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Apache Spark 2.x for Java Developers

Apache Spark 2.x for Java Developers

By : Kumar, Gulati
2 (4)
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Apache Spark 2.x for Java Developers

Apache Spark 2.x for Java Developers

2 (4)
By: Kumar, Gulati

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 (12 chapters)
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Let Us Spark

This chapter serves the purpose of providing instructions so that the reader becomes familiar with the process of installing Apache Spark in standalone mode, along with its dependencies. Then we will start our first interaction with Apache Spark by doing a couple of hands on exercises using Spark CLI as known as REPL.

We will move on to discuss Spark components and common terminologies associated with spark, and then finally discuss the life cycle of a Spark Job in a clustered environment. We will also explore the execution of Spark jobs in a graphical sense, from creation of DAG to execution of the smallest unit of tasks by the utilities provided in Spark Web UI.

Finally, we will conclude the chapter by discussing different methods of Spark Job configuration and submission using Spark-Submit tool and Rest APIs.

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