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.

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Distributed Computing with Python
Apr, 2016 | 170 pages
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Table of Contents (15 chapters)
Distributed Computing with Python
Distributed Computing with Python
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
An Introduction to Parallel and Distributed Computing
An Introduction to Parallel and Distributed Computing
Parallel computing
Distributed computing
Shared memory versus distributed memory
Amdahl's law
The mixed paradigm
Summary
2
Asynchronous Programming
Asynchronous Programming
Coroutines
An asynchronous example
Summary
3
Parallelism in Python
Parallelism in Python
Multiple threads
Multiple processes
Multiprocess queues
Closing thoughts
Summary
4
Distributed Applications – with Celery
Distributed Applications – with Celery
Establishing a multimachine environment
Installing Celery
Testing the installation
A tour of Celery
More complex Celery applications
Celery in production
Celery alternatives – Python-RQ
Celery alternatives – Pyro
Summary
5
Python in the Cloud
Python in the Cloud
Cloud computing and AWS
Creating an AWS account
Creating an EC2 instance
Storing data in Amazon S3
Amazon elastic beanstalk
Creating a private cloud
Summary
6
Python on an HPC Cluster
Python on an HPC Cluster
Your typical HPC cluster
Job schedulers
Running a Python job using HTCondor
Running a Python job using PBS
Debugging
Summary
7
Testing and Debugging Distributed Applications
Testing and Debugging Distributed Applications
The big picture
Common problems – clocks and time
Common problems – software environments
Common problems – permissions and environments
Common problems – the availability of hardware resources
Challenges – the development environment
A useful strategy – logging everything
A useful strategy – simulating components
Summary
8
The Road Ahead
The Road Ahead
The first two chapters
The tools
The cloud and the HPC world
Debugging and monitoring
Where to go next
Index
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.

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