FakeIt Series (Part 4 of 5): Working With Existing Data

So far in our FakeIt series, we’ve seen how we can generate fake data, share data and dependencies, and use definitions for smaller models. Today, we are going to look at the last major feature of FakeIt, which is working with existing data through inputs.

Rarely as developers do we get the advantage of working on greenfield applications. Our domains are more often than not a comprised of different legacy databases and applications. As we are modeling and building new applications, we need to reference and use this existing data. FakeIt allows you to provide existing data to your models through JSON, CSV, or CSON files. This data is exposed as inputs an  variable in each of a model's *run and *build functions.

Users Model

We will start with our users.yaml model that we updated to in our most recent post to use Address and Phone definitions.

name: Users
type: object
key: _id
data:
  min: 1000
  max: 2000
properties:
  _id:
    type: string
    description: The document id built by the prefix "user_" and the users id
    data:
      post_build: "`user_${this.user_id}`"
  doc_type:
    type: string
    description: The document type
    data:
      value: "user"
  user_id:
    type: integer
    description: An auto-incrementing number
    data:
      build: document_index
  first_name:
    type: string
    description: The users first name
    data:
      build: faker.name.firstName()
  last_name:
    type: string
    description: The users last name
    data:
      build: faker.name.lastName()
  username:
    type: string
    description: The username
    data:
      build: faker.internet.userName()
  password:
    type: string
    description: The users password
    data:
      build: faker.internet.password()
  email_address:
    type: string
    description: The users email address
    data:
      build: faker.internet.email()
  created_on:
    type: integer
    description: An epoch time of when the user was created
    data:
      build: new Date(faker.date.past()).getTime()
  addresses:
    type: object
    description: An object containing the home and work addresses for the user
    properties:
      home:
        description: The users home address
        schema:
         $ref: '#/definitions/Address'
      work:
        description: The users work address
        schema:
         $ref: '#/definitions/Address'
  main_phone:
    description: The users main phone number
    schema:
     $ref: '#/definitions/Phone'
    data:
      post_build: |
       delete this.main_phone.type
       return this.main_phone
  additional_phones:
    type: array
    description: The users additional phone numbers
    items:
     $ref: '#/definitions/Phone'
      data:
        min: 1
        max: 4
definitions:
  Phone:
    type: object
    properties:
      type:
        type: string
        description: The phone type
        data:
          build: faker.random.arrayElement([ 'Home', 'Work', 'Mobile', 'Other' ])
      phone_number:
        type: string
        description: The phone number
        data:
          build: faker.phone.phoneNumber().replace(/[^0-9]+/g, '')
      extension:
        type: string
        description: The phone extension
        data:
          build: chance.bool({ likelihood: 30 }) ? chance.integer({ min: 1000, max: 9999 }) : null
  Address:
    type: object
    properties:
      address_1:
        type: string
        description: The address 1
        data:
          build: `${faker.address.streetAddress()} ${faker.address.streetSuffix()}`
      address_2:
        type: string
        description: The address 2
        data:
          build: chance.bool({ likelihood: 35 }) ? faker.address.secondaryAddress() : null
      locality:
        type: string
        description: The city / locality
        data:
          build: faker.address.city()
      region:
        type: string
        description: The region / state / province
        data:
          build: faker.address.stateAbbr()
      postal_code:
        type: string
        description: The zip code / postal code
        data:
          build: faker.address.zipCode()
      country:
        type: string
        description: The country code
        data:
          build: faker.address.countryCode()

Currently, our Address definition is generating a random country. What if our e-commerce site only supports a small subset of the 195 countries? Let’s say we support six countries to start with: US, CA, MX, UK, ES, and DE. We could update the definitions country property to grab a random array element:

For brevity, the other properties have been left off of the model definition.

...
      country:
        type: string
        description: The country code
        data:
          build: faker.random.arrayElement(['US', 'CA', 'MX', 'UK', 'ES', 'DE']);

While this would work, what if we have other models that rely on this same country info? We would have to duplicate this logic. We can achieve this same thing by creating a countries.json file and adding an inputs property to the data property that can be an absolute or relative path to our input. When our model is generated, our countries.json file will be exposed to each of the models build functions via the inputs argument as inputs.countries.

