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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

Rebalance the parallelism of a topology

As explained in the previous chapter, one of the key features of Storm is that it allows us to modify the parallelism of a topology at runtime. The process of updating a topology parallelism at runtime is called rebalance.

There are two ways to rebalance the topology:

  • Using Storm Web UI
  • Using Storm CLI

The Storm Web UI was covered in the previous chapter. This section covers how we can rebalance the topology using the Storm CLI tool. Here are the commands that we need to execute on Storm CLI to rebalance the topology:

> bin/storm rebalance [TopologyName] -n [NumberOfWorkers] -e [Spout]=[NumberOfExecutos] -e [Bolt1]=[NumberOfExecutos] [Bolt2]=[NumberOfExecutos]

The rebalance command will first deactivate the topology for the duration of the message timeout and then redistribute the workers evenly around the Storm cluster. After a few...

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