@faker-js/faker vs randexp vs faker vs chance vs lorem-ipsum vs casual vs mockjs
JavaScript Data Generation Libraries Comparison
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@faker-js/fakerrandexpfakerchancelorem-ipsumcasualmockjsSimilar Packages:
What's JavaScript Data Generation Libraries?

These libraries are designed to generate fake data for testing and development purposes. They help developers create realistic data structures without the need for real user data, which can be sensitive or unavailable. By using these libraries, developers can easily populate databases, create mock APIs, and test applications with various data scenarios, ensuring robustness and reliability in their code without compromising user privacy.

Package Weekly Downloads Trend
Github Stars Ranking
Stat Detail
Package
Downloads
Stars
Size
Issues
Publish
License
@faker-js/faker8,736,917
14,3034.39 MB12217 days agoMIT
randexp4,941,063
1,859-137 years agoMIT
faker2,125,121
-10.1 MB--MIT
chance1,850,118
6,5412.13 MB1764 months agoMIT
lorem-ipsum254,378
295133 kB7-ISC
casual187,588
3,016-426 years agoMIT
mockjs63,558
19,641-3426 years ago-
Feature Comparison: @faker-js/faker vs randexp vs faker vs chance vs lorem-ipsum vs casual vs mockjs

Data Variety

  • @faker-js/faker:

    @faker-js/faker provides a rich set of data types, including names, addresses, dates, and even images. It supports multiple locales, allowing for culturally relevant data generation, which is essential for international applications.

  • randexp:

    Randexp focuses on generating random strings that match specified regular expressions, providing a unique approach to data generation that is useful for testing input validation scenarios.

  • faker:

    Faker is known for its extensive variety of data types, similar to @faker-js/faker. However, it may lack some of the latest features and updates found in the newer library.

  • chance:

    Chance excels in providing a diverse range of data types, including random integers, floating-point numbers, and even custom data types. Its flexibility allows developers to generate data tailored to specific needs.

  • lorem-ipsum:

    Lorem-ipsum is specialized in generating placeholder text, offering various text lengths and formats. It is ideal for UI design and testing where text content is needed without specific meaning.

  • casual:

    Casual offers a limited but straightforward set of data types, focusing on simplicity. It includes names, addresses, and other basic types, making it suitable for quick data generation without the need for extensive configuration.

  • mockjs:

    Mockjs allows for the creation of complex data structures and nested objects, making it suitable for simulating realistic API responses. It enables developers to define the shape and content of the data returned from mock endpoints.

Ease of Use

  • @faker-js/faker:

    @faker-js/faker is designed with a user-friendly API that makes it easy to generate data with minimal setup. Its documentation is clear, providing examples for quick reference.

  • randexp:

    Randexp has a simple API for generating strings based on regex patterns, but understanding regular expressions is necessary for effective use. This may pose a challenge for those unfamiliar with regex.

  • faker:

    Faker has a more complex API compared to others, which might require a learning curve. However, its extensive documentation helps users navigate its features effectively.

  • chance:

    Chance offers a balance between functionality and usability, with a clear API that is easy to understand. It provides a good amount of flexibility while remaining accessible to developers of all skill levels.

  • lorem-ipsum:

    Lorem-ipsum is extremely easy to use, requiring only a few parameters to generate text. Its focused purpose makes it straightforward for generating placeholder content.

  • casual:

    Casual stands out for its simplicity, allowing developers to generate random data with just a few lines of code. Its straightforward API is ideal for beginners or quick tasks.

  • mockjs:

    Mockjs has a slightly steeper learning curve due to its ability to define complex data structures. However, once understood, it provides powerful capabilities for simulating API responses.

Customization

  • @faker-js/faker:

    @faker-js/faker allows for extensive customization of data generation, including the ability to create custom data types and formats, making it highly adaptable to specific project needs.

  • randexp:

    Randexp allows for customization through regular expressions, enabling developers to generate strings that meet specific patterns. This is particularly useful for testing scenarios.

