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

Loading the data

In order to build a machine learning model, we need data. Thus, before we start, we need to read some data. In this recipe, and throughout this chapter, we will be using the 1994 census income data. 

Getting ready

To execute this recipe, you need to have a working Spark environment. If you do not have one, you might want to go back to Chapter 1Installing and Configuring Spark and follow the recipes you will find there. 

The dataset was sourced from


The dataset is located in the data folder in the GitHub repository for the book.

All the code that you will need in this chapter can be found in the GitHub repository we set up for the book:; go to Chapter05 and open the 5. Machine Learning with MLlib.ipynb notebook. 

No other prerequisites are required.

How to do it...

We will read the data into a DataFrame so it is easier for us to work with. Later on, we will convert it into an RDD of labeled points....