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

Introduction


Graphs enable solving certain data problems more easily and intuitively. At the core of a graph lies concepts of edges, nodes (or vertices), and their properties. For example, the following are two seemingly disconnected graphs. The left one represents a social network and the relationship (the edges of the graph) between friends (the vertices of the graph), while the right one is a graph that represents restaurant recommendations. Note that the vertices for our restaurant recommendations are not only the restaurants themselves but also the cuisine type (for example, Ramen) and location (for example, Vancouver, B.C., Canada); these are the properties of the vertices. This ability to assign nodes to virtually anything and use edges to define the relationship between these nodes is the greatest virtue of graphs, that is, their flexibility:

This flexibility allows us to conceptually connect these two seemingly disparate graphs into one common graph. In this case, we can join the...