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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

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.

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