Strategies for real-time event processing
Sean T. Allen, Peter Pathirana, and Matthew Jankowski
MEAP Began: December 2013
Softbound print: February 2015 (est.) | 275 pages
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Table of Contents, MEAP Chapters & Resources
|Table of Contents||Resources|
Part 1: Foundations
1 Introducing Storm - FREE
2 Storm Concepts - AVAILABLE
3 Topology Design - AVAILABLE
4 Creating Robust Topologies - AVAILABLE
Part 2: Eye of the Storm - Monitoring / Tuning / Troubleshooting
5 Moving From Local To Remote Topologies- AVAILABLE
6 Tuning in Storm - AVAILABLE
7 Resource Contention in a Storm Cluster - AVAILABLE
Part 3: Building on top of Storm
9 Internals API
It's a lot harder to make sense out of data when it's coming at you fast. Apache Storm, an efficient distributed stream processing platform, does for real-time computation what Hadoop did for batch analysis. Its efficient stream processing capabilities are relied upon by giants like Twitter and Yahoo for swiftly extracting intelligence from their Big Data streams. Fault tolerant guarantees of Storm make it an invaluable and highly versatile platform in the Big Data landscape. It integrates seamlessly with battle-tested message queuing systems (like Kafka) and NoSQL databases (like Cassandra). Storm is built to run on the JVM but provides straightforward extensions for working with non-JVM languages like Ruby and Python.
Storm Applied is a practical guide to using Apache Storm for the real-world tasks associated with processing and analyzing real-time data streams. This immediately-useful book starts by building a solid foundation of the Storm essentials so that you learn how to think about designing Storm solutions the right way from day one. But it quickly dives into real-world case studies that will bring the novice up to speed with productionizing Storm: the knowledge needed to scale a high throughput stream processor and ensure smooth operation within a production cluster. With that mastery of Storm in hand, it moves on to teach you how to use Trident to treat streams as batches for solving a different class of problems. Finally, it will cover all the different and interesting tools available within the Storm open source community that are crucial for any seasoned Storm developer.
- Demystifying Storm terminology: bolts, spouts, topologies, tuples and streams
- How to break down a problem and map it to Storm components
- How Storm's fault tolerance comes into play
- Adding much needed transparency for performance tuning and scaling productions systems
- Approaches for troubleshooting and debugging production systems
- Trident and highly sought after exactly-once processing techniques
This book concentrates on applying Storm to real-world use cases. While prior experience with Storm is not necessary, acquaintance with related Big Data problem solving is helpful. Basic understanding of Java or similar JVM language and concurrency is assumed.
ABOUT THE AUTHORS
Sean T. Allen, Peter Pathirana, and Matthew Jankowski are leaders on the development team at TheLadders, a high-volume, search-intensive web application. Sean is a seasoned architect with an abiding interest in distributed systems. You can follow Sean on Twitter at @SeanTAllen. Peter is a lead engineer specializing in search and recommendations platform architecture. Matthew has spent the last couple of years integrating Storm into TheLadders problem domain and continues to search for new uses for Storm in solving big data problems. You can follow Matthew on Twitter at @mattjanks16.
ABOUT THE EARLY ACCESS VERSION
This Early Access version of Storm Applied enables you to receive new chapters as they are being written. You can also interact with the authors to ask questions, provide feedback and errata, and help shape the final manuscript on the Author Online forum.
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