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

Examples


Now let's look at a couple of examples in order to understand how to use the new GraphFrames API.

First of all, we need to create a GraphFrames object from data files. Before we do this, we need to install GraphFrames. Fortunately, this is very easy as the package is available on a Maven repository and we can just pass the dependency to the spark-submit and spark-shell commands:

spark-shell --packages graphframes:graphframes:0.5.0-spark2.1-s_2.11

Then, we need to import the required package:

import org.graphframes._

As our graph is based on two CSV files--one for the vertices and one for the edges--we need to create two DataFrames out of them:

val vertex = spark.read.option("header","true").csvgraph1_vertex.csv")
val edges = spark.read.option("header","true").csvgraph1_edges.csv")

Finally, we create a GraphFrames object out of these:

val graph = GraphFrame(vertex, edges)

Example 1 – counting

The graph has been loaded and we know the data volumes in the data files but what about the data content...