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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

Stream groupings

Next we need to get acquainted with various stream groupings (a stream grouping is basically the mechanism that defines how Storm partitions and distributes the streams of tuples amongst tasks of bolts) provided by Storm. Streams are the basic wiring component of a Storm topology, and understanding them provides a lot of flexibility to the developer to handle various problems in programs efficiently.

Local or shuffle grouping

Local or shuffle grouping is the most common grouping that randomly distributes the tuples emitted by the source ensuring equal distribution, that is, each instance of the bolt gets to process the same number of events. Load balancing is automatically taken care of by this grouping.

Due to the random nature of distribution of this grouping, it's useful only for atomic operations by specifying a single parameter—source of stream. The following snippet is from WordCount topology (which we reated earlier), which demonstrates the usage of shuffle...

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