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Mastering Apache Spark 2.x

You're reading from   Mastering Apache Spark 2.x Advanced techniques in complex Big Data processing, streaming analytics and machine learning

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781786462749
Length 354 pages
Edition 2nd Edition
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Author (1):
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Romeo Kienzler Romeo Kienzler
Author Profile Icon Romeo Kienzler
Romeo Kienzler
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Table of Contents (15) Chapters Close

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

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...
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