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

Introducing Pipelines


The Pipeline class helps to sequence, or streamline, the execution of separate blocks that lead to an estimated model; it chains multiple Transformers and Estimators to form a sequential execution workflow.

Pipelines are useful as they avoid explicitly creating multiple transformed datasets as the data gets pushed through different parts of the overall data transformation and model estimation process. Instead, Pipelines abstract distinct intermediate stages by automating the data flow through the workflow. This makes the code more readable and maintainable as it creates a higher abstraction of the system, and it helps with code debugging.

In this recipe, we will streamline the execution of a generalized linear regression model.

Getting ready

To execute this recipe, you will need a working Spark environment and you would have already loaded the data into the forest DataFrame.

No other prerequisites are required.

How to do it...

The following code provides a streamlined version...