Search Algorithm
- fuse.js:
fuse.jsemploys a fuzzy matching algorithm that calculates the similarity between the search term and the target strings. It allows for configurable thresholds, weights, and scoring, making it highly flexible and accurate for searching within complex data structures. - fuzzysort:
fuzzysortutilizes a high-performance fuzzy matching algorithm that ranks results based on their similarity to the search term. It focuses on sorting the matches by their fuzziness score, providing a fast and accurate way to retrieve and display results. - fuzzy-search:
fuzzy-searchimplements a simple fuzzy matching algorithm that compares the search term with the target strings or object properties. It provides a straightforward approach to finding matches with a customizable level of fuzziness, making it efficient for quick searches. - fuzzysearch:
fuzzysearchuses a fast substring search algorithm that allows for approximate matching of a substring within a string. It is designed for quick lookups and is particularly efficient for checking the presence of fuzzy matches without extensive processing. - elasticlunr:
elasticlunruses an inverted index for full-text search, allowing for fast retrieval of documents based on indexed terms. It supports stemming and tokenization, which helps in matching terms more effectively, even if they are not an exact match.
Data Structure Support
- fuse.js:
fuse.jsis designed to work with various data structures, including arrays of strings, objects, and nested data. It provides flexible options for specifying which fields to search, making it suitable for complex and hierarchical data. - fuzzysort:
fuzzysortis compatible with arrays of strings and objects, providing fuzzy matching and sorting capabilities. It can handle both simple and structured data, making it versatile for various applications. - fuzzy-search:
fuzzy-searchworks with arrays of strings and objects, allowing for fuzzy matching on both simple and complex data. It supports searching through object properties, making it adaptable to different data formats. - fuzzysearch:
fuzzysearchoperates on strings, making it ideal for searching within text data. It is focused on substring matching and does not support complex data structures, which keeps it simple and efficient. - elasticlunr:
elasticlunrsupports indexing and searching through plain text, as well as structured data like objects and arrays. It allows for custom tokenization and analysis, making it versatile for different types of data.
Customization
- fuse.js:
fuse.jsoffers extensive customization options for the search algorithm, including the ability to adjust the fuzziness level, weight different fields, and define custom matching functions. This makes it highly adaptable to different use cases and data types. - fuzzysort:
fuzzysortallows for some customization of the matching and sorting process, including the ability to adjust the fuzziness threshold and define custom scoring functions. It is designed to be fast and efficient while still providing flexibility for developers. - fuzzy-search:
fuzzy-searchprovides basic customization features, such as adjusting the fuzziness level and specifying which properties to search in objects. However, it is more limited compared to other libraries, focusing on simplicity and ease of use. - fuzzysearch:
fuzzysearchis minimalistic and does not offer much customization beyond adjusting the fuzziness of the substring search. Its simplicity is its strength, making it easy to use without complex configuration. - elasticlunr:
elasticlunrallows for customization of the indexing and searching process, including the ability to define custom tokenizers, analyzers, and scoring algorithms. This flexibility enables developers to tailor the search functionality to meet specific requirements.
Performance
- fuse.js:
fuse.jsis performant for client-side searches, but its speed can vary depending on the size of the dataset and the complexity of the search configuration. It is best used with optimized data and reasonable fuzziness settings to maintain good performance. - fuzzysort:
fuzzysortis known for its high performance, particularly in ranking and sorting fuzzy matches. It is optimized for speed, making it a great choice for applications that need quick search results with minimal latency. - fuzzy-search:
fuzzy-searchis designed for efficiency, providing fast fuzzy search capabilities on arrays. Its performance is generally good, making it suitable for applications that require quick lookups without significant overhead. - fuzzysearch:
fuzzysearchis extremely fast, especially for checking the presence of a substring within a string. Its lightweight nature and simple algorithm make it ideal for performance-critical applications where speed is essential. - elasticlunr:
elasticlunris optimized for fast search performance, especially after the initial indexing phase. The use of an inverted index allows for quick lookups, making it suitable for real-time search applications with moderate-sized datasets.
Ease of Use: Code Examples
- fuse.js:
Basic Usage of
fuse.js// Import the library const Fuse = require('fuse.js'); // Sample data const data = [ { id: 1, title: 'Apple', tags: ['fruit', 'food'] }, { id: 2, title: 'Banana', tags: ['fruit', 'yellow'] }, { id: 3, title: 'Carrot', tags: ['vegetable', 'orange'] }, ]; // Configure Fuse.js const options = { keys: ['title', 'tags'], // Fields to search threshold: 0.3, // Fuzziness threshold }; // Create a Fuse instance const fuse = new Fuse(data, options); // Perform a search const result = fuse.search('app'); console.log(result); - fuzzysort:
Basic Usage of
fuzzysort// Import the library const fuzzysort = require('fuzzysort'); // Sample data const items = ['apple', 'banana', 'cherry', 'date']; // Perform a fuzzy sort const results = fuzzysort.go('ap', items); console.log(results); - fuzzy-search:
Basic Usage of
fuzzy-search// Import the library const { fuzzySearch } = require('fuzzy-search'); // Sample data const items = ['apple', 'banana', 'cherry', 'date']; // Perform a fuzzy search const results = fuzzySearch(items, 'appl'); console.log(results); // Output: ['apple'] - fuzzysearch:
Basic Usage of
fuzzysearch// Import the library const fuzzysearch = require('fuzzysearch'); // Sample strings const str = 'hello world'; const pattern = 'hlo wr'; // Perform a fuzzy search const isMatch = fuzzysearch(pattern, str); console.log(isMatch); // Output: true - elasticlunr:
Basic Usage of
elasticlunr// Import the library const elasticlunr = require('elasticlunr'); // Create an index const index = elasticlunr(function () { this.addField('title'); this.addField('body'); this.setRef('id'); }); // Add documents to the index index.addDoc({ id: 1, title: 'Hello World', body: 'This is a test document.' }); index.addDoc({ id: 2, title: 'Elastic Search', body: 'Searching with elasticlunr is fun!' }); // Search the index const results = index.search('elastic', { expand: true }); console.log(results);
