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PySpark Cookbook

You're reading from  PySpark Cookbook

Product type Book
Published in Jun 2018
Publisher Packt
ISBN-13 9781788835367
Pages 330 pages
Edition 1st Edition
Languages
Authors (2):
Denny Lee Denny Lee
Profile icon Denny Lee
Tomasz Drabas Tomasz Drabas
Profile icon Tomasz Drabas
View More author details
Toc

Table of Contents (13) Chapters close

Title Page
Packt Upsell
Contributors
Preface
1. Installing and Configuring Spark 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 Index

Running queries against the graph


Now that you have created your graph, start off by creating and running some simple queries against your GraphFrame.

Getting ready

Ensure that you have created the graph GraphFrame (derived from the vertices and edges DataFrames) from the previous section.

How to do it...

Let's start with some simple count queries to determine the number of airports (nodes or vertices; remember?) and the number of flights (the edges), which can be determined by applying count(). The call to count() is similar to a DataFrame except that you also need to include whether you are countingverticesoredges:

print "Airport count: %d" % graph.vertices.count()
print "Trips count: %d" % graph.edges.count()

The output of these queries should be similar to the following output, denoting the 279 vertices (that is, airports) and more than 1.3 million edges (that is, flights):

Output:
  Airports count: 279 
  Trips count: 1361141

Similar to DataFrames, you can also execute the filter and groupBy...

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