python-architecture

Structure Python applications using domain-driven design by defining entities, value objects, aggregates, and repository interfaces. This approach enforces clean separation of concerns, isolating pure business logic from IO operations to improve maintainability and testability.

The functional core / imperative shell pattern in Python architecture separates pure functions for business logic from thin orchestration layers handling IO. This design ensures domain logic remains side-effect free, making code highly testable and scalable.

Map data between API requests, domain models, and database records in Python using from_request, to_response, from_record, and to_record transformations. These data modeling patterns cleanly translate Pydantic input/output models to internal dataclass entities.

Yes, the Python architecture guidance supports refactoring existing projects by guiding developers through layering, validation, and bounded contexts. It helps enforce clean separation of concerns during project setup and generates architectural documentation.

Start building a clean architecture Python project by creating a small domain example with a dataclass entity, a Pydantic input/output model, and a repository interface. This demonstrates end-to-end flow from request handling to persistence.

Use bounded contexts in Python application architecture when scaling complex domains to maintain clean separation of concerns. They provide practical guidance for isolating domain models and testing strategies within specific business boundaries.