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Think of Data Connect like a private GraphQL API for you, the developer, building your app.

💨 tl;dr

Firebase Data Connect now supports SQL via Cloud SQL (PostgreSQL). It integrates AI features, generates APIs, and unifies data models, queries, and authentication into a Query Defined Infrastructure (QDI) using GraphQL. It offers type-safe client code generation, efficient query execution, and easy schema management via VS Code or Firebase CLI.

💡 Key Ideas

• Firebase Data Connect introduces SQL support using Cloud SQL, a PostgreSQL database.
• Data Connect integrates AI development features like vector search and function-calling definitions.
• It generates tool definitions consumable by GenKit for natural language database queries.
• Data Connect combines data model, queries, and authentication into a unified Query Defined Infrastructure (QDI).
• QDI is written like front-end code but operates like server code, using GraphQL for data modeling and queries.
• It supports type-safe, multi-platform client code generation for Android, iOS, and JavaScript.
• The VS Code extension facilitates testing and iterating over data models and queries.
• Declarative approach ensures database schema, queries, and security policies are predefined and synchronized.
• Data Connect generates API endpoints, authentication/authorization policies, and strongly-typed client code from a single query.
• It supports creating and customizing database schemas, relationships, and queries using GraphQL types and directives.
• Data Connect provides tools for secure query execution, automatic mutation fields, and server values for authenticated user fields.
• It offers efficient SQL query joins, SQL views, and schema synchronization.
• Vector similarity search and LLM embeddings are supported, with configuration options for computing embeddings.
• Data Connect enables building agent-like flows in apps without extensive boilerplate code.
• Deployment and schema migration are handled via VS Code extension or Firebase CLI.
• Firebase Console provides tools like playground and Query Editor for data management.
• Data Connect is in gated preview with invites being rolled out gradually.

🎓 Lessons Learnt

• Use Firebase Data Connect for backend efficiency: It auto-generates APIs, authentication, and client code from a single query, speeding up development.
• Predefine queries for security and client alignment: Writing queries upfront helps create type-safe client code and integrated security policies.
• Utilize declarative sources for automatic updates: Simple objects representing the database schema ensure changes reflect across the model, client libraries, and security policies.
• Combine data model, queries, and authentication in QDI: Manage these elements together for efficient and secure data handling.
• Employ GraphQL for data modeling and operations: Simplifies managing and updating database structures with defined queries and mutations.
• Leverage VS Code extension for rapid testing: Test and iterate data models and queries quickly with a local emulator and mock authentication.
• Organize code into schema, queries, and mutations files: Maintain clarity and organization by structuring code into these files.
• Secure queries with auth directives: Ensure only authenticated users can access queries, enhancing security.
• Test queries and mutations in VS Code: Use integrated tools for easier testing and debugging of data changes.
• Create composite indexes for small tables: Improve performance by using composite indexes when tables have few columns.
• Use automatic joining fields for relationships: Simplifies table relationships by auto-generating joining fields.
• Customize and deploy schema changes easily: Use VS Code extension or Firebase CLI for schema adjustments and handle migrations for existing schemas.
• Leverage vector similarity search for querying: Enhance search capabilities by utilizing vector similarity search in Data Connect.
• Automate embedding computation during mutations: Simplify the process by configuring automatic embedding computations with Vertex AI.
• Generate fake embeddings for local testing: Enable development and testing without external dependencies by generating fake embeddings locally.
• Predefine queries and mutations for LLM integration: Streamline client SDKs and function calling definitions for easy integration with LLMs.
• Build agent-like flows without boilerplate code: Use GenKit and Data Connect to create agent-like flows in your apps efficiently.

🌚 Conclusion

Firebase Data Connect boosts backend efficiency by auto-generating APIs and client code from a single query. It ensures security with predefined queries and auth directives, simplifies schema management with GraphQL, and supports AI features like vector search. Ideal for rapid development and secure data handling.

