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Learning Real-time Analytics with Storm and Cassandra

You're reading from   Learning Real-time Analytics with Storm and Cassandra Solve real-time analytics problems effectively using Storm and Cassandra

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Product type Paperback
Published in Mar 2015
Publisher
ISBN-13 9781784395490
Length 220 pages
Edition 1st Edition
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Author (1):
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Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
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Toc

Table of Contents (14) Chapters Close

Preface 1. Let's Understand Storm 2. Getting Started with Your First Topology FREE CHAPTER 3. Understanding Storm Internals by Examples 4. Storm in a Clustered Mode 5. Storm High Availability and Failover 6. Adding NoSQL Persistence to Storm 7. Cassandra Partitioning, High Availability, and Consistency 8. Cassandra Management and Maintenance 9. Storm Management and Maintenance 10. Advance Concepts in Storm 11. Distributed Cache and CEP with Storm A. Quiz Answers Index

The Storm isolation scheduler


The Storm isolation scheduler was released in Storm Version 0.8.2. This was a very handy feature that is very actively being used ever since its release, in the case of the shared Storm cluster. Let's understand its working and capability through an example; say, we have a four supervisor node Storm cluster with four slots each, so in total I have 16 slots. Now I want to employ three Storm topologies here, say, Topo1, Topo2, and Topo3; each has four workers allocated to it.

So by probable default, the scheduling behavior of the Storm distribution will be as follows:

 

Supervisor 1

Supervisor 2

Supervisor 3

Supervisor 4

Topo1

Worker 1

Worker 2

Worker 3

Worker 4

Topo2

Worker 2

Worker 1

Worker 1

Worker 1

Topo3

Worker 3

Worker 3

Worker 2

Worker 2

Storm will respect load distribution and will spawn one worker of each topology on each node.

Now let's tweak the scenario a bit and introduce a requirement that Topo1 is a very resource-intensive topology...

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