Real-time data processing is no longer a luxury exercised by a few big companies but has become a necessity for businesses that want to compete, and Apache Storm is becoming the de facto standard to develop real-time processing pipelines. The key features of Storm are that it is horizontally scalable, fault-tolerant, and provides guaranteed message processing. Storm can solve various types of analytical problems, such as machine learning, log processing, and graph analysis.
Learning Storm will serve both as a getting-started guide for inexperienced developers and as a reference to implement advanced use cases with Storm for experienced developers. In the first two chapters, you will learn the basics of a Storm topology and various components of a Storm cluster. In the later chapters, you will learn how to build a Storm application that can interact with various other Big Data technologies and how to create transactional topologies. Finally, the last two chapters cover case studies for log processing and machine learning.
Chapter 1, Setting Up Storm on a Single Machine, gives an introduction to Storm and its components, followed by setting up a single-node Storm cluster, developing a sample Storm topology, and deploying it on a single-node cluster.
Chapter 2, Setting Up a Storm Cluster, covers the deployment of Storm in the cluster, deploys sample topology on a Storm cluster, discusses how we can achieve parallelism in Storm and how we can change the parallelism of the Storm topology in runtime, and even covers the basic Storm commands.
Chapter 3, Monitoring the Storm Cluster, introduces you to various ways of monitoring a Storm cluster, including the Storm UI and the Nimbus thrift client.
Chapter 4, Storm and Kafka Integration, introduces Apache Kafka, a message-queuing system, and shows how to integrate it with Storm to interact with data coming from external systems.
Chapter 5, Exploring High-level Abstraction in Storm with Trident, gives an introduction to Trident's function, filter, projection, aggregator, and repartitioning operations. It also covers a description of the transactional, non-transactional, and opaque transactional topologies. At the end, we cover how we can develop the sample Trident topology and how we can use the distributed RPC feature.
Chapter 6, Integration of Storm with Batch Processing Tools, shows you how to integrate Storm with Hadoop using the Storm-YARN framework.
Chapter 7, Integrating Storm with JMX, Ganglia, HBase, and Redis, shows you how to integrate Storm with various other Big Data technologies. It also focuses on how we can publish Storm's JVM metrics on Ganglia.
Chapter 8, Log Processing with Storm, covers a sample log processing application in which, we parse Apache web server logs and generate some business information from logfiles.
Chapter 9, Machine Learning, walks you through a case study of implementing a machine learning topology in Storm.