  • faker:

    Faker allows for some customization, but it may not be as flexible as @faker-js/faker in terms of creating new data types or formats. It is still useful for generating a wide variety of data.

  • chance:

    Chance provides a good level of customization, allowing developers to specify ranges for numbers and formats for strings, making it versatile for various use cases.

  • lorem-ipsum:

    Lorem-ipsum is not customizable in terms of content but allows for customization of text length and format, making it useful for specific design needs.

  • casual:

    Casual offers limited customization options, focusing on ease of use rather than flexibility. It is best suited for straightforward data generation without the need for complex configurations.

  • mockjs:

    Mockjs excels in customization, allowing developers to define the structure of the mock data and responses in detail. This makes it ideal for simulating complex API behaviors.

Community and Support

  • @faker-js/faker:

    @faker-js/faker has a growing community and is actively maintained, ensuring that users receive updates and support. Its documentation is comprehensive, making it easier to find help.

  • randexp:

    Randexp has a smaller community, but it is well-documented for its specific purpose. Users may need to rely on general regex resources for more complex use cases.

  • faker:

    Faker has a large user base and extensive documentation, but it is less actively maintained than newer alternatives. Users may encounter issues that are not addressed promptly.

  • chance:

    Chance has a moderate community with reasonable support. It is well-documented, but users may find fewer resources compared to more popular libraries.

  • lorem-ipsum:

    Lorem-ipsum has a niche community focused on placeholder text generation. It is simple enough that most users won't require extensive support, but resources may be limited.

  • casual:

    Casual has a smaller community, which may result in less frequent updates. However, its simplicity means that most users can quickly get started without needing extensive support.

  • mockjs:

    Mockjs has a dedicated community, particularly among frontend developers. It is well-documented, and users can find examples and support for common use cases.

Performance

  • @faker-js/faker:

    @faker-js/faker is optimized for performance, allowing for fast data generation even with large datasets. It efficiently handles multiple locales and data types without significant overhead.

  • randexp:

    Randexp performs well for generating strings based on regex patterns, but complex patterns may lead to slower performance. Users should balance complexity with performance needs.

  • faker:

    Faker may experience performance issues with very large datasets due to its older architecture. Users should be cautious when generating extensive data to avoid slowdowns.

  • chance:

    Chance is designed for performance, providing efficient data generation without excessive resource consumption. It is suitable for applications that require large volumes of random data.

  • lorem-ipsum:

    Lorem-ipsum performs exceptionally well for generating text, as it focuses solely on placeholder content. It is optimized for speed, making it ideal for UI mockups.

  • casual:

    Casual is lightweight and performs well for small to medium data generation tasks. Its simplicity contributes to quick execution times, making it suitable for rapid prototyping.

  • mockjs:

    Mockjs is efficient in generating mock data for APIs, but performance may vary based on the complexity of the defined data structures. Users should optimize their mock definitions for better performance.

How to Choose: @faker-js/faker vs randexp vs faker vs chance vs lorem-ipsum vs casual vs mockjs
  • @faker-js/faker:

    Choose @faker-js/faker if you need a comprehensive and actively maintained library with a wide range of data types and localization support. It is a modern fork of the original Faker library, ensuring better performance and community support.

  • randexp:

    Select randexp if you require a library that generates random strings based on regular expressions. This is particularly useful for testing input validation and ensuring that your application can handle various string formats.

  • faker:

    Use faker if you are looking for a well-established library with a large user base and extensive documentation. It provides a rich set of features but may not be actively maintained, so consider the project's longevity.

  • chance:

    Select chance if you require a library that offers a variety of random data generation options, including random numbers, strings, and even unique IDs. It is particularly useful for generating data in a more controlled manner, such as ensuring uniqueness.

  • lorem-ipsum:

    Choose lorem-ipsum if your primary need is to generate placeholder text. It is specifically designed for creating dummy text, making it ideal for UI mockups and design prototypes.

  • casual:

    Opt for casual if you prefer a simpler API and need to generate random data quickly without extensive configuration. It is lightweight and easy to use, making it suitable for small projects or quick prototypes.

