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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 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

Cassandra fault tolerance


Well, one of the prime reasons for using Cassandra as a data store is its fault-tolerant capabilities. It's not driven by a typical master-slave architecture, where failure of the master becomes a single point of system breakdown. Instead, it harbors a concept of operating in a ring mode so that there is no single point of failure. Whenever required, we can restart the nodes without the dread of bringing the whole cluster down; there are various situations where this capability comes in handy.

There are situations where we need to restart Cassandra, but Cassandra's ring architecture equips the administrator to do this seamlessly with zero downtime for the cluster. This means that in situations such as the following that requires a Cassandra cluster to be restarted, a Cassandra administrator can restart the nodes one by one instead of bringing down the entire cluster and then starting it:

  • Starting the Cassandra daemon with changes in the memory configuration

  • Enabling...

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