FakeIt Series 2 of 5: Shared Data and Dependencies

Aaron Benton is an experienced architect who specializes in creative solutions to develop innovative mobile applications. He has over 10 years experience in full stack development, including ColdFusion, SQL, NoSQL, JavaScript, HTML, and CSS. Aaron is currently an Applications Architect for Shop.com in Greensboro, North Carolina and is a Couchbase Community Champion.

FakeIt Series 2 of 5: Shared Data and Dependencies

In FakeIt Series 1 of 5: Generating Fake Data we learned that FakeIt can generate a large amount of random data based off a single YAML file and output the results to various formats and destination, including Couchbase Server. Today we are going to explore what makes FakeIt truly unique and powerful in the world of data generation.

There are tons of random data generators available, a simple Google Search will give you more than enough to choose from. However, almost all of these have the same frustrating flaw, which is they can only ever deal with a single model. Rarely as developers do we have the luxury of dealing with a single model, more often than not we are developing against multiple models for our projects. This is where FakeIt stands out, it allows for multiple models and those models to have dependencies.

Let’s take a look at the possible models we’ll have within our e-commerce application:

• Users
• Products
• Cart
• Orders
• Reviews

Users, the first model that we defined does not have any dependencies and the same can be said for the Products model, which we will define next. However, it would be logical to say that our Orders model would depend on both the Users and Products model. If we truly want test data, the documents created by our Orders model should be the actual random data generated from both the Users and Products models.

Products Model

Before we look at how model dependencies work in FakeIt let’s define what our Products model is going to look like.

This model is a little more complex than our previous Users model. Let’s examine a few of this property in more detail:

• _id: This value is being set after every property in the document has been build and is available to the post build function. The this context is that of the current document being generated
• sale_price: This using defining a 30% chance of a sale price and if there is a sale price ensuring that the value is less than that of the price property
• keywords: Is an array. This defined similarly to Swagger, we define our array items and how we want them constructed using the build / post_build functions. Additionally, we can define min and max values and FakeIt will generate a random number of array elements between these values. There is also a fixed property that can be used to generate a set number of array elements.

Now that we’ve constructed our Products model let’s generate some random data and output it to the console to see what it looks like using the command:

Orders Model

For our project we have already defined the following models:

• users.yaml
• products.yaml

Let’s start by defining or Orders model without any properties and specifying its dependencies:

We have defined two dependencies for our Orders model, and referenced them by their file name. Since all of our models are stored in the same directory there is no reason to specify the full path. At runtime, FakeIt will first parse all of the models before attempting to generate documents, and it will determine a run order based on each of the models dependencies (if any).

Each of the build functions in a FakeIt model is a function body, with the following arguments passed to it.

Once the run order has been established, each of the dependencies are saved in-memory and made available to the dependant model through the documents argument. This argument is an object containing a key for each model whose value is an array of each document that has been generated. For our example of the documents property it will look similar to this:

We can take advantage of this to retrieve random Product and User documents assigning their properties to properties within our Orders model. For example, we can retrieve a random user_id from the documents generated by the Users model and assign that to the user_id of the Orders model through a build function

Let’s define what the rest of our Orders model will look like:

And output it to the console using the command:

As you can see from the console output, the documents were generated for the Users and Products models, and those documents were made available to the Orders model. However, they were excluded from output because all that was requested to be output was the Orders model.

Now that we have defined 3 models with dependencies (Users, Products and Orders), we need to be able to generate multiple documents for each of these and output them to Couchbase Server. Up to this point we have been specifying the number of documents to generate via the –count command line argument. We can specify the number of documents or a range of documents by using the data: property at the root of the model.

We can now generate random sets of related document models and output those documents directly into Couchbase Server using the command:

Conclusion

We’ve seen through three simple FakeIt YAML models how we can create model dependencies allowing for randomly generated data to be related across models and streamed into Couchbase Server. We’ve also seen how we can specify the number of documents to generate by model by using the data: property at the root of a model.

These models can be stored in your projects repository, taking up very little space and allow your developers to generate the same data structures with completely different data. Another advantage of being able to generate documents through multi-model relationships is to explore different document models and see how they perform with various N1QL queries.

Up Next

• FakeIt Series 3 of 5: Lean Models through Definitions
• FakeIt Series 4 of 5: Working with Existing Data
• FakeIt Series 5 of 5: Rapid Mobile Development w/ Sync-Gateway

Previous

• FakeIt Series 1 of 5: Generating Fake Data

 This post is part of the Couchbase Community Writing Program