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Real-Time Big Data Analytics

You're reading from   Real-Time Big Data Analytics Design, process, and analyze large sets of complex data in real time

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Product type Paperback
Published in Feb 2016
Publisher
ISBN-13 9781784391409
Length 326 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 (12) Chapters Close

Preface 1. Introducing the Big Data Technology Landscape and Analytics Platform FREE CHAPTER 2. Getting Acquainted with Storm 3. Processing Data with Storm 4. Introduction to Trident and Optimizing Storm Performance 5. Getting Acquainted with Kinesis 6. Getting Acquainted with Spark 7. Programming with RDDs 8. SQL Query Engine for Spark – Spark SQL 9. Analysis of Streaming Data Using Spark Streaming 10. Introducing Lambda Architecture Index

Storm simple patterns

While we work with Storm, there are a variety of patterns you can recognize. In the segment here, without using Trident, we are attempting to capture some common patterns with Storm topologies.

Joins

As the name suggests, this is the most common pattern. The output from two or more different streams is joined on some common field and is emitted as a single tuple. In Storm, that's effectively achieved using fields grouping, which ensures that all the tuples with same field value are emitted to the same task. The following figure and code snippet captures its essence:

 TopologyBuilder builder = new TopologyBuilder();
    builder.setSpout("gender", genderSpout);
    builder.setSpout("age", ageSpout);
    builder.setBolt("join", new SingleJoinBolt(new Fields("gender", "age"))).fieldsGrouping("gender", new Fields("id"))
        .fieldsGrouping("age", new Fields("id"));

That's...

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