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 state

Trident provides an abstraction for reading from and writing results to stateful sources. We can maintain the state either internally to the topology (memory), or we can store it in external sources (Memcached or Cassandra).

Let's consider that we are maintaining the output of the preceding hello world Trident topology in a database. Every time you process the tuple, the count of country present in a tuple is increased in the database. We can't achieve exactly-once processing by only maintaining a count in the database. The reason is that if any tuple failed during processing, then the failed tuple is retried. This gives us a problem while updating the state, because we are not sure whether the state of this tuple was updated previously or not. If the tuple has failed before updating the state, then retrying the tuple will increase the count in the database and make the state consistent. But if the tuple has failed after updating the state, then retrying the same tuple will...