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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 internals

The moment people start talking about Storm, a few key aspects of this framework stand apart:

  • Storm parallelism
  • Storm internal message processing

Now, let's pick each of these attributes and figure out how Storm is able to deliver these capabilities.

Storm parallelism

If we want to enlist the processes that thrive within a Storm cluster, the following are key components to be tracked:

  • Worker process: These are the processes executing on the supervisor node and process a subset of the topology. Each worker process executes in its own JVM. The number of workers allocated to a topology can be specified in the topology builder template and is applicable at the time of topology submission.
  • Executors: These are the threads that are spawned within the worker processes for execution of a bolt or spout. Each executor can run multiple tasks, but being a single thread, these tasks on the executor are performed sequentially. The number of executors is specified while wiring in the bolts...
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