Introduction

Quickly about what is FastAPI: FastAPI is a modern web framework for building APIs with Python that is designed to be fast, easy to use, and highly scalable. It uses Python’s async/await syntax for efficient, non-blocking I/O and provides automatic API documentation using OpenAPI and JSON Schema.

What are FastAPI Background Tasks

FastAPI background tasks allow you to run non-blocking tasks in the background while your application continues to respond to user requests. These tasks are designed to be lightweight and efficient, making them ideal for handling tasks that would otherwise slow down your application’s performance, such as sending emails, processing large files, or executing long-running database queries. FastAPI background tasks are simple to implement and can be configured to run automatically at specified intervals or times, making them a powerful tool for building performant and scalable web applications.

Example Use Case

Let’s say you’re building a web application that allows users to upload and process large files, such as images or videos. Processing these files can take a long time and may slow down your application’s response time. With FastAPI background tasks, you could offload this processing to a separate task that runs in the background, allowing your application to continue to respond quickly to user requests.

Here’s how it could work:

1. When a user uploads a file, your application would initiate a FastAPI background task to process the file in the background.

2. The task would run asynchronously, allowing your application to continue to respond to other requests while the file is being processed.

3. Once the file processing is complete, the task would update the application’s database with the results.

4. When the user requests the processed file, your application would retrieve the processed file data from the database and return it to the user.

Here’s an example of how you could use FastAPI background tasks to process a file uploaded by a user:

from fastapi import BackgroundTasks, FastAPI

app = FastAPI()

def process_file(file_id: str):
    # This is where you would do the actual file processing
    # In this example, we'll just simulate a delay
    time.sleep(10)
    print(f"File with ID {file_id} has been processed.")

@app.post("/upload-file/")
async def upload_file(background_tasks: BackgroundTasks):
    # This is where you would handle the file upload
    # In this example, we'll just assume that the file ID is passed as a parameter
    file_id = "1234"

    # Initiate a background task to process the file
    background_tasks.add_task(process_file, file_id)

    # Return a response to the user
    return {"message": f"File with ID {file_id} has been uploaded and is being processed in the background."}

In this example, the upload_file endpoint receives a file ID as a parameter and initiates a background task to process the file using the process_file function. The process_file function simulates a delay of 10 seconds (to simulate the time it would take to process the file), and then prints a message to the console indicating that the file has been processed.

When a user uploads a file, the upload_file endpoint returns a response immediately, letting the user know that their file has been uploaded and is being processed in the background. Meanwhile, the process_file function runs asynchronously in the background, allowing your application to continue to respond to other requests while the file is being processed. Once the file processing is complete, the task updates the application's database with the results, and the user can request the processed file data from the database.

Problem

FastAPI is built on top of Starlette. Starlette provides the ASGI framework for building asynchronous web applications, while FastAPI extends it with additional features specifically tailored for building APIs quickly and efficiently.

FastAPI leverages Starlette's capabilities for handling HTTP requests and responses asynchronously, allowing developers to define routes, request handlers, and middleware using Python's async/await syntax.

Background tasks in Starlette are typically handled within the framework's event loop. When you define a background task using Starlette's BackgroundTasks class, it gets executed within the same process and thread as the main application. Starlette leverages Python's asynchronous features, such as asyncio, to manage these tasks efficiently without the need for additional threads or processes.

This approach can be suitable for handling moderately heavy tasks within the same process. However, if you have tasks that are particularly CPU-intensive or blocking for long periods, it may not be the most efficient solution. Since background tasks share the same event loop and execution context with the main application, heavy tasks can potentially block the event loop and degrade the responsiveness of the web server.

For heavy tasks that need to be processed concurrently without blocking the main application, it's often recommended to offload them to separate processes or threads. You can achieve this by using other libraries or frameworks in conjunction with Starlette, such as concurrent.futures for threading or multiprocessing for processes.

But you could ask: what about python GIL?

The Global Interpreter Lock (GIL) is a mechanism used in CPython, the default implementation of Python, to ensure that only one thread executes Python bytecode at a time. This means that even in a multi-threaded Python program, only one thread can execute Python code at any given moment.

So if you need to perform a truly parallel computing task you should probably look towards Celery.

FastAPI Background Tasks vs Celery

FastAPI background tasks and Celery are both tools for running asynchronous tasks in the background of a web application. However, they have some differences that are worth noting:

1. Integration: FastAPI background tasks are built into the FastAPI framework, while Celery is a separate library that needs to be integrated into your application.

2. Complexity: Celery is a more complex tool than FastAPI background tasks, with a wider range of features and configuration options. This complexity can be an advantage in some cases, as it allows for greater flexibility and scalability, but it can also make it more challenging to set up and maintain.

3. Performance: Both tools are designed to be highly performant and efficient, but FastAPI background tasks have an advantage in this area because they are built on top of Starlette, a lightweight ASGI framework. This means that FastAPI can take full advantage of Python’s async/await syntax for highly efficient, non-blocking I/O.

4. Scaling: Celery is a better choice if you need to scale your application across multiple servers or processes, as it includes built-in support for distributed task queues. FastAPI background tasks are designed to be lightweight and efficient, making them ideal for handling tasks that can be run within a single application instance.

In summary, if you need a lightweight, efficient tool for running simple background tasks within a FastAPI application, FastAPI background tasks are a great choice. If you need more complex features or need to scale your application across multiple servers or processes, Celery may be a better choice.

Conclusion

In conclusion, FastAPI background tasks are a powerful tool for running asynchronous tasks in the background of a FastAPI web application. They allow your application to remain fast and responsive while handling time-consuming tasks in the background, making for a better user experience. With its lightweight and efficient design, FastAPI is a great choice for building high-performance web applications that can handle complex processing tasks in the background. Whether you’re processing large files or performing complex calculations, FastAPI background tasks can help you get the job done quickly and efficiently.