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Learning PySpark

You're reading from   Learning PySpark Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786463708
Length 274 pages
Edition 1st Edition
Languages
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Authors (2):
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Denny Lee Denny Lee
Author Profile Icon Denny Lee
Denny Lee
Tomasz Drabas Tomasz Drabas
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Tomasz Drabas
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Table of Contents (13) Chapters Close

Preface 1. Understanding Spark 2. Resilient Distributed Datasets FREE CHAPTER 3. DataFrames 4. Prepare Data for Modeling 5. Introducing MLlib 6. Introducing the ML Package 7. GraphFrames 8. TensorFrames 9. Polyglot Persistence with Blaze 10. Structured Streaming 11. Packaging Spark Applications Index

Determining airport ranking using PageRank


Because GraphFrames is built on top of GraphX, there are several algorithms that we can immediately leverage. PageRank was popularized by the Google Search Engine and created by Larry Page. To quote Wikipedia:

"PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites."

While the preceding example refers to web pages, this concept readily applies to any graph structure whether it is created from web pages, bike stations, or airports. Yet the interface via GraphFrames is as simple as calling a method. GraphFrames.PageRank will return the PageRank results as a new column appended to the vertices DataFrame to simplify our downstream analysis.

As there are many flights and connections through the various airports included in this dataset, we can use the PageRank algorithm...

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