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Distributed Computing with Python

You're reading from   Distributed Computing with Python Harness the power of multiple computers using Python through this fast-paced informative guide

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Product type Paperback
Published in Apr 2016
Publisher
ISBN-13 9781785889691
Length 170 pages
Edition 1st Edition
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Author (1):
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Rasheedh B Rasheedh B
Author Profile Icon Rasheedh B
Rasheedh B
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Table of Contents (10) Chapters Close

Preface 1. An Introduction to Parallel and Distributed Computing 2. Asynchronous Programming FREE CHAPTER 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

A useful strategy – logging everything


Oftentimes, logging is like taking backups or eating vegetables—we all know we should do it, but most of us forget. In distributed applications, we simply have no other choice—logging is essential. Not only that, logging everything is essential.

With many different processes running on potentially ephemeral remote resources at difficult-to-predict times, the only way to understand what happens is to have logging information and have it readily available and in an easily searchable format/system.

At the bare minimum, we should log process startup and exit time, exit code and exceptions (if any), all input arguments, all outputs, the full execution environment, the name and IP of the execution host, the current working directory, the user account as well as the full application configuration, and all software versions.

The idea is that if something goes wrong, we should be able to use this information to log onto the same machine (if still available), go...

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