FastAPI is described as a modern and high-performance web framework for developing APIs with Python 3.7+.
True to its name, FastAPI is fast. It offers high performance on par with NodeJS and GO. It is used by top companies like Uber and Netflix to build their applications.
What is API ?
API is the mediator between our database and system of application or website.
To take request from browser through internet and pass to database through server and give the response back again.
What is ASGI (Asynchronous Server Gateway Interface) and WSGI ( Web Server Gateway Interface ) ?
As we already discussed, FastAPI works on ASGI while Flask works on WSGI.
What is Data Validation ?
(i) pip install pydantic
(ii) From pydantic import Basemodel
(iii) After that create a class by giving classname (:here bank:) and define column name with its datatype which means that age, duration,campaign ,etc. value should only allow integer data type value as a user input while emp_var_rate should allow float data type.
(iv) Next when you are creating api, give data validation by giving class name as
an argument inside a user defined function.Make sure that both the variables the columns are the same.
What is Documentation ?
By using the downside arrow and Try It Out tab you can pass input and run the functionalities as well as see the output by clicking on the Execute function.
As well can see response code of error and success.
If code faces any error it can also show the error like
No. | Flask | Fast API |
1. | Works on WSGIn | Works on ASGI |
2. | Need to do data validation manually | In-built feature of data validation |
3. | http methods:@app.route(“/”, methods = [“GET”]) | http methods:@app.get(“/”) |
4. | Does not support asynchronous task | Supports asynchronous task |
5. | Need to make documentation manually | Automatic document support |
6. | Larger community support | Smaller community support |
7. | Comparingly less easy | Easy and faster |
8. | Error messages are displayed in html format | Error messages are displayed in clear format of json |
What is Uvicorn in FastAPI?
Uvicorn is a lightning-fast ASGI (Asynchronous Server Gateway Interface) server that is used by FastAPI to run web applications.
Uvicorn is designed to take advantage of Python’s asyncio library to handle multiple connections asynchronously and efficiently, making it an excellent choice for high-performance web applications.
How do I open up adopting FastAPI?
Create a new instance of the FastAPI class and define a new route with the @app.get decorator.
( Here : The @app.get decorator maps the /hello URL endpoint to the hello() function.)
Define a new asynchronous function, which returns the output when the route is requested. The async keyword indicates that the function is asynchronous, which means that it can run concurrently with other functions and I/O operations.
If you want to take user input you can go for by specifying variable into curly brackets {}.
If a user tries to access the endpoint without a name parameter or with an invalid name value, FastAPI will return a JSON response with a 422 Unprocessable Entity status code and an error message indicating the validation error.
(Here : Code has declared variable “name” which is user input and should be of string data type only)
uvicorn file_name.py:fast_api instance name –reload
(Here : we have created instance of FastAPI in object called app and our file_name is credit_fastapi.py)
How should the global community expand by employing FASTAPI?
FastAPI’s asynchronous capabilities enable real-time data processing and analysis. It can be used to build applications that can process and analyze data streams in real-time, enabling organizations to make faster decisions and respond to events more quickly.
FastAPI’s support for Python’s type hints and easy integration with popular machine learning libraries, such as TensorFlow and PyTorch, make it an ideal choice for building machine learning-based applications. These applications can be used for a wide range of use cases, such as predictive analytics, natural language processing, and computer vision.
FastAPI’s support for Docker and Kubernetes make it easy to build cloud-native applications that can run on any cloud provider or on-premises infrastructure. This enables organizations to build scalable and resilient applications that can adapt to changing business needs.
In conclusion, FastAPI can be used efficiently in digital transformation by enabling the creation of APIs, microservices-based applications, real-time data processing, machine learning-based applications, and cloud-native applications. Its high performance, scalability, and ease of use make it an excellent choice for modern digital transformation projects.
For more reference : https://fastapi.tiangolo.com/
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