4. Building the schema
3m

Overview

Time to put what we learned about to work.

In this lesson, we will:

  • Add our dependencies
  • Define our schema

Adding schema dependencies

To get started with our schema, we'll need to bring in our DGS dependencies. DGS gives us a couple of helpful starter packages that we can integrate seamlessly with our basic Spring Boot project:

implementation 'com.netflix.graphql.dgs:graphql-dgs-spring-boot-starter'
implementation(platform('com.netflix.graphql.dgs:graphql-dgs-platform-dependencies:7.6.0'))

Open up the build.gradle file in the root of your project, and copy these lines into dependencies.

build.gradle
dependencies {
implementation 'org.springframework.boot:spring-boot-starter-web'
implementation 'org.springframework.boot:spring-boot-starter-webflux'
implementation 'com.netflix.graphql.dgs:graphql-dgs-spring-boot-starter'
implementation(platform('com.netflix.graphql.dgs:graphql-dgs-platform-dependencies:7.6.0'))
// ... other dependencies
}

These packages are responsible for all of the wiring we'll need to get our project up and running. DGS automatically scours our project for schema files (specifically, by looking in the project's src/main/resources/schema folder, which we are about to create), along with certain annotations and functions that we'll define shortly.

Task!

But before it can do anything for us, we actually need to define a schema!

Building the schema

Let's navigate to the resources package, in src/main. In there, we'll find schema directory, which houses our schema file: schema.graphqls.

📂 src
┣ 📂 main
┃ ┣ 📂 java
┃ ┣ 📂 resources
┃ ┃ ┣ 📄 application.properties
┃ ┃ ┣ 📂 schema
┃ ┃ ┃ ┗ 📄 schema.graphqls

✏️ Let's define that schema

Referring back to our featured listings mockup, we identified that we need some data for each listing.

A screenshot of Airlock, focused on the row of featured listings and their required fields

For this course, we're going to skip the listing photo and overall rating, and tackle a slightly pared-down version of the mockup.

Here are the basic we'll start with:

  • title
  • numOfBeds
  • costPerNight
  • closedForBookings

We'll also need a that we can use to differentiate one listing from another—we'll give this field the name id.

With our set of in mind, let's bring the Listing type to life!

The Listing type

Let's define the Listing type in our schema.graphqls file and add a description right away.

schema.graphqls
"A particular intergalactic location available for booking"
type Listing {
# Fields go here
}

Now for the listing's , we'll have:

  • id of type ID!
  • title of type String!
  • numOfBeds of type Int
  • costPerNight of type Float
  • closedForBookings of type Boolean

So we should end up with a Listing type that looks like this:

"A particular intergalactic location available for booking"
type Listing {
id: ID!
"The listing's title"
title: String!
"The number of beds available"
numOfBeds: Int
"The cost per night"
costPerNight: Float
"Indicates whether listing is closed for bookings (on hiatus)"
closedForBookings: Boolean
}

Be sure to note the nullability of each of your Listing type's !

The Listing type is complete for now, but we need a way to actually ask our for listing data. For that, we have a separate Query type.

The Query type

The Query type is defined like any other :

type Query {
# Fields go here
}

The of this type are entry points into the rest of our schema. These are the top-level that our client can for.

For now, we're only interested in fetching the list of featured listings for our homepage. Let's name the featuredListings to make it as descriptive as possible. We want this to return a list of Listings. We'll also add a nice description:

type Query {
"A curated array of listings to feature on the homepage"
featuredListings: [Listing!]!
}

Our schema is now fully defined to support our first feature!

Key takeaways

  • With just a few dependencies, DGS automatically searches for a schema.graphqls file housed in a directory called schema.

Up next

Now that our base schema is ready, we can start working on the next piece of our API—writing the functions that actually return some data. In the next lesson, we'll write our first datafetcher.

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