faker vs chance vs lorem-ipsum vs random-words
JavaScript Data Generation Libraries Comparison
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What's JavaScript Data Generation Libraries?

Data generation libraries are essential tools in web development that help developers create random data for testing, prototyping, and populating databases. These libraries provide various methods to generate realistic and diverse data types, such as names, addresses, and text snippets, which can be particularly useful for simulating user interactions and testing application functionality. By using these libraries, developers can save time and ensure their applications are robust against a variety of data inputs.

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faker2,234,544-10.1 MB--MIT
chance1,799,9116,5102.13 MB1768 months agoMIT
lorem-ipsum236,427295133 kB7-ISC
random-words93,94725546.1 kB11a year agoMIT
Feature Comparison: faker vs chance vs lorem-ipsum vs random-words

Data Variety

  • faker:

    Faker excels in producing realistic data sets that include names, addresses, and other personal information. It supports multiple locales, allowing for culturally relevant data generation, which is crucial for global applications.

  • chance:

    Chance provides a broad spectrum of data types, including numbers, strings, booleans, and even custom objects. It allows for the generation of complex data structures, making it suitable for various testing scenarios.

  • lorem-ipsum:

    Lorem Ipsum focuses on generating placeholder text, specifically designed to mimic the flow of natural language. It is ideal for creating dummy content that helps visualize layouts without the need for real text.

  • random-words:

    Random Words generates lists of random words, which can be useful for applications that require word-based inputs, such as games or creative writing tools. It is simple and effective for generating non-complex data.

Customization

  • faker:

    Faker provides some customization options, such as locale selection, but is primarily focused on generating realistic data. It is less flexible than Chance for creating custom data types but excels in generating realistic fake data.

  • chance:

    Chance allows for extensive customization of generated data, enabling developers to specify formats, ranges, and types. This flexibility makes it suitable for generating tailored data sets for specific testing needs.

  • lorem-ipsum:

    Lorem Ipsum has limited customization options, mainly focusing on the length and quantity of generated text. It is straightforward and does not require complex configurations, making it user-friendly for quick text generation.

  • random-words:

    Random Words is very straightforward with minimal customization, primarily allowing the user to specify the number of words to generate. It is designed for simplicity rather than extensive configuration.

Ease of Use

  • faker:

    Faker is also easy to use, with a straightforward API that allows developers to generate data quickly. Its extensive documentation and examples make it easy to implement in various projects.

  • chance:

    Chance has a user-friendly API that is easy to integrate into projects. Its documentation is clear, making it accessible for developers of all skill levels, from beginners to advanced users.

  • lorem-ipsum:

    Lorem Ipsum is extremely easy to use, requiring minimal setup. Its simplicity makes it an excellent choice for designers needing quick placeholder text without any complexity.

  • random-words:

    Random Words is very simple to implement, with a basic API that allows for quick generation of random words. Its ease of use makes it ideal for developers looking for a quick solution.

Performance

  • faker:

    Faker is also performant but may have some overhead due to the realism of the generated data. It is efficient for generating moderate amounts of data but may slow down with extensive datasets.

  • chance:

    Chance is optimized for performance and can generate large amounts of data quickly without significant overhead. It is suitable for applications that require bulk data generation.

  • lorem-ipsum:

    Lorem Ipsum is lightweight and performs well, as it only generates text without complex data structures. It is efficient for generating large volumes of placeholder text quickly.

  • random-words:

    Random Words is highly efficient for generating random words, with minimal performance impact. It can quickly produce large lists of words without significant resource usage.

Community and Support

  • faker:

    Faker has a large user base and extensive community support, with numerous resources, tutorials, and examples available. Its popularity ensures ongoing maintenance and updates.

  • chance:

    Chance has a supportive community and is actively maintained, ensuring that developers can find help and resources easily. Its popularity means that many examples and use cases are available online.

  • lorem-ipsum:

    Lorem Ipsum is widely used, and while it may not have a large community, its simplicity means that support is generally not needed. Many resources are available online for quick reference.

  • random-words:

    Random Words has a smaller community but is straightforward enough that developers can easily find information. Its simplicity means that extensive support is often unnecessary.

How to Choose: faker vs chance vs lorem-ipsum vs random-words
  • faker:

    Select Faker if you require highly realistic and locale-specific fake data, such as names, addresses, and company information. Faker is ideal for applications that need to simulate real-world data closely, making it suitable for testing and development environments.

  • chance:

    Choose Chance if you need a versatile library that offers a wide range of data generation capabilities, including random numbers, strings, and even custom data types. It is particularly useful for generating complex data structures and has a simple API for quick implementation.

