Book Image

Mastering Apache Storm

By : Ankit Jain
Book Image

Mastering Apache Storm

By: Ankit Jain

Overview of this book

Apache Storm is a real-time Big Data processing framework that processes large amounts of data reliably, guaranteeing that every message will be processed. Storm allows you to scale your data as it grows, making it an excellent platform to solve your big data problems. This extensive guide will help you understand right from the basics to the advanced topics of Storm. The book begins with a detailed introduction to real-time processing and where Storm fits in to solve these problems. You’ll get an understanding of deploying Storm on clusters by writing a basic Storm Hello World example. Next we’ll introduce you to Trident and you’ll get a clear understanding of how you can develop and deploy a trident topology. We cover topics such as monitoring, Storm Parallelism, scheduler and log processing, in a very easy to understand manner. You will also learn how to integrate Storm with other well-known Big Data technologies such as HBase, Redis, Kafka, and Hadoop to realize the full potential of Storm. With real-world examples and clear explanations, this book will ensure you will have a thorough mastery of Apache Storm. You will be able to use this knowledge to develop efficient, distributed real-time applications to cater to your business needs.
Table of Contents (19 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

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.


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...