这里主要介绍几种常见的日志的source来源，包括监控文件型，监控文件内容增量，TCP和HTTP。
Spool类型
用于监控指定目录内数据变更，若有新文件，则将新文件内数据读取上传
在教你一步搭建Flume分布式日志系统最后有介绍此案例
Exec
EXEC执行一个给定的命令获得输出的源,如果要使用tail命令，必选使得file足够大才能看到输出内容
创建agent配置文件
# vi /usr/local/flume170/conf/exec_tail.conf
a1.sources =r1
a1.channels=c1 c2
a1.sinks=k1 k2
#Describe/configure the source
a1.sources.r1.type =exec
a1.sources.r1.channels=c1 c2
a1.sources.r1.command= tail -F /var/log/haproxy.log#Use a channel which buffers events in memory
a1.channels.c1.type =memory
a1.channels.c1.capacity= 1000
a1.channels.c1.transactionCapacity= 100
a1.channels.c2.type=file
a1.channels.c2.checkpointDir= /usr/local/flume170/checkpoint
a1.channels.c2.dataDirs= /usr/local/flume170/data#Describe the sink
a1.sinks.k1.type =logger
a1.sinks.k1.channel=c1
a1.sinks.k2.type=FILE_ROLL
a1.sinks.k2.channel=c2
a1.sinks.k2.sink.directory= /usr/local/flume170/files
a1.sinks.k2.sink.rollInterval= 0
启动flume agent a1
# /usr/local/flume170/bin/flume-ng agent -c . -f /usr/local/flume170/conf/exec_tail.conf -n a1 -Dflume.root.logger=INFO,console
生成足够多的内容在文件里
# for i in {1..100};do echo "exec tail$i" >> /usr/local/flume170/log_exec_tail;echo $i;sleep 0.1;done
在H32的控制台，可以看到以下信息：
Http
JSONHandler型
基于HTTP POST或GET方式的数据源，支持JSON、BLOB表示形式
创建agent配置文件
# vi /usr/local/flume170/conf/post_json.conf
a1.sources =r1
a1.channels=c1
a1.sinks=k1#Describe/configure the source
a1.sources.r1.type =org.apache.flume.source.http.HTTPSource
a1.sources.r1.port= 5142
a1.sources.r1.channels=c1#Use a channel which buffers events in memory
a1.channels.c1.type =memory
a1.channels.c1.capacity= 1000
a1.channels.c1.transactionCapacity= 100#Describe the sink
a1.sinks.k1.type =logger
a1.sinks.k1.channel= c1
启动flume agent a1
# /usr/local/flume170/bin/flume-ng agent -c . -f /usr/local/flume170/conf/post_json.conf -n a1 -Dflume.root.logger=INFO,console
生成JSON 格式的POST request
# curl -X POST -d '[{ "headers" :{"a" : "a1","b" : "b1"},"body" : "idoall.org_body"}]' http://localhost:8888
在H32的控制台，可以看到以下信息：
Tcp
Syslogtcp监听TCP的端口做为数据源
创建agent配置文件
# vi /usr/local/flume170/conf/syslog_tcp.conf
a1.sources =r1
a1.channels=c1
a1.sinks=k1#Describe/configure the source
a1.sources.r1.type =syslogtcp
a1.sources.r1.port= 5140
a1.sources.r1.host=H32
a1.sources.r1.channels=c1#Use a channel which buffers events in memory
a1.channels.c1.type =memory
a1.channels.c1.capacity= 1000
a1.channels.c1.transactionCapacity= 100#Describe the sink
a1.sinks.k1.type =logger
a1.sinks.k1.channel= c1
启动flume agent a1
# /usr/local/flume170/bin/flume-ng agent -c . -f /usr/local/flume170/conf/syslog_tcp.conf -n a1 -Dflume.root.logger=INFO,console
测试产生syslog
# echo "hello idoall.org syslog" | nc localhost 5140
在H32的控制台，可以看到以下信息：
Flume Sink Processors和Avro类型
Avro可以发送一个给定的文件给Flume，Avro 源使用AVRO RPC机制。
failover的机器是一直发送给其中一个sink，当这个sink不可用的时候，自动发送到下一个sink。channel的transactionCapacity参数不能小于sink的batchsiz
在H32创建Flume_Sink_Processors配置文件