For brevity, the other properties have been left off of the model definition.

name: Users
type: object
key: _id
data:
  min: 1000
  max: 2000
  inputs: ./countries.json
properties:
...
definitions:
...
      country:
        type: string
        description: The country code
        data:
          build: faker.random.arrayElement(inputs.countries);

countries.json

[
 "US",
 "CA",
 "MX",
 "UK",
 "ES",
 "DE"
]

By changing one existing line and adding another line in the model, we have provided existing data to our Users model. We can still generate a random country based on the countries our application supports. Let's test our changes by using the following command:

fakeit console --count 1 models/users.yaml

Products Model

Our e-commerce application is using a separate system for categorization. We need to expose that data to our randomly generated products so that we are using valid category information. We will start with the products.yaml that we defined in the FakeIt Series (Part 2 of 5): Shared Data and Dependencies post.

products.yaml

name: Products
type: object
key: _id
data:
  min: 4000
  max: 5000
properties:
  _id:
    type: string
    description: The document id
    data:
      post_build: `product_${this.product_id}`
  doc_type:
    type: string
    description: The document type
    data:
      value: product
  product_id:
    type: string
    description: Unique identifier representing a specific product
    data:
      build: faker.random.uuid()
  price:
    type: double
    description: The product price
    data:
      build: chance.floating({ min: 0, max: 150, fixed: 2 })
  sale_price:
    type: double
    description: The product price
    data:
      post_build: |
       let sale_price = 0;
       if (chance.bool({ likelihood: 30 })) {
         sale_price = chance.floating({ min: 0, max: this.price * chance.floating({ min: 0, max: 0.99, fixed: 2 }), fixed: 2 });
       }
       return sale_price;
  display_name:
    type: string
    description: Display name of product.
    data:
      build: faker.commerce.productName()
  short_description:
    type: string
    description: Description of product.
    data:
      build: faker.lorem.paragraphs(1)
  long_description:
    type: string
    description: Description of product.
    data:
      build: faker.lorem.paragraphs(5)
  keywords:
    type: array
    description: An array of keywords
    items:
      type: string
      data:
        min: 0
        max: 10
        build: faker.random.word()
  availability:
    type: string
    description: The availability status of the product
    data:
      build: |
       let availability = 'In-Stock';
       if (chance.bool({ likelihood: 40 })) {
         availability = faker.random.arrayElement([ 'Preorder', 'Out of Stock', 'Discontinued' ]);
       }
       return availability;
  availability_date:
    type: integer
    description: An epoch time of when the product is available
    data:
      build: faker.date.recent()
      post_build: new Date(this.availability_date).getTime()
  product_slug:
    type: string
    description: The URL friendly version of the product name
    data:
      post_build: faker.helpers.slugify(this.display_name).toLowerCase()
  category:
    type: string
    description: Category for the Product
    data:
      build: faker.commerce.department()
  category_slug:
    type: string
    description: The URL friendly version of the category name
    data:
      post_build: faker.helpers.slugify(this.category).toLowerCase()
  image:
    type: string
    description: Image URL representing the product.
    data:
      build: faker.image.image()
  alternate_images:
    type: array
    description: An array of alternate images for the product
    items:
      type: string
      data:
        min: 0
        max: 4
        build: faker.image.image()

Our existing categories data has been provided in CSV format.

categories.csv

"category_id","category_name","category_slug"
23,"Electronics","electronics"
1032,"Office Supplies","office-supplies"
983,"Clothing & Apparel","clothing-and-apparel"
483,"Movies, Music & Books","movies-music-and-books"
3023,"Sports & Fitness","sports-and-fitness"
4935,"Automotive","automotive"
923,"Tools","tools"
5782,"Home Furniture","home-furniture"
9783,"Health & Beauty","health-and-beauty"
2537,"Toys","toys"
10,"Video Games","video-games"
736,"Pet Supplies","pet-supplies"

Now we need to update our products.yaml model to use this existing data.

For brevity, the other properties have been left off of the model definition.

name: Products
type: object
key: _id
data:
  min: 4000
  max: 5000
  inputs:
    - ./categories.csv
  pre_build: globals.current_category = faker.random.arrayElement(inputs.categories);
properties:
...
  category_id:
    type: integer
    description: The Category ID for the Product
    data:
      build: globals.current_category.category_id
  category:
    type: string
    description: Category for the Product
    data:
      build: globals.current_category.category_name
  category_slug:
    type: string
    description: The URL friendly version of the category name
    data:
      post_build: globals.current_category.category_slug
...

There are a few things to notice about how we’ve updated our products.yaml model.

• inputs: is defined as an array, not a string. While we are only using a single input, you can provide as many input files to your model as necessary.
• A pre_build function is defined at the root of the model. This is because we cannot grab a random array element for each of our three category properties, as the values would not match. Each time an individual document is generated for our model, this pre_build function will run first.
• Each of our category property's build functions references the global variable set by the pre_build function on our model.

We can test our changes by using the following command:

fakeit console --count 1 models/products.yaml

Conclusion

Being able to work with existing data is an extremely powerful feature of FakeIt. It can be used to maintain the integrity of randomly generated documents to work with existing systems and can even be used to transform existing data and import it into Couchbase Server.