node package manager

mocker-data-generator

mocker-data-generator

NPM

Greenkeeper badge

npm versionGitHub licenseStandard Version

Linux Build statusWindows Build statusCodecov coverageCodecov coverage

Dependency StatusDevDependency Status

bitHound Overall Score bitHound Dependencies bitHound Dev Dependencies bitHound Code

GitHub licenseAwesome license

Support link

A simplified way to generate massive mock data based on a schema, using the awesome fake/random data generators like (FakerJs, ChanceJs, CasualJs and RandExpJs), all in one tool to generate your fake data for testing.

Now the library has been migrated 100% to typescript typing are included.

Getting started

Install the module with: npm install mocker-data-generator

Import it

var mocker = require('mocker-data-generator').default (vainilla way)
 
or
 
import mocker from 'mocker-data-generator' (ES6 or Typescript way)

Then use it:

var user = {
    firstName: {
        faker: 'name.firstName'
    },
    lastName: {
        faker: 'name.lastName'
    },
    country: {
        faker: 'address.country'
    },
    createdAt: {
        faker: 'date.past'
    },
    username:{
        functionfunction() {
            return this.object.lastName.substring(0, 5) + this.object.firstName.substring(0, 3) + Math.floor(Math.random() * 10)
        }
    }
};
var group = {
    description: {
        faker: 'lorem.paragraph'
    },
    users: [{
        functionfunction() {
            return this.faker.random.arrayElement(this.db.user).username
        },
        length: 10,
        fixedLength: false
    }]
};
var conditionalField = {
    type:{
        values: ['HOUSE', 'CAR', 'MOTORBIKE']
    },
    'object.type=="HOUSE",location':{
        faker: 'address.city'
    },
    'object.type=="CAR"||object.type=="MOTORBIKE",speed':{
        faker: 'random.number'
    }
};
 
mocker()
    .schema('user', user, 2)
    .schema('group', group, 2)
    .schema('conditionalField', conditionalField, 2)
    .build(function(data) {
        console.log(util.inspect(data, { depth: 10 }))
//This returns an object 
// { 
//      user:[array of users], 
//      group: [array of groups], 
//      conditionalField: [array of conditionalFields] 
// } 
        })

Documentation

Data generation goes with model based composed by generators, the generators can have access to the data generated and to the entity generated. Generators run syncronously, take care of the related entities!!

Methods

  • schema(name, schema, generationType): Add a new schema, you must specify this params:

    • name (String): Name of the schema.
    • schema (JSON): The schema you define
    • generationType (integer or JSON): In this field you specify how you will generate this schema. 3 ways:
      • Integer to specify how many objects of this schema you want.
      • JSON with this object {max: '<maximunValues>'} you can also optionally pass min {max: '<maximunValues>', min: '<minimumValues>', this will generate a range of objects of this schema, between (0 and max) or (min and max) randomly.
      • JSON with this object {uniqueField: '<yourUniqueField>'} this means that this field () is an array and you want to generate entities with this unique values
  • reset(): Clean the internal DB.

  • restart(): Clean the internal DB and all the schemas inside.

  • build(callback): This methods start to produce the data and wrap it to the callback

Schema definition

Every schema should contains the specified fields. Key can be 2 types:

  • Normal string key: indicates the key.
  • Commaseparated string key: indicates that there is a conditional, before the comma you must specify a conditional (you have all level fields generated in this moment), then you must specify the field if the conditional is true see the example.

Inside every value you can put:

  • static: For fixed fields

          { static: 'hello im fixed field' }
  • self: get himself object, and evaluate the string, so you can get calculated fields.

          { self: 'id' } //will get the id of the generated entity 
  • db: get the db, and evaluate the string, so you can access to this entities.

          { db: 'user[0].id' } //will get the first user id 
  • eval: evaluate the current string, remember that i inject all the awesome methods, faker, chance, casual, randexp, and also the db and object methods. With this eval field, you must pass an exactly JSON syntax:

          { eval: 'object.id' }
     
          //OR 
     
          { eval: 'db.user[0]' }
     
          //OR 
     
          { eval: 'faker.lorem.words()' }
  • hasOne: You can pass 2 parameters:

    • hasOne: the name of the related entity, get one random.

    • get (Optional): String that will be evaluated over the random related entity.

          {
              hasOne: 'user' //this populate the field with one random user 
          }
       
          //OR: 
       
          {
              hasOne: 'user',
              get: 'id' //this populate the field with one id of a random user 
          }
  • hasMany: You can pass 4 parameters:

    • hasMany: the name of the related entity, get one random.

    • amount (Optional): Fixed number of related entities to get.

    • min (Optional): Minimum entities to get.

    • max (Optional): Maximum entities to get.

    • get (Optional): String that will be evaluated over the random related entity.

          // In this case we will get 1 user (hasMany) 
          {
              hasMany: 'user' //this populate the field with one random user 
          }
       
          //OR: 
          // In this case we will get 1 (amount) user (hasMany) 
       
          {
              hasMany: 'user',
              amount: 1, //optional 
          }
       
          //OR: 
          // In this case we will get as max 3 (max) users (hasMany) 
       
          {
              hasMany: 'user',
              max: 3 //optional 
          }
       
          //OR: 
          // In this case we will get bettween min 1 (min) and max 3 (max) users (hasMany) 
       
          {
              hasMany: 'user',
              min: 1 //optional 
              max: 3 //optional 
          }
       
          //OR: 
          // In this case we will get the id (get) from 1 random user (hasMany) 
       
          {
              hasMany: 'user',
              get: 'id'
          }
  • incrementalId: For incremental numeric ids, pass the start number to increment. If you put incrementalId = true it takes from 0 the ids.