# vi /usr/local/flume170/conf/Flume_Sink_Processors.conf
a1.sources =r1
a1.channels=c1 c2
a1.sinks=k1 k2#Describe/configure the source
a1.sources.r1.type =syslogtcp
a1.sources.r1.port= 5140a1.sources.r1.channels=c1 c2
a1.sources.r1.selector.type=replicating#Use a channel which buffers events in memory
a1.channels.c1.type =memory
a1.channels.c1.capacity= 1000
a1.channels.c1.transactionCapacity= 100
a1.channels.c2.type=memory
a1.channels.c2.capacity= 1000
a1.channels.c2.transactionCapacity= 100#Describe the sink
a1.sinks.k1.type =avro
a1.sinks.k1.channel=c1
a1.sinks.k1.hostname=H32
a1.sinks.k1.port= 5141a1.sinks.k2.type=avro
a1.sinks.k2.channel=c2
a1.sinks.k2.hostname=H33
a1.sinks.k2.port= 5141#这个是配置failover的关键，需要有一个sink groupa1.sinkgroups =g1
a1.sinkgroups.g1.sinks=k1 k2#处理的类型是failover
a1.sinkgroups.g1.processor.type =failover#优先级，数字越大优先级越高，每个sink的优先级必须不相同
a1.sinkgroups.g1.processor.priority.k1 = 5
a1.sinkgroups.g1.processor.priority.k2= 10#设置为10秒，当然可以根据你的实际状况更改成更快或者很慢
a1.sinkgroups.g1.processor.maxpenalty = 10000
在H32创建Flume_Sink_Processors_avro配置文件
# vi/usr/local/flume170/conf/Flume_Sink_Processors_avro.conf
a1.sources =r1
a1.channels=c1
a1.sinks=k1#Describe/configure the source
a1.sources.r1.type =avro
a1.sources.r1.channels=c1
a1.sources.r1.bind= 0.0.0.0
a1.sources.r1.port= 5141#Use a channel which buffers events in memory
a1.channels.c1.type =memory
a1.channels.c1.capacity= 1000
a1.channels.c1.transactionCapacity= 100#Describe the sink
a1.sinks.k1.type =logger
a1.sinks.k1.channel= c1
将2个配置文件复制到H33上一份
/usr/local/flume170# scp -r /usr/local/flume170/conf/Flume_Sink_Processors.conf H33:/usr/local/flume170/conf/Flume_Sink_Processors.conf
/usr/local/flume170# scp -r /usr/local/flume170/conf/Flume_Sink_Processors_avro.conf H33:/usr/local/flume170/conf/Flume_Sink_Processors_avro.conf
打开4个窗口，在H32和H33上同时启动两个flume agent
# /usr/local/flume170/bin/flume-ng agent -c . -f /usr/local/flume170/conf/Flume_Sink_Processors_avro.conf -n a1 -Dflume.root.logger=INFO,console
# /usr/local/flume170/bin/flume-ng agent -c . -f /usr/local/flume170/conf/Flume_Sink_Processors.conf -n a1 -Dflume.root.logger=INFO,console
然后在H32或H33的任意一台机器上，测试产生log
# echo "idoall.org test1 failover" | nc H32 5140
因为H33的优先级高，所以在H33的sink窗口，可以看到以下信息，而H32没有：
这时我们停止掉H33机器上的sink(ctrl+c)，再次输出测试数据
# echo "idoall.org test2 failover" | nc localhost 5140
可以在H32的sink窗口，看到读取到了刚才发送的两条测试数据：
我们再在H33的sink窗口中，启动sink：
# /usr/local/flume170/bin/flume-ng agent -c . -f /usr/local/flume170/conf/Flume_Sink_Processors_avro.conf -n a1 -Dflume.root.logger=INFO,console
输入两批测试数据：
# echo "idoall.org test3 failover" | nc localhost 5140 && echo "idoall.org test4 failover" | nc localhost 5140
在H33的sink窗口，我们可以看到以下信息，因为优先级的关系，log消息会再次落到H33上：
Load balancing Sink Processor
load balance type和failover不同的地方是，load balance有两个配置，一个是轮询，一个是随机。两种情况下如果被选择的sink不可用，就会自动尝试发送到下一个可用的sink上面。
在H32创建Load_balancing_Sink_Processors配置文件
