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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

Chapter 7. Testing and Debugging Distributed Applications

Distributed systems, both large and small, can be extremely challenging to test and debug, as they are spread over a network, run on computers that can be quite different from each other, and might even be physically located in different continents altogether.

Moreover, the computers we use could have different user accounts, different disks with different software packages, different hardware resources, and very uneven performance. Some can even be in a different time zone. Developers of distributed systems need to consider all these pieces of information when trying to foresee failure conditions. Operators have to work around all of these challenges when debugging errors.

So far, in this book, we have not spent much time on the extremely important issue of what to do when something goes either wrong or differently than we expect, and we instead concentrated on some of the tools that we can use to write and deploy our applications...

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