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Python Django Celery Course: Creating a New Standalone Celery Worker 

Very Academy
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Python Django Celery Course. Creating a new standalone Celery Worker
Udemy Course Link:
www.udemy.com/...
RU-vid 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.

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3 окт 2024

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Комментарии : 13   
@fernandopopocaortiz1071
@fernandopopocaortiz1071 Год назад
Even though it's a long video, thank you for not removing the mistakes. This way, we can learn what might cause problems. Not naming your file 'celery.py' seems like one of those small mistakes that can take a lot of time to figure out.
@leoramirez7022
@leoramirez7022 Год назад
Hi Very Academy. You are one of the best programming channels I have seen, especially from Django. You have helped me a lot in my professional life. Thank you very much for making your videos. Cheer up, keep it up.
@veryacademy
@veryacademy Год назад
Happy to hear that! Thank you!
@liorbm1
@liorbm1 4 месяца назад
Also, a real-world example can be very helpful. for example the celery-with-django machine to do some DB related work & the standalone will do something that doesnt need the DB.
@pythonpathwithhimalay
@pythonpathwithhimalay Год назад
keep building and removing containers. that's where my all time goes
@veryacademy
@veryacademy Год назад
Keep making and trying to think of better ways to ensure courses might be compatible and future proof as possible, that’s where all my time goes
@pythonpathwithhimalay
@pythonpathwithhimalay Год назад
Appreciating your effects.. 💖💖
@shubhamjha5738
@shubhamjha5738 Год назад
Would be possible to integrate this with sqs using beat. Sqs is not sending task to worker to execute at the time beat sends it in queue for execution. My need is 2 exexute at the same time
@muhammad-bilal1
@muhammad-bilal1 Год назад
@veryacademy : Your content is very interesting and useful. I got a question: How can I use an "async" method in my celery task? So far, I saw only the use of "sync" methods in your celery task.
@liorbm1
@liorbm1 4 месяца назад
didnt understand the standalone celery folder. Is it just for the case of when one dont want it with the django code? the usecase is still to call it from inside the django app, right?. (just to change the `broker_url` & `result_backend`)
@LongLe-mh1lu
@LongLe-mh1lu Год назад
Can we use celery worker in another machine 2 and get a task from this queue on machine 1?. Thanks
@TemporaryEmail-nk3rf
@TemporaryEmail-nk3rf Год назад
Very nice.. thanks!!! 😍
@veryacademy
@veryacademy Год назад
Thank you too!
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