In-Depth

All Key Ideas

Firebase Data Connect Features

• Firebase now supports SQL with a service called Data Connect.
• Data Connect uses Cloud SQL, a PostgreSQL database with flexible queries, extensions, and scalability.
• Data Connect is designed for AI development with features like vector search and function-calling definitions.
• Data Connect can generate tool definitions consumable by GenKit for natural language database queries.
• Data Connect re-imagines Firebase development and aims to speed up application development.
• With Data Connect, a single query generates an API endpoint, authentication/authorization policy, strongly-typed client code, and tool definitions for GenAI workflows.
• Traditional SQL-based Firebase development requires separate code for data model, client libraries, and authorization.
• Traditional approaches lack type safety, require manual updates for database changes, and necessitate writing queries multiple times for cross-platform apps.
• Client-side queries pose security challenges, requiring developers to write comprehensive security policies.
• Firebase proposes a declarative approach where database schema, queries, and security policies are predefined and in sync, allowing for type-safe, multi-platform client code generation.

Features of Query Defined Infrastructure (QDI) and Firebase Data Connect

• Combines data model, queries, and authentication in one place called Query Defined Infrastructure (QDI)
• QDI is written like front-end code but can be reasoned about like server code
• Users have full control over which operations can be performed by a client application
• Data modeling, queries, and update operations are modeled with GraphQL in Firebase Data Connect
• GraphQL code is generated into Android, iOS, and JavaScript libraries
• Data Connect comes with a VS Code extension for testing and iterating over data models and queries
• Code is organized into three main files: schema, queries, and mutations
• Database schema is declared with GraphQL types, creating tables and columns
• Relationships are created through object types, e.g., one-to-many relationship between customer and orders
• Customization of tables and columns is possible using @table and @call directives
• Queries can be created with specific names and query criteria, using variables and operators
• Built-in language server provides type-ahead hints
• Queries are protected with the auth directive for security
• Queries can be executed within VS Code, allowing for mock authentication and execution history
• Mutations in GraphQL define data changes and can specify security policies
• Automatic mutation fields map to common database update operations
• Server values can set fields to the currently-authenticated user

Features of Firebase Data Connect

• Creating a reference table for toppings and a join table for order toppings, using a composite index
• Firebase Data Connect provides automatic joining fields for relationships between tables
• Renaming keys using aliases in Data Connect for better app data structure
• Data Connect generates SDKs, ensuring type safety and up-to-date query variables
• Data Connect allows configuring the output destination of the generated SDK and package names
• Deployment can be done using VS Code extension or Firebase CLI, with migration handling for schema changes
• Firebase Console offers tools like the playground and Query Editor for data insertion and query writing
• Data Connect generates efficient SQL query joins and allows SQL views for custom queries
• Data Connect uses GraphQL for type safety but manages schema synchronization and security internally
• Data Connect provides a private GraphQL API, restricting client queries to predefined ones

Data Connect Features

• Data Connect currently has support for vector similarity search and generated tooling definitions to work with function calling.
• You can use a vector type on a field in Data Connect to store LLM embeddings.
• Configuration is provided for computing embeddings during mutations.
• Embeddings can be generated either locally with fake embeddings or in production using Vertex AI.
• Vector Search can be used to query for items based on similarity.
• Data Connect allows specifying embedding models and comparison methods (L2, cosine, inner product) for similarity queries.
• Data Connect has an AI feature for function calling definition generation between GenKit and other LLMs.
• Tools Definition in GenKit is a JSON file showing the structure of queries and mutations.
• Data Connect enables building agent-like flows in apps without needing to create all boilerplate steps.
• Data Connect is in gated preview with invites rolling out over time.

All Lessons Learnt

Firebase Development Tips

• Use Firebase Data Connect for simplified backend development: It generates APIs, authentication, and client code from a single query, speeding up app development.
• Predefine queries to align security and client code: Writing queries upfront allows for type-safe client code generation and integrated security policies.
• Avoid manual updates by using declarative sources: Using simple objects to represent the database schema ensures that changes are automatically reflected across the data model, client libraries, and security policies.

