Introduction
FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. On the other hand, REST (Representational State Transfer) is an architectural style for building APIs that use HTTP to exchange data. So, while FastAPI is a specific web framework for building APIs, REST is a design pattern for building APIs.
Yes, FastAPI is a web framework that makes it easy to build APIs following the REST architectural style. REST is a set of guidelines for building APIs that use HTTP to exchange data between clients and servers. FastAPI is built on top of these guidelines, so by using FastAPI, you are automatically following the REST architectural style. This means that FastAPI APIs are REST APIs.
Security
FastAPI includes several security features to help you protect your API from common security threats. Some of the key security features of FastAPI include:
Automatic HTTPS: FastAPI automatically redirects HTTP requests to HTTPS to help protect your API from attackers who might try to intercept your data over an unsecured connection.
Secure Password Hashing: FastAPI uses the Argon2 password hashing algorithm to securely store user passwords. Argon2 is considered one of the most secure password hashing algorithms available, and FastAPI uses it by default.
Dependency Injection: FastAPI uses Dependency Injection to help prevent vulnerabilities that can result from direct use of global variables. This helps to ensure that your code is more secure and easier to test.
Cross-Site Request Forgery (CSRF) Protection: FastAPI provides built-in protection against CSRF attacks, which are a common type of attack that can be used to steal user data.
Encryption and Decryption: FastAPI provides built-in support for encrypting and decrypting data, which can be useful for storing sensitive information like user passwords or access tokens.
Overall, FastAPI provides a number of security features that can help you build secure, reliable APIs. It is important to remember, however, that security is an ongoing process, and you should always be vigilant about keeping your API and its data secure.
Best practices
Here are some best practices for using FastAPI:
Use the latest version of FastAPI: FastAPI is a rapidly-evolving framework, and new versions are released regularly. Make sure you are using the latest version of FastAPI to take advantage of the latest features and security fixes.
Use the asyncio module: FastAPI is built on top of the asyncio module in Python, which enables asynchronous programming. Asynchronous programming can help improve the performance of your API by allowing it to handle multiple requests concurrently.
Define request parameters using Pydantic models: Pydantic is a Python library that FastAPI uses to validate request parameters. By defining your request parameters using Pydantic models, you can automatically validate the data that is sent to your API and return clear, user-friendly error messages if the data is invalid.
Use automatic API documentation: FastAPI includes automatic API documentation, which is generated from your API code. This documentation is interactive and can be used by developers to understand how to use your API.
Use dependencies wisely: FastAPI allows you to define and use dependencies in your API code. Dependencies can make your code more modular and easier to test, but they can also make your code harder to understand and maintain. Use dependencies wisely to avoid making your code more complex than it needs to be.
These are just a few of the best practices for using FastAPI. There are many other best practices that you can follow to make the most of this powerful framework.
Here are some pros and cons of using FastAPI:
Pros:
FastAPI is fast: FastAPI is one of the fastest web frameworks available for Python. It is built on top of the asyncio module, which enables asynchronous programming, and it uses modern Python features like type hints to improve performance.
FastAPI is easy to use: FastAPI is designed to be easy to use, even for developers who are new to web development or to Python. It has clear, concise documentation and a straightforward API that makes it easy to get started.
FastAPI is well-documented: FastAPI has comprehensive documentation that covers all of its features and includes tutorials, examples, and references. This makes it easy to learn how to use FastAPI and find answers to common questions.
FastAPI is built on best practices: FastAPI is built on top of the REST architectural style and follows best practices for building APIs. This makes it a good choice for building APIs that are scalable, maintainable, and easy to use.
Cons:
FastAPI is relatively new: FastAPI is a relatively new web framework, and it is still evolving. This means that it may not have as much support or as many third-party libraries as older, more established frameworks.
FastAPI is Python-only: FastAPI is a Python-only framework, so it is not suitable for projects that require a polyglot (multi-language) approach.
FastAPI may have a steep learning curve: Because FastAPI is a modern, high-performance web framework, it may have a steep learning curve for developers who are new to web development or to Python. It may require a significant investment of time and effort to learn how to use FastAPI effectively.
Overall, FastAPI has many pros that make it a good choice for building APIs with Python. However, it also has some cons that you should consider before deciding to use it for your project.
Python Pydantic models
Python Pydantic models are classes that are used to define the structure of data that is sent to and received from an API. Pydantic is a Python library that FastAPI uses to validate and convert data types in request and response parameters. By defining your data using Pydantic models, you can automatically validate the data that is sent to your API and ensure that it conforms to the expected format.
