML5', author: 'Remy Sharp' },
  { title: 'JavaScript: The Good Parts', author: 'Douglas Crockford' }
]

const fuse = new Fuse(books, {
  keys: ['title', 'author']
})

fuse.search('javscript')
// → [{ item: { title: 'JavaScript: The Good Parts', ... }, ... }]

Features

Fuzzy Search

The core of Fuse.js. Uses the Bitap algorithm for approximate string matching — handles typos, misspellings, and partial matches out of the box.

fuse.search('javscript')
// → [{ item: { title: 'JavaScript: The Good Parts', author: 'Douglas Crockford' } }]

Weighted Keys

Search across multiple fields with different importance levels. Title matches can rank higher than description matches.

const fuse = new Fuse(docs, {
  keys: [
    { name: 'title', weight: 2 },
    { name: 'description', weight: 1 }
  ]
})

Extended Search

Use operators for precise control: exact match (=), prefix (^), suffix (!), and more. Enable with useExtendedSearch: true.

const fuse = new Fuse(list, {
  useExtendedSearch: true,
  keys: ['title']
})

fuse.search('=exact match')   // exact match
fuse.search('^prefix')        // starts with
fuse.search('!term')          // does not include

Token Search

Splits multi-word queries into individual terms, fuzzy-matches each independently, and ranks results using BM25-style IDF weighting. Enable with useTokenSearch: true.

const fuse = new Fuse(docs, {
  useTokenSearch: true,
  keys: ['title', 'body']
})

fuse.search('express midleware rout')
// Finds "Express Middleware" and "Express Routing Guide" despite typos
  • Typo tolerance per word — each term is fuzzy-matched independently
  • Relevance ranking — rare terms are weighted higher than common ones
  • Word order independent — "patterns javascript" and "javascript patterns" return identical results
  • No query length limit — long multi-word queries work naturally since each term is searched separately

Available in the full build. See TOKEN_SEARCH.md for details and performance benchmarks.

Logical Search

Combine conditions with $and and $or for complex queries. Available in the full build.

fuse.search({
  $and: [
    { title: 'javascript' },
    { author: 'crockford' }
  ]
})

Match Highlighting

Get character-level match indices for highlighting search results in your UI.

const fuse = new Fuse(list, {
  includeMatches: true,
  keys: ['title']
})

const result = fuse.search('javscript')
// result[0].matches[0].indices → [[0, 9]]

Single String Matching

Use Fuse.match() to fuzzy-match a pattern against a single string without creating an index. Useful for one-off comparisons or custom filtering.

const result = Fuse.match('javscript', 'JavaScript: The Good Parts')
// → { isMatch: true, score: 0.04, indices: [[0, 9]] }

Dynamic Collections

Add and remove documents from a live index without rebuilding.

fuse.add({ title: 'New Book', author: 'New Author' })
fuse.remove((doc) => doc.title === 'Old Book')

Builds

Fuse.js ships in two variants:

BuildIncludesMin + gzip
FullFuzzy + Extended + Logical + Token search~8 kB
BasicFuzzy search only~6.5 kB

Use the basic build if you only need fuzzy search and want the smallest bundle size.

Documentation

For the full API reference, configuration options, scoring theory, and interactive demos, visit fusejs.io.

Supporting Fuse.js

Develop

See DEVELOPERS.md for setup, scripts, and project structure.

Contribute

See CONTRIBUTING.md for guidelines on issues and pull requests.

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