Key Features and Best Practices for Using Query Defined Infrastructure (QDI)

• Combine data model, queries, and authentication in one place: Using Query Defined Infrastructure (QDI) allows for efficient management of data models, queries, and authentication together.
• Full control over client operations: You can define which operations are allowed by the client application, ensuring secure and precise data access.
• Use GraphQL for data modeling and operations: GraphQL is used to model data and define queries and mutations, making it easier to manage and update the database structure.
• Leverage VS Code extension for rapid testing: The Firebase Data Connect VS Code extension helps quickly test and iterate over data models and queries with a local emulator and mock authentication.
• Organize code into schema, queries, and mutations files: Structuring your code into these three main files helps maintain clarity and organization.
• Define database schema with GraphQL types: Creating GraphQL types sets up the database tables and their relationships, streamlining database schema definition.
• Customize tables and columns using @table and @call directives: These directives allow for customization of tables and columns, such as setting a primary key.
• Utilize type-ahead hints for efficient query writing: The built-in language server provides type-ahead hints, simplifying the process of writing queries.
• Set query criteria with variables and operators: Use variables and query operators to define specific criteria for data retrieval, similar to SQL.
• Secure queries with auth directives: Protect your queries by specifying that only authenticated users can access them, enhancing security.
• Test queries within VS Code: Use the Code Lens button to execute queries and view results directly in VS Code, facilitating easier testing and debugging.
• Define data changes with GraphQL mutations: Use mutations to specify data changes and apply security policies to limit access to these operations.
• Use automatic mutation fields for common updates: These fields map to common database update operations, making mutation authoring simpler.
• Test mutations with Arguments panel: Fill out values in the Arguments panel and run mutations to verify successful data changes.
• Ensure queries return necessary fields: When queries aren't returning expected results, like missing toppings on pizza orders, check and adjust the query to include all required fields.

Firebase Data Connect Best Practices

• Create composite indexes for tables with few columns - When a table has only a few columns, using composite indexes can improve performance by setting the IDs as the primary key.
• Use automatic joining fields for table relationships - Firebase Data Connect simplifies the process by automatically generating joining fields for table relationships, reducing manual effort.
• Rename keys for better data structure in apps - Use aliases to rename automatically generated keys to suit your app’s data structure preferences.
• Leverage Data Connect's SDK for type information - Data Connect ensures up-to-date type information for queries, arguments, and UI components, enhancing development efficiency.
• Customize SDK output destinations and package names - Data Connect allows configuration of SDK output destinations and custom package names for easy integration into web apps.
• Deploy schema changes using VS Code extension or Firebase CLI - Use these tools to deploy database schema changes and keep your development environment synchronized.
• Handle existing schemas with migrations - When deploying schema changes, handle existing schemas by performing necessary migrations to track and accept all changes.
• Use Firebase Console for schema management - Inspect, insert data, write queries, and view keys/relationships directly in the Firebase Console for better database management.
• Avoid complex GraphQL management issues with Data Connect - Data Connect simplifies GraphQL security and complexity by managing synchronization and resolving schema-to-database issues.
• Create controlled client queries - Limit client applications to only call pre-defined queries to avoid unexpected or complex query execution.
• Utilize SQL views within GraphQL types for efficiency - Embed SQL views in GraphQL types to maintain type safety and reduce the number of API calls by encapsulating complex data retrieval.

Data Connect Features

• Leverage vector similarity search for efficient querying: Utilize the vector similarity search in Data Connect to efficiently query items based on similarity, enhancing search capabilities.
• Automate embedding computation during data mutations: Configure Data Connect to automatically compute embeddings during data mutations using Vertex AI, simplifying the process.
• Generate fake embeddings for local testing: When running locally, Data Connect can generate fake embeddings, enabling development and testing without external dependencies.
• Use predefined queries and mutations for LLM function calling: Predefine queries and mutations in Data Connect to generate client SDKs and function calling definitions, streamlining integration with LLMs.
• Comment query names and variables for better LLM context: Add comments to query names and variables to provide more context for LLMs, improving their ability to call operations accurately.
• Build agent-like flows without boilerplate code: Create agent-like flows in your apps using GenKit and Data Connect without needing extensive boilerplate code, simplifying development.
• Sign up for gated preview to get early access: Sign up for the gated preview of Data Connect to get early access and provide feedback, helping shape the tool’s development.