chance vs faker vs casual vs random-words vs mockjs
JavaScript Data Generation Libraries
chancefakercasualrandom-wordsmockjsSimilar Packages:
JavaScript Data Generation Libraries

These libraries are designed to generate random data for various purposes, such as testing, prototyping, and populating databases. They provide developers with tools to create realistic data sets quickly, which can be crucial for testing applications without relying on real user data. Each library has its unique features and capabilities, catering to different needs and preferences in data generation.

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chance1,763,7256,5562.13 MB1778 months agoMIT
faker1,682,280-10.1 MB--MIT
casual182,5943,019-417 years agoMIT
random-words108,49125746.1 kB112 years agoMIT
mockjs60,56919,651-3426 years ago-
Feature Comparison: chance vs faker vs casual vs random-words vs mockjs

Data Variety

  • chance:

    Chance offers a broad spectrum of data types, from basic strings and numbers to more complex types like dates and custom distributions. This makes it versatile for various testing scenarios.

  • faker:

    Faker excels in generating a wide range of realistic data types, including names, addresses, company information, and even financial data. It is particularly useful for applications needing detailed and realistic datasets.

  • casual:

    Casual provides a limited but useful range of data types, focusing on everyday items like names, addresses, and dates. It is ideal for generating simple datasets quickly without extensive configuration.

  • random-words:

    Random Words focuses solely on generating random words or phrases, making it straightforward and lightweight. It is best suited for applications that require simple text data.

  • mockjs:

    Mock.js allows you to define complex data structures and generate data that adheres to those structures. This feature is particularly beneficial for simulating API responses and testing front-end applications.

Customization

  • chance:

    Chance provides extensive customization options, allowing you to define your own data distributions and types. This flexibility is ideal for developers needing specific data characteristics.

  • faker:

    Faker allows for some customization, especially in formatting and data types, but it is primarily designed to generate realistic data without extensive configuration.

  • casual:

    Casual offers minimal customization options, focusing on ease of use and quick data generation. It is suitable for developers who prefer simplicity over configurability.

  • random-words:

    Random Words has limited customization options, focusing on generating random words without additional features. It is straightforward and easy to use.

  • mockjs:

    Mock.js offers high customization through its ability to define data templates and structures. This makes it particularly powerful for developers needing tailored mock data for testing.

Ease of Use

  • chance:

    Chance has a moderate learning curve, with a straightforward API that allows for easy data generation once you understand its features.

  • faker:

    Faker is user-friendly and well-documented, making it easy for developers to get started quickly. Its comprehensive API is intuitive for generating realistic data.

  • casual:

    Casual is designed for simplicity and ease of use, making it an excellent choice for developers who want to quickly generate random data without a steep learning curve.

  • random-words:

    Random Words is extremely easy to use, with a simple API that allows for quick random word generation without any setup.

  • mockjs:

    Mock.js has a slightly steeper learning curve due to its templating system, but it is well-documented, making it manageable for developers familiar with API design.

Performance

  • chance:

    Chance is efficient and can handle larger datasets without significant performance issues, making it suitable for more extensive testing scenarios.

  • faker:

    Faker can generate large amounts of data, but performance may vary depending on the complexity of the data being generated. It is generally efficient for most use cases.

  • casual:

    Casual performs well for generating small datasets quickly, but may not be optimized for generating large volumes of data due to its simplicity.

  • random-words:

    Random Words is lightweight and performs exceptionally well for generating random words, making it suitable for applications that require minimal overhead.

  • mockjs:

    Mock.js is optimized for generating structured mock data and can handle complex data generation efficiently, making it ideal for simulating API responses.

Community and Support

  • chance:

    Chance has a moderate community with decent support and documentation, making it easier to find help and examples online.

  • faker:

    Faker has a large and active community, providing extensive documentation, examples, and support, making it a reliable choice for developers.

  • casual:

    Casual has a smaller community compared to others, which may result in limited support and fewer resources available for troubleshooting.

  • random-words:

    Random Words has a smaller community, but its simplicity means that most developers can quickly understand and implement it without extensive support.

  • mockjs:

    Mock.js has a growing community and good documentation, which helps developers implement it effectively in their projects.

How to Choose: chance vs faker vs casual vs random-words vs mockjs
  • chance:

    Select Chance if you require a more extensive set of data generation capabilities, including complex data types and custom distributions. Chance offers a wide variety of options and is great for generating random data in a more controlled manner.

  • faker:

    Opt for Faker if you need a comprehensive library that mimics real-world data closely. It is particularly useful for generating realistic names, addresses, and other personal information, making it ideal for applications that require a high level of detail in the data.

  • casual:

    Choose Casual if you need a simple and straightforward library for generating random data without too much configuration. It is particularly useful for generating casual data types like names, addresses, and dates quickly.

  • random-words:

    Choose Random Words if your primary need is to generate random words or phrases. This library is lightweight and straightforward, making it suitable for applications that require simple text generation.

  • mockjs:

    Use Mock.js if you are looking to create mock data for APIs. It allows you to define data structures and generate data that matches those structures, making it perfect for front-end development and testing without a backend.

README for chance

Chance

Chance Logo

Build Status GitHub license GitHub stars npm jsDelivr Hits npm Coverage Status awesomeness

Chance - Random generator helper for JavaScript

Homepage: http://chancejs.com

Many more details on http://chancejs.com but this single library can generate random numbers, characters, strings, names, addresses, dice, and pretty much anything else.

It includes the basic building blocks for all these items and is built on top of a Mersenne Twister so it can generate these things with repeatability, if desired.

Usage

See the full docs for details on installation and usage.

Dependent tools

  • Chance CLI - Use Chance on the command line.
  • Chance Token Replacer - Replace tokens in a string with Chance generated items.
  • Dream.js - Lightweight json data generator
  • Fake JSON Schema - Use chance generators to populate JSON Schema samples.
  • Mocker Data Generator - Minimal JSON data generator.
  • swagger-mock-api - Generate API mocks from a Swagger spec file enriched with Chance types and constraints
  • fony - A simple command line tool for generating fake data from a template string

Or view all of the dependents on npm

Know a library that uses Chance that isn't here? Update the README and submit a PR!

Author

Victor Quinn

https://www.victorquinn.com @victorquinn

Please feel free to reach out to me if you have any questions or suggestions.

Contributors

THANK YOU!

Contribute!

Be a part of this project! You can run the test using the following.

Note: Make sure you have Yarn installed globally

  1. Install dependencies from package.json by running yarn
  2. Run the test suite via yarn test
  3. Make some fun new modules!

This project is licensed under the MIT License so feel free to hack away :)

Proudly written in Washington, D.C.