Book Image

Apache Spark 2.x for Java Developers

By : Sourav Gulati, Sumit Kumar
Book Image

Apache Spark 2.x for Java Developers

By: Sourav Gulati, Sumit Kumar

Overview of this book

Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone. The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark streaming, Machine Learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages. By the end of the book, you will have a solid foundation in implementing components in the Spark framework in Java to build fast, real-time applications.
Table of Contents (19 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Common RDD actions

In the previous section, we explored various RDD transformations that resulted in another RDD, however, an RDD operation that triggers Job computation and returns a non RDD value(s) is called an action. All the various transformations discussed previously do not get evaluated until an action gets called. Different transformations only help in materializing the lazily evaluated DAGs. It is the only action that triggers the data to be loaded in RDDs, transformation to be computed, and the final result to be either sent to the Driver program or written to a filesystem.

Some of the common actions widely used in Spark are discussed as follows:


isEmpty returns a Boolean value as True, if the RDD contains no element at all. It is important to note that RDD can be empty even when it is one or more partition. In the following example, we create the RDD with three integers and then filter an element that does not exist in the parent RDD hence the resultant creates an empty...