Here is an example of a Pydantic model that defines a user object:
from pydantic import BaseModel
class User(BaseModel):
id: int
name: str
email: str
age: int
In this example, the User model defines four fields: id, name, email, and age. The type of each field is specified using Python type hints, and Pydantic uses these hints to validate and convert the data that is sent to the API.
Once you have defined a Pydantic model, you can use it in your FastAPI code to validate and convert request and response parameters. For example, you can use a Pydantic model as the input or output parameter of an API endpoint, and FastAPI will automatically validate and convert the data that is sent to or received from that endpoint. This can help ensure that your API always receives and returns data in the expected format, which can make it easier to work with and less prone to errors.
Asyncio module
The asyncio module is a built-in Python library for writing asynchronous code. Asynchronous code is code that can run concurrently with other code, without blocking or waiting for other operations to complete. This can be useful for improving the performance of your code, especially when working with I/O-bound or high-latency operations.
FastAPI is built on top of the asyncio module, which means that it uses asynchronous programming by default. This allows FastAPI to handle multiple requests concurrently, which can improve the performance of your API.
Here is an example of how to use the asyncio module to define an asynchronous function in FastAPI:
Example :
import asyncio
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
async def index():
await asyncio.sleep(1)
return {"message": "Hello, world!"}
In this example, the index() function is marked as asynchronous using the async keyword. This tells the asyncio module that the function can be run concurrently with other code. The await keyword is used to pause the execution of the function and wait for an asynchronous operation (in this case, the asyncio.sleep() function) to complete.
Using the asyncio module in FastAPI can help improve the performance of your API by allowing it to handle multiple requests concurrently. However, asynchronous programming can also make your code more complex and harder to understand, so it is important to use it wisely.
Pls explain FastAPI automatic API documentation
FastAPI includes automatic API documentation, which is generated from your API code. This documentation is interactive and can be used by developers to understand how to use your API.
To enable automatic API documentation in FastAPI, you simply need to add the @app.get_api_docs() decorator to your API code. For example:
from fastapi import FastAPI
app = FastAPI()
@app.get("/users/{user_id}")
def get_user(user_id: int):
return {"user_id": user_id}
@app.get_api_docs()
def docs():
return {"title": "My API", "version": "1.0.0"}
In this example, the get_user() function is an API endpoint that returns the user ID of a specified user. The docs() function is a documentation endpoint that is decorated with the @app.get_api_docs() decorator. This tells FastAPI to generate automatic API documentation for the get_user() endpoint and to make it available at the /docs URL.
When you access the /docs URL in your browser, you will see the automatic API documentation for your API. This documentation includes the endpoint URL, the HTTP method (GET in this case), the request parameters, the response parameters, and other details about the endpoint. It also includes a built-in test client that you can use to test your API and see the responses it returns.
Automatic API documentation can be a valuable tool for developers who are using your API. It makes it easy for them to understand how to use your API and can help them get started quickly. It can also make it easier for you to maintain and update your API, because the documentation is generated automatically from your code.
Pls explain how to use FastAPI dependencies wisely
FastAPI allows you to define and use dependencies in your API code. Dependencies are objects or functions that your code uses, and they can make your code more modular and easier to test. However, dependencies can also make your code more complex and harder to understand, so it is important to use them wisely.
Here are some tips for using FastAPI dependencies wisely:
Use dependencies to make your code more modular: Dependencies can help you break your code into smaller, more manageable pieces. This can make it easier to write and maintain your code, and it can also make it easier to test and debug.
Avoid creating unnecessary dependencies: Don't create dependencies just for the sake of having dependencies. Only create dependencies if they are necessary to improve the structure or maintainability of your code.
Use clear, descriptive names for dependencies: Give your dependencies clear, descriptive names that reflect their purpose and use. This can make it easier for other developers to understand what your dependencies do and how to use them.
Avoid creating circular dependencies: Circular dependencies are dependencies that depend on each other in a loop. These can make your code difficult to understand and maintain, and they can also cause runtime errors. Avoid creating circular dependencies in your code.
Overall, FastAPI dependencies can be a powerful tool for improving the structure and maintainability of your code. By using them wisely, you can make your code more modular and easier to work with.
