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Real-time Analytics with Storm and Cassandra

You're reading from  Real-time Analytics with Storm and Cassandra

Product type Book
Published in Mar 2015
Publisher
ISBN-13 9781784395490
Pages 220 pages
Edition 1st Edition
Languages
Author (1):
Shilpi Saxena Shilpi Saxena
Profile icon Shilpi Saxena

Table of Contents (19) Chapters

Real-time Analytics with Storm and Cassandra
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Let's Understand Storm 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 Quiz Answers Index

Building a Trident topology


Trident gives a batching edge to the Storm computation. It lets developers use the abstracted layer for computations over the Storm framework, giving the advantage of stateful processing with high throughput for distributed queries.

Well the architecture of Trident is the same as Storm; it's built on top of Storm to abstract a layer that adds the functionality of micro-batching and execution of SQL-like functions on top of Storm.

For the sake of analogy, one can say that Trident is a lot like Pig for batch processing in terms of concept. It has support for joins, aggregates, grouping, filters, functions, and so on.

Trident has basic batch processing features such as consistent processing and execution of process logic over the tuples exactly once.

Now to understand Trident and its working; let's look at a simple example.

The example we have picked up would achieve the following:

  • Word count over the stream of sentences (a standard Storm word count kind of topology)

  • A...

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