FastAPI vs Tornado: When to Use Each?

Table of Contents

1. Fundamental Concepts
   • FastAPI
   • Tornado
2. Usage Methods
   • FastAPI
   • Tornado
3. Common Practices
   • FastAPI
   • Tornado
4. Best Practices
   • FastAPI
   • Tornado
5. Conclusion
6. References

Fundamental Concepts

FastAPI

FastAPI is built on top of Starlette for the web parts and Pydantic for data validation. It leverages Python type hints to automatically validate, serialize, and deserialize data. This not only makes the code more readable but also helps in generating interactive API documentation automatically. FastAPI uses an asynchronous programming model, but it also supports synchronous code, making it easy for developers who are new to asynchronous programming.

Tornado

Tornado is an open - source Python web framework and asynchronous networking library. It is designed to handle a large number of concurrent connections with low latency. Tornado uses a non - blocking I/O model and an event - driven architecture. This means that it can handle multiple requests simultaneously without waiting for each request to complete before moving on to the next one. Tornado is well - suited for applications that require real - time communication, such as chat applications and long - polling APIs.

Usage Methods

FastAPI

Here is a simple example of creating a basic API using FastAPI:

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
def read_root():
    return {"Hello": "World"}

@app.get("/items/{item_id}")
def read_item(item_id: int, q: str = None):
    return {"item_id": item_id, "q": q}

To run this application, you can use uvicorn, a lightning - fast ASGI server. Save the above code in a file named main.py and run the following command in the terminal:

uvicorn main:app --reload

Tornado

Here is a simple example of creating a basic web application using Tornado:

import tornado.ioloop
import tornado.web

class MainHandler(tornado.web.RequestHandler):
    def get(self):
        self.write("Hello, world")

def make_app():
    return tornado.web.Application([
        (r"/", MainHandler),
    ])

if __name__ == "__main__":
    app = make_app()
    app.listen(8888)
    tornado.ioloop.IOLoop.current().start()

To run this application, simply save the code in a file named tornado_app.py and run it using Python:

python tornado_app.py

Common Practices

FastAPI

• Data Validation: Use Pydantic models to validate incoming data. This helps in ensuring that the data received by the API is in the correct format.
• Dependency Injection: FastAPI has a powerful dependency injection system. You can use it to share common functionality across multiple endpoints, such as authentication and database connections.
• Automatic Documentation: Take advantage of FastAPI’s automatic documentation generation. You can access the Swagger UI at /docs and the ReDoc at /redoc to test and explore your API.

Tornado

• Asynchronous Programming: Use Tornado’s asynchronous functions and coroutines to handle I/O - bound tasks efficiently. For example, when making HTTP requests or accessing databases, use asynchronous versions of the libraries.
• WebSocket Support: Tornado has built - in support for WebSockets. If your application requires real - time communication, use WebSockets to enable bidirectional communication between the client and the server.
• Error Handling: Implement proper error handling in your Tornado application. You can override the write_error method in the RequestHandler class to customize the error response.

Best Practices

FastAPI

• Use Async Functions: If your API has I/O - bound tasks, use asynchronous functions to improve performance. FastAPI supports both synchronous and asynchronous functions, but using asynchronous functions can make your application more efficient.
• Testing: Write unit and integration tests for your FastAPI application. You can use pytest along with the TestClient provided by FastAPI to test your endpoints.
• Security: Implement proper security measures such as authentication and authorization. FastAPI provides built - in support for various authentication methods, such as OAuth2 and API keys.

Tornado

• Scaling: If your application needs to handle a large number of concurrent connections, consider scaling Tornado horizontally by running multiple instances behind a load balancer.
• Logging: Implement proper logging in your Tornado application. Tornado has a built - in logging module that you can use to log important events and errors.
• Code Organization: Organize your Tornado code into modules and classes to make it more maintainable. Separate the business logic from the request handlers.

Conclusion

Both FastAPI and Tornado are powerful Python web frameworks, but they are suited for different use cases. FastAPI is a great choice for building modern APIs with automatic documentation, data validation, and easy - to - use dependency injection. It is a good option for developers who want to quickly build APIs with Python and take advantage of the latest Python features.

Tornado, on the other hand, is well - suited for applications that require high - performance and real - time communication. Its non - blocking I/O model and event - driven architecture make it ideal for handling a large number of concurrent connections with low latency.

When choosing between FastAPI and Tornado, consider the requirements of your project, such as the type of application you are building, the expected number of concurrent connections, and the complexity of the data validation and business logic.

References

• FastAPI Documentation: https://fastapi.tiangolo.com/
• Tornado Documentation: https://www.tornadoweb.org/en/stable/