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PySpark Cookbook

You're reading from   PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788835367
Length 330 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
Denny Lee Denny Lee
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Denny Lee
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Toc

Table of Contents (9) Chapters Close

Preface 1. Installing and Configuring Spark FREE CHAPTER 2. Abstracting Data with RDDs 3. Abstracting Data with DataFrames 4. Preparing Data for Modeling 5. Machine Learning with MLlib 6. Machine Learning with the ML Module 7. Structured Streaming with PySpark 8. GraphFrames – Graph Theory with PySpark

Using PageRank to determine airport ranking


PageRank is an algorithm popularized by the Google Search Engine and created by Larry Page. Ian Rogers says (see http://www.cs.princeton.edu/~chazelle/courses/BIB/pagerank.htm):

"(...)PageRank is a “vote”, by all the other pages on the Web, about how important a page is. A link to a page counts as a vote of support. If there’s no link there’s no support (but it’s an abstention from voting rather than a vote against the page)."

As you might imagine, this method can be applied to other problems and not only to ranking web pages. In our context, we can use it to determine airport ranking. To achieve this, we can use the number of flights and connections to and from various airports included that are in this departure delay dataset. 

Getting ready

Ensure that you have created the graph GraphFrame from the preceding subsections.

How to do it...

Execute the following code snippet to determine the most important airport in our dataset via the PageRank algorithm...

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