1.案例場景

A、B兩臺日志服務(wù)機器實時生產(chǎn)日志主要類型為access.log、nginx.log、web.log 

現(xiàn)在要求：
把A、B 機器中的access.log、nginx.log、web.log 采集匯總到C機器上然后統(tǒng)一收集到hdfs中。
但是在hdfs中要求的目錄為：
/source/logs/access/20160101/**
/source/logs/nginx/20160101/**
/source/logs/web/20160101/**

2．場景分析

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3．?dāng)?shù)據(jù)流程處理分析

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4．功能實現(xiàn)

①　在服務(wù)器A和服務(wù)器B上

創(chuàng)建配置文件 exec_source_avro_sink.conf

# Name the components on this agent
a1.sources = r1 r2 r3
a1.sinks = k1
a1.channels = c1

# Describe/configure the source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /root/data/access.log
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = static
##  static攔截器的功能就是往采集到的數(shù)據(jù)的header中插入自##   己定義的key-value對
a1.sources.r1.interceptors.i1.key = type
a1.sources.r1.interceptors.i1.value = access

a1.sources.r2.type = exec
a1.sources.r2.command = tail -F /root/data/nginx.log
a1.sources.r2.interceptors = i2
a1.sources.r2.interceptors.i2.type = static
a1.sources.r2.interceptors.i2.key = type
a1.sources.r2.interceptors.i2.value = nginx
a1.sources.r3.type = exec
a1.sources.r3.command = tail -F /root/data/web.log
a1.sources.r3.interceptors = i3
a1.sources.r3.interceptors.i3.type = static
a1.sources.r3.interceptors.i3.key = type
a1.sources.r3.interceptors.i3.value = web

# Describe the sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = 192.168.200.101
a1.sinks.k1.port = 41414

# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 20000
a1.channels.c1.transactionCapacity = 10000

# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sources.r2.channels = c1
a1.sources.r3.channels = c1
a1.sinks.k1.channel = c1

②　在服務(wù)器C上創(chuàng)建配置文件 avro_source_hdfs_sink.conf 文件內(nèi)容為

#定義agent名， source、channel、sink的名稱
a1.sources = r1
a1.sinks = k1
a1.channels = c1

#定義source
a1.sources.r1.type = avro
a1.sources.r1.bind = mini2
a1.sources.r1.port =41414

#添加時間攔截器
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = 
org.apache.flume.interceptor.TimestampInterceptor$Builder

#定義channels
a1.channels.c1.type = memory
a1.channels.c1.capacity = 20000
a1.channels.c1.transactionCapacity = 10000

#定義sink
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path=hdfs://192.168.200.101:9000/source/logs/%{type}/%Y%m%d
a1.sinks.k1.hdfs.filePrefix =events
a1.sinks.k1.hdfs.fileType = DataStream
a1.sinks.k1.hdfs.writeFormat = Text
#時間類型
a1.sinks.k1.hdfs.useLocalTimeStamp = true
#生成的文件不按條數(shù)生成
a1.sinks.k1.hdfs.rollCount = 0
#生成的文件按時間生成
a1.sinks.k1.hdfs.rollInterval = 30
#生成的文件按大小生成
a1.sinks.k1.hdfs.rollSize  = 10485760
#批量寫入hdfs的個數(shù)
a1.sinks.k1.hdfs.batchSize = 10000
flume操作hdfs的線程數(shù)（包括新建，寫入等）
a1.sinks.k1.hdfs.threadsPoolSize=10
#操作hdfs超時時間
a1.sinks.k1.hdfs.callTimeout=30000

#組裝source、channel、sink
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

③　配置完成之后，在服務(wù)器A和B上的/root/data有數(shù)據(jù)文件access.log、nginx.log、web.log。先啟動服務(wù)器C上的flume，啟動命令
在flume安裝目錄下執(zhí)行 ：

bin/flume-ng agent -c conf -f conf/avro_source_hdfs_sink.conf -name a1 -Dflume.root.logger=DEBUG,console    

然后在啟動服務(wù)器上的A和B，啟動命令
在flume安裝目錄下執(zhí)行 ：

bin/flume-ng agent -c conf -f conf/exec_source_avro_sink.conf -name a1 -Dflume.root.logger=DEBUG,console