<br />

Firebase Data Connect client SDKs let you call your server-side queries and
mutations directly from a Firebase app. You generate a custom client SDK in
parallel as you design the schemas, queries and mutations you deploy to your
Data Connect service. Then, you integrate methods from this SDK into
your client logic.

As we've mentioned elsewhere, it's important to note that Data Connect
queries and mutations are not submitted by client code and executed on the
server. Instead, when deployed, Data Connect operations are stored on
the server like Cloud Functions. This means you need to deploy corresponding
client-side changes to avoid breaking existing users (for example, on older app
versions).

That's why Data Connect provides you with a developer environment and
tooling that lets you prototype your server-deployed schemas, queries and mutations.
It also generates client-side SDKs automatically, while you prototype.

When you've iterated updates to your service and client apps, both server- and
client-side updates are ready to deploy.

## What is the client development workflow?

If you followed the [Get started](https://firebase.google.com/docs/data-connect/quickstart), you were
introduced to the overall development flow for Data Connect. In this
guide, you'll find more detailed information about generating Android SDKs from
your schema and working with client queries and mutations.
| You can learn client development with a quickstart app repository and build a fully-featured Data Connect app by following our [codelab for Android](https://firebase.google.com/codelabs/firebase-dataconnect-android).

To summarize, to use generated Android SDKs in your client apps, you'll follow
these prerequisite steps:

1. Add Firebase to your [Android](https://firebase.google.com/docs/android/setup) app.
2. Configure Data Connect as a dependency in Gradle.
3. Add the Kotlin Serialization Gradle plugin and Gradle dependency.

Then:

1. Develop your app schema.
2. Set up SDK generation:

   - With the **Add SDK to app** button in our Data Connect VS Code extension
   - By [updating your `connector.yaml`](https://firebase.google.com/docs/data-connect/android-sdk#generate-kotlin)
3. [Initialize your client code and import libraries](https://firebase.google.com/docs/data-connect/android-sdk#set-client).

4. [Implement calls to queries and mutations](https://firebase.google.com/docs/data-connect/android-sdk#queries-mutations).

5. [Set up and use the Data Connect emulator](https://firebase.google.com/docs/data-connect/android-sdk#prototype-and) and
   iterate.

## Generate your Kotlin SDK

Use the Firebase CLI to set up Data Connect generated SDKs in your apps.
The `init` command should detect all apps in the current folder and install
generated SDKs automatically.  

    firebase init dataconnect:sdk

### Update SDKs while prototyping

If you have Data Connect VS Code extension installed, it will always keep generated
SDKs up to date.

If you don't use Data Connect VS Code extension, you can use Firebase CLI
to keep generated SDKs up to date.  

    firebase dataconnect:sdk:generate --watch

### Generate SDKs in build pipelines

You can use the Firebase CLI to generate Data Connect SDKs in CI/CD build processes.  

    firebase dataconnect:sdk:generate

## Set up client code

### Incorporate Data Connect into your client code

To set up your client code to use Data Connect and your generated SDK,
first follow the [standard Firebase setup
instructions](https://firebase.google.com/docs/android/setup).

Then, add the following into the `plugins` section in `app/build.gradle.kts`:  

    // The Firebase team tests with version 1.8.22; however, other 1.8 versions,
    // and all newer versions are expected work too.
    kotlin("plugin.serialization") version "1.8.22" // MUST match the version of the Kotlin compiler

Then, add the following into the `dependencies` section in
`app/build.gradle.kts`:  

    implementation(platform("com.google.firebase:firebase-bom:34.8.0"))
    implementation("com.google.firebase:firebase-dataconnect")
    implementation("com.google.firebase:firebase-auth") // Optional
    implementation("com.google.firebase:firebase-appcheck") // Optional
    implementation("org.jetbrains.kotlinx:kotlinx-coroutines-core:1.7.3") // Newer versions should work too
    implementation("org.jetbrains.kotlinx:kotlinx-serialization-core:1.5.1") // Newer versions should work too

### Initialize the Data Connect Android SDK

Initialize your Data Connect instance using the information you used
to set up Data Connect (all available in the Firebase console
Data Connect tab).

#### The ConnectorConfig object

The SDK requires a connector configuration object.

This object is automatically generated from `serviceId` and `location` in
`dataconnect.yaml`, and `connectorId` in `connector.yaml`.

