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

Different types of stream grouping in the Storm cluster

When defining a topology, we create a graph of computation with the number of bolt-processing streams. At a more granular level, each bolt executes multiple tasks in the topology. Thus, each task of a particular bolt will only get a subset of the tuples from the subscribed streams.

Stream grouping in Storm provides complete control over how this partitioning of tuples happens among the many tasks of a bolt subscribed to a stream. Grouping for a bolt can be defined on the instance of org.apache.storm.topology.InputDeclarer returned when defining bolts using the org.apache e.storm.topology.TopologyBuilder.setBolt method.

Storm supports the following types of stream groupings.

Shuffle grouping

Shuffle grouping distributes tuples in a uniform, random way across the tasks. An equal number of tuples will be processed by each task. This grouping is ideal when you want to distribute your processing load uniformly across the tasks and where there...