Chapter 1, Real-Time Processing and Storm Introduction, gives an introduction to Storm and its components.

Chapter 2, Storm Deployment, Topology Development, and Topology Options, covers deploying Storm into the cluster, deploying the sample topology on a Storm cluster, how we can monitor the storm pipeline using storm UI, and how we can dynamically change the log level settings.

Chapter 3, Storm Parallelism and Data Partitioning, covers the parallelism of topology, how to configure parallelism at the code level, guaranteed message processing, and Storm internally generated tuples.

Chapter 4, Trident Introduction, covers an introduction to Trident, an understanding of the Trident data model, and how we can write Trident filters and functions. This chapter also covers repartitioning and aggregation operations on Trident tuples.

Chapter 5, Trident Topology and Uses, introduces Trident tuple grouping, non-transactional topology, and a sample Trident topology. The chapter also introduces Trident state and distributed RPC.

Chapter 6, Storm Scheduler, covers different types of scheduler available in Storm: the default scheduler, isolation scheduler, resource-aware scheduler, and custom scheduler.

Chapter 7, Monitoring of the Storm Cluster, covers monitoring Storm by writing custom monitoring UIs using the stats published by Nimbus. We explain the integration of Ganglia with Storm using JMXTrans. This chapter also covers how we can configure Storm to publish JMX metrics.

Chapter 8, Integration of Storm and Kafka, shows the integration of Storm with Kafka. This chapter starts with an introduction to Kafka, covers the installation of Storm, and ends with the integration of Storm with Kafka to solve any real-world problem.

Chapter 9, Storm and Hadoop Integration, covers an overview of Hadoop, writing the Storm topology to publish data into HDFS, an overview of Storm-YARN, and deploying the Storm topology on YARN.

Chapter 10, Storm Integration with Redis, Elasticsearch, and HBase, teaches you how to integrate Storm with various other big data technologies.

Chapter 11, Apache Log Processing with Storm, covers a sample log processing application in which we parse Apache web server logs and generate some business information from log files.

Chapter 12, Twitter Tweets Collection and Machine Learning, walks you through a case study implementing a machine learning topology in Storm.

All of the code in this book has been tested on CentOS 6.5. It will run on other variants of Linux and Windows as well with appropriate changes in commands.

We have tried to keep the chapters self-contained, and the setup and installation of all the software used in each chapter are included in the chapter itself. These are the software packages used throughout the book:

If you are a Java developer and are keen to enter into the world of real-time stream processing applications using Apache Storm, then this book is for you. No previous experience in Storm is required as this book starts from the basics. After finishing this book, you will be able to develop not-so-complex Storm applications.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning. Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Add the following line in the storm.yaml file of the Nimbus machine to enable JMX on the Nimbus node."

A block of code is set as follows:

<dependency>
  <groupId>org.apache.storm</groupId>
  <artifactId>storm-core</artifactId>
  <version>1.0.2</version>
  <scope>provided<scope>
</dependency>

Any command-line input or output is written as follows:

cd $ZK_HOME/conf
touch zoo.cfg

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Now, click on the Connect button to view the metrics of the supervisor node."

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