Book Image

Learning PySpark

By : Tomasz Drabas, Denny Lee
Book Image

Learning PySpark

By: Tomasz Drabas, Denny Lee

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. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.
Table of Contents (20 chapters)
Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Chapter 7. GraphFrames

Graphs are an interesting way to solve data problems because graph structures are a more intuitive approach to many classes of data problems.

In this chapter, you will learn about:

  • Why use graphs?

  • Understanding the classic graph problem: the flights dataset

  • Understanding the graph vertices and edges

  • Simple queries

  • Using motif finding

  • Using breadth first search

  • Using PageRank

  • Visualizing flights using D3

Whether traversing social networks or restaurant recommendations, it is easier to understand these data problems within the context of graph structures: vertices, edges, and properties:

For example, within the context of social networks, the vertices are the people while the edges are the connections between them. Within the context of restaurant recommendations, the vertices (for example) involve the location, cuisine type, and restaurants while the edges are the connections between them (for example, these three restaurants are in Vancouver, BC, but only two of them serve ramen...