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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 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

Examples and illustrations


One of the other out-of-the-box and popular implementations of Trident is reach topology, which is a pure DRPC topology that finds the reach of a URL on demand. Let's first understand some of the jargon before we delve deeper.

Reach is basically a sum total of the count of Twitter users exposed to a URL.

Reach computation is a multistep process that can be attained by the following examples:

  • Get all the users who have ever tweeted a URL

  • Fetch the follower tree of each of these users

  • Assemble the huge follower sets fetched previously

  • Count the set

Well, looking at the skeletal algorithm entailed previously, you can make out that it is beyond the capability of a single machine and we'd need a distributed compute engine to achieve it. It's an ideal candidate of the Storm Trident framework, as you have the capability to execute highly parallel computations at each step across the cluster.

  • Our Trident reach topology would be sucking data from two large data banks

  • Bank A is the...

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