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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

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781786462749

Length 354 pages

Edition 2nd Edition

Languages

Scala

Tools

Apache Spark

Concepts

Big Data

Author (1):

Romeo Kienzler

View More author details

Table of Contents (15) Chapters

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

Project Tungsten

This chapter introduces Project Tungsten. CPU and main memory performance became the new bottlenecks in big data processing after the massive increase in I/O performance due to the usage of solid state disks (SSDs) and 10 Gbps Ethernet. Therefore, Project Tungsten, the core of the Apache Spark execution engine, aims at improving performance at the CPU and main memory level. This chapter will cover the following topics:

Memory management beyond the Java Virtual Machine (JVM) Garbage Collector (GC)
Cache-friendly layout of data in memory
Code generation

We will have an in-depth look at all these three topics now.

The rest of the chapter is locked

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Authors (1)

Romeo Kienzler

Romeo Kienzler

Romeo Kienzler works as the chief data scientist in the IBM Watson IoT worldwide team, helping clients to apply advanced machine learning at scale on their IoT sensor data. He holds a Master's degree in computer science from the Swiss Federal Institute of Technology, Zurich, with a specialization in information systems, bioinformatics, and applied statistics.

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