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 transformations

As described in Chapter 1, Introduction of Spark, if an operation on an RDD outputs an RDD then it is a transformation. In other words, transformation on an RDD executes a function on the elements of the RDD and creates a new RDD, which represents the transformed elements. The number of elements in the target RDD can be different than the source RDD.

Transformations in Spark are lazy operations that get triggered with an action; that is, a chain of transformations will only execute when an action is performed.

One important aspect of transformation also deals with the dependency on partition of the parent RDD. If each partition of the generated RDD depends on a fixed partition of the parent RDD then it's called a narrow dependency. Similarly, a wide dependency pertains to operations where the generated RDD depends on multiple partitions of the parent RDD. Typically, map, filter, and similar operations (which will be discussed in the coming section) have a narrow...