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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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Table of Contents (14) Chapters Close

Preface 1. Let's Understand Storm FREE CHAPTER 2. Getting Started with Your First Topology 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

Building high availability of components


Now we are at an opportune juncture to look for high availability of various components in the cluster. We will do this as a series of exercises wherein we assume that each component is installed in the clustered mode and more than one instance of it exists in the ecosystem.

The high availability of RabbitMQ can be checked only after you have a mirrored queue in place. Let's assume:

  • We have two nodes in the RabbitMQ cluster: node1 and node2

  • MyExchange is the name of the exchange that is created for the purpose of this exercise

  • MyQueue is a mirrored queue that is created for this exercise

Next, we will just run the fixedEmitter code we created in the Creating a RabbitMQ feeder component section. Now perform the Litmus test:

  • Let's assume the queue MyQueue has 100 messages

  • Now bring down node2 (this means, one node on the cluster is down)

  • All the 100 messages will be retained and will be visible on the console; node1 fills in when there is an absence of node2...

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