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Spark Cookbook

You're reading from   Spark Cookbook With over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries this is the perfect Spark book to always have by your side

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Product type Paperback
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Length 226 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 (14) Chapters Close

Preface 1. Getting Started with Apache Spark 2. Developing Applications with Spark FREE CHAPTER 3. External Data Sources 4. Spark SQL 5. Spark Streaming 6. Getting Started with Machine Learning Using MLlib 7. Supervised Learning with MLlib – Regression 8. Supervised Learning with MLlib – Classification 9. Unsupervised Learning with MLlib 10. Recommender Systems 11. Graph Processing Using GraphX 12. Optimizations and Performance Tuning Index

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 page link data to calculate page rank. Wikipedia publishes its data in the form of a database dump. We are going to use link data from http://haselgrove.id.au/wikipedia.htm, which has the data in two files:

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

I have put both of them on Amazon S3 at s3n://com.infoobjects.wiki/links and s3n://com.infoobjects.wiki/nodes. Since the data size is larger, it is recommended that you run it on either Amazon EC2 or your local cluster. Sandbox may be very slow.

You can load the files to hdfs using the following commands...

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