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Apache Flume: distributed log collection for Hadoop : design and implement a series of Flume agents to send streamed data into Hadoop /
If you are a Hadoop programmer who wants to learn about Flume to be able to move datasets into Hadoop in a timely and replicable manner, then this book is ideal for you. No prior knowledge about Apache Flume is necessary, but a basic knowledge of Hadoop and the Hadoop File System (HDFS) is assumed.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2015.
|
| Edición: | Second edition. |
| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Hoffman, Steve, |e author. | |
| 245 | 1 | 0 | |a Apache Flume: distributed log collection for Hadoop : |b design and implement a series of Flume agents to send streamed data into Hadoop / |c Steve Hoffman. |
| 246 | 3 | 0 | |a Design and implement a series of Flume agents to send streamed data into Hadoop |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2015. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 490 | 1 | |a Community experience distilled | |
| 588 | 0 | |a Online resource; title from cover page (Safari, viewed March 26, 2015). | |
| 500 | |a Includes index. | ||
| 505 | 0 | |a Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Overview and Architecture; Flume 0.9; Flume 1.X (Flume-NG); The problem with HDFS and streaming data/logs; Sources, channels, and sinks; Flume events; Interceptors, channel selectors, and sink processors; Tiered data collection (multiple flows and/or agents); The Kite SDK; Summary; Chapter 2: A Quick Start Guide to Flume; Downloading Flume; Flume in Hadoop distributions; An overview of the Flume configuration file; Starting up with ""Hello, World!""; Summary. | |
| 505 | 8 | |a Chapter 3: ChannelsThe memory channel; The file channel; Spillable Memory Channel; Summary; Chapter 4: Sinks and Sink Processors; HDFS sink; Path and filename; File rotation; Compression codecs; Event Serializers; Text output; Text with headers; Apache Avro; User-provided Avro schema; File type; SequenceFile; DataStream; CompressedStream; Timeouts and workers; Sink groups; Load balancing; Failover; MorphlineSolrSink; Morphline configuration files; Typical SolrSink configuration; Sink configuration; ElasticSearchSink; LogStash Serializer; Dynamic Serializer; Summary. | |
| 505 | 8 | |a Chapter 5: Sources and Channel SelectorsThe problem with using tail; The Exec source; Spooling Directory Source; Syslog sources; The syslog UDP source; The syslog TCP source; The multiport syslog TCP source; JMS source; Channel selectors; Replicating; Multiplexing; Summary; Chapter 6: Interceptors, ETL, and Routing; Interceptors; Timestamp; Host; Static; Regular expression filtering; Regular expression extractor; Morphline interceptor; Custom interceptors; The plugins directory; Tiering flows; The Avro source/sink; Compressing Avro; SSL Avro flows; The Thrift source/sink. | |
| 505 | 8 | |a Using command-line AvroThe Log4J appender; The Log4J load-balancing appender; The embedded agent; Configuration and startup; Sending data; Shutdown; Routing; Summary; Chapter 7: Putting it All Together; Web logs to searchable UI; Setting up the web server; Configuring log rotation to the spool directory; Setting up the target -- Elasticsearch; Setting up Flume on collector/relay; Setting up Flume on the client; Creating more search fields with an interceptor; Setting up a better user interface -- Kibana; Archiving to HDFS; Summary; Chapter 8: Monitoring Flume; Monitoring the agent process. | |
| 505 | 8 | |a MonitNagios; Monitoring performance metrics; Ganglia; Internal HTTP server; Custom monitoring hooks; Summary; Chapter 9: There Is No Spoon -- the Realities of Real-time Distributed Data Collection; Transport time versus log time; Time zones are evil; Capacity planning; Considerations for multiple data centers; Compliance and data expiry; Summary; Index. | |
| 520 | |a If you are a Hadoop programmer who wants to learn about Flume to be able to move datasets into Hadoop in a timely and replicable manner, then this book is ideal for you. No prior knowledge about Apache Flume is necessary, but a basic knowledge of Hadoop and the Hadoop File System (HDFS) is assumed. | ||
| 546 | |a English. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 630 | 0 | 0 | |a Apache Hadoop. |
| 630 | 0 | 7 | |a Apache Hadoop |2 fast |
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| 650 | 0 | |a File organization (Computer science) | |
| 650 | 6 | |a Traitement réparti. | |
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| 650 | 7 | |a File organization (Computer science) |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Hoffman, Steve. |t Apache Flume : Distributed Log Collection for Hadoop. |d Birmingham : Packt Publishing, ©2015 |z 9781784392178 |
| 830 | 0 | |a Community experience distilled. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781784392178/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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