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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 2. Apache Spark SQL FREE CHAPTER 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

How to go from Unresolved Logical Execution Plan to Resolved Logical Execution Plan

The ULEP basically reflects the structure of an AST. So again, the AST is generated from the user's code implemented either on top of the relational API of DataFrames and Datasets or using SQL, or all three. This AST can be easily transformed into a ULEP. But, of course, a ULEP can't be executed. The first thing that is checked is if the referred relations exist in the catalog. This means all table names and fields expressed in the SQL statement or relational API have to exist. If the table (or relation) exists, the column names are verified. In addition, the column names that are referred to multiple times are given an alias in order to read them only once. This is already a first stage optimization taking place here. Finally, the data types of the columns are determined in order to...

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