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Parallel Programming with Python

You're reading from  Parallel Programming with Python

Product type Book
Published in Jun 2014
Publisher
ISBN-13 9781783288397
Pages 124 pages
Edition 1st Edition
Languages

Table of Contents (16) Chapters

Parallel Programming with Python
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Contextualizing Parallel, Concurrent, and Distributed Programming Designing Parallel Algorithms Identifying a Parallelizable Problem Using the threading and concurrent.futures Modules Using Multiprocessing and ProcessPoolExecutor Utilizing Parallel Python Distributing Tasks with Celery Doing Things Asynchronously Index

Understanding Celery


Celery is a framework that offers mechanisms to lessen difficulties while creating distributed systems. The Celery framework works with the concept of distribution of work units (tasks) by exchanging messages among the machines that are interconnected as a network, or local workers. A task is the key concept in Celery; any sort of job we must distribute has to be encapsulated in a task beforehand.

Why use Celery?

We could justify the use of Celery by listing some of its positive points:

  • It distributes tasks in a transparent way among workers that are spread over the Internet, or local workers

  • It changes, in a simple way, the concurrence of workers through setup (processes, threads, Gevent, Eventlet)

  • It supports synchronous, asynchronous, periodic, and scheduled tasks

  • It re-executes tasks in case of errors

Tip

It is common for some developers to claim that synchronous tasks are the same as real-time tasks. This is an unnecessary confusion as the concepts are totally different...

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