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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

Storm architecture and its components


Let's discuss Storm architecture and how it works. The following figure depicts the Storm cluster:

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  • The Nimbus node acts as the master node in a Storm cluster. It is responsible for analyzing topology and distributing tasks to different supervisors as per their availability. Also, it monitors failure; if one of the supervisors dies, it redistributes the tasks among available supervisors. The Nimbus node uses Zookeeper to keep track of tasks to maintain it's state. If the Nimbus node fails, it can be restarted so that it reads the state of Zookeeper and starts from same point where it failed earlier.
  • Supervisors act as slave nodes in the Storm cluster. One or more workers, that is, JVM processes, can run in each supervisor node. A supervisor coordinates with workers to complete the tasks assigned by Nimbus node. In the case of worker process failure, the supervisor finds available workers to complete the tasks.
  • A worker process is a JVM running in a...
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