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Mastering Apache Storm : processing big data streams in real time /
Master the intricacies of Apache Storm and develop real-time stream processing applications with ease About This Book Exploit the various real-time processing functionalities offered by Apache Storm such as parallelism, data partitioning, and more Integrate Storm with other Big Data technologies lik...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2017.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 1 | 0 | |a Mastering Apache Storm : |b processing big data streams in real time / |c Ankit Jain. |
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| 505 | 0 | |a Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Real-Time Processing and Storm Introduction; Apache Storm; Features of Storm; Storm components; Nimbus; Supervisor nodes; The ZooKeeper cluster; The Storm data model; Definition of a Storm topology; Operation modes in Storm; Programming languages; Summary; Chapter 2: Storm Deployment, Topology Development, and Topology Options; Storm prerequisites; Installing Java SDK 7; Deployment of the ZooKeeper cluster; Setting up the Storm cluster. | |
| 505 | 8 | |a Developing the hello world exampleThe different options of the Storm topology; Deactivate; Activate; Rebalance; Kill; Dynamic log level settings; Walkthrough of the Storm UI; Cluster Summary section; Nimbus Summary section; Supervisor Summary section; Nimbus Configuration section; Topology Summary section; Dynamic log level settings; Updating the log level from the Storm UI; Updating the log level from the Storm CLI; Summary; Chapter 3: Storm Parallelism and Data Partitioning; Parallelism of a topology; Worker process; Executor; Task; Configure parallelism at the code level. | |
| 505 | 8 | |a Worker process, executor, and task distributionRebalance the parallelism of a topology; Rebalance the parallelism of a SampleStormClusterTopology topology; Different types of stream grouping in the Storm cluster; Shuffle grouping; Field grouping; All grouping; Global grouping; Direct grouping; Local or shuffle grouping; None grouping; Custom grouping; Guaranteed message processing; Tick tuple; Summary; Chapter 4: Trident Introduction; Trident introduction; Understanding Trident's data model; Writing Trident functions, filters, and projections; Trident function; Trident filter. | |
| 505 | 8 | |a Trident projectionTrident repartitioning operations; Utilizing shuffle operation; Utilizing partitionBy operation; Utilizing global operation; Utilizing broadcast operation; Utilizing batchGlobal operation; Utilizing partition operation; Trident aggregator; partitionAggregate; aggregate; ReducerAggregator; Aggregator; CombinerAggregator; persistentAggregate; Aggregator chaining; Utilizing the groupBy operation; When to use Trident; Summary; Chapter 5: Trident Topology and Uses; Trident groupBy operation; groupBy before partitionAggregate; groupBy before aggregate; Non-transactional topology. | |
| 505 | 8 | |a Trident hello world topologyTrident state; Distributed RPC; When to use Trident; Summary; Chapter 6: Storm Scheduler; Introduction to Storm scheduler; Default scheduler; Isolation scheduler; Resource-aware scheduler; Component-level configuration; Memory usage example; CPU usage example; Worker-level configuration; Node-level configuration; Global component configuration; Custom scheduler; Configuration changes in the supervisor node; Configuration setting at component level; Writing a custom supervisor class; Converting component IDs to executors; Converting supervisors to slots. | |
| 500 | |a Registering a CustomScheduler class. | ||
| 520 | |a Master the intricacies of Apache Storm and develop real-time stream processing applications with ease About This Book Exploit the various real-time processing functionalities offered by Apache Storm such as parallelism, data partitioning, and more Integrate Storm with other Big Data technologies like Hadoop, HBase, and Apache Kafka An easy-to-understand guide to effortlessly create distributed applications with Storm Who This Book Is For If you are a Java developer who wants 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. What You Will Learn Understand the core concepts of Apache Storm and real-time processing Follow the steps to deploy multiple nodes of Storm Cluster Create Trident topologies to support various message-processing semantics Make your cluster sharing effective using Storm scheduling Integrate Apache Storm with other Big Data technologies such as Hadoop, HBase, Kafka, and more Monitor the health of your Storm cluster In Detail 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. Style and approach This easy-to-f ... | ||
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