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

Importing and saving data


We wanted to add this section about importing and saving data here, even though it is not purely about Spark SQL, so that concepts such as Parquet and JSON file formats could be introduced. This section also allows us to cover how to access saved data in loose text as well as the CSV, Parquet, and JSON formats conveniently in one place.

Processing the text files

Using SparkContext, it is possible to load a text file in RDD using the textFile method. Additionally, the wholeTextFile method can read the contents of a directory to RDD. The following examples show you how a file, based on the local filesystem (file://) or HDFS (hdfs://), can be read to a Spark RDD. These examples show you that the data will be divided into six partitions for increased performance. The first two examples are the same as they both load a file from the Linux filesystem, whereas the last one resides in HDFS:

sc.textFile("/data/spark/tweets.txt",6)
sc.textFile("file:///data/spark/tweets.txt...