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

Chapter 6. Spark on Cluster

In the previous chapters, we have seen Spark examples using Spark shell in local mode or executing Spark programs using local master mode. As Spark is a distributed processing engine, it is designed to run on a cluster for high performance, scalability, and fault-tolerant behavior.

In this chapter, we will discuss Spark application architecture in distributed-mode. This chapter will also explain how various components of Spark in distributed mode interact. Along with that, this chapter will focus on various cluster managers that can be used to run Spark jobs in clusters. Also, we will discuss some effective performance parameters for running jobs in cluster mode. After this chapter, the reader will be able to execute Spark jobs effectively in distributed mode.