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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

Apache Spark GraphFrames

GraphFrames is the new graph library of Apache Spark. It supports loss-less transformations of graph representations between GraphFrames and GraphX. While introducing another Apache Spark library equivalent to an existing one, we have to explain the motivation behind this decision. The motivation is the same as it was when introducing SparkML over MLlib. The idea was to have an equivalent to GraphX, which supports DataFrames, in order to make use of the optimizations that Catalyst and Tungsten bring.

Another goal was to progress further with unification by integrating graph algorithms and graph queries and optimizing their execution.

Although GraphX supports relational queries on graph structures, it doesn't support graph queries.

Therefore, GraphFrames was implemented with the goal of unifying graph algorithms, graph queries, and DataFrames (and...

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