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

Reading data from files

For this recipe, we will create an RDD by reading a local file in PySpark. To create RDDs in Apache Spark, you will need to first install Spark as noted in the previous chapter. You can use the PySpark shell and/or Jupyter notebook to run these code samples. Note that while this recipe is specific to reading local files, a similar syntax can be applied for Hadoop, AWS S3, Azure WASBs, and/or Google Cloud Storage:

Storage type


Local files

sc.textFile('/local folder/filename.csv')

Hadoop HDFS


AWS S3 (


Azure WASBs (


Google Cloud Storage (