          { incrementalId: 0 }
  • funcion: No params are passed, only context (this), in this you have {db, object, faker, chance}, and you can use faker or chance functions, object (the specified model), db (actual data generated)

          { functionfunction(){
              //this.db 
              //this.object 
              //this.faker 
              //this.chance 
              //this.casual 
              return yourValue
          } }
     
          //OR: 
     
          { function(){
              //this.db 
              //this.object 
              //this.faker 
              //this.chance 
              //this.casual 
              return yourValue
          } }
  • faker: you can use directly faker functions like: (note that, db (actual entities generated), object (actual entity generated) are injected), you must pass an exactly JSON syntax:

          { faker: 'lorem.words' }                            //Run faker.lorem.words() 
          { faker: 'lorem.words()' }                          //Run faker.lorem.words() 
          { faker: 'lorem.words(1)' }                         //Run faker.lorem.words(1) 
          { faker: 'integer({"min": 1, "max": 10})' }         //Run faker.lorem.words(1) and take the first 
          { faker: 'random.arrayElement(db.users)' }          //Run faker.arrayElement over a generated user entity 
          { faker: 'random.arrayElement(db.users)["userId"]' }  //Run faker.arrayElement over a generated user entity and take the userId only 
  • chance: you can use directly chance functions, you can do: (note that, db (actual entities generated), object (actual entity generated) are injected), you must pass an exactly JSON syntax:

          { chance: 'integer' }                                //Run chance.integer() 
          { chance: 'integer()' }                              //Run chance.integer() 
          { chance: 'integer({"min": 1, "max": 10})' }         //Run chance.integer({"min": 1, "max": 10}) 
          { chance: 'street_suffixes()[0]["name"]' }           //Run chance.street_suffixes() takes first result and the name inside 
  • casual: you can use directly use casualJs functions, you can do: (note that, db (actual entities generated), object (actual entity generated) are injected), you must pass an exactly JSON syntax:

          { casual: 'country' }
          { chance: 'array_of_digits()' }
          { casual: 'array_of_digits(3)[0]' }
  • randexp: pass a regexp string to use randexp generator.

          { randexp: /hello+ (world|to you)/ }
  • [Array]: you can pass an array that indicates an array of data you can create, passing in the first field the generator (function, faker, or array(not Tested)), and in the second field pass a config object (length, fixedLentgh)

    • length: to know how many values

    • fixedLength (Optional): true to create always same amount of values in the array, false to generate a random number between 0 and 'length' value. False by default.

    • concat (Optional): An stringuified array ex: '[object.id, db.users.id]'. This should be an evaluable string to concat with the array that are generating. Also takes in mind that if you have a fixedLength, should not increase the length.

    • strictConcat (Optional): true to remove duplicates in the concatenated string array, when it is calculated. False by default.

      [{
         //Any generator 
             //Faker 
         faker: 'random.arrayElement(db.users).userId'
             //Chance 
         chance: 'integer'
             //Function that has included index, length and self that refers at the actual array generation 
         functionfunction (index, length, self){ return /**/ }
       
         //Array config 
         length: 10,
         fixedLength: true
       
         //Concat 
         concat: '[db.users[0].userId, db.users[1].userId]'
         strictConcat: true
      }]

Optional fields

  • [virtual]: Boolean, if you pass this option, this mean that this field will not appear at the output entity. But you can use during the generation.
    {
        //Any generator 
            //Faker 
        faker: 'random.arrayElement(db.users)[userId]'
            //Chance 
        chance: 'integer'
            //static 
        static: 'any static field'
            //Function 
        functionfunction (){ return /**/ }
 
        //with the virtual option 
        virtual: true
 
    }

Data generation

Initialize mocker with the config, and then generate any entity with promises style, use generate function that accepts the name of the model and the amount of data to generate. Like the example:

mocker()
    .schema('user', user, 2)
    .schema('group', group, 2)
    .schema('conditionalField', conditionalField, 2)
    .build(function(data) {
        console.log(util.inspect(data, { depth: 10 }))
//This returns an object 
// { 
//      user:[array of users], 
//      group: [array of groups], 
//      conditionalField: [array of conditionalFields] 
// } 
        })

You can also pass instead of the number, an object with the a config, from now {uniqueField}. If this field exists tells to the generator that instead of init a fixed length of data, generate an amount of data depending of the values of the field you will specify. You have 2 way to deal with this, check the examples See the output of this example:

// 
// First way, using an 'values' embedded object 
// 
 
var cat = {
    name: {
        values: ['txuri', 'pitxi', 'kitty']
    }
};
var m = mocker()
    .schema('cat', cat, 10)
    .schema('cat2', cat, {uniqueField: 'name'})
    .build(function(data){
        console.log(util.inspect(data, {depth:10}))
    })
 
// 
// Second way, without 'values' embedded. 
// 
 
var cat = {
    name: ['txuri', 'pitxi', 'kitty']
};
var m = mocker()
    .schema('cat', cat, 10)
    .schema('cat2', cat, {uniqueField: 'name'})
    .build(function(data){
        console.log(util.inspect(data, {depth:10}))
    })

More, Coming soon

Online API

You can visit the repo url here: https://github.com/danibram/mocker-api-tester

Or visit the api directly: https://mocker-api.herokuapp.com/

Development

Run npm install;npm run dev to watch the project, webpack compile the code automatically. Run npm build to build the normal and minified version.

Why not use json-schema-faker?

json-schema-faker is awesome and works really nice, but i need a simplified and fast way to generate mock data for my projects, so i created this.

Credits

I couldn't do this without this awesome libraries, so thanks to all:

License

Licensed under the MIT license. 2015