Python Django Celery Course: Configuring Task Prioritization (Redis)

Python Django Celery Course. Configuring Task Prioritization (Redis)
Udemy Course Link:
www.udemy.com/...
KZbin Playlist:
• Django Celery Mastery ...
In today's fast-paced web development landscape, efficiently handling time-consuming and resource-intensive tasks is crucial for building high-performance applications. Django Celery, a powerful asynchronous task-processing library, provides the perfect solution to address this challenge. This comprehensive course, "Django Celery Mastery: Python Asynchronous Task Processing," is designed to empower you with the knowledge and skills necessary to harness the full potential of Django Celery and elevate your Python web applications to new heights of scalability and responsiveness.
Course Description: The course begins by guiding you through the process of setting up a fully functional Django Celery working environment. You'll learn the essentials of Django Celery, explore task producers and consumers, and gain hands-on experience building Docker containers for Django, Redis (the message broker), and Celery workers. Additionally, you'll understand the role of a results backend and create a Redis Docker container to facilitate effective task communication.
Moving forward, you'll dive deep into defining and executing Celery tasks within a Django application. You'll discover how to create and register tasks, start and manage Celery workers, and configure task routing for optimized task distribution. Advanced concepts such as task prioritization, task grouping, task chaining, task rate limits, and passing arguments and returning results from Celery tasks will be thoroughly covered. You'll also explore both synchronous and asynchronous task execution approaches and leverage the Flower monitoring tool to track and monitor Celery workers and tasks.
Handling task failures and retries is a critical aspect of asynchronous task processing, and this course provides a comprehensive introduction to this topic. You'll gain insights into common types of exceptions and errors in Celery tasks and explore various error-handling strategies. You'll implement automatic retries, handle errors in task groups and chains, and discover techniques for handling failed tasks and task timeouts. Additionally, you'll learn how to gracefully shut down tasks, clean up failed tasks, and leverage error tracking and monitoring tools such as Sentry.
Task scheduling and periodic tasks play a vital role in managing recurring tasks efficiently. In this course, you'll understand the fundamentals of task scheduling, including scheduling tasks to run at specific times or intervals. You'll explore the customization of periodic tasks, implement crontab schedules, and ensure schedule persistence in a Django application. Furthermore, you'll learn how to schedule Django custom commands using Celery Beat and monitor service status using custom event tracking and alerting mechanisms.
Throughout the course, hands-on exercises, practical examples, and real-world scenarios will enhance your learning experience and enable you to apply the concepts directly in your own projects. By the end of this course, you'll have gained mastery over Django Celery and be equipped with the skills to implement efficient asynchronous task processing in Python applications, ensuring scalability, responsiveness, and optimal resource utilization.
Whether you are a Python developer, Django developer, web application developer, software engineer, backend developer, or a technical lead/architect, this course will empower you to unlock the full potential of Django Celery and revolutionize your approach to asynchronous task processing. Don't miss this opportunity to level up your skills and supercharge your applications with the power of Celery.
Trademark Usages and Fees Disclosures:
Usage of Django Logo: The Django logo used in this product is for identification purposes only, to signify that the content or service is Django-related. It does not imply that this product is officially endorsed by the Django Software Foundation (DSF) or the Django Core team as representatives of the Django project.
Fees Disclosure: We would like to clarify that 100% of the fees will be retained by the author to support the ongoing development and maintenance of this product. Currently, 0% of the fees, if applicable, will be contributed back to the DSF as a donation to further support the Django community.
Usage of Celery Logo: The Celery logo used in this product is for identification purposes only, to signify that the content or service is Celery-Project-related. It does not imply that this product is officially endorsed by the Celery Project or the logo licensor. Author Ty Wilkins - Licensed under the Creative Commons Attribution-Share Alike 4.0 International license.

[@alexburla1973]

4:50 as I understood from docs priority_steps sets the actual queues amount. By default it will be 4.

[@GlennMonteiro-w5p]

That's also what I understood from the docs. The doc also mentions the 10 queues that would be created based on the priority_steps (10) chosen in this video: ['celery', 'celery:1', 'celery:2', 'celery:3', 'celery:4', 'celery:5', 'celery:6', 'celery:7', 'celery:8', 'celery:9']