Book Image

Apache Spark 2.x for Java Developers

By : Sourav Gulati, Sumit Kumar
Book Image

Apache Spark 2.x for Java Developers

By: Sourav Gulati, Sumit Kumar

Overview of this book

Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone. The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark streaming, Machine Learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages. By the end of the book, you will have a solid foundation in implementing components in the Spark framework in Java to build fast, real-time applications.
Table of Contents (19 chapters)
Title Page
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Introduction to GraphX


As per the Apache Spark documentation: "GraphX is Apache Spark's API for graphs and graph-parallel computation". Graph based computations have become very popular with the advancement of technologies. Whether it is finding the shortest path between two points, matching DNA, or social media, graph computations have become ubiquitous.

Graph consists of a vertex and edges, where a vertex defines entities or nodes and edges defines the relationships from entities. Edges can be one directional or bidirectional based on the requirement. For example, an edge describing friendship relations between two users on Facebook is bidirectional; however, an edge describing follower relations between two users on Twitter may or may not be bidirectional because one can follow another person on Twitter without being followed by that person.

The Spark Graphx library helps to run graph-based computations on top of Spark. It provides a graph-based abstraction on the Spark RDD called a Property...