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Apache Spark 2.x for Java Developers

You're reading from   Apache Spark 2.x for Java Developers Explore big data at scale using Apache Spark 2.x Java APIs

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787126497
Length 350 pages
Edition 1st Edition
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Authors (2):
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Sourav Gulati Sourav Gulati
Author Profile Icon Sourav Gulati
Sourav Gulati
Sumit Kumar Sumit Kumar
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Sumit Kumar
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Table of Contents (12) Chapters Close

Preface 1. Introduction to Spark FREE CHAPTER 2. Revisiting Java 3. Let Us Spark 4. Understanding the Spark Programming Model 5. Working with Data and Storage 6. Spark on Cluster 7. Spark Programming Model - Advanced 8. Working with Spark SQL 9. Near Real-Time Processing with Spark Streaming 10. Machine Learning Analytics with Spark MLlib 11. Learning Spark GraphX

Graph operations


The Graphx library provides various inbuilt operations that can be executed on Property Graphs. In this section, we will discuss those operations.

mapVertices

Similar to map transformation on RDD, mapVertices allows users to transform the vertices of graphs. The following is the signature of the mapVertices method:

mapVertices(scala.Function2<Object,VD,VD2> map, scala.reflect.ClassTag<VD2> evidence$3, scala.Predef.$eq$colon$eq<VD,VD2> eq)

To implement scala.Function2 in Java, scala.runtime.AbstractFunction2 can be used because implementing scala.Function2 in Java requires users to implement a large number of abstract methods and this generates a lot of byte code.

AbstractFunction0, AbstractFunction1, AbstractFunction2, ... were introduced to reduce the byte code generated by anonymous functions such as scala.Function0, scala.Function1, scala.Function2, ..... That is why they are kept in the scala.runtime package. We can leverage them while calling graph operations...

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