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一、概述
1.通过搭建高可用flume来实现对数据的收集并存储到hdfs上，架构图如下：
二、配置Agent
1.cat flume-client.properties
#name the components on this agent 声明source、channel、sink的名称 a1.sources = r1 a1.sinks = k1 k2 a1.channels = c1 #Describe/configure the source 声明source的类型为通过tcp的方式监听本地端口5140 a1.sources.r1.type = syslogtcp a1.sources.r1.port = 5140 a1.sources.r1.host = localhost a1.sources.r1.channels = c1 #define sinkgroups 此处配置k1、k2的组策略，类型为均衡负载方式 a1.sinkgroups=g1 a1.sinkgroups.g1.sinks=k1 k2 a1.sinkgroups.g1.processor.type=load_balance a1.sinkgroups.g1.processor.backoff=true a1.sinkgroups.g1.processor.selector=round_robin #define the sink 1 数据流向，都是通过avro方式发到两台collector机器 a1.sinks.k1.type=avro a1.sinks.k1.hostname=hadoop1 a1.sinks.k1.port=5150 #define the sink 2 a1.sinks.k2.type=avro a1.sinks.k2.hostname=hadoop2 a1.sinks.k2.port=5150 # Use a channel which buffers events in memory 指定channel的类型为内存模式 a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1 a1.sinks.k2.channel=c1
#a2和a3的配置和a1相同
三、配置Collector
1.cat flume-server.properties
#name the components on this agent 声明source、channel、sink的名称 collector1.sources = r1 collector1.channels = c1 collector1.sinks = k1 # Describe the source 声明source的类型为avro collector1.sources.r1.type = avro collector1.sources.r1.port = 5150 collector1.sources.r1.bind = 0.0.0.0 collector1.sources.r1.channels = c1 # Describe channels c1 which buffers events in memory 指定channel的类型为内存模式 collector1.channels.c1.type = memory collector1.channels.c1.capacity = 1000 collector1.channels.c1.transactionCapacity = 100 # Describe the sink k1 to hdfs 指定sink数据流向hdfs collector1.sinks.k1.type = hdfs collector1.sinks.k1.channel = c1 collector1.sinks.k1.hdfs.path = hdfs://master/user/flume/log collector1.sinks.k1.hdfs.fileType = DataStream collector1.sinks.k1.hdfs.writeFormat = TEXT collector1.sinks.k1.hdfs.rollInterval = 300 collector1.sinks.k1.hdfs.filePrefix = %Y-%m-%d collector1.sinks.k1.hdfs.round = true collector1.sinks.k1.hdfs.roundValue = 5 collector1.sinks.k1.hdfs.roundUnit = minute collector1.sinks.k1.hdfs.useLocalTimeStamp = true
#collector2配置和collector1相同
四、启动
1.在Collector上启动fulme-ng
flume-ng agent -n collector1 -c conf -f /usr/local/flume/conf/flume-server.properties -Dflume.root.logger=INFO,console # -n 后面接配置文件中的Agent Name
2.在Agent上启动flume-ng
flume-ng agent -n a1 -c conf -f /usr/local/flume/conf/flume-client.properties -Dflume.root.logger=INFO,console
五、测试
[root@hadoop5 ~]# echo "hello" | nc localhost 5140 #需要安装nc
17/09/03 22:56:58 INFO source.AvroSource: Avro source r1 started. 17/09/03 22:59:09 INFO ipc.NettyServer: [id: 0x60551752, /192.168.100.15:34310 => /192.168.100.11:5150] OPEN 17/09/03 22:59:09 INFO ipc.NettyServer: [id: 0x60551752, /192.168.100.15:34310 => /192.168.100.11:5150] BOUND: /192.168.100.11:5150 17/09/03 22:59:09 INFO ipc.NettyServer: [id: 0x60551752, /192.168.100.15:34310 => /192.168.100.11:5150] CONNECTED: /192.168.100.15:34310 17/09/03 23:03:54 INFO hdfs.HDFSDataStream: Serializer = TEXT, UseRawLocalFileSystem = false 17/09/03 23:03:54 INFO hdfs.BucketWriter: Creating hdfs://master/user/flume/log/2017-09-03.1504494234038.tmp
六、总结
高可用flume-ng一般有两种模式：load_balance和failover。此次使用的是load_balance，failover的配置如下：
#set failover a1.sinkgroups.g1.processor.type = failover a1.sinkgroups.g1.processor.priority.k1 = 10 a1.sinkgroups.g1.processor.priority.k2 = 1 a1.sinkgroups.g1.processor.maxpenalty = 10000
一些常用的source、channel、sink类型如下：
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