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Apache Spark 2.x for Java Developers

You're reading from   Apache Spark 2.x for Java Developers Explore big data at scale using Apache Spark 2.x Java APIs

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787126497
Length 350 pages
Edition 1st Edition
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Authors (2):
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Sourav Gulati Sourav Gulati
Author Profile Icon Sourav Gulati
Sourav Gulati
Sumit Kumar Sumit Kumar
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Sumit Kumar
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Table of Contents (12) Chapters Close

Preface 1. Introduction to Spark FREE CHAPTER 2. Revisiting Java 3. Let Us Spark 4. Understanding the Spark Programming Model 5. Working with Data and Storage 6. Spark on Cluster 7. Spark Programming Model - Advanced 8. Working with Spark SQL 9. Near Real-Time Processing with Spark Streaming 10. Machine Learning Analytics with Spark MLlib 11. Learning Spark GraphX

Cluster managers


Cluster managers are used to deploy Spark applications in cluster mode. Spark can be configured to run various cluster managers. Spark distribution provides an inbuilt cluster manager known as Spark standalone. Apart from that Spark can run on top of other popular cluster managers in the big data world such as YARN and Mesos. In this section, we will discuss how to deploy Spark applications with Spark standalone and YARN.

Spark standalone

Spark standalone manager is available in the Spark distribution. It helps to deploy Spark applications in cluster mode in a very efficient and convenient way.

Spark standalone manager follows the master-slave architecture. It consists of a Spark master and multiple worker nodes where worker nodes are the slave nodes for Spark master node. Similar to other master-slave frameworks, Spark master works a scheduler for the submitted Spark applications. It schedules the applications on worker nodes and the processes that executed the application...

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