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

Spark components

Before moving any further let's first understand the common terminologies associated with Spark:

  • Driver: This is the main program that oversees the end-to-end execution of a Spark job or program. It negotiates the resources with the resource manager of the cluster for delegate and orchestrate the program into smallest possible data local parallel programming unit.
  • Executors: In any Spark job, there can be one or more executors, that is, processes that execute smaller tasks delegated by the driver. The executors process the data, preferably local to the node and store the result in memory, disk, or both.
  • Master: Apache Spark has been implemented in master-slave architecture and hence master refers to the cluster node executing the driver program.
  • Slave: In a distributed cluster mode, slave refers to the nodes on which executors are being run and hence there can be (and mostly is) more than one slave in the cluster.
  • Job: This is a collection of operations performed on any set of...