目录
Flume的扇入与扇出
之前的案例都只有一个Flume的客户端工作，其实Flume之间也可以进行数据传递，这样的传递我们可以称为扇入和扇出。
案例四：单Flume多Channel、Sink（扇出）
目标：使用flume1监控文件变动，flume1将变动内容传递给flume-2，flume-2负责存储到HDFS。同时flume1将变动内容传递给flume-3，flume-3负责输出到local。由于整个流程图的出口像“扇子”一样，所以称为扇出。
实现过程：
（1）创建flume1.conf，用于监控某文件的变动，同时产生两个channel和sink分别输送给flume2和flume3。
# 1.agent source->channel对应关系1/n sink->channel对应关系1/1
a1.sources = r1
a1.sinks = k1 k2
a1.channels = c1 c2
# 将数据流复制给多个channel
a1.sources.r1.selector.type = replicating
# 2.source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /opt/stephenyou
a1.sources.r1.shell = /bin/bash -c
# 3.sink1
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = bigdata112
a1.sinks.k1.port = 4141
# sink2
a1.sinks.k2.type = avro
a1.sinks.k2.hostname = bigdata113
a1.sinks.k2.port = 4141
# 4.channel—1
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
# 4.channel—2
a1.channels.c2.type = memory
a1.channels.c2.capacity = 1000
a1.channels.c2.transactionCapacity = 100
# Bind the source and sink to the channel
a1.sources.r1.channels = c1 c2
a1.sinks.k1.channel = c1
a1.sinks.k2.channel = c2
（2) 创建flume-2.conf，用于接收flume1的event，同时产生1个channel和1个sink，将数据输送给hdfs：
# 1 agent
a2.sources = r1
a2.sinks = k1
a2.channels = c1
# 2 source
a2.sources.r1.type = avro
a2.sources.r1.bind = bigdata112
a2.sources.r1.port = 4141
# 3 sink
a2.sinks.k1.type = hdfs
a2.sinks.k1.hdfs.path = hdfs://bigdata111:9000/flume2/%H
#上传文件的前缀
a2.sinks.k1.hdfs.filePrefix = flume2-
#是否按照时间滚动文件夹
a2.sinks.k1.hdfs.round = true
#多少时间单位创建一个新的文件夹
a2.sinks.k1.hdfs.roundValue = 1
#重新定义时间单位
a2.sinks.k1.hdfs.roundUnit = hour
#是否使用本地时间戳
a2.sinks.k1.hdfs.useLocalTimeStamp = true
#积攒多少个Event才flush到HDFS一次
a2.sinks.k1.hdfs.batchSize = 100
#设置文件类型，可支持压缩
a2.sinks.k1.hdfs.fileType = DataStream
#多久生成一个新的文件
a2.sinks.k1.hdfs.rollInterval = 600
#设置每个文件的滚动大小大概是128M
a2.sinks.k1.hdfs.rollSize = 134217700
#文件的滚动与Event数量无关
a2.sinks.k1.hdfs.rollCount = 0
#最小副本数
a2.sinks.k1.hdfs.minBlockReplicas = 1
# 4 channel
a2.channels.c1.type = memory
a2.channels.c1.capacity = 1000
a2.channels.c1.transactionCapacity = 100
#5 Bind
a2.sources.r1.channels = c1
a2.sinks.k1.channel = c1
（3）创建flume-3.conf，用于接收flume1的event，同时产生1个channel和1个sink，将数据输送给本地目录：
#1 agent
a3.sources = r1
a3.sinks = k1
a3.channels = c1
# 2 source
a3.sources.r1.type = avro
a3.sources.r1.bind = bigdata113
a3.sources.r1.port = 4141
#3 sink
a3.sinks.k1.type = file_roll
#备注：此处的文件夹需要先创建好
a3.sinks.k1.sink.directory = /opt/flume3
# 4 channel
a3.channels.c1.type = memory
a3.channels.c1.capacity = 1000
a3.channels.c1.transactionCapacity = 100
# 5 Bind
a3.sources.r1.channels = c1
a3.sinks.k1.channel = c1
（4）执行测试：分别开启对应flume-job（依次启动flume1，flume-2，flume-3），同时产生文件变动并观察结果：
#bigadata111
flume-ng agent --conf conf/ --name a1 --conf-file myconf/flume1.conf -Dflume.root.logger==INFO,console
#bigdata112
flume-ng agent --conf conf/ --name a2 --conf-file myconf/flume2.conf
#bigdata113
flume-ng agent --conf conf/ --name a3 --conf-file myconf/flume3.conf
（5）结果
a.说明flume2的监控产生效果
b.查看flume3的记录文件
案例五：多Flume汇总数据到单Flume
目标：flume11监控文件hive.log，flume-22监控某一个端口的数据流，flume11与flume-22将数据发送给flume-33，flume33将最终数据写入到HDFS。
实现：
（1）创建flume11.conf，用于监控hive.log文件，同时sink数据到flume-33：
# 1 agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# 2 source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /opt/stephenyou
a1.sources.r1.shell = /bin/bash -c
# 3 sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = bigdata113
a1.sinks.k1.port = 4141
# 4 channel
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
# 5. Bind
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
（2）创建flume-22.conf，用于监控端口44444数据流，同时sink数据到flume-33：
# 1 agent
a2.sources = r1
a2.sinks = k1
a2.channels = c1
# 2 source
a2.sources.r1.type = netcat
a2.sources.r1.bind = bigdata112
a2.sources.r1.port = 44444
#3 sink
a2.sinks.k1.type = avro
a2.sinks.k1.hostname = bigdata113
a2.sinks.k1.port = 4141
# 4 channel
a2.channels.c1.type = memory
a2.channels.c1.capacity = 1000
a2.channels.c1.transactionCapacity = 100
# 5 Bind
a2.sources.r1.channels = c1
a2.sinks.k1.channel = c1
（3）创建flume33.conf，用于接收flume11与flume22发送过来的数据流，最终合并后sink到HDFS
# 1 agent
a3.sources = r1
a3.sinks = k1
a3.channels = c1
# 2 source
a3.sources.r1.type = avro
a3.sources.r1.bind = bigdata113
a3.sources.r1.port = 4141
# 3 sink
a3.sinks.k1.type = hdfs
a3.sinks.k1.hdfs.path = hdfs://bigdata111:9000/flume3/%H
#上传文件的前缀
a3.sinks.k1.hdfs.filePrefix = flume3-
#是否按照时间滚动文件夹
a3.sinks.k1.hdfs.round = true
#多少时间单位创建一个新的文件夹
a3.sinks.k1.hdfs.roundValue = 1
#重新定义时间单位
a3.sinks.k1.hdfs.roundUnit = hour
#是否使用本地时间戳
a3.sinks.k1.hdfs.useLocalTimeStamp = true
#积攒多少个Event才flush到HDFS一次
a3.sinks.k1.hdfs.batchSize = 100
#设置文件类型，可支持压缩
a3.sinks.k1.hdfs.fileType = DataStream
#多久生成一个新的文件
a3.sinks.k1.hdfs.rollInterval = 600
#设置每个文件的滚动大小大概是128M
a3.sinks.k1.hdfs.rollSize = 134217700
#文件的滚动与Event数量无关
a3.sinks.k1.hdfs.rollCount = 0
#最小冗余数
a3.sinks.k1.hdfs.minBlockReplicas = 1
# 4 channel
a3.channels.c1.type = memory
a3.channels.c1.capacity = 1000
a3.channels.c1.transactionCapacity = 100
# 5 Bind
a3.sources.r1.channels = c1
a3.sinks.k1.channel = c1