Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (18 chapters)
Learning Apache Spark 2
Credits
About the Author
About the Reviewers
www.packtpub.com
Customer Feedback
Preface

Graph algorithms in GraphX


GraphX supports sophisticated Graph processing and while you can build your own graph algorithms, GraphX provides a number of algorithms as a part of GraphX directly available as methods of graph or GraphOps objects. The three major components that GraphX supports include:

  • PageRank
  • Connected components
  • Triangle counting

PageRank

PageRank measure the importance of a vertex in the graph. For example, a Twitter user with lots of Twitter followers, or a LinkedIn user with lots of LinkedIn connections and followers is often considered influential and ranked highly.

GraphX supports both static and dynamic versions of PageRank, where static PageRank runs for a fixed number of iterations, while a dynamic PageRank runs until convergence (changes less than the tolerance level after each iteration).

Let's look at a very simple example. We have a set of call detail records (dummy data), and we are trying to identify the most important person in the community. There are other metrics...