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Practical Real-time Data Processing and Analytics

You're reading from   Practical Real-time Data Processing and Analytics Distributed Computing and Event Processing using Apache Spark, Flink, Storm, and Kafka

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787281202
Length 360 pages
Edition 1st Edition
Languages
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Authors (2):
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Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
Saurabh Gupta Saurabh Gupta
Author Profile Icon Saurabh Gupta
Saurabh Gupta
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introducing Real-Time Analytics FREE CHAPTER 2. Real Time Applications – The Basic Ingredients 3. Understanding and Tailing Data Streams 4. Setting up the Infrastructure for Storm 5. Configuring Apache Spark and Flink 6. Integrating Storm with a Data Source 7. From Storm to Sink 8. Storm Trident 9. Working with Spark 10. Working with Spark Operations 11. Spark Streaming 12. Working with Apache Flink 13. Case Study

RabbitMQ – integration with Storm


Now that we have accomplished basic setup and publish, and subscribe next let's move on to integration of RabbitMQ with Storm. We'll execute this as an end-to-end example.

AMQPSpout

Storm integrates with RabbitMQ using an AMQPSpout, which reads the messages from RabbitMQ and pushes them to Storm topology for further processing. The following code snippet captures the key aspects of encoding the AMQPSpout:

.. 
public class AMQPSpout implements IRichSpout { 
      private static final long serialVersionUID = 1L; 
      /** 
       * Logger instance 
       */ 
      private static final Logger log = 
      LoggerFactory.getLogger(AMQPSpout.class); 
      private static final long CONFIG_PREFETCH_COUNT = 0; 
      private static final long DEFAULT_PREFETCH_COUNT = 0; 
      private static final long WAIT_AFTER_SHUTDOWN_SIGNAL = 0; 
      private static final long WAIT_FOR_NEXT_MESSAGE = 1L; 
 
      private static final String EXCHANGE_NAME = "MYExchange"; 
 ...
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