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

Summary


This was a long chapter! We looked at Celery as a powerful package for writing distributed applications in Python. We then looked at Python-RQ, a lightweight and simpler alternative. Both packages use a distributed task queue architecture, which is a multimachine implementation of the same system that is used to distribute work that we saw in Chapter 3, Parallelism in Python.

Pyro was then introduced as an alternative approach to both Celery and Python-RQ. Pyro has a very different philosophy that is firmly rooted in the proxy pattern and remote-procedure-call (RPC) architecture for distributed systems.

Both approaches have their merits and their strengths, and undoubtedly you will find yourselves preferring one or the other.

The next chapter will look at one way to deploy our distributed applications to the cloud—it is going to be a fascinating read.