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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
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Table of Contents (13) Chapters Close

Preface 1. Understanding Spark FREE CHAPTER 2. Resilient Distributed Datasets 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

Understanding vertex degrees


Within the context of graph theory, the degrees around a vertex are the number of edges around the vertex. In our flights example, the degrees are then the total number of edges (that is, flights) to the vertex (that is, airports). Therefore, if we were to obtain the top 20 vertex degrees (in descending order) from our graph, then we would be asking for the top 20 busiest airports (most flights in and out) from our graph. This can be quickly determined using the following query:

display(tripGraph.degrees.sort(desc("degree")).limit(20))

Because we're using the display command, we can quickly view a bar graph of this data:

Diving into more details, here are the top 20 inDegrees (that is, incoming flights):

display(tripGraph.inDegrees.sort(desc("inDegree")).limit(20))

While here are the top 20 outDegrees (that is, outgoing flights):

display(tripGraph.outDegrees.sort(desc("outDegree")).limit(20))

Interestingly, while the top 10 airports (Atlanta/ATL to Charlotte/CLT) are...

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