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Apache Spark 2.x Cookbook

You're reading from   Apache Spark 2.x Cookbook Over 70 cloud-ready recipes for distributed Big Data processing and analytics

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 pages
Edition 1st Edition
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Apache Spark FREE CHAPTER 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
I have put both of them on Amazon S3 at s3a://com.infoobjects.wiki/links and s3a://com.infoobjects...
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