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 application in distributed-mode

A Spark application is made of up of Driver and Executor(s) processes. Each application in Spark contains one driver process and one or more executor processes. The driver is the central coordinator of the application that drives the application. Spark Driver communicates and divides work among one or more executors. In distributed mode, Spark driver and each executor runs in separate JVM.

Logical Representation of a Spark Application in Distributed Mode

Driver program

SparkContext is initialized in the Driver JVM. Spark driver can be considered as the master of Spark applications. The following are the responsibilities of Spark Driver program:

  • It creates the physical plan of execution of tasks based on the DAG of operations.
  • It schedules the tasks on the executors. It passes the task bundle to executors based. Data locality principle is used while passing the tasks to executors.
  • Spark driver tracks RDD partitions to executor mapping for executing future tasks...