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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

Trident aggregator

The Trident aggregator is used to perform the aggregation operation on the input batch, partition, or input stream. For example, if a user wants to count the number of tuples present in each batch, then we can use the count aggregator to count the number of tuples in each batch. The output of the aggregator completely replaces the value of the input tuple. There are three types of aggregator available in Trident:

partitionAggregate
aggregate
persistenceAggregate

Let's understand each type of aggregator in detail.

partitionAggregate

As the name suggests, the partitionAggregate works on each partition instead of the whole batch. The output of partitionAggregate completely replaces the input tuple. Also, the output of partitionAggregate contains a single-field tuple. Here is a piece of code that shows how we can use partitionAggregate :

mystream.partitionAggregate(new Fields("x"), new Count() ,new new Fields("count"))

For example, we get an input stream containing the fields x 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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