Top 6 JavaScript Search Libraries

JavaScript search libraries are tools that enable you to perform full-text search on data using JavaScript, either on the browser or on the server. They can help you create fast and user-friendly search experiences for your web applications, without relying on external search services or databases.

1. Fuse.js (17.6k ⭐) — A powerful, lightweight fuzzy-search library, with zero dependencies. It allows you to perform approximate string matching on the client-side or the backend, without setting up a dedicated search service. You can use it to search through arrays of strings or objects, with different options for weighting, nesting, and extended search syntax.

2. List.js (11.2k ⭐) — A vanilla JavaScript library that adds search, sort, filters and flexibility to plain HTML lists, tables, or anything. It can work with existing HTML or create its own HTML from scratch. You can customize the options for value names, item templates, pagination and more.

3. FlexSearch.js (12k ⭐) — A next-generation full-text search library for Browser and Node.js with zero dependencies. It claims to be the web’s fastest and most memory-efficient search library, with features like contextual indexing, document indexing, web workers, and more.

4. Lunr.js (8.8k ⭐) — A small, full-text search library for use in the browser or on the server with Node.js. It indexes JSON documents and provides a simple search interface for retrieving documents that best match text queries. It is designed to be easy to set up and use, without the need for external search services.

5. Orama (8.2k ⭐) — A fast, batteries-included, full-text search engine entirely written in TypeScript, designed to work on any runtime and has no dependencies. Orama also supports plugins to extend its functionality and integrate with other frameworks.

6. MiniSearch (4.2k ⭐) — A tiny but powerful in-memory fulltext search engine written in JavaScript. It is respectful of resources, and it can comfortably run both in Node and in the browser. MiniSearch addresses use cases where full-text search features are needed (e.g. prefix search, fuzzy search, ranking, boosting of fields…), but the data to be indexed can fit locally in the process memory.

Common features

Above libraries vary in their specific features and capabilities, but there are several common features that are often found across them:

• They are all JavaScript libraries that can run on the browser or on Node.js.
• They all support full-text search on arrays of strings or objects, with different options for ranking, filtering, stemming, tokenizing, etc.
• They all have zero dependencies and are easy to install and use.
• They all provide fast and flexible search capabilities, with some claiming to be the fastest or most memory-efficient in the web.

It’s important to note that different search libraries may prioritize different features, and the specific implementation details may vary. It’s always recommended to consult the documentation and explore the capabilities of each library to understand how well they align with your project’s needs.

Full-text search

Full-text search is a comprehensive search method that compares every word of the search request against every word within the document or database. Full-text search can perform linguistic searches on text data, such as finding synonyms, stemming, or ranking results by relevance.

Full-text search can also support various types of data and queries, such as numerical, geolocation, structured, or fuzzy. Full-text search is often used to provide fast and user-friendly search experiences for web and mobile applications.

Client-side fuzzy search

Client-side fuzzy searching is the technique of finding strings that are approximately equal to a given pattern (rather than exactly) on the browser, without relying on a server or a database. It can be useful for situations where you want to provide fast and user-friendly search experiences for small to moderately large data sets, and you don’t need a dedicated backend or a complex search service.

Client-side fuzzy searching can be implemented using JavaScript libraries such as Fuse.js, which can perform approximate string matching on arrays of strings or objects, with different options for ranking, filtering, stemming, tokenizing, etc.

Server-side search services

Server-side search services are tools or platforms that provide search functionality on the server side, usually by indexing and querying data stored in a database or a file system. Some examples of server-side search services are:

• Elasticsearch — A distributed search and analytics engine built on top of the Lucene library. It offers real-time search, full-text search, and analytics capabilities. Elasticsearch is highly scalable, distributed, and can handle large volumes of data. It provides RESTful APIs and supports various query types, filtering, aggregations, and relevancy scoring.

• Algolia — A cloud-based service that provides AI-powered search and discovery capabilities for web and mobile applications. Algolia search allows you to index and query various types of data, such as text, geolocation, numeric, and structured data. Algolia search also offers features such as autocomplete, typo tolerance, synonyms, facets, personalization, analytics, and more. You can use Algolia search by integrating with its API or using one of its InstantSearch libraries for different platforms.

Client-side vs server-side search

Client-side search and server-side search are different approaches to performing search operations on data. The main difference is where the search logic and the data are located and executed. Client-side search runs on the user’s browser, while server-side search runs on a remote server.

Some of the advantages of client-side search are:

• It can be faster and more responsive, as it does not require network communication or server processing.
• It can work offline, as it does not depend on a server connection.
• It can be more flexible and customizable, as it can leverage the features and capabilities of the browser.

Some of the disadvantages of client-side search are:

• It can be less efficient and scalable, as it requires loading and processing all the data on the client side, which can consume more bandwidth and memory.
• It can be less secure and reliable, as it exposes the data and the search logic to the user, which can be tampered with or compromised.
• It can be less accurate and consistent, as it may not support complex or advanced search features, such as stemming, ranking, filtering, etc.

Some of the advantages of server-side search are:

• It can be more efficient and scalable, as it only sends the relevant data and results to the client side, which can reduce bandwidth and memory usage.
• It can be more secure and reliable, as it hides the data and the search logic from the user, which can prevent unauthorized access or manipulation.
• It can be more accurate and consistent, as it can support complex or advanced search features, such as stemming, ranking, filtering, etc.

Some of the disadvantages of server-side search are:

• It can be slower and less responsive, as it requires network communication and server processing.
• It can not work offline, as it depends on a server connection.
• It can be less flexible and customizable, as it may not leverage the features and capabilities of the browser.

Fuse vs Flexsearch

Fuse and Flexsearch are both JavaScript libraries that enable client-side fuzzy searching on text data. They have some similarities and differences in terms of features, performance, and size. Some of the main points of comparison are:

• Fuse is older and more popular than Flexsearch, with more stars and mentions on GitHub.
• Flexsearch claims to be faster and more memory-efficient than Fuse, according to its own benchmarks. However, these benchmarks may not reflect real-world scenarios or use cases.
• Fuse has a simpler and more intuitive API than Flexsearch, with fewer options and parameters to configure. Flexsearch has a more complex and flexible API, with more features and customization possibilities.
• Fuse supports weighted search, which allows assigning different weights to different fields or properties of the data. Flexsearch does not support weighted search, but it supports contextual scoring, which determines the relevance of a match based on its position and distance in the text.
• Fuse supports extended search syntax, which allows using logical operators (AND, OR, NOT) and wildcards (*) in the query. Flexsearch does not support extended search syntax, but it supports query expressions, which allow using functions and operators to manipulate the query.
• Fuse has a larger bundle size than Flexsearch, especially for the full version that includes all the language support. Flexsearch has a smaller bundle size, especially for the light version that only supports English.