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

Debugging and monitoring


Logging, monitoring, profiling, and debugging distributed systems, as discussed in Chapter 7, Testing and Debugging Distributed Applications, even today is not an easy task, especially when using languages other than C, C++, and Fortran. There is not much more to say here other than the fact that there is an important vacuum to be filled.

Most medium-to-large teams end up developing their own custom solutions based on log aggregators such as Sentry (https://getsentry.com) and monitoring solutions such as Ganglia (http://ganglia.sourceforge.net).

What would be nice to have are the equivalent of I/O monitoring tools such as Darshan (http://www.mcs.anl.gov/research/projects/darshan/) and distributed profilers such as Allinea MAP (http://www.allinea.com/products/map) for Python distributed applications.