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

Multiple threads


Python has had support for threads for a very long time now (at least since version 1.4). It also offers a robust high-level interface to OS-native (that is, POSIX on Linux and Mac OS X) threads in the threading module, which is what we will use for the examples in this section.

It should be noted that on single CPU systems, the use of multiple threads would not give true concurrency, since only one thread will be executed at any given point in time (remember that a CPU runs only one task at any given point in time). It is only on a multiprocessor system that threads can run in parallel. We will assume that we will make use of a multiprocessor/multicore system for the remainder of the chapter.

Let's start by writing a simple program that makes use of multiple threads to download data from the Web. In your favorite editor, create a new Python script (currency.py) with the following code:

from threading import Thread
from queue import Queue
import urllib.request


URL = 'http...