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Mastering Apache Storm

You're reading from Mastering Apache Storm

Product type Book

Published in Aug 2017

Publisher

ISBN-13 9781787125636

Pages 284 pages

Edition 1st Edition

Languages

Concepts

Data Processing

Author (1):

Ankit Jain

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

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

Setting up the Storm cluster

In this chapter, we will learn how to set up a three nodes Storm cluster, of which one node will be the active master node (Nimbus) and the other two will be worker nodes (supervisors).

The following is the deployment diagram of our three node Storm cluster:

The following are the steps that need to be performed to set up a three node Storm cluster:

Install and run the ZooKeeper cluster. The steps for installing ZooKeeper are mentioned in the previous section.
Download the latest stable Storm release from https://storm.apache.org/downloads.html; at the time of writing, the latest version is Storm 1.0.2.

Once you have downloaded the latest version, copy and unzip it in all three machines. Now, we will set the $STORM_HOME environment variable on each machine to make the setup easier. The $STORM_HOME environment contains the path of the Storm home folder (for example, export STORM_HOME=/home/user/storm-1.0.2).
Go to the $STORM_HOME/conf directory in the master nodes and...

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Authors (1)

Ankit 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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