  • mockjs:

    Pick mockjs if you need to create mock APIs for frontend development. It allows you to define data structures and responses, making it easier to simulate server interactions without a backend.

Similar Npm Packages to @faker-js/faker

@faker-js/faker is a popular library for generating fake data in JavaScript applications. It is particularly useful for testing, prototyping, and populating databases with realistic-looking data without the need for real user information. The library provides a wide range of data types, including names, addresses, phone numbers, and much more, making it a versatile tool for developers. While @faker-js/faker is a robust option for generating fake data, there are several alternatives available in the ecosystem. Here are a few noteworthy alternatives:

  • casual is a simple and lightweight library for generating fake data in JavaScript. It provides a straightforward API and supports a variety of data types, including names, addresses, and even custom data types. Casual is easy to use and can be a great choice for projects that require basic fake data generation without the overhead of a more complex library.

  • chance is another library that offers a wide range of random data generation capabilities. It provides a rich set of functions for generating everything from random numbers and strings to names and addresses. Chance is highly customizable, allowing developers to create their own data types and formats, making it a flexible option for generating fake data.

  • lorem-ipsum is a specialized library focused on generating placeholder text, specifically the classic "Lorem Ipsum" text. It is particularly useful for developers who need to fill content areas with text during the design and development phases. While it doesn't offer the breadth of data types found in other libraries, it excels in its niche of generating text content.

  • mockjs is a powerful library for generating mock data and intercepting HTTP requests. It allows developers to create complex data structures and simulate API responses, making it ideal for front-end development when the back-end is not yet available. Mockjs is particularly useful for testing and development, as it can help create realistic data scenarios without relying on real data sources.

  • randexp is a library designed for generating random strings based on regular expressions. It allows developers to create highly specific random data that adheres to defined patterns, making it useful for generating data that needs to fit certain criteria. While it is more specialized than other libraries, randexp can be a powerful tool for generating complex data formats.

To see how @faker-js/faker compares with casual, chance, lorem-ipsum, mockjs, and randexp, check out the comparison: Comparing @faker-js/faker vs casual vs chance vs lorem-ipsum vs mockjs vs randexp.

Similar Npm Packages to faker

faker is a popular library for generating fake data in JavaScript applications. It is widely used for testing, prototyping, and populating databases with realistic-looking data. Faker provides a variety of methods to create names, addresses, phone numbers, emails, and much more, making it an essential tool for developers who need to create sample data quickly and easily. While faker is a robust solution for generating fake data, there are several alternatives worth considering:

  • casual is a lightweight library that offers a simple API for generating fake data. It provides a wide range of data types, including names, addresses, dates, and even custom data generators. Casual is particularly useful for developers looking for a straightforward and easy-to-use library without the overhead of more complex solutions. Its flexibility allows for the creation of custom generators, making it a versatile choice for various data generation needs.
  • chance is another powerful library for generating random data. It offers a rich set of features, including the ability to generate random numbers, strings, booleans, and even entire objects. Chance is designed to be both flexible and easy to use, making it suitable for a variety of applications, from testing to game development. With its extensive documentation and active community, chance is a reliable alternative for developers who need to generate random data quickly and efficiently.
  • randexp is a specialized library focused on generating random strings based on regular expressions. It allows developers to create complex patterns and generate random data that adheres to those patterns. Randexp is particularly useful when you need to generate data that follows specific formats, such as email addresses or phone numbers. If your data generation needs require strict adherence to patterns, randexp is an excellent choice.

To see how faker compares with casual, chance, and randexp, check out the comparison: Comparing casual vs chance vs faker vs randexp.

Similar Npm Packages to chance

chance is a popular JavaScript library for generating random data. It is particularly useful for developers who need to create mock data for testing, development, or demonstration purposes. With a wide array of methods, chance can generate everything from random names and addresses to dates, numbers, and even custom data structures. Its flexibility and ease of use make it a go-to choice for many developers looking to populate their applications with realistic data.

