Matching Algorithm
- fuse.js:
fuse.jsuses 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-searchuses 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:
fuzzysetuses 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.jsis 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-searchworks 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:
fuzzysetis 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.jsoffers 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-searchprovides 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 asfuse.js. - fuzzyset:
fuzzysetallows 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.jsis 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-searchis 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:
fuzzysetperformance 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.jsimport 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-searchimport { 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
fuzzysetimport 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);
