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

Advanced transformations

As stated earlier in this book, if an RDD operation returns an RDD, then it is called a transformation. In Chapter 4, Understanding the Spark Programming Model, we learnt about commonly used useful transformations. Now we are going to look at some advanced level transformations.


The working of this transformation is similar to map transformation. However, instead of acting upon each element of the RDD, it acts upon each partition of the RDD. So, the map function is executed once per RDD partition. Therefore, there will one-to-one mapping between partitions of the source RDD and the target RDD.

As a partition of an RDD is stored as a whole on a node, this transformation does not require shuffling.

In the following example, we will create an RDD of integers and increment all elements of the RDD by 1 using mapPartitions:

JavaRDD<Integer> intRDD = jsc.parallelize(Arrays.asList(1,2,3,4,5,6,7,8,9,10),2);

Java 7:

intRDD.mapPartitions(new FlatMapFunction&lt...