Flume 扇入（fanin）扇出（fanout）案例

案例三、Flume 与 Flume 之间数据传递，多 Flume 汇总数据到单 Flume。

目标：flume-fanin-1监控某一个端口的数据流，flume-fanin-2 监控文件，flume-fanin-1 和 flume-fanin-2 将数据发送给 flume-fanin-3，flume-fanin-3 将最终数据写入到HDFS。
分步实现
1.创建 flume-fanin-1.conf，用于监控端口 55555，同时 sink 数据到 flume-fanin-3

# 1 agent
a1.sources = netcat-a1
a1.sinks = avro113
a1.channels = c1

# 2 source
a1.sources.netcat-a1.type = netcat
a1.sources.netcat-a1.bind = bigdata111
a1.sources.netcat-a1.port = 55555

#3 sink
a1.sinks.avro113.type = avro
a1.sinks.avro113.hostname = bigdata113
a1.sinks.avro113.port = 4141

# 4 channel
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

# 5 Bind
a1.sources.netcat-a1.channels = c1
a1.sinks.avro113.channel = c1

2.创建 flume-fanni-2.conf 用于监控文件，同时 Sink 到 flume-fanin-3。

# 1 agent
a2.sources = tail-file
a2.sinks = avro13
a2.channels = c1

# 2 source
a2.sources.tail-file.type = exec
a2.sources.tail-file.command = tail -F /opt/wind
a2.sources.tail-file.shell = /bin/bash -c

# 3 sink
a2.sinks.avro13.type = avro
a2.sinks.avro13.hostname = bigdata113
a2.sinks.avro13.port = 4141

# 4 channel
a2.channels.c1.type = memory
a2.channels.c1.capacity = 1000
a2.channels.c1.transactionCapacity = 100

# 5. Bind
a2.sources.tail-file.channels = c1
a2.sinks.avro13.channel = c1

创建 flume-fanin-3.conf 用于接收 flume-fanini-1 和 flume-fanin-2 发送来的数据流，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

**先启动 flume-fanin-3 **

案例四、Flume 与 Flume 之间数据传递：单 Flume 多 Channel、Sink

目标：使用 flume-fanout-1 监控文件变动，flume-fanout-1将变动数据传递给 flume-fanout-2，flume-fanout-2 负责储存到 HDFS。同时， flume-fanout-1 将变动内容传递给 flume-fanout-3，flume-fanout-3 负责输出到 local。
分步实现：
1.创建 flume-fanout-1.conf 用于监控某个文件的变动，同时产生两个 Channel 和两个 Sink 分别输出到 flume-fanout-2和 flume-fanout-3：

# 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/wind
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-fanout-2.conf，用于接收 flume-fanout-1的event ，同时产生1个 sink，将数据传输给 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-fanout-3.conf，用于接收 flume-fanout-1 的 event，同时产生1个 Chanel 和1个 Sink，将数据传输到本地目录。

#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

尖叫提示：输出的本地目录必须是已经存在的目录，如果该目录不存在，并不会创建新的目录。