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Apache Spark 2.x for Java Developers

You're reading from  Apache Spark 2.x for Java Developers

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781787126497
Pages 350 pages
Edition 1st Edition
Languages
Authors (2):
Sourav Gulati Sourav Gulati
Profile icon Sourav Gulati
Sumit Kumar Sumit Kumar
Profile icon Sumit Kumar
View More author details

Table of Contents (19) Chapters

Title Page
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Introduction to Spark 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 algorithms


The Spark Graphx library provides built-in implementations for some very popular graph algorithms. These implementations help to perform various graph-based analytics in a simplified manner. The org.apache.Spark.graphx.GraphOps API allows for executing these operations on the graph. In this section, we will run graph-based analytics using the following implementations:

PageRank

PageRank is one of the most popular algorithms in graph theory. It is used to rank the vertices based on their importance. The importance of a vertex is calculated by the number of edges directed to the vertex. For example, a user is highly ranked on Twitter based on their followers, that is, the number of directed edges to that user vertex.

The PageRank algorithm was developed by Google founders Larry Page and Sergey Brin to measure the importance of web pages. Thus, the best example of PageRank implementation is the Google Search Engine. Google ranks pages based on their importance. For example, if...

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