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

Chapter 12. Apache Spark GraphFrames

GraphFrames is the new graph library of Apache Spark. It supports loss-less transformations of graph representations between GraphFrames and GraphX. While introducing another Apache Spark library equivalent to an existing one, we have to explain the motivation behind this decision. The motivation is the same as it was when introducing SparkML over MLlib. The idea was to have an equivalent to GraphX, which supports DataFrames, in order to make use of the optimizations that Catalyst and Tungsten bring.

Another goal was to progress further with unification by integrating graph algorithms and graph queries and optimizing their execution.

Although GraphX supports relational queries on graph structures, it doesn't support graph queries.

Therefore, GraphFrames was implemented with the goal of unifying graph algorithms, graph queries, and DataFrames (and therefore relational queries).

This chapter will cover the following topics:

  • Architecture of GraphFrames
  • Performance...