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 repartitioning operations

By performing repartitioning operations, a user can partition tuples across multiple tasks. The repartitioning operation doesn't make any changes to the content of the tuples. Also, the tuples will only pass over the network for the repartitioning operation. Here are the different types of repartitioning operation.

Utilizing shuffle operation

This repartitioning operation partitions the tuples in a uniform, random way across multiple tasks. This repartitioning operation is generally used when we want to distribute the processing load uniformly across the tasks. The following diagram shows how the input tuples are repartitioned using the shuffle operation:

Here is a piece of code that shows how we can use the shuffle operation:

mystream.shuffle().each(new Fields("a","b"), new myFilter()).parallelismHint(2) 

Utilizing partitionBy operation

This repartitioning operation enables you to partition the stream on the basis of the fields in the tuples. For example, if...