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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

Shared variables – broadcast variables and accumulators

While working in distributed compute programs and modules, where the code executes on different nodes and/or different workers, a lot of time a need arises to share data across the execution units in the distributed execution setup. Thus Spark has the concept of shared variables. The shared variables are used to share information between the parallel executing tasks across various workers or the tasks and the drivers. Spark supports two types of shared variable:

  • Broadcast variables
  • Accumulators

In the following sections, we will look at these two types of Spark variables, both conceptually and pragmatically.

Broadcast variables

These are the variables that the...

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