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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

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

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. So the basic workflow is:

  1. Java source code gets compiled into Java byte-code.
  2. Java byte-code gets interpreted by the JVM.
  3. The JVM...
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