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

Kafka architecture

Kafka has an architecture that differs significantly from other messaging systems. Kafka is a peer to peer system (each node in a cluster has the same role) in which each node is called a broker. The brokers coordinate their actions with the help of a ZooKeeper ensemble. The Kafka metadata managed by the ZooKeeper ensemble is mentioned in the section Sharing ZooKeeper between Storm and Kafka:

Figure 8.1: A Kafka cluster

The following are the important components of Kafka:


producer is an entity that uses the Kafka client API to publish messages into the Kafka cluster. In a Kafka broker, messages are published by the producer entity to named entities called topics. A topic is a persistent queue (data stored into topics is persisted to disk).

For parallelism, a Kafka topic can have multiple partitions. Each partition data is represented in a different file. Also, two partitions of a single topic can be allocated on a different broker, thus increasing throughput as all...