  • lorem-ipsum:

    Opt for Lorem Ipsum if your primary need is to generate placeholder text for design mockups or content layout. It specializes in creating dummy text that mimics the structure of natural language, making it perfect for UI/UX design.

  • random-words:

    Use Random Words if you need to generate lists of random words for applications like games or brainstorming tools. It is straightforward and efficient for creating word-based data without additional complexity.

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 random-words

random-words is a simple npm package that generates random words, making it useful for various applications such as testing, placeholder content, or even games. With its straightforward API, developers can easily generate one or more random words, which can be beneficial for creating mock data or enhancing user interfaces with dynamic content. While random-words serves its purpose effectively, there are several alternatives that offer additional features or different approaches to generating random content. Here are a few notable alternatives:

  • chance is a comprehensive library for generating random data. It provides a wide range of functionalities, including generating random numbers, strings, dates, and even entire objects. Chance is particularly useful when you need more than just random words, as it can create complex data structures that mimic real-world scenarios. If your project requires a broader set of random data generation capabilities, Chance is an excellent choice.
  • faker is another powerful library designed for generating fake data for testing and development purposes. It can create realistic-looking data such as names, addresses, phone numbers, and even entire user profiles. Faker is especially useful for populating databases or creating realistic test scenarios. If you need a library that can generate a wide variety of fake data beyond just words, Faker is a strong contender.
  • lorem-ipsum is a specialized library that generates placeholder text based on the classic "Lorem Ipsum" text. It is particularly useful for web developers and designers who need to fill content areas with text during the design phase. If your primary need is to generate placeholder text rather than random words or data, lorem-ipsum is the most suitable option.

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

README for faker

faker.js - generate massive amounts of fake data in the browser and node.js

Faker.js

Build Status Coverage Status

npm version

OpenCollective OpenCollective Gitter chat

Demo

https://rawgit.com/Marak/faker.js/master/examples/browser/index.html

Faker Cloud

Don't have a local development setup ready?

Try our hosted version of Faker at https://fakercloud.com/api

https://github.com/faker/faker-cloud

Usage

Browser

    <script src = "faker.js" type = "text/javascript"></script>
    <script>
      var randomName = faker.name.findName(); // Caitlyn Kerluke
      var randomEmail = faker.internet.email(); // Rusty@arne.info
      var randomCard = faker.helpers.createCard(); // random contact card containing many properties
    </script>

Node.js

    var faker = require('faker');

    var randomName = faker.name.findName(); // Rowan Nikolaus
    var randomEmail = faker.internet.email(); // Kassandra.Haley@erich.biz
    var randomCard = faker.helpers.createCard(); // random contact card containing many properties

