Firebase Data Connect

Firebase SQL Connect

Firebase SQL Connect is a relational database service using Cloud SQL for

PostgreSQL with GraphQL schema, auto-generated queries/mutations, and type-safe

SDKs.

> [!NOTE] Product Rename: Firebase Data Connect was renamed to **Firebase

> SQL Connect**. All instructions, references, and examples in this skill

> repository referring to "Data Connect" or "Firebase Data Connect" apply to

> "SQL Connect" and "Firebase SQL Connect" as well.

Project Structure

dataconnect/
├── dataconnect.yaml      # Service configuration
├── seed_data.gql         # LOCAL ONLY — prototype/test data
├── schema/
│   └── schema.gql        # Data model (types with @table)
└── connector/
    ├── connector.yaml    # Connector config + SDK generation
    ├── queries.gql       # Queries
    └── mutations.gql     # Mutations

Key Tools for Validation

Rely on these two mechanisms to ensure project correctness:

• Review GraphQL Schema: Both user-defined and generated extensions (in

.dataconnect/schema/main/).

• Validate Operations: Run

npx -y firebase-tools@latest dataconnect:compile against the schema.

Operation Strategies: GraphQL vs. Native SQL

Always default to Native GraphQL. Native SQL lacks type safety and

bypasses schema-enforced structures. Only use Native SQL when the user

explicitly requests it or when the task requires advanced database features.

| Strategy | When to use | Implementation |

| ---------------------------- | ---------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |

| Native GraphQL (Default) | Almost all use cases. Standard CRUD, basic filtering/sorting, simple relational joins. Requires full type safety. | Auto-generated fields (movie_insert, movies). Strong typing and schema enforcement. |

| Native SQL (Advanced) | PostgreSQL extensions (e.g., PostGIS), window functions (RANK()), complex aggregations, or highly tuned sub-queries. | Raw SQL string literals via _select, _execute, etc. Requires strict positional parameters ($1). No type safety. |

Development Workflow

Follow this strict workflow to build your application. You must read the

linked reference files for each step to understand the syntax and available

features.

1. Define Data Model (schema/schema.gql)

Define your GraphQL types, tables, and relationships (which map to a Postgres

schema).

> Read reference/schema.md for:

>

> - @table, @col, @default

> - Relationships (@ref, one-to-many, many-to-many)

> - Data types (UUID, Vector, JSON, etc.)

2. Define Authorized Operations (connector/queries.gql, connector/mutations.gql)

Write the queries and mutations your client will use, including authorization

logic. SQL Connect is secure by default.

> Read reference/operations.md for:

>

> - Queries: Filtering (where), Ordering (orderBy), Pagination

> (limit/offset).

> - Mutations: Create (_insert), Update (_update), Delete (_delete).

> - Upserts: Use _upsert to "insert or update" records (CRITICAL for user

> profiles).

> - Transactions: Use @transaction for multi-step atomic operations. Use

> _expr: "response.<prevStep>" to pass data between steps.

>

> Read reference/security.md for authorization:

>

> - @auth(level: ...) for PUBLIC, USER, or NO_ACCESS.

> - @check and @redact for row-level security and validation.

>

> Read reference/realtime.md for real-time

> subscriptions:

>

> - @refresh directive for time-based polling and event-driven updates.

> - CEL conditions to scope refresh triggers precisely.

>

> Read reference/native_sql.md for Native SQL

> operations:

>

> - Embedding raw SQL with _select, _selectFirst, _execute

> - Strict rules for positional parameters ($1, $2), quoting, and CTEs

> - Advanced PostgreSQL features (PostGIS, Window Functions)

3. Use type-safe SDK in your apps

Generate type-safe code for your client platform.

Configure SDK generation in connector.yaml:

connectorId: my-connector
generate:
  javascriptSdk:
    outputDir: "../web-app/src/lib/dataconnect"
    package: "@movie-app/dataconnect"
  kotlinSdk:
    outputDir: "../android-app/app/src/main/kotlin/com/example/dataconnect"
    package: "com.example.dataconnect"
  swiftSdk:
    outputDir: "../ios-app/DataConnect"

Generate SDKs:

npx -y firebase-tools@latest dataconnect:sdk:generate

For platform-specific instructions on how to use the generated SDKs, read:

• Web (TypeScript): reference/sdk_web.md
• Android (Kotlin): reference/sdk_android.md
• iOS (Swift): reference/sdk_ios.md
• Admin (Node.js):

reference/sdk_admin_node.md

• Flutter (Dart): reference/sdk_flutter.md

______________________________________________________________________

Feature Capability Map

If you need to implement a specific feature, consult the mapped reference file:

| Feature | Reference File | Key Concepts |

| :------------------------------ | :----------------------------------------------------------- | :------------------------------------------------- |

| Data Modeling | reference/schema.md | @table, @unique, @index, Relations |

| Vector Search | reference/search.md | Vector, @col(dataType: "vector"), embeddings |

| Full-Text Search | reference/search.md | @searchable, movies_search |

| Upserting Data | reference/operations.md | _upsert mutations |

| Complex Filters | reference/operations.md | _or, _and, _not, eq, contains |

| Transactions | reference/operations.md | @transaction, response binding |

| Environment Config | reference/config.md | dataconnect.yaml, connector.yaml |

| Realtime Subscriptions | reference/realtime.md | @refresh, subscribe(), auto-refresh |

| Cloud Functions Integration | reference/cloud_functions.md | onMutationExecuted, triggering events |

| Data Seeding & Migrations | reference/data_seeding.md | seed_data.gql, _insertMany, Admin SDK bulk |

| Starter Templates | templates.md | CRUD, user-owned resources, many-to-many, SDK init |

______________________________________________________________________

Deployment & CLI

> Read reference/config.md for deep dive on

> configuration.

Follow these patterns based on your current task:

How to initialize SQL Connect in a Firebase project

• Understand the app idea. Ask clarification questions if unclear.
• Run npx -y firebase-tools@latest init dataconnect.
• Validate that the app template and generated SDK are setup.

How to build apps using SQL Connect locally

• Start the emulator:

npx -y firebase-tools@latest emulators:start --only dataconnect.

• Write schema and operations.
• Seed local test data into seed_data.gql. Read

reference/data_seeding.md.

• Run npx -y firebase-tools@latest dataconnect:compile or

npx -y firebase-tools@latest dataconnect:sdk:generate to validate them.

• Use the operations in your app and build it.

How to deploy SQL Connect to Cloud SQL

• Run npx -y firebase-tools@latest deploy --only dataconnect.

Examples

For complete, working code examples of schemas and operations, see

examples.md.

For ready-to-use starter templates (CRUD, user-owned resources, many-to-many,

YAML configs, SDK init), see templates.md.