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Getting Started with Storm : Continuous Streaming Computation with Twitter's Cluster Technology by Jonathan Leibiusky, Gabriel Eisbruch and Dario Simonassi (2012, Trade Paperback)

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About this product

Product Identifiers

PublisherO'reilly Media, Incorporated

ISBN-101449324010

ISBN-139781449324018

eBay Product ID (ePID)113127340

Product Key Features

Number of Pages106 Pages

Publication NameGetting Started with Storm : Continuous Streaming Computation with Twitter's Cluster Technology

LanguageEnglish

SubjectSystems Architecture / Distributed Systems & Computing, Databases / Data Warehousing, Data Processing, Databases / General

Publication Year2012

TypeTextbook

AuthorJonathan Leibiusky, Gabriel Eisbruch, Dario Simonassi

Subject AreaComputers

FormatTrade Paperback

Dimensions

Item Height0.2 in

Item Weight7 Oz

Item Length9.2 in

Item Width7 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN2012-472502

IllustratedYes

Table Of ContentPreface; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgements;Chapter 1: Basics; 1.1 The Components of Storm; 1.2 The Properties of Storm;Chapter 2: Getting Started; 2.1 Operation Modes; 2.2 Hello World Storm; 2.3 Creating Our First Topology; 2.4 Conclusion;Chapter 3: Topologies; 3.1 Stream Grouping; 3.2 LocalCluster versus StormSubmitter; 3.3 DRPC Topologies;Chapter 4: Spouts; 4.1 Reliable versus Unreliable Messages; 4.2 Getting Data; 4.3 Conclusion;Chapter 5: Bolts; 5.1 Bolt Lifecycle; 5.2 Bolt Structure; 5.3 Reliable versus Unreliable Bolts; 5.4 Multiple Streams; 5.5 Multiple Anchoring; 5.6 Using IBasicBolt to Ack Automatically;Chapter 6: A Real-Life Example; 6.1 The Node.js Web Application; 6.2 Starting the Node.js Web Application; 6.3 The Storm Topology; 6.4 The Redis Server; 6.5 Testing the Topology; 6.6 Notes on Scalability and Availability;Chapter 7: Using Non-JVM Languages with Storm; 7.1 The Multilang Protocol Specification;Chapter 8: Transactional Topologies; 8.1 The Design; 8.2 Transactions in Action; 8.3 Partitioned Transactional Spouts; 8.4 Opaque Transactional Topologies;Installing the Storm Client;Installing Storm Cluster;Real Life Example Setup; Installing Redis; Installing Node.js; Building and Testing; Running the Topology; Playing with the Example;

SynopsisEven as data is turning the world upside down, the next revolution is taking shape: realtime data analysis. Data tools like Hadoop are batch-oriented; they're good at taking a set of data, analyzing it, and giving you the result later. But these tools are not particularly good at handling streaming data: data that is constantly arriving, and results that are changing as fast as the data arrives. Storm is a new tool that solves that problem. This book gets you started with it., Even as big data is turning the world upside down, the next phase of the revolution is already taking shape: real-time data analysis. This hands-on guide introduces you to Storm, a distributed, JVM-based system for processing streaming data. Through simple tutorials, sample Java code, and a complete real-world scenario, you'll learn how to build fast, fault-tolerant solutions that process results as soon as the data arrives. Discover how easy it is to set up Storm clusters for solving various problems, including continuous data computation, distributed remote procedure calls, and data stream processing. Learn how to program Storm components: spouts for data input and bolts for data transformation Discover how data is exchanged between spouts and bolts in a Storm topology Make spouts fault-tolerant with several commonly used design strategies Explore bolts--their life cycle, strategies for design, and ways to implement them Scale your solution by defining each component's level of parallelism Study a real-time web analytics system built with Node.js, a Redis server, and a Storm topology Write spouts and bolts with non-JVM languages such as Python, Ruby, and Javascript, Even as data is turning the world upside down, the next revolution is taking shape: realtime data analysis. Data tools like Hadoop are batch-oriented; they're good at taking a set of data, analyzing it, and giving you the result later.

LC Classification NumberMLCM 2016/40329 (Q)