feature-driven-architecture-python

Feature-driven architecture organizes FastAPI projects into self-contained directories based on distinct business capabilities. This structure prevents tight coupling between distinct features like billing and authentication, enabling independent development and maintaining clear boundaries as the project grows.

Strict boundaries between Python feature modules are enforced using import-linter to maintain separation. This tooling ensures features remain independent by preventing unauthorized cross-feature imports, establishing clear inter-feature communication patterns like events or shared read models.

Database model ownership in feature-driven architecture places models within their respective feature directories. Establishes clear guidelines for cross-feature references and provides specific migration strategies to transition from traditional layered structures without breaking database integrity.

Feature-driven architecture supports both FastAPI and Flask applications. It addresses structural challenges in both frameworks by organizing distinct features into separate directories, defining inter-feature communication, and establishing clear database model ownership rules.

Migrating from layered to feature-driven architecture involves reorganizing code into self-contained feature directories. Establishes clear strategies for moving database models, defining boundaries with import-linter, and implementing cross-feature testing layers to ensure architectural integrity during transition.

Feature-driven architecture becomes necessary when a Python project grows complex and traditional layered structures cause architectural drift. If managing multiple distinct features like products and orders creates tight coupling, this approach maintains independence and long-term maintainability.