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Distributed Computing with Python

Distributed Computing with Python

By : Pierfederici
4.3 (3)
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Distributed Computing with Python

Distributed Computing with Python

4.3 (3)
By: Pierfederici

Overview of this book

CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications. This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.
Table of Contents (10 chapters)
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9
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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