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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

Code generation

Apache Spark V1.5 introduced code generation for expression evaluation. In order to understand this, let's start with an example. Let's have a look at the following expression:

val i = 23
val j = 5
var z = i*x+j*y

Imagine x and y are data coming from a row in a table. Now, consider that this expression is applied for every row in a table of, let's say, one billion rows. Now the Java Virtual Machine has to execute (interpret) this expression one billion times, which is a huge overhead. So what Tungsten actually does is transform this expression into byte-code and have it shipped to the executor thread.

As you might know, every class executed on the JVM is byte-code. This is an intermediate abstraction layer to the actual machine code specific for each different micro-processor architecture. This was one of the major selling points of Java decades ago...

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