Parallel and distributed computing is a fascinating subject that only a few years ago developers in only a very few large companies and national labs were privy to. Things have changed dramatically in the last decade or so, and now everybody can build small- and medium-scale distributed applications in a variety of programming languages including, of course, our favorite one: Python.

This book is a very practical guide for Python programmers who are starting to build their own distributed systems. It starts off by illustrating the bare minimum theoretical concepts needed to understand parallel and distributed computing in order to lay the basic foundations required for the rest of the (more practical) chapters.

It then looks at some first examples of parallelism using nothing more than modules from the Python standard library. The next step is to move beyond the confines of a single computer and start using more and more nodes. This is accomplished using a number of third-party libraries, including Celery and Pyro.

The remaining chapters investigate a few deployment options for our distributed applications. The cloud and classic High Performance Computing (HPC) clusters, together with their strengths and challenges, take center stage.

Finally, the thorny issues of monitoring, logging, profiling, and debugging are touched upon.

All in all, this is very much a hands-on book, teaching you how to use some of the most common frameworks and methodologies to build parallel and distributed systems in Python.