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
Contributors
Preface
Index

Overview of DataFrame transformations


Just like RDDs, DataFrames have both transformations and actions. As a reminder, transformations convert one DataFrame into another, while actions perform some computation on a DataFrame and normally return the result to the driver. Also, just like the RDDs, transformations in DataFrames are lazy.

In this recipe, we will review the most common transformations. 

Getting ready

To execute this recipe, you need to have a working Spark 2.3 environment. You should have gone through the Specifying schema programmatically recipe, as we will be using the sample_data_schema DataFrame we created there.

There are no other requirements.

How to do it...

In this section, we will list some of the most common transformations available for DataFrames. The purpose of this list is not to provide a comprehensive enumeration of all available transformations, but to give you some intuition behind the most common ones.

The .select(...) transformation

The .select(...) transformation...