API

JSDoc API Browser

http://marak.github.io/faker.js/

API Methods

  • address
    • zipCode
    • zipCodeByState
    • city
    • cityPrefix
    • citySuffix
    • cityName
    • streetName
    • streetAddress
    • streetSuffix
    • streetPrefix
    • secondaryAddress
    • county
    • country
    • countryCode
    • state
    • stateAbbr
    • latitude
    • longitude
    • direction
    • cardinalDirection
    • ordinalDirection
    • nearbyGPSCoordinate
    • timeZone
  • animal
    • dog
    • cat
    • snake
    • bear
    • lion
    • cetacean
    • horse
    • bird
    • cow
    • fish
    • crocodilia
    • insect
    • rabbit
    • type
  • commerce
    • color
    • department
    • productName
    • price
    • productAdjective
    • productMaterial
    • product
    • productDescription
  • company
    • suffixes
    • companyName
    • companySuffix
    • catchPhrase
    • bs
    • catchPhraseAdjective
    • catchPhraseDescriptor
    • catchPhraseNoun
    • bsAdjective
    • bsBuzz
    • bsNoun
  • database
    • column
    • type
    • collation
    • engine
  • datatype
    • number
    • float
    • datetime
    • string
    • uuid
    • boolean
    • hexaDecimal
    • json
    • array
  • date
    • past
    • future
    • between
    • betweens
    • recent
    • soon
    • month
    • weekday
  • fake
  • finance
    • account
    • accountName
    • routingNumber
    • mask
    • amount
    • transactionType
    • currencyCode
    • currencyName
    • currencySymbol
    • bitcoinAddress
    • litecoinAddress
    • creditCardNumber
    • creditCardCVV
    • ethereumAddress
    • iban
    • bic
    • transactionDescription
  • git
    • branch
    • commitEntry
    • commitMessage
    • commitSha
    • shortSha
  • hacker
    • abbreviation
    • adjective
    • noun
    • verb
    • ingverb
    • phrase
  • helpers
    • randomize
    • slugify
    • replaceSymbolWithNumber
    • replaceSymbols
    • replaceCreditCardSymbols
    • repeatString
    • regexpStyleStringParse
    • shuffle
    • mustache
    • createCard
    • contextualCard
    • userCard
    • createTransaction
  • image
    • image
    • avatar
    • imageUrl
    • abstract
    • animals
    • business
    • cats
    • city
    • food
    • nightlife
    • fashion
    • people
    • nature
    • sports
    • technics
    • transport
    • dataUri
    • lorempixel
    • unsplash
    • lorempicsum
  • internet
    • avatar
    • email
    • exampleEmail
    • userName
    • protocol
    • httpMethod
    • url
    • domainName
    • domainSuffix
    • domainWord
    • ip
    • ipv6
    • port
    • userAgent
    • color
    • mac
    • password
  • lorem
    • word
    • words
    • sentence
    • slug
    • sentences
    • paragraph
    • paragraphs
    • text
    • lines
  • mersenne
    • rand
    • seed
    • seed_array
  • music
    • genre
  • name
    • firstName
    • lastName
    • middleName
    • findName
    • jobTitle
    • gender
    • prefix
    • suffix
    • title
    • jobDescriptor
    • jobArea
    • jobType
  • phone
    • phoneNumber
    • phoneNumberFormat
    • phoneFormats
  • random
    • number
    • float
    • arrayElement
    • arrayElements
    • objectElement
    • uuid
    • boolean
    • word
    • words
    • image
    • locale
    • alpha
    • alphaNumeric
    • hexaDecimal
  • system
    • fileName
    • commonFileName
    • mimeType
    • commonFileType
    • commonFileExt
    • fileType
    • fileExt
    • directoryPath
    • filePath
    • semver
  • time
    • recent
  • unique
  • vehicle
    • vehicle
    • manufacturer
    • model
    • type
    • fuel
    • vin
    • color
    • vrm
    • bicycle

Faker.fake()

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

Example:

console.log(faker.fake("{{name.lastName}}, {{name.firstName}} {{name.suffix}}"));
// outputs: "Marks, Dean Sr."

This will interpolate the format string with the value of methods name.lastName(), name.firstName(), and name.suffix()

Localization

As of version v2.0.0 faker.js has support for multiple localities.

The default language locale is set to English.

Setting a new locale is simple:

// sets locale to de
faker.locale = "de";
  • az
  • ar
  • cz
  • de
  • de_AT
  • de_CH
  • en
  • en_AU
  • en_AU_ocker
  • en_BORK
  • en_CA
  • en_GB
  • en_IE
  • en_IND
  • en_US
  • en_ZA
  • es
  • es_MX
  • fa
  • fi
  • fr
  • fr_CA
  • fr_CH
  • ge
  • hy
  • hr
  • id_ID
  • it
  • ja
  • ko
  • nb_NO
  • ne
  • nl
  • nl_BE
  • pl
  • pt_BR
  • pt_PT
  • ro
  • ru
  • sk
  • sv
  • tr
  • uk
  • vi
  • zh_CN
  • zh_TW

Individual Localization Packages

faker.js supports incremental loading of locales.

By default, requiring faker will include all locale data.

In a production environment, you may only want to include the locale data for a specific set of locales.

// loads only de locale
var faker = require('faker/locale/de');

Setting a randomness seed

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

faker.seed(123);

var firstRandom = faker.datatype.number();

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

var secondRandom = faker.datatype.number();

console.log(firstRandom === secondRandom);

Tests

npm install .
make test

You can view a code coverage report generated in coverage/lcov-report/index.html.

Building faker.js

faker uses gulp to automate its build process. Each build operation is a separate task which can be run independently.

Browser Bundle

npm run browser

Building JSDocs

JSDOC v3 HTML API documentation

npm run jsdoc

Building ReadMe

The ReadMe.md file for faker.js is automatically generated and should not be modified directly. All updates to ReadMe.md should be performed in ./build/src/docs.md and then the build script should be run.

npm run readme

Version Release Schedule

faker.js is a popular project used by many organizations and individuals in production settings. Major and Minor version releases are generally on a monthly schedule. Bugs fixes are addressed by severity and fixed as soon as possible.

If you require the absolute latest version of faker.js the master branch @ http://github.com/marak/faker.js/ should always be up to date and working.

Maintainer

Marak Squires

faker.js - Copyright (c) 2020 Marak Squires www.marak.com http://github.com/marak/faker.js/

faker.js was inspired by and has used data definitions from:

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Support us with a monthly donation and help us continue our activities. [Become a backer]

Sponsors

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