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

DataFrames


We have already used DataFrames in previous examples; it is based on a columnar format. Temporary tables can be created from it but we will expand on this in the next section. There are many methods available to the data frame that allow data manipulation and processing.

Let's start with a simple example and load some JSON data coming from an IoT sensor on a washing machine. We are again using the Apache Spark DataSource API under the hood to read and parse JSON data. The result of the parser is a data frame. It is possible to display a data frame schema as shown here:

As you can see, this is a nested data structure. So, the doc field contains all the information that we are interested in, and we want to get rid of the meta information that Cloudant/ApacheCouchDB added to the original JSON file. This can be accomplished by a call to the select method on the DataFrame:

This is the first time that we are using the DataFrame API for data processing. Similar to RDDs, a set of methods...