Book Image

Mastering Apache Spark 2.x - Second Edition

Book Image

Mastering Apache Spark 2.x - Second Edition

Overview of this book

Apache Spark is an in-memory, cluster-based Big Data processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and more. This book will take your knowledge of Apache Spark to the next level by teaching you how to expand Spark’s functionality and build your data flows and machine/deep learning programs on top of the platform. The book starts with a quick overview of the Apache Spark ecosystem, and introduces you to the new features and capabilities in Apache Spark 2.x. You will then work with the different modules in Apache Spark such as interactive querying with Spark SQL, using DataFrames and DataSets effectively, streaming analytics with Spark Streaming, and performing machine learning and deep learning on Spark using MLlib and external tools such as H20 and Deeplearning4j. The book also contains chapters on efficient graph processing, memory management and using Apache Spark on the cloud. By the end of this book, you will have all the necessary information to master Apache Spark, and use it efficiently for Big Data processing and analytics.
Table of Contents (21 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
10
Deep Learning on Apache Spark with DeepLearning4j and H2O

Summary


This chapter showed by example how Scala-based code can be used to call GraphX algorithms in Apache Spark. Scala has been used because it requires less code to develop the examples than Java, which saves time. Note that GraphX is not available for Python or R. A Scala-based shell can be used, and the code can be compiled into Spark applications.

The most common graph algorithms have been covered and you should have an idea now on how to solve any graph problem with GraphX. Especially since you've understood that a Graph in GraphX is still represented and backed by RDDs, so you are already familiar with using them. The configuration and code examples from this chapter will also be available for download with the book.

We hope that you found this chapter useful. The next chapter will delve into graph frames, which make use of DataFrames, Tungsten, and Catalyst for graph processing.