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

Common problems – clocks and time


Time is a handy variable for use. For instance, using timestamps is very natural when we want to join different streams of data, sort database records, and in general, reconstruct the timeline for a series of events, which we oftentimes observe out of order. In addition, some tools (for example, GNU make) rely solely on file modification time and are easily confused by a clock skew between machines.

For these reasons, clock synchronization among all computers and systems we use is very important. If our computers are in different time zones, we might want to not only synchronize their clocks but also set them to Coordinated Universal Time (UTC) for simplicity. In all the cases, when changing clocks to UTC is not possible, a good piece of advice is to always process time in UTC within our code and to only convert to local time for display purposes.

In general, clock synchronization in distributed systems is a fascinating and complex topic, and it is out of...