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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.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Hoffman, Steve (Autor)
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)
Tabla de Contenidos:
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.