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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

The SQL abstract syntax tree

As explained earlier, it doesn't matter if you are using SQL, DataFrame, or Dataset, the Apache Spark SQL parser returns an abstract syntax tree. However, DataFrames or Datasets can be used as starting points. The result of all these methods is again a tree-based structure called ULEP. The following figure is an example of such an AST (abstract syntax tree) adding an attribute coming from a row in a table and two literals together:

So as you can see, at the bottom of the tree (those nodes are also called leaves since they don't have any further nodes connecting to them) we have two integer literals: one and two. On top we have an operation taking those two literals and adding them together. You should note that those literals could also be loaded from a persistent data store. The Add operation virtually turns into another literal (three...

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