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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
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Authors (2):
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Tomasz Drabas Tomasz Drabas
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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

Building the graph


In the preceding sections, you installed GraphFrames and built the DataFrames required for the graph; now, you can start building the graph itself.

How to do it...

The first component of this recipe involves importing the necessary libraries, in this case, the PySpark SQL functions (pyspark.sql.functions) and GraphFrames (graphframes). In the previous recipe, we had created the src and dst columns as part of creating the deptsDelays_geo DataFrame. When creating edges within GraphFrames, it is specifically looking for the src and dst columns to create the edges as per edges. Similarly, GraphFrames is looking for the column id to represent the graph vertex (as well as join to the src and dst columns). Therefore, when creating the vertexes, vertices, we rename the IATA column to id:

from pyspark.sql.functions import *
from graphframes import *

# Create Vertices (airports) and Edges (flights)
vertices = airports.withColumnRenamed("IATA", "id").distinct()
edges = deptsDelays_geo...
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