Get started with Firebase Data Connect locally

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In this quickstart, you will learn how to build Firebase Data Connect in your application locally without setting up a production SQL instance. You will:

• Add Firebase Data Connect to your Firebase project.
• Set up a development environment including Visual Studio Code extensions to work with a local instance.
• Then we will show you how to:
  • Use VS Code extension tooling, with Gemini Code Assist, to:
    • Create a schema for an app
    • Create administrative queries and mutations to populate your local database
    • Help you implement queries and mutations for your app in a deployable connector
  • Test your queries and mutations with sample data against a local emulator
  • Generate strongly typed SDKs and use them in your app
  • Deploy your final schema and connector to the cloud (optional, with a Blaze plan upgrade).

Choose a local development flow

Data Connect offers you two ways to install development tools and work locally.

Prerequisites

To use this quickstart, you'll need the following.

• Visual Studio Code.
• A Node.js installation, using nvm-windows for Windows or nvm for macOS or Linux.
• A Firebase project. If you haven't already created one, do so in the Firebase console.

Local flow: Set up the development environment

1. Create a new directory for your local project.
2. Open VS Code in the new directory.
3. Install the Firebase Data Connect extension from the Visual Studio Code Marketplace.

Set up your project directory

To set up your local project, initialize your project directory. In the IDE window, in the left-hand panel, click the Firebase icon to open the Data Connect VS Code extension UI:

1. Click the Sign in with Google button.
2. Click the Connect a Firebase project button and select the project you created earlier in the console.
3. Click the Run firebase init button.

4. Click the Start emulators button.

Create a schema

In your Firebase project directory, in the /dataconnect/schema/schema.gql file, start defining a GraphQL schema about, for example, movie reviews.

Use Gemini Code Assist to build a schema

To create a movie review app schema using Gemini Code Assist:

1. Click the Data Connect VS Code extension icon to open its sidebar.
2. Click Try Gemini with @data-connect. The Gemini Code Assist chat window opens.
3. Click the chat interface, and begin typing @data-connect to filter relevant commands.

4. Select the /generate_schema command and at the prompt, complete the command, asking Gemini to build a schema for the app you're developing.

   For example:

   @data-connect /generate_schema I want to build an app to track movie reviews from multiple users
   

5. After a few moments, a recommended schema appears. Review the schema.

6. To add the code to schema.gql:

   1. Click the Insert to bottom of file button
   2. Or, to insert the code at your cursor position, click the + button at the top of the chat response.

Movie

In Data Connect, GraphQL fields are mapped to columns. Movie has id, title, imageUrl and genre. Data Connect recognizes primitive data types: String and UUID.

Copy the following snippet or uncomment the corresponding lines in the file.

# By default, a UUID id key will be created by default as primary key.
# If you want to specify a primary key, say title, which you can do through
# the @table(key: "title") directive
type Movie @table {
  id: UUID! @default(expr: "uuidV4()")
  title: String!
  imageUrl: String!
  genre: String
}

MovieMetadata

Copy the following snippet or uncomment the corresponding lines in the file.

# Movie - MovieMetadata is a one-to-one relationship
type MovieMetadata @table {
  # This time, we omit adding a primary key because
  # you can rely on Data Connect to manage it.

  # @unique indicates a 1-1 relationship
  movie: Movie! @unique
  # movieId: UUID <- this is created by the above reference
  rating: Float
  releaseYear: Int
  description: String
}

Notice that the movie field is mapped to a type of Movie. Data Connect understands that this is a relationship between Movie and MovieMetadata and will manage this relationship for you.

Learn more about Data Connect schemas in the documentation

Add data to your tables

In the IDE editor panel, you will see CodeLens buttons appear over the GraphQL types in /dataconnect/schema/schema.gql. You can use the Add data and Run (Local) buttons add data to your local database.

To add records to the Movie, and MovieMetadata tables:

1. In schema.gql, click the Add data button above the Movie type declaration.
   
2. In the Movie_insert.gql file that is generated, hard code data for the three fields.
3. Click the Run (Local) button.
   
4. Repeat the previous steps to add a record to the MovieMetadata table, supplying the id of your Movie in the movieId field, as prompted in the generated MovieMetadata_insert mutation.

