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


Python has had support for asynchronous programming since version 1.5.2, with the introduction of the asyncore and asynchat modules for asynchronous network programming. Version 2.5 introduced the ability to send data to coroutines via yield expressions, allowing us to write asynchronous code in a simpler but more powerful way. Python 3.4 introduced a new library for asynchronous I/O called asyncio.

Python 3.5 introduced true coroutine types via async def and await. Interested readers are encouraged to explore these new developments. One word of warning though: asynchronous programming is a powerful tool that can dramatically improve the performance of I/O-intensive code. It does not come without issues, though, the main of which is complexity.

Any important asynchronous code has to carefully select nonblocking libraries in order to avoid using blocking code. It has to implement a coroutine scheduler (since the OS does not schedule coroutines for us like it does with threads), which...