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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

Common problems – permissions and environments


Different computers might run our code under different user accounts, and our application might expect to be able to read a file or write data into a specific directory and hit an unexpected permission error. Even in cases where the user accounts used by our code are all the same (down to the same user ID and group ID), their environment may be different on different hosts. Therefore, an environment variable we assumed to be defined might not be or, even worse, might be set to an incompatible value.

These problems are common when our code runs as a special, unprivileged user, such as nobody. Defensive coding, especially when accessing the environment, and making sure to always fall back to sensible defaults when variables are undefined (that is, value = os.environ.get('SOME_VAR', fallback_value) instead of simply value = os.environ.get['SOME_VAR']) is often necessary.

A common approach, when this is possible, is to only run our applications under...

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