# vi /usr/local/flume170/conf/Load_balancing_Sink_Processors.conf
a1.sources =r1
a1.channels=c1
a1.sinks=k1 k2#Describe/configure the sourcea1.sources.r1.type =syslogtcp
a1.sources.r1.port= 5140
a1.sources.r1.channels=c1#Use a channel which buffers events in memory
a1.channels.c1.type =memory
a1.channels.c1.capacity= 1000
a1.channels.c1.transactionCapacity= 100#Describe the sink
a1.sinks.k1.type =avro
a1.sinks.k1.channel=c1
a1.sinks.k1.hostname=H32
a1.sinks.k1.port= 5141
a1.sinks.k2.type=avro
a1.sinks.k2.channel=c1
a1.sinks.k2.hostname=H33
a1.sinks.k2.port= 5141#这个是配置failover的关键，需要有一个sink group
a1.sinkgroups =g1
a1.sinkgroups.g1.sinks=k1 k2#处理的类型是load_balance
a1.sinkgroups.g1.processor.type =load_balance
a1.sinkgroups.g1.processor.backoff=true
a1.sinkgroups.g1.processor.selector=round_robin
在H32创建Load_balancing_Sink_Processors_avro配置文件
# vi /usr/local/flume170/conf/Load_balancing_Sink_Processors_avro.conf
a1.sources =r1
a1.channels=c1
a1.sinks=k1#Describe/configure the source
a1.sources.r1.type =avro
a1.sources.r1.channels=c1
a1.sources.r1.bind= 0.0.0.0
a1.sources.r1.port= 5141#Use a channel which buffers events in memory
a1.channels.c1.type =memory
a1.channels.c1.capacity= 1000
a1.channels.c1.transactionCapacity= 100#Describe the sink
a1.sinks.k1.type =logger
a1.sinks.k1.channel= c1
将2个配置文件复制到H33上一份
/usr/local/flume170# scp -r /usr/local/flume170/conf/Load_balancing_Sink_Processors.conf H33:/usr/local/flume170/conf/Load_balancing_Sink_Processors.conf
/usr/local/flume170# scp -r /usr/local/flume170/conf/Load_balancing_Sink_Processors_avro.conf H33:/usr/local/flume170/conf/Load_balancing_Sink_Processors_avro.conf
打开4个窗口，在H32和H33上同时启动两个flume agent
# /usr/local/flume170/bin/flume-ng agent -c . -f /usr/local/flume170/conf/Load_balancing_Sink_Processors_avro.conf -n a1 -Dflume.root.logger=INFO,console
# /usr/local/flume170/bin/flume-ng agent -c . -f /usr/local/flume170/conf/Load_balancing_Sink_Processors.conf -n a1 -Dflume.root.logger=INFO,console
然后在H32或H33的任意一台机器上，测试产生log，一行一行输入，输入太快，容易落到一台机器上
# echo "idoall.org test1" | nc H32 5140
# echo "idoall.org test2" | nc H32 5140
# echo "idoall.org test3" | nc H32 5140
# echo "idoall.org test4" | nc H32 5140
在H32的sink窗口，可以看到以下信息
1. 14/08/10 15:35:29 INFO sink.LoggerSink: Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 32 idoall.org test2 }
2. 14/08/10 15:35:33 INFO sink.LoggerSink: Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 34 idoall.org test4 }
在H33的sink窗口，可以看到以下信息：
1. 14/08/10 15:35:27 INFO sink.LoggerSink: Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 31 idoall.org test1 }
2. 14/08/10 15:35:29 INFO sink.LoggerSink: Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 33 idoall.org test3 }
说明轮询模式起到了作用。
以上均是建立在H32和H33能互通，且Flume配置都正确的情况下运行，且都是非常简单的场景应用，值得注意的一点是Flume说是日志收集，其实还可以广泛的认为“日志”可以当作是信息流，不局限于认知的日志。
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