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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
Toc

Table of Contents (21) Chapters close

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

Summary


This chapter showed by example how Scala-based code can be used to call GraphX algorithms in Apache Spark. Scala has been used because it requires less code to develop the examples than Java, which saves time. Note that GraphX is not available for Python or R. A Scala-based shell can be used, and the code can be compiled into Spark applications.

The most common graph algorithms have been covered and you should have an idea now on how to solve any graph problem with GraphX. Especially since you've understood that a Graph in GraphX is still represented and backed by RDDs, so you are already familiar with using them. The configuration and code examples from this chapter will also be available for download with the book.

We hope that you found this chapter useful. The next chapter will delve into graph frames, which make use of DataFrames, Tungsten, and Catalyst for graph processing.

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