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Mastering Apache Spark 2.x - Second Edition

You're reading from Mastering Apache Spark 2.x - Second Edition

Product type Book

Published in Jul 2017

Publisher Packt

ISBN-13 9781786462749

Pages 354 pages

Edition 2nd Edition

Languages

Concepts

Table of Contents (21) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. A First Taste and What’s New in Apache Spark V2

2. Apache Spark SQL

3. The Catalyst Optimizer

4. Project Tungsten

5. Apache Spark Streaming

6. Structured Streaming

7. Apache Spark MLlib

8. Apache SparkML

9. Apache SystemML

10. Deep Learning on Apache Spark with DeepLearning4j and H2O

11. Apache Spark GraphX

12. Apache Spark GraphFrames

13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience

14. Apache Spark on Kubernetes

Using Kubernetes for provisioning containerized Spark applications

So what's in it for Apache Spark here? Let's assume we have a set of powerful nodes in our local data center. What is the advantage of using Kubernetes for deployment over just installing Apache Spark on bare metal? Let's take the question the other way round. Let's have a look at the disadvantages of using Kubernetes in this scenario. Actually, there is no disadvantage at all.

Note

The link http://domino.research.ibm.com/library/cyberdig.nsf/papers/0929052195DD819C85257D2300681E7B/$File/rc25482.pdf is a 2014 paper of IBM Research, stating that performance within a Docker container is nearly identical to bare metal.

So this means that the only disadvantage is the effort you invest in installing and maintaining Kubernetes. But what you gain are the following:

Easy installation and updates of Apache Spark and other additional software packages (such as Apache Flink, Jupyter, or Zeppelin)
Easy switching between different versions...

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