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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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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 – messaging that works


RabbitMQ is one of the most sought after broker/queue services that works in production implementation with Storm. It's a very robust and versatile messaging system, that is supported both in open source as well as in a commercial version across all major operating systems. It has both durable and in-memory configuration on queues where the developers get enough flexibility to decide and choose on trade-offs between reliability and performance.

A few terms that would be used very often in context to RabbitMQ in particular, or any other queuing system are described as follows:

  • Producer/publisher: It's the client component that writes or sends the messages to the queue
  • Queue: It's actually the in-memory buffer that stores the message, from the time it's sent to the queue to the time it's read off the queue by a consumer application
  • Consumer/subscriber: It's the client component that receives or reads the messages off the queue

In the case of RabbitMQ, the producer...

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