從flume到kafka，日誌收集

實時日誌分析：

本篇文章主要測試 從flume到kafka的日誌收集，storm日誌分析，學習中！

flume 配置檔案

#collector

collector.sources=cs
collector.sinks=ck hbasesink
collector.channels=cc
hbasechannel
collector.sources.cs.type =exec
collector.sources.cs.command = tail -f /data/hudonglogs/self/channel.log
collector.sources.cs.channels=cc
hbasechannel
collector.channels.
cc.type =memory
collector.channels.
cc.capacity = 1000
collector.channels.
cc.transactioncapacity = 100
collector.channels.hbasechannel.type =memory
collector.channels.hbasechannel.capacity = 1000
collector.channels.hbasechannel.transactioncapacity = 100
#sink kafka
collector.sinks.ck.type =org.apache.flume.sink.kafka.kafkasink
collector.sinks.ck.topic =logs
collector.sinks.ck.brokerlist = localhost:9092
collector.sinks.ck.requiredacks = 1
collector.sinks.ck.batchsize = 20
collector.sinks.ck.channel = cc
#hbase sink
collector.sinks.hbasesink.type =asynchbase
collector.sinks.hbasesink.channel =hbasechannel
collector.sinks.hbasesink.table =logs
collector.sinks.hbasesink.columnfamily =content
collector.sinks.hbasesink.batchsize = 5

kafka consumer

public

class kafkaconsumer extends
thread 
private
consumerconfig createconsumerconfig() 
@override
public
void
run()  
catch
(interruptedexception e) }}
}

hbase檢視：

以上所有環境都是單節點部署！

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