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@@ -1,16 +1,18 @@
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a1.channels.c1.type = org.apache.flume.channel.kafka.KafkaChannel
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-a1.channels.c1.kafka.bootstrap.servers = 10.26.22.124:8092,10.26.22.121:8092,10.26.22.120:8092,10.26.22.102:8092,10.26.22.122:8092
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-a1.channels.c1.kafka.topic = api_searchlog_novel
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+a1.channels.c1.kafka.bootstrap.servers = 10.26.27.71:8092,10.26.22.121:8092,10.26.22.120:8092,10.26.22.102:8092,10.26.22.122:8092
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+a1.channels.c1.kafka.topic = api_easou_com_nginx
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a1.channels.c1.kafka.consumer.group.id = flume2kafka
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+
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# Define an Avro source called r1 on a1 and tell it
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# to bind to 0.0.0.0:41414. Connect it to channel c1.
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#注意这里分出2个channel出来,一个是到k1,一个到k2
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#如果这里只出一个channel c1的话,下面2个sink channel都为c1,则消息就会随机分到2个sink上,而不是2个sink都有一份
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a1.sources.r1.channels = c1
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a1.sources.r1.type = exec
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-a1.sources.r1.command = tail -F /log/resin-books/words/access.log
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+a1.sources.r1.command = tail -F /data/log/resin-books/bo/visit/access.log
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a1.sources.r1.restart = true
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-a1.sources.r1.batchSize = 10000
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+a1.sources.r1.selector.optional = c1
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+a1.sources.r1.batchSize = 100000
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a1.sources.r1.interceptors = i1 i2 i3
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a1.sources.r1.interceptors.i1.type = timestamp
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a1.sources.r1.interceptors.i2.type = host
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@@ -23,51 +25,6 @@ a1.sources.r1.interceptors.i3.excludeEvents = true
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# Finally, now that we've defined all of our components, tell
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# a1 which ones we want to activate.
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-a1.sources = r1 r2 r3
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-a1.channels = c1 c2 c3
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-a1.sinks =
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-
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-#########searchrecord apirs的BI统计搜索日志
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-a1.channels.c2.type = org.apache.flume.channel.kafka.KafkaChannel
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-a1.channels.c2.kafka.bootstrap.servers = 10.26.22.124:8092,10.26.22.121:8092,10.26.22.120:8092,10.26.22.102:8092,10.26.22.122:8092
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-a1.channels.c2.kafka.topic = api_searchrecord_bi
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-a1.channels.c2.kafka.consumer.group.id = flume2kafka
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-# Define an Avro source called r1 on a1 and tell it
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-#注意这里分出2个channel出来,一个是到k1,一个到k2
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-#如果这里只出一个channel c1的话,下面2个sink channel都为c1,则消息就会随机分到2个sink上,而不是2个sink都有一份
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-a1.sources.r2.channels = c2
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-a1.sources.r2.type = exec
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-a1.sources.r2.command = tail -F /log/resin-books/search/searchrecord.log
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-a1.sources.r2.restart = true
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-a1.sources.r2.batchSize = 10000
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-a1.sources.r2.interceptors = i1 i2 i3
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-a1.sources.r2.interceptors.i1.type = timestamp
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-a1.sources.r2.interceptors.i2.type = host
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-a1.sources.r2.interceptors.i2.useIP = false
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-a1.sources.r2.interceptors.i3.type = regex_filter
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-a1.sources.r2.interceptors.i3.regex = ^\\s*$
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-a1.sources.r2.interceptors.i3.excludeEvents = true
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-#默认是replicationg,还有multiplexer
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-#a1.sources.r1.selector.type = replicationg
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-
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-#####bookincharge
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-a1.channels.c3.type = org.apache.flume.channel.kafka.KafkaChannel
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-a1.channels.c3.kafka.bootstrap.servers = 10.26.22.124:8092,10.26.22.121:8092,10.26.22.120:8092,10.26.22.102:8092,10.26.22.122:8092
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-a1.channels.c3.kafka.topic = api_bookincharge_novel
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-a1.channels.c3.kafka.consumer.group.id = flume2kafka
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-# Define an Avro source called r1 on a1 and tell it
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-# to bind to 0.0.0.0:41414. Connect it to channel c1.
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-a1.sources.r3.channels = c3
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-a1.sources.r3.type = exec
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-a1.sources.r3.command = tail -F /data/log/bookincharge/bookincharge.log
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-a1.sources.r3.restart = true
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-a1.sources.r3.batchSize = 10000
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-a1.sources.r3.interceptors = i1 i2 i3
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-a1.sources.r3.interceptors.i1.type = timestamp
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-a1.sources.r3.interceptors.i2.type = host
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-a1.sources.r3.interceptors.i2.useIP = false
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-a1.sources.r3.interceptors.i3.type = regex_filter
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-a1.sources.r3.interceptors.i3.regex = ^\\s*$
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-a1.sources.r3.interceptors.i3.excludeEvents = true
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-#默认是replicationg,还有multiplexer
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-#a1.sources.r2.selector.type = replicationg
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+a1.sources = r1
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+a1.channels = c1
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+a1.sinks =
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