Python Guru Series ?????? - Part 6: Asynchronous Programming and Task Management with Celery

Hello everyone, continuing the topic of asynchronous programming in Python.

In part 6 of today’s series, I would like to introduce you to Celery, a powerful library used to manage and run asynchronous tasks and message queues in Python applications.

Okay, let's get started!

1. Overview of Celery

When developing Python web applications, most of the time, we handle tasks that require immediate results (synchronous tasks). For example, finding information about the girl you like, checking your bank account balance, etc. Besides these, there are also many tasks that do not need to return results right away. These are asynchronous tasks, such as sending email notifications to users, exporting files, etc.

For these asynchronous tasks, users don’t need to wait until the task is completed. The idea to solve this problem is to offload the task elsewhere for execution, avoiding blocking the main thread, and thus increasing the application's adaptability.

For simple tasks, to improve performance, we can delegate them to a background thread for processing. However, if there are many requests and errors occur during task execution, how can we manage them efficiently? Worse yet, you might be affected by the Global Interpreter Lock (GIL), which only allows one thread to execute at a time.

An alternative solution is to use a task queue to provide independent processing from the server. This is where Celery comes into play. Celery is often used for asynchronous tasks that do not require immediate results or for running scheduled tasks.

Here’s an illustration of a FastAPI system using Celery to manage the task queue, with Redis acting as the message broker and the place to store backend results.

2. Key Features

• Asynchronous task processing: Celery allows tasks to be executed without waiting for them to complete, improving application performance.
• Task queues: Celery manages message queues and coordinates the execution of tasks for workers from these queues.
• Scheduled tasks: Celery supports running scheduled tasks, similar to cron jobs. With Celery Beat, you can schedule tasks to run periodically at specific times, such as automatically sending a goodnight message to your loved one at 10 PM.
• Scalability: Celery can easily scale horizontally by increasing the number of workers to handle tasks in parallel.
• Retry and error management: When a task fails, Celery can automatically retry the task or retry according to the specified configuration.

3. Main Structure of Celery

• Workers: These are the processes responsible for fetching and executing tasks from the queue.
• Broker: Between the application and the worker are message brokers. This component manages the message queue. The most common brokers used are Redis or RabbitMQ, and Amazon SQS. Celery uses these brokers to send tasks to workers for processing.
• Task: The unit of work in Celery is called a task. Tasks are distributed to workers for execution.
• Backend Result: A place used to store the results of tasks upon completion in databases such as Redis, Postgres, etc.

Celery is a popular task management system in Python. It integrates easily with web frameworks like Django, Flask, and FastAPI to create high-performance web applications. Additionally, we can use Flower, a tool for visually monitoring and managing Celery workers and tasks through a web interface.

4. Conclusion

Above is an overview of Celery and its applications in developing asynchronous applications in Python.

I hope you will find this information useful.

Once again, I’m Phan, a curious and dedicated developer.

See you in upcoming articles!