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

The SparkSession--your gateway to structured data processing


The SparkSession is the starting point for working with columnar data in Apache Spark. It replaces SQLContext used in previous versions of Apache Spark. It was created from the Spark context and provides the means to load and save data files of different types using DataFrames and Datasets and manipulate columnar data with SQL, among other things. It can be used for the following functions:

  • Executing SQL via the sql method
  • Registering user-defined functions via the udf method
  • Caching
  • Creating DataFrames
  • Creating Datasets

Note

The examples in this chapter are written in Scala as we prefer the language, but you can develop in Python, R, and Java as well. As stated previously, the SparkSession is created from the Spark context.

Using the SparkSession allows you to implicitly convert RDDs into DataFrames or Datasets. For instance, you can convert RDD into a DataFrame or Dataset by calling the toDF or toDS methods:

 import spark.implicits._...