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Mastering Apache Storm

You're reading from   Mastering Apache Storm Real-time big data streaming using Kafka, Hbase and Redis

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Product type Paperback
Published in Aug 2017
Publisher
ISBN-13 9781787125636
Length 284 pages
Edition 1st Edition
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Author (1):
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Ankit Jain Ankit Jain
Author Profile Icon Ankit Jain
Ankit Jain
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Table of Contents (13) Chapters Close

Preface 1. Real-Time Processing and Storm Introduction FREE CHAPTER 2. Storm Deployment, Topology Development, and Topology Options 3. Storm Parallelism and Data Partitioning 4. Trident Introduction 5. Trident Topology and Uses 6. Storm Scheduler 7. Monitoring of Storm Cluster 8. Integration of Storm and Kafka 9. Storm and Hadoop Integration 10. Storm Integration with Redis, Elasticsearch, and HBase 11. Apache Log Processing with Storm 12. Twitter Tweet Collection and Machine Learning

Introduction to Storm scheduler

As mentioned in the first two chapters, the Nimbus is responsible for deploying the topology and the supervisor is responsible for performing the computation tasks as defined in a Storm topology's spouts and bolts components. As we have shown, we can configure the number of worker slots for each supervisor node that are assigned to a topology as per the scheduler policy, as well as the number of workers allocated to a topology. In short, Storm schedulers help the Nimbus to decide the worker distribution of any given topology.

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