目錄
Flume的扇入與扇出
之前的案例都只有一個Flume的客戶端工作，其實Flume之間也可以進行數據傳遞，這樣的傳遞我們可以稱爲扇入和扇出。
案例四：單Flume多Channel、Sink（扇出）
目標：使用flume1監控文件變動，flume1將變動內容傳遞給flume-2，flume-2負責存儲到HDFS。同時flume1將變動內容傳遞給flume-3，flume-3負責輸出到local。由於整個流程圖的出口像“扇子”一樣，所以稱爲扇出。
實現過程：
（1）創建flume1.conf，用於監控某文件的變動，同時產生兩個channel和sink分別輸送給flume2和flume3。
# 1.agent source->channel對應關係1/n sink->channel對應關係1/1
a1.sources = r1
a1.sinks = k1 k2
a1.channels = c1 c2
# 將數據流複製給多個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) 創建flume-2.conf，用於接收flume1的event，同時產生1個channel和1個sink，將數據輸送給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
#上傳文件的前綴
a2.sinks.k1.hdfs.filePrefix = flume2-
#是否按照時間滾動文件夾
a2.sinks.k1.hdfs.round = true
#多少時間單位創建一個新的文件夾
a2.sinks.k1.hdfs.roundValue = 1
#重新定義時間單位
a2.sinks.k1.hdfs.roundUnit = hour
#是否使用本地時間戳
a2.sinks.k1.hdfs.useLocalTimeStamp = true
#積攢多少個Event才flush到HDFS一次
a2.sinks.k1.hdfs.batchSize = 100
#設置文件類型，可支持壓縮
a2.sinks.k1.hdfs.fileType = DataStream
#多久生成一個新的文件
a2.sinks.k1.hdfs.rollInterval = 600
#設置每個文件的滾動大小大概是128M
a2.sinks.k1.hdfs.rollSize = 134217700
#文件的滾動與Event數量無關
a2.sinks.k1.hdfs.rollCount = 0
#最小副本數
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）創建flume-3.conf，用於接收flume1的event，同時產生1個channel和1個sink，將數據輸送給本地目錄：
#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
#備註：此處的文件夾需要先創建好
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）執行測試：分別開啓對應flume-job（依次啓動flume1，flume-2，flume-3），同時產生文件變動並觀察結果：
#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）結果
a.說明flume2的監控產生效果
b.查看flume3的記錄文件
案例五：多Flume彙總數據到單Flume
目標：flume11監控文件hive.log，flume-22監控某一個端口的數據流，flume11與flume-22將數據發送給flume-33，flume33將最終數據寫入到HDFS。
實現：
（1）創建flume11.conf，用於監控hive.log文件，同時sink數據到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）創建flume-22.conf，用於監控端口44444數據流，同時sink數據到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）創建flume33.conf，用於接收flume11與flume22發送過來的數據流，最終合併後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
#上傳文件的前綴
a3.sinks.k1.hdfs.filePrefix = flume3-
#是否按照時間滾動文件夾
a3.sinks.k1.hdfs.round = true
#多少時間單位創建一個新的文件夾
a3.sinks.k1.hdfs.roundValue = 1
#重新定義時間單位
a3.sinks.k1.hdfs.roundUnit = hour
#是否使用本地時間戳
a3.sinks.k1.hdfs.useLocalTimeStamp = true
#積攢多少個Event才flush到HDFS一次
a3.sinks.k1.hdfs.batchSize = 100
#設置文件類型，可支持壓縮
a3.sinks.k1.hdfs.fileType = DataStream
#多久生成一個新的文件
a3.sinks.k1.hdfs.rollInterval = 600
#設置每個文件的滾動大小大概是128M
a3.sinks.k1.hdfs.rollSize = 134217700
#文件的滾動與Event數量無關
a3.sinks.k1.hdfs.rollCount = 0
#最小冗餘數
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