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

Parallelism of a topology

Parallelism means the distribution of jobs on multiple nodes/instances where each instance can work independently and can contribute to the processing of data. Let's first look at the processes/components that are responsible for the parallelism of a Storm cluster.

Worker process

A Storm topology is executed across multiple supervisor nodes in the Storm cluster. Each of the nodes in the cluster can run one or more JVMs called worker processes, which are responsible for processing a part of the topology.

A worker process is specific to one of the specific topologies and can execute multiple components of that topology. If multiple topologies are being run at the same time, none of them will share any of the workers, thus providing some degree of isolation between topologies.


Within each worker process, there can be multiple threads executing parts of the topology. Each of these threads is called an executor. An executor can execute only one of the components...