python-api-development

FastAPI uses dependency injection and Pydantic models for request validation. You define Pydantic models to structure data and inject dependencies into path operations to manage shared logic like database connections or authentication requirements automatically.

Choosing between FastAPI and Flask depends on your async needs. FastAPI natively supports asynchronous endpoints and automatic validation, making it ideal for scalable microservices, while Flask offers flexibility for smaller services with its extensive ecosystem and synchronous design.

JWT authentication and OAuth2 strategies in Python APIs are implemented by defining security dependencies. You create token generation logic and use framework-specific security utilities to enforce access control, protecting endpoints by validating bearer tokens or API keys on each request.

FastAPI fully supports async endpoint patterns for data-driven microservices. By using async def for your route handlers, you can handle concurrent requests and non-blocking I/O operations, which is essential for building scalable REST services that interact with external databases or APIs.

You should avoid Flask for API development when your application requires native asynchronous request handling or heavy concurrent I/O operations. Flask's synchronous nature makes it less suitable for high-throughput data-driven microservices compared to async-focused frameworks like FastAPI.