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

Overview of RDD transformations

As noted in preceding sections, there are two types of operation that can be used to shape data in an RDD: transformations and actions. A transformation, as the name suggests, transforms one RDD into another. In other words, it takes an existing RDD and transforms it into one or more output RDDs. In the preceding recipes, we had used a map() function, which is an example of a transformation to split the data by its tab-delimiter.

Transformations are lazy (unlike actions). They only get executed when an action is called on an RDD. For example, calling the count() function is an action; more information is available in the following section on actions.

Getting ready

This recipe will be reading a tab-delimited (or comma-delimited) file, so please ensure that you have a text (or CSV) file available. For your convenience, you can download theairport-codes-na.txt anddeparturedelays.csv files from