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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 comprehensive introduction of 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. Enroll now and take the first step towards mastering asynchronous task processing in Python!
Legal Notice: “Celery Logo” is licensed under the Creative Commons Attribution-Share Alike 4.0 International license. To view a copy of this license, visit creativecommons org licenses by-sa 4.0. No changes were made to the original file. This course is not accredited, certified, affiliated with, or endorsed by Celery Project.
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.
Note: The Django name and logo are registered trademarks of the Django Software Foundation, and their usage is subject to the Django Trademark License Agreement.
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.
Setup Guides
Preparing a Django Celery Working Environment
Defining and Executing Celery Tasks
Handling Task Failures and Retries
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17Creating and Registering Celery Tasks in Django
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18Starting the Celery Worker
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19Initiating a Celery Task
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20Creating a new standalone Celery Worker
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21Introducing Tasks Routing
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22Configuring Task Routing
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23Introducing Celery Task Prioritization
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24Configuring Task Prioritization (Redis)
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25The Primitives - Task Grouping
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26The Primitives - Task Chaining
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27Task Rate limits
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28Configuring Task Prioritization (RabbitMQ)
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29Passing arguments and returning results from Celery tasks
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30Executing tasks synchronously and asynchronously
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31Monitoring Celery Workers and Tasks with Flower
Task Scheduling and Periodic Tasks
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32Common Types of Exceptions and Errors in Celery Tasks
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33Dynamic Task Discovery in Celery: Auto-discovering Tasks in a Directory
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34Error Handling: Try Except Blocks
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35Handling Errors in Celery Tasks with Custom Task Classes
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36Implementing Automatic Retries
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37Error Handling in Groups
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38Towards Error Handling in Task Chains
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39Towards Dead-letter Queues: Handling Failed Tasks
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40Task Timeouts and Task Revoking (Using task time limits and timeouts)
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41Handling Errors in Task Result Callbacks
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42Task Signals Graceful Shutdown and Cleanup of Failed Tasks
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43Error Tracking and Monitoring with Sentry
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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