To effectively run and understand the code in this chapter, ensure you have the following set up. If you’ve followed Chapter 1, First Steps with FastAPI, you should already have some of these installed:

• Python: Make sure you’ve installed Python version 3.9 or higher on your computer.
• FastAPI: Install FastAPI along with all its dependencies using the pip install fastapi[all] command. As we saw in Chapter 1, First Steps with FastAPI, this command also installs Uvicorn, an ASGI server that’s necessary to run your FastAPI application.
• Integrated development environment (IDE): A suitable IDE such as VS Code or PyCharm should be installed. These IDEs offer excellent support for Python and FastAPI development, providing features such as syntax highlighting, code completion, and easy debugging.
• MongoDB: For the NoSQL database portions of this chapter, MongoDB needs to be installed on your local machine. Download and install the...

In the world of data handling, the power of Python meets the efficiency of SQL databases. This recipe aims to introduce you to how to integrate SQL databases within your application, a crucial skill for any developer looking to build robust and scalable web applications.

SQL is the standard language for managing and manipulating relational databases. When combined with FastAPI, it unlocks a world of possibilities in data storage and retrieval.

FastAPI’s compatibility with SQL databases is facilitated through ORMs. The most popular one is SQLAlchemy. We will focus on it in this recipe.

Getting ready

To begin, you’ll need to have FastAPI and SQLAlchemy installed in your virtual environment. If you followed the steps in Chapter 1, First Steps with FastAPI, you should have FastAPI already set up. For SQLAlchemy, a simple pip command is all that’s needed:

$ pip install sqlalchemy

Once installed, the next step is to configure...

After setting up your SQL database with FastAPI, the next crucial step is creating database models. This process is central to how your application interacts with the database. Database models in SQLAlchemy are essentially Python classes that represent tables in your SQL database. They provide a high-level, object-oriented interface to manipulate database records as if they were regular Python objects.

In this recipe, we will set up the create, read, update and delete (CRUD) endpoints to interact with the database.

Getting ready

With the models set up, you can now implement CRUD operations. These operations form the backbone of most web applications, allowing you to interact with the database.

How to do it…

For each operation, we will create a dedicated endpoint implementing the interacting operation with the database.

Creating a new user

To add a new user, we’ll use a POST request. In the main.py file...

Transitioning from SQL to NoSQL databases opens up a different paradigm in data storage and management. NoSQL databases, like MongoDB, are known for their flexibility, scalability, and ability to handle large volumes of unstructured data. In this recipe, we’ll explore how to integrate MongoDB, a popular NoSQL database, with FastAPI.

NoSQL databases differ from traditional SQL databases in that they often allow for more dynamic and flexible data models. MongoDB, for example, stores data in binary JSON (BSON) format, which can easily accommodate changes in data structure. This is particularly useful in applications that require rapid development and frequent updates to the database schema.

Getting ready

Make sure you’ve installed MongoDB on your machine. If you haven’t done it yet, you can download the installer from https://www.mongodb.com/try/download/community.

FastAPI doesn’t provide a built-in ORM...

Effective data validation stands as a cornerstone of robust web applications, ensuring that incoming data meets predefined criteria and remains safe for processing.

FastAPI harnesses the power of Pydantic, a Python library dedicated to data validation and serialization. By integrating Pydantic models, FastAPI streamlines the process of validating and serializing data, offering an elegant and efficient solution. This recipe shows how to utilize Pydantic models within FastAPI applications, exploring how they enable precise validation and seamless data serialization.

Getting ready

Pydantic models are essentially Python classes that define the structure and validation rules of your data. They use Python’s type annotations to validate that incoming data matches the expected format. When you use a Pydantic model in your FastAPI endpoints, FastAPI automatically validates incoming request data against the model.

In this recipe...

Handling files is a common requirement in web applications, whether it’s uploading user avatars, downloading reports, or processing data files. FastAPI provides efficient and easy-to-implement methods for both uploading and downloading files. This recipe will guide you through how to set up and implement file handling in FastAPI.

Getting ready

Let’s create a new project directory called uploads_and_downloads that contains a main.py module with a folder called uploads. This will contain the files from the application side. The directory structure will look like this:

uploads_and_downloads/
|─ uploads/
|─ main.py

We can now proceed to create the appropriate endpoints.

How to do it…

To handle file uploads in FastAPI, you must use the File and UploadFile classes from FastAPI. The UploadFile class is particularly useful as it provides an asynchronous interface and spools large files to disk to avoid...

Asynchronous programming is a core feature of FastAPI that allows you to develop highly efficient web applications. It allows your application to handle multiple tasks concurrently, making it particularly well-suited for I/O-bound operations, such as database interactions, file handling, and network communication.

Let’s delve into leveraging asynchronous programming in FastAPI for data operations, enhancing the performance and responsiveness of your applications.

Getting ready

FastAPI is built on Starlette and Pydantic, which provide a robust foundation for writing asynchronous code in Python using the asyncio library with async/await syntax.

The asyncio library allows you to write non-blocking code that can pause its execution while waiting for I/O operations to complete, and then resume where it left off, all without blocking the main execution thread.

This recipe demonstrates the benefits of using asyncio with FastAPI in...

In the realm of web development, the security of sensitive data is paramount.

This recipe is a checklist of best practices for securing sensitive data in your FastAPI applications.

Getting ready

First and foremost, it’s crucial to understand the types of data that need protection. Sensitive data can include anything from passwords and tokens to personal user details. Handling such data requires careful consideration and adherence to security best practices.

Understanding the types of data that require protection sets the foundation for implementing robust security measures, such as leveraging environment variables for sensitive configurations, a key aspect of data security in app development.

Instead of hardcoding these values in your source code, they should be stored in environment variables, which can be accessed securely within your application. This approach not only enhances security but also makes your application...