Pls raise an example to use a FastAPI dependencies
Here is an example of how to use FastAPI dependencies:
from fastapi import FastAPI, Depends
app = FastAPI()
Define a dependency
def get_current_user(user_id: int):
return {"user_id": user_id}
Use the dependency in an API endpoint
@app.get("/users/{user_id}")
def get_user(user: dict = Depends(get_current_user)):
return user
In this example, the get_current_user() function is a dependency that returns the user ID of the current user. This function is then used in the get_user() API endpoint as a dependency. The get_user() endpoint uses the Depends() decorator to specify that it depends on the get_current_user() function. When the get_user() endpoint is called, FastAPI will automatically call the get_current_user() function and inject the returned user object into the user parameter of the get_user() function.
This example shows how to use FastAPI dependencies to make your code more modular and easier to maintain. By defining your dependencies as separate functions, you can reuse them in multiple places in your code, and you can easily test and debug them independently of your API endpoints.
FastAPI includes built-in support for testing your API endpoints. This makes it easy to write automated tests for your API and ensure that it is working as expected.
Here is an example of how to test a FastAPI endpoint:
from fastapi.testclient import TestClient
client = TestClient(app)
Test the /users/{user_id} endpoint
response = client.get("/users/1")
assert response.status_code == 200
assert response.json() == {"user_id": 1}
In this example, the TestClient class is used to create a test client that can be used to send HTTP requests to your API. The client.get() method is used to send a GET request to the /users/{user_id} endpoint and retrieve the response. The response is then checked to make sure that it has the expected status code (200 in this case) and that it contains the expected data (in this case, a JSON object with the user ID set to 1).
This is a simple example of how to test a FastAPI endpoint, but you can use the TestClient class to test many different aspects of your API. You can also use it to test different HTTP methods, request parameters, and response data, and you can use it to write comprehensive, automated tests for your API.
Best pratice for FastAPI project layout
FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. It is built on top of Starlette for the web parts and Pydantic for the data parts.
There is no single "best" way to layout a FastAPI project, as the ideal project structure will depend on the specific needs and requirements of your project. However, there are some general best practices that you can follow to ensure that your FastAPI project is organized and easy to maintain.
One important aspect of project layout is organizing your files and directories in a logical and consistent way. This will make it easier for you and other developers to navigate and understand your project. A good starting point is to follow the FastAPI tutorial, which recommends organizing your project into the following directories:
app/: This directory contains the main code for your FastAPI application, including the views, models, and templates.
tests/: This directory contains your application's test suite.
venv/: This directory contains the virtual environment for your FastAPI project.
In addition to organizing your files and directories in a logical way, you should also try to keep your FastAPI project as modular and extensible as possible. This will make it easier to maintain and reuse your code, and it will also make it easier to add new features and functionality to your application.
One way to achieve modularity in your FastAPI project is to use modules to organize your application into smaller, reusable components. Each module can have its own views, templates, and static files, and they can be imported and used in different parts of your application. This makes it easy to extend and reuse your FastAPI application's code.
Another way to improve the modularity of your FastAPI project is to use FastAPI plugins. FastAPI plugins are third-party libraries that provide additional functionality for FastAPI applications. They can be used to add features such as database support, authentication, and form validation, without having to write the code yourself.
Overall, the best practices for FastAPI project layout include organizing your files and directories in a logical and consistent way, and making your project as modular and extensible as possible. This will make it easier to maintain and extend your FastAPI application, and it will also make it easier for other developers to understand and work with your code.
Connect to both sqlAlchemy / MongoDB
It is possible to connect FastAPI to both SQLAlchemy and MongoDB at the same time. First, you will need to install the relevant packages for each database. For SQLAlchemy, you can use the sqlalchemy package, and for MongoDB, you can use the motor package.
Once you have the packages installed, you can connect to each database using their respective APIs. For SQLAlchemy, you can use the create_engine function to create a connection to the database, and for MongoDB, you can use the AsyncIOMotorClient class to create a client for the database.
Once you have established a connection to each database, you can use them in your FastAPI application by injecting the connection objects into your application as dependencies. For example, you could use the @inject decorator from the pydantic library to inject the database connections into your application, like so:
from pydantic import inject
from sqlalchemy import create_engine
from motor.motor_asyncio import AsyncIOMotorClient
app = FastAPI()
Create a connection to the SQL database
sql_engine = create_engine("")
Create a connection to the MongoDB database
mongo_client = AsyncIOMotorClient("")
Inject the database connections into the application
@app.get("/")
async def root(
sql_engine: Engine = inject(sql_engine),
mongo_client: AsyncIOMotorClient = inject(mongo_client)):
# Use the database connections here
pass
After you have injected the database connections into your application, you can use them to perform operations on the respective databases, such as querying for data or updating records. You can find more detailed documentation for each database's API on their respective websites.