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 2. Apache Spark SQL

In this chapter, we will examine ApacheSparkSQL, SQL, DataFrames, and Datasets on top of Resilient Distributed Datasets (RDDs). DataFrames were introduced in Spark 1.3, basically replacing SchemaRDDs, and are columnar data storage structures roughly equivalent to relational database tables, whereas Datasets were introduced as experimental in Spark 1.6 and have become an additional component in Spark 2.0.

We have tried to reduce the dependency between individual chapters as much as possible in order to give you the opportunity to work through them as you like. However, we do recommend that you read this chapter because the other chapters are dependent on the knowledge of DataFrames and Datasets.

This chapter will cover the following topics:

  • SparkSession
  • Importing and saving data
  • Processing the text files
  • Processing the JSON files
  • Processing the Parquet files
  • DataSource API
  • DataFrames
  • Datasets
  • Using SQL
  • User-defined functions
  • RDDs versus DataFrames versus Datasets

Before moving...