Book Image

PySpark Cookbook

By : Denny Lee, Tomasz Drabas
Book Image

PySpark Cookbook

By: Denny Lee, Tomasz Drabas

Overview of this book

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.
Table of Contents (13 chapters)
Title Page
Packt Upsell

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...