Search icon CANCEL
Subscription
0
Cart icon
Close icon
You have no products in your basket yet
Save more on your purchases!
Savings automatically calculated. No voucher code required
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Apache Spark 2.x for Java Developers

You're reading from  Apache Spark 2.x for Java Developers

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781787126497
Pages 350 pages
Edition 1st Edition
Languages
Authors (2):
Sourav Gulati Sourav Gulati
Profile icon Sourav Gulati
Sumit Kumar Sumit Kumar
Profile icon Sumit Kumar
View More author details

Table of Contents (19) Chapters

Title Page
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Introduction to Spark 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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €14.99/month. Cancel anytime}