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Mastering Apache Storm

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

Product type Paperback

Published in Aug 2017

Publisher

ISBN-13 9781787125636

Length 284 pages

Edition 1st Edition

Languages

Java

Tools

Apache Storm

Concepts

Big Data

Author (1):

Jain

View More author details

Table of Contents (19) Chapters

Title Page

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

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

Walkthrough of the Storm UI

This section will show you how we can start the Storm UI daemon. However, before starting the Storm UI daemon, we assume that you have a running Storm cluster. The Storm cluster deployment steps are mentioned in the previous sections of this chapter. Now, go to the Storm home directory (cd $STORM_HOME) at the leader Nimbus machine and run the following command to start the Storm UI daemon:

$> cd $STORM_HOME$> bin/storm ui &

By default, the Storm UI starts on the 8080 port of the machine where it is started. Now, we will browse to the http://nimbus-node:8080 page to view the Storm UI, where Nimbus node is the IP address or hostname of the the Nimbus machine.

The following is a screenshot of the Storm home page:

Cluster Summary section

This portion of the Storm UI shows the version of Storm deployed in the cluster, the uptime of the Nimbus nodes, number of free worker slots, number of used worker slots, and so on. While submitting a topology to the cluster...

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Authors (1)

Jain

Jain

Ankit Jain holds a bachelor's degree in computer science and engineering. He has 6 years, experience in designing and architecting solutions for the big data domain and has been involved with several complex engagements. His technical strengths include Hadoop, Storm, S4, HBase, Hive, Sqoop, Flume, Elasticsearch, machine learning, Kafka, Spring, Java, and J2EE. He also shares his thoughts on his personal blog. You can follow him on Twitter at @mynameisanky. He spends most of his time reading books and playing with different technologies. When not at work, he spends time with his family and friends watching movies and playing games.

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