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Apache Spark 2.x Cookbook

You're reading from  Apache Spark 2.x Cookbook

Product type Book
Published in May 2017
Publisher
ISBN-13 9781787127265
Pages 294 pages
Edition 1st Edition
Languages
Author (1):
Rishi Yadav Rishi Yadav
Profile icon Rishi Yadav
Toc

Table of Contents (19) Chapters close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Apache Spark 2. Developing Applications with Spark 3. Spark SQL 4. Working with External Data Sources 5. Spark Streaming 6. Getting Started with Machine Learning 7. Supervised Learning with MLlib — Regression 8. Supervised Learning with MLlib — Classification 9. Unsupervised Learning 10. Recommendations Using Collaborative Filtering 11. Graph Processing Using GraphX and GraphFrames 12. Optimizations and Performance Tuning

Using PageRank


PageRank measures the importance of each vertex in a graph. PageRank was started by Google's founders, who used the theory that the most important pages on the Internet are the pages with the most links leading to them. PageRank also looks at the importance of a page leading to the target page. So, if a given web page has incoming links from higher rank pages, it will be ranked higher.

Getting ready

We are going to use Wikipedia's page link data to calculate the page rank. Wikipedia publishes its data in the form of a database dump. We are going to use link data from, which has the data in two files:

  • links-simple-sorted.txt
  • titles-sorted.txt

Note

I have put both of them on Amazon S3 at s3a://com.infoobjects.wiki/links and s3a://com.infoobjects.wiki/nodes. Since the data size is larger, it is recommended that you run it on either Databricks Cloud or EMR.

How to do it...

  1. Import the graphx related classes:
scala> import org.apache.spark.graphx._
  1. Load the edges from Amazon S3:
scala&gt...
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