Book Image

Distributed Computing with Python

Book Image

Distributed Computing with Python

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 (15 chapters)
Distributed Computing with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Establishing a multimachine environment


The first thing to do before we dive into Celery and the other Python packages that we will explore is set up a test environment. We are developing distributed applications, which means that we ideally need a multimachine environment.

Those of you who have access to at least two machines in a properly set up network environment (meaning that these test machines have DNS-resolvable names) can skip to the next section. All the rest, please keep reading.

For those without easy access to multiple machines for development and testing, there are still a number of solutions that are easy to implement and either free or very inexpensive.

One is to simply use virtual machines running on the local host (for instance, using VirtualBox: https://www.virtualbox.org). Just create a couple of VMs, install your favorite Linux distribution on it, and keep them running in the background. Since these do not need a graphical desktop for development purposes, they can be very...