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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

Understanding the workings of the Catalyst Optimizer

So how does the optimizer work? The following figure shows the core components and how they are involved in a sequential optimization process:

First of all, it has to be understood that it doesn't matter if a DataFrame, the Dataset API, or SQL is used. They all result in the same Unresolved Logical Execution Plan (ULEP). A QueryPlan is unresolved if the column names haven't been verified and the column types haven't been looked up in the catalog. A Resolved Logical Execution Plan (RLEP) is then transformed multiple times, until it results in an Optimized Logical Execution Plan. LEPs don't contain a description of how something is computed, but only what has to be computed. The optimized LEP is transformed into multiple Physical Execution Plans (PEP) using so-called strategies. Finally, an optimal PEP is...

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