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

Creating an RDD for training

Before we can train an ML model, we need to create an RDD where each element is a labeled point. In this recipe, we will use the final_data RDD we created in the previous recipe to prepare our RDD for training.

Getting ready

To execute this recipe, you need to have a working Spark environment. You would have already gone through the previous recipe when we standardized the encoded census data.

No other prerequisites are required.

How to do it...

Many of the MLlib models require an RDD of labeled points to train. The next code snippets will create such an RDD for us to build classification and regression model.


Here's the snippet to create the classification RDD of labeled points that we will be using to predict whether someone is making more than $50,000:

final_data_income = (
    .map(lambda row: reg.LabeledPoint(
        , row[1:]


Here's the snippet to create the regression RDD of labeled points that...