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Distributed Computing with Python

You're reading from  Distributed Computing with Python

Product type Book
Published in Apr 2016
Publisher
ISBN-13 9781785889691
Pages 170 pages
Edition 1st Edition
Languages
Toc

Table of Contents (15) Chapters close

Distributed Computing with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. An Introduction to Parallel and Distributed Computing 2. Asynchronous Programming 3. Parallelism in Python 4. Distributed Applications – with Celery 5. Python in the Cloud 6. Python on an HPC Cluster 7. Testing and Debugging Distributed Applications 8. The Road Ahead Index

Celery alternatives – Pyro


Pyro (http://pythonhosted.org/Pyro4/), which stands for Python Remote Objects, is a package that has been around for quite some time, since 1998 or so. As a result, it is remarkably stable and feature-complete.

It takes a very different approach to task distribution than Celery or Python-RQ in that it exposes Python objects as servers on a network. It then creates proxy objects to them so that the calling code sees them as local objects. This architecture was very popular at the end of the 90s with systems such as CORBA and Java RMI.

The fact that Pyro is somewhat hiding the fact that some of the objects in one's code are local and some are remote is at times a source of criticism. The reason being that there are a number of failure modes intrinsic to running remote code that are easy to forget when remote code execution is hidden behind a proxy object.

Another source of criticism is that Pyro can be somewhat difficult to get running correctly on an ad hoc network...

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