python-fastapi-patterns

Structure your FastAPI application using a modular service layer to coordinate repositories, async SQLAlchemy 2.0 for session management, and Pydantic v2 for strict data validation. This pattern enforces domain rules and clear boundaries for scalable APIs.

The best way to manage async database sessions in FastAPI is through dependency injection patterns. This approach ensures clean routing, proper session handling with async SQLAlchemy 2.0, and testable components that can be composed into scalable APIs.

Dependency injection in FastAPI wires async database sessions into a service layer, which coordinates repositories and enforces domain rules. This architecture separates business logic from routing, resulting in robust error handling and testable components.

Yes, you can use Pydantic v2 for data validation in a production FastAPI service. It provides strict type validation, schema generation, and model integration suitable for complex APIs and operational readiness.

Handle errors and authentication in FastAPI by implementing robust error handling and authorization patterns within your service layer. These production-grade patterns ensure complex APIs remain secure and maintainable.

Use testable component patterns that leverage dependency injection to override database sessions. This allows you to isolate service layer logic and apply operational testing guidance for FastAPI applications using async SQLAlchemy 2.0.