Description:

All modern systems use distributed and asynchronous tasks to make proper use of the available hardware and software resources in a safe and reliable manner. The ability to create and distribute asynchronous tasks drastically impact the performance as well as the capability of a system.

The most popular way of creating asynchronous task queues involves using streams or pub/sub infrastructures like Apache Kafka, RabbitMQ, Kinesis etc, more or less, are equally suitable for a variety of scenarios.

___Then, the question is why we’re talking something else i.e. “Celery” over here? The answer is “Python Programming Language”.___

If you're creating a system (especially microservices based and/or distributed systems) in python and want to create an asynchronous (and distributed) task queue which is pure python, simple, super fast, and diminishes the boundary between distributed systems, then you should consider using ___“Celery”___.

Using something like “Celery” is not about being better than other solutions out there, it’s all about the ability to make use of the python ecosystem and create things faster without leaving the python programming language for something else.

Welcome to this tutorial on Celery, the open source distributed task queues. In this tutorial, we'll learn how to use “Celery” and create an end to end system. We’ll also learn about how we can also visualize the distributed task queues at runtime using “Flower”

This tutorial will have classroom exercises, post class homework as well as complimentary readings.

Prerequisites:

This tutorial is for all python developers irrespective of their experience level. However basic knowledge and understanding of Microservices / Distributed Systems and data streams will be helpful in understanding and comprehending this tutorial

Video URL:

https://www.youtube.com/watch?v=b2kdhkUXI2U

Speaker Info:

Daksh Gupta (a.k.a Deepak K Gupta) is a software product development consultant with 23 years of experience in software development in the areas of Application Programming, Cloud Computing, Big Data Management & Machine Learning.

Daksh has used python to create multiple production level software in the areas of cloud computing and machine learning. During the course of his career he has worked with startups, mid-sized as well as multinational organizations in multiple Dev Advocate roles.

Daksh is an avid public speaker and has spoken in multiple public platforms and conferences including 4 times in PyCon US (2019, 2020, 2022, 2024)

Daksh also writes blogs in medium and creates vlogs on YouTube. More details about him can be found @ https://www.codesports.ai/

Speaker Links:

• PyCon US 2024 - https://us.pycon.org/2024/schedule/presentation/35/ (Not yet uploaded to YouTube - Should be available in a week or so)
• PyCon US 2022 - https://www.youtube.com/watch?v=akfsWPsvmrM
• PyCon US 2020 - https://www.youtube.com/watch?v=NjMTf2UWPsw
• PyCon US 2019 - https://www.youtube.com/watch?v=bCDcI8SdjD8

GitHub https://github.com/CognitiveProgrammer/

YouTube https://www.youtube.com/@Cognitive-Programmer

Medium https://medium.com/@CognitiveProgrammer

Website https://cognitiveprogrammer.tech/