#### Getting a connector instance

Now that you've set up a configuration object, get a Data Connect
connector instance. The code for your connector will be generated by the
Data Connect emulator. If your connector name is `movies` and the
Kotlin package is `com.myapplication`, as specified in `connector.yaml`, then
retrieve the connector object by calling:  

    val connector = com.myapplication.MoviesConnector.instance

## Use queries and mutations from your Android SDK

With the connector object, you can run queries and mutations as defined in the
GraphQL source code. Suppose your connector has these operations defined:  

    mutation createMovie($title: String!, $releaseYear: Int!, $genre: String!, $rating: Int!) {
      movie_insert(data: {
        title: $title
        releaseYear: $releaseYear
        genre: $genre
        rating: $rating
      })
    }

    query getMovieByKey($key: Movie_Key!) {
      movie(key: $key) { id title }
    }

    query listMoviesByGenre($genre: String!) {
      movies(where: {genre: {eq: $genre}}) {
        id
        title
      }
    }

then you could create and retrieve a movie as follows:  

    val connector = MoviesConnector.instance

    val addMovieResult1 = connector.createMovie.execute(
      title = "Empire Strikes Back",
      releaseYear = 1980,
      genre = "Sci-Fi",
      rating = 5
    )

    val movie1 = connector.getMovieByKey.execute(addMovieResult1.data.key)

    println("Empire Strikes Back: ${movie1.data.movie}")

You can also retrieve multiple movies:  

    val connector = MoviesConnector.instance

    val addMovieResult2 = connector.createMovie.execute(
      title="Attack of the Clones",
      releaseYear = 2002,
      genre = "Sci-Fi",
      rating = 5
    )

    val listMoviesResult = connector.listMoviesByGenre.execute(genre = "Sci-Fi")

    println(listMoviesResult.data.movies)

You can also collect a `Flow` that will only produce a result when a new query
result is retrieved using a call to the query's `execute()` method.
**Note:** This Flow is not updated in realtime.  

    val connector = MoviesConnector.instance

    connector.listMoviesByGenre.flow(genre = "Sci-Fi").collect { data ->
      println(data.movies)
    }

    connector.createMovie.execute(
      title="A New Hope",
      releaseYear = 1977,
      genre = "Sci-Fi",
      rating = 5
    )

    connector.listMoviesByGenre.execute(genre = "Sci-Fi") // will cause the Flow to get notified

### Handle changes to enumeration fields

An app's schema can [contain enumerations](https://firebase.google.com/docs/data-connect/schemas-guide#enumeration-fields),
which can be accessed by your [GraphQL queries](https://firebase.google.com/docs/data-connect/queries-guide#query-enumerations).

As an app's design changes, you may add new enum supported values. For example,
imagine that later in your application's lifecycle you decide to add a
FULLSCREEN value to the `AspectRatio` enum.

In the Data Connect workflow, you can use local development tooling to
update your queries and SDKs.

However, before you release an updated version of your clients, older deployed
clients may break.

#### Example resilient implementation

The generated SDK forces handling of unknown values as the customer's code must
unwrap the `EnumValue` object, which is either `EnumValue.Known` for known enum
values or `EnumValue.Unknown` for unknown values.  

    val result = connector.listMoviesByAspectRatio.execute(AspectRatio.WIDESCREEN)
    val encounteredAspectRatios = mutableSetOf<String>()

    result.data.movies
      .mapNotNull { it.otherAspectRatios }
      .forEach { otherAspectRatios ->
        otherAspectRatios
          .filterNot { it.value == AspectRatio.WIDESCREEN }
          .forEach {
            when (it) {
              is EnumValue.Known -> encounteredAspectRatios.add(it.value.name)
              is EnumValue.Unknown ->
                encounteredAspectRatios.add("[unknown ratio: ${it.stringValue}]")
            }
          }
      }

    println(
      "Widescreen movies also include additional aspect ratios: " +
        encounteredAspectRatios.sorted().joinToString()
    )

## Prototype and test your Android application

### Instrument clients to use a local emulator

You can use the Data Connect emulator, whether from the
Data Connect VS Code extension or from the CLI.

Instrumenting the app to connect to the emulator is the same for both scenarios.  

    val connector = MoviesConnector.instance

    // Connect to the emulator on "10.0.2.2:9399"
    connector.dataConnect.useEmulator()

    // (alternatively) if you're running your emulator on non-default port:
    connector.dataConnect.useEmulator(port = 9999)

    // Make calls from your app

To switch to production resources, comment out lines for connecting to the
emulator.

## Data types in Data Connect SDKs

The Data Connect server represents common and custom GraphQL data
types. These are represented in the SDK as follows.

| Data Connect Type | Kotlin |
|---|---|
| String | String |
| Int | Int (32-bit integer) |
| Float | Double (64-bit float) |
| Boolean | Boolean |
| UUID | java.util.UUID |
| Date | com.google.firebase.dataconnect.LocalDate (was java.util.Date until 16.0.0-beta03) |
| Timestamp | com.google.firebase.Timestamp |
| Int64 | Long |
| Any | com.google.firebase.dataconnect.AnyValue |