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Mastering Python

You're reading from   Mastering Python Master the art of writing beautiful and powerful Python by using all of the features that Python 3.5 offers

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Product type Paperback
Published in Apr 2016
Publisher Packt
ISBN-13 9781785289729
Length 486 pages
Edition 1st Edition
Languages
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Author (1):
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Rick Hattem Rick Hattem
Author Profile Icon Rick Hattem
Rick Hattem
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Table of Contents (17) Chapters Close

Preface 1. Getting Started – One Environment per Project FREE CHAPTER 2. Pythonic Syntax, Common Pitfalls, and Style Guide 3. Containers and Collections – Storing Data the Right Way 4. Functional Programming – Readability Versus Brevity 5. Decorators – Enabling Code Reuse by Decorating 6. Generators and Coroutines – Infinity, One Step at a Time 7. Async IO – Multithreading without Threads 8. Metaclasses – Making Classes (Not Instances) Smarter 9. Documentation – How to Use Sphinx and reStructuredText 10. Testing and Logging – Preparing for Bugs 11. Debugging – Solving the Bugs 12. Performance – Tracking and Reducing Your Memory and CPU Usage 13. Multiprocessing – When a Single CPU Core Is Not Enough 14. Extensions in C/C++, System Calls, and C/C++ Libraries 15. Packaging – Creating Your Own Libraries or Applications Index

Remote processes

So far, we have only executed our scripts on multiple local processors, but we can actually expand this further. Using the multiprocessing library, it's actually very easy to execute jobs on remote servers, but the documentation is currently still a bit cryptic. There are actually a few ways of executing processes in a distributed way, but the most obvious one isn't the easiest one. The multiprocessing.connection module has both the Client and Listener classes, which facilitate secure communication between the clients and servers in a simple way. Communication is not the same as process management and queue management however, those features requires some extra effort. The multiprocessing library is still a bit bare in this regard, but it's most certainly possible given a few different processes.

Distributed processing using multiprocessing

First of all, we will start with a module with containing a few constants which should be shared between all clients and...

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