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

Chapter 4. Distributed Applications – with Celery

This chapter is a follow up on some of the topics that we discussed so far. In particular, it explores asynchronous programming and distributed computing in detail with some example applications. It concentrates on Celery, a sophisticated Python framework that is used to build distributed applications. Toward the end, the chapter explores some alternative packages to Celery: Pyro and Python-RQ.

You should be familiar, at this point, with the basic ideas behind parallelism, distributed computing, and asynchronous programming. If not, it might be worthwhile to skim the previous chapters to get a refresher.