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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

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 in this case), which then again is used by another Add operation as one of its inputs. The other input labeled as Attribute(x...

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