However, there are several alternatives to chance that also provide similar functionality:

  • casual is another library for generating fake data in JavaScript. It offers a straightforward API and a variety of data types, including names, addresses, and even user profiles. One of the key features of casual is its ability to create custom generators, allowing developers to tailor the data generation process to their specific needs. If you're looking for a simple and customizable solution for generating random data, casual is a great option.

  • faker is one of the most widely used libraries for generating fake data. It provides a rich set of methods for creating realistic data, including names, addresses, phone numbers, and much more. faker is highly versatile and can be used in various environments, including Node.js and the browser. Its extensive documentation and active community make it a reliable choice for developers who need to generate large amounts of mock data quickly.

  • mockjs is a library that not only generates random data but also allows you to create mock APIs. It is particularly useful for front-end developers who want to simulate server responses without needing a real backend. mockjs can generate complex data structures and supports template syntax for defining the shape of the data. If you need to mock API responses along with generating random data, mockjs is a powerful tool to consider.

  • random-words is a lightweight library focused specifically on generating random words. It is ideal for scenarios where you need to create placeholder text or random strings without the complexity of generating full data objects. If your requirements are simple and you only need random words, random-words offers a straightforward solution.

To see how chance compares with casual, faker, mockjs, and random-words, check out the comparison: Comparing casual vs chance vs faker vs mockjs vs random-words.

Similar Npm Packages to casual

casual is a versatile JavaScript library designed for generating random data. It is particularly useful for creating mock data for testing and development purposes. With a wide range of data types available, including names, addresses, dates, and more, casual makes it easy for developers to populate their applications with realistic-looking data without the need for manual input. This can significantly speed up the development process and enhance testing scenarios.

While casual is a great option for generating random data, there are several alternatives available in the ecosystem that serve similar purposes. Here are a few noteworthy alternatives:

  • chance is a random generator for JavaScript that provides a wide variety of data types, including names, addresses, phone numbers, and even custom data types. Chance is highly customizable and allows developers to create random data in a straightforward manner. Its extensive API and flexibility make it a popular choice for generating random data in various applications.
  • faker is another widely-used library for generating fake data. It offers a comprehensive set of features for creating realistic-looking data, including names, addresses, company names, and product details. Faker is particularly useful for populating databases and creating mock APIs. Its rich set of locales allows developers to generate data that is culturally relevant and diverse.
  • mockjs is a library that not only generates random data but also allows developers to create mock APIs. It can intercept AJAX requests and return mock data based on predefined templates. This makes it an excellent tool for front-end developers who want to work with realistic data without relying on a back-end service during development.
  • random-words is a simple library focused specifically on generating random words. It can be useful for generating placeholder text, usernames, or any scenario where random words are needed. While it does not offer the extensive features of other libraries, its simplicity and focused functionality make it a good choice for specific use cases.

To see how casual compares with chance, faker, mockjs, and random-words, check out the comparison: Comparing casual vs chance vs faker vs mockjs vs random-words.

Similar Npm Packages to mockjs

mockjs is a popular JavaScript library used for generating mock data and simulating API responses. It is particularly useful for front-end developers who need to work with realistic data during the development and testing phases of their applications. By allowing developers to create random data structures, mockjs helps streamline the development process without relying on a live backend. This can be especially beneficial when the backend is not yet available or when testing various scenarios.

While mockjs is a powerful tool for generating mock data, there are several alternatives available that also serve similar purposes. Here are a few noteworthy options:

  • chance is a minimalist library for generating random data. It provides a wide range of functions to create random strings, numbers, dates, and even entire objects. Chance is highly customizable, allowing developers to specify the type of data they need. It is particularly useful for generating test data or populating forms with realistic values without the overhead of setting up a mock server.

  • faker is another widely used library for generating fake data. It offers a rich set of features to create realistic data for various use cases, including names, addresses, phone numbers, and more. Faker is often used in testing and development environments to simulate real-world data. Its extensive API makes it easy to generate complex data structures, making it a popular choice among developers.