To quickly verify data was added:

1. Back in schema.gql, click the Read data button above the Movie type declaration.
2. In the resulting Movie_read.gql file, click the Run (Local) button to execute the query.

Learn more about Data Connect mutations in the documentation

Define a query

Now for more fun: define the queries you will need in your application. As a developer, you're accustomed to writing SQL queries rather than GraphQL queries, so this can feel a bit different at first.

However, GraphQL is far more terse and type-safe than raw SQL. And, our VS Code extension eases the development experience, both for queries and mutations.

To create a query using Gemini Code Assist:

1. Click the Data Connect VS Code extension icon to open its sidebar.
2. Click Try Gemini with @data-connect. The Gemini Code Assist chat window opens.
3. Click the chat interface, and begin typing @data-connect to filter relevant commands.

4. Select the /generate_operation command and at the prompt, complete the command, asking Gemini to build a query.

   For example:

   @data-connect /generate_operation List all movies with titles start with "A".
   

5. After a few moments, a recommended query appears. Review the query.

6. To add the code to queries.gql:

   1. Click the Insert to bottom of file button
   2. Or, to insert the code at your cursor position, click the + button at the top of the chat response.

Execute the query using the nearby CodeLens button.

Learn more about Data Connect queries in the documentation

Generate SDKs and use them in your app

In the IDE left-hand panel, click the Firebase icon to open the Data Connect VS Code extension UI:

1. Click the Add SDK to app button.

2. In the dialog that appears, select a directory containing code for your app. Data Connect SDK code will be generated and saved there.

3. Select your app platform, then note that SDK code is immediately generated in your selected directory.

Use the SDKs to call your query from an app

You can use the SDK that Data Connect generated to implement a call to your ListMovies query. You can then execute this query locally using the Data Connect emulator.

import 'package:firebase_core/firebase_core.dart';
import 'package:flutter/material.dart';
import 'firebase_options.dart';

// Generated queries.
// Update as needed with the path to your generated SDK

import 'movies_connector/movies.dart';

void main() async {
  WidgetsFlutterBinding.ensureInitialized();
  
  await Firebase.initializeApp(
    options: DefaultFirebaseOptions.currentPlatform,
  );
  
  MoviesConnector.instance.dataConnect
      .useDataConnectEmulator(Uri.base.host, 443, isSecure: true);
  
  runApp(const MyApp());
}

class MyApp extends StatelessWidget {
  const MyApp({super.key});
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
        home: Scaffold(
            body: Column(children: [
      ConstrainedBox(
        constraints: const BoxConstraints(maxHeight: 200),
        child: FutureBuilder(
            future: MoviesConnector.instance.listMovies().execute(),
            builder: (context, snapshot) {
              if (snapshot.connectionState == ConnectionState.done) {
                return ListView.builder(
                  scrollDirection: Axis.vertical,
                  itemBuilder: (context, index) => Card(
                      child: Text(
                    snapshot.data!.data.movies[index].title,
                  )),
                  itemCount: snapshot.data!.data.movies.length,
                );
              }
              return const CircularProgressIndicator();
            }),
      )
    ])));
  }
}

Deploy your schema and query to production

Once you have your local setup on your app, now you can deploy your schema and connector to the cloud. You need a Blaze plan project to set up a Cloud SQL instance.

1. Navigate to Data Connect section of the Firebase console and create a free trial Cloud SQL instance.

2. In the IDE integrated Terminal, run firebase init dataconnect and select the Region/Service ID you just created on the console.

3. Select "Y" when prompted with "File dataconnect/dataconnect.yaml already exists, Overwrite?".

4. In the IDE window, in the VS Code Extension UI, click the Deploy to production button.

5. Once deployed, go to the Firebase console to verify the schema, operations and data has been uploaded to the cloud. You should be able to view the schema, and run your operations on the console as well. The Cloud SQL for PostgreSQL instance will be updated with its final deployed generated schema and data.

Next steps

Review your deployed project and discover more tools:

• Add data to your database, inspect and modify your schemas, and monitor your Data Connect service in the Firebase console.

Access more information in the documentation. For example, since you've completed the quickstart:

• Learn more about schema, query and mutation development
• Learn about generating client SDKs and calling queries and mutations from client code for web, Android, and iOS, and Flutter.