  • json-server is a full-fledged fake REST API that can be set up quickly to simulate a backend server. It allows developers to create a mock server with a simple JSON file, enabling them to perform CRUD operations without writing any server-side code. This is particularly useful for front-end developers who want to test their applications against a realistic API without needing a dedicated backend.

For a detailed comparison of these libraries, check out the following link: Comparing chance vs faker vs json-server vs mockjs.

README for @faker-js/faker

Faker

Generate massive amounts of fake (but realistic) data for testing and development.

npm version npm downloads Continuous Integration codecov Chat on Discord Open Collective sponsor

⚡️ Try it Online

Open in StackBlitz

📙 API Documentation

🚀 Features

  • 🧍 Person - Generate Names, Genders, Bios, Job titles, and more.
  • 📍 Location - Generate Addresses, Zip Codes, Street Names, States, and Countries!
  • ⏰ Date - Past, present, future, recent, soon... whenever!
  • 💸 Finance - Create stubbed out Account Details, Transactions, and Crypto Addresses.
  • 👠 Commerce - Generate Prices, Product Names, Adjectives, and Descriptions.
  • 👾 Hacker - “Try to reboot the SQL bus, maybe it will bypass the virtual application!”
  • 🔢 Number and String - Of course, we can also generate random numbers and strings.
  • 🌏 Localization - Pick from over 70 locales to generate realistic looking Names, Addresses, and Phone Numbers.

Note: Faker tries to generate realistic data and not obvious fake data. The generated names, addresses, emails, phone numbers, and/or other data might be coincidentally valid information. Please do not send any of your messages/calls to them from your test setup.

📦 Install

npm install --save-dev @faker-js/faker

🪄 Usage

// ESM
import { faker } from '@faker-js/faker';

// CJS
const { faker } = require('@faker-js/faker');

export function createRandomUser() {
  return {
    userId: faker.string.uuid(),
    username: faker.internet.username(),
    email: faker.internet.email(),
    avatar: faker.image.avatar(),
    password: faker.internet.password(),
    birthdate: faker.date.birthdate(),
    registeredAt: faker.date.past(),
  };
}

export const users = faker.helpers.multiple(createRandomUser, {
  count: 5,
});

💎 Modules

An in-depth overview of the API methods is available in the documentation for v10 (stable) and v10.* (next).

Templates

Faker contains a generator method faker.helpers.fake for combining faker API methods using a mustache string format.

console.log(
  faker.helpers.fake(
    'Hello {{person.prefix}} {{person.lastName}}, how are you today?'
  )
);

🌏 Localization

Faker has support for multiple locales.

The main faker instance uses the English locale. But you can also import instances using other locales.

// ESM
import { fakerDE as faker } from '@faker-js/faker';

// CJS
const { fakerDE: faker } = require('@faker-js/faker');

See our documentation for a list of provided languages.

Please note: Not every locale provides data for every module. In our pre-made faker instances, we fall back to English in such a case as this is the most complete and most commonly used language. If you don't want that or prefer a different fallback, you can also build your own instances.

import { de, de_CH, Faker } from '@faker-js/faker';

export const faker = new Faker({
  locale: [de_CH, de],
});

⚙️ Setting a randomness seed

If you want consistent results, you can set your own seed:

faker.seed(123);

const firstRandom = faker.number.int();

// Setting the seed again resets the sequence.
faker.seed(123);

const secondRandom = faker.number.int();

console.log(firstRandom === secondRandom);

🤝 Sponsors

Faker is an MIT-licensed open source project with its ongoing development made possible entirely by the support of these awesome backers

Sponsors

Backers

✨ Contributing

Please make sure to read the Contributing Guide before making a pull request.

📘 Credits

Thanks to all the people who already contributed to Faker!

The fakerjs.dev website is generously hosted by Netlify, with search functionality powered by Algolia.

📝 Changelog

Detailed changes for each release are documented in the release notes.

📜 What happened to the original faker.js?

Read the team update (January 14th, 2022).

🔑 License

MIT

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