Flume之进阶（一）

Put事务:

• doPut:将批数据先写入临时缓冲区putList
• doCommit:检查channel内存队列是否足够合并
• doRollback:channel内存队列空间不足，回滚数据

Take事务:

• doTake:将数据取到临时缓冲区takeList，并将数据发送到HDFS
• doCommit:如果数据全部发送成功，则清除临时缓冲区takeList
• doRollback:数据发送过程中如果出现异常，rollback将临时缓冲区t akeList中的数据归还给channel内存队列

SinkProcessor:

• DefaultSinkProcessor 对应的是单个的 Sink
• LoadBalancingSinkProcessor对应的是 Sink Group，可以实现负
  载均衡的功能
• FailoverSinkProcessor 对应的是 Sink Group，可以实现错误恢复的功能

此模式不建议桥接过多的 flume 数量， flume 数量过多不仅会影响传输速
率，而且一旦传输过程中某个节点 flume 宕机，会影响整个传输系统

Flume 支持将事件流向一个或者多个目的地。这种模式可以将相同数据复制到多个
channel 中，或者将不同数据分发到不同的 channel 中，sink 可以选择传送到不同的目的地

使用 Flume-1 监控文件变动，Flume-1 将变动内容传递给 Flume-2，Flume-2 负责存储到 HDFS。同时 Flume-1 将变动内容传递给 Flume-3，Flume-3 负责输出到 Local FileSystem。

1. 准备工作

   • 在/opt/module/flume-1.9.0/jobs 目录下创建 group1 文件夹

     [codecat@hadoop102 jobs]$ mkdir group1
     

   • 在/opt/module/datas/目录下创建 flume3 文件夹

     [codecat@hadoop102 datas]$ mkdir flume3
     

2. 创建 flume-file-flume.conf

   配置 1 个接收日志文件的 source 和两个 channel、两个 sink，分别输送给 flume-flume-hdfs 和 flume-flume-dir。添加如下内容

   # Name the components on this agent
   a1.sources = r1
   a1.sinks = k1 k2
   a1.channels = c1 c2
   # 将数据流复制给所有 channel
   a1.sources.r1.selector.type = replicating
   # Describe/configure the source
   a1.sources.r1.type = exec
   a1.sources.r1.command = tail -F /opt/module/hive-3.1.2/logs/hive.log
   a1.sources.r1.shell = /bin/bash -c
   # Describe the sink
   # sink 端的 avro 是一个数据发送者
   a1.sinks.k1.type = avro
   a1.sinks.k1.hostname = hadoop102
   a1.sinks.k1.port = 4141
   a1.sinks.k2.type = avro
   a1.sinks.k2.hostname = hadoop102
   a1.sinks.k2.port = 4142
   # Describe the channel
   a1.channels.c1.type = memory
   a1.channels.c1.capacity = 1000
   a1.channels.c1.transactionCapacity = 100
   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
   

3. 创建 flume-flume-hdfs.conf

   配置上级 Flume 输出的 Source，输出是到 HDFS 的 Sink。添加如下内容

   # Name the components on this agent
   a2.sources = r1
   a2.sinks = k1
   a2.channels = c1
   # Describe/configure the source
   # source 端的 avro 是一个数据接收服务
   a2.sources.r1.type = avro
   a2.sources.r1.bind = hadoop102
   a2.sources.r1.port = 4141
   # Describe the sink
   a2.sinks.k1.type = hdfs
   a2.sinks.k1.hdfs.path = hdfs://hadoop102:8020/flume2/%Y%m%d/%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 = 30
   #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
   # Describe the channel
   a2.channels.c1.type = memory
   a2.channels.c1.capacity = 1000
   a2.channels.c1.transactionCapacity = 100
   # Bind the source and sink to the channel
   a2.sources.r1.channels = c1
   a2.sinks.k1.channel = c1
   

4. 创建 flume-flume-dir.conf

   配置上级 Flume 输出的 Source，输出是到本地目录的 Sink。添加如下内容

   # Name the components on this agent
   a3.sources = r1
   a3.sinks = k1
   a3.channels = c2
   # Describe/configure the source
   a3.sources.r1.type = avro
   a3.sources.r1.bind = hadoop102
   a3.sources.r1.port = 4142
   # Describe the sink
   a3.sinks.k1.type = file_roll
   a3.sinks.k1.sink.directory = /opt/module/datas/flume3
   # Describe the channel
   a3.channels.c2.type = memory
   a3.channels.c2.capacity = 1000
   a3.channels.c2.transactionCapacity = 100
   # Bind the source and sink to the channel
   a3.sources.r1.channels = c2
   a3.sinks.k1.channel = c2
   

   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

5. 执行配置文件

   由于avro的source为服务端，所以启动顺序应该为：

   [codecat@hadoop102 flume-1.9.0]$ flume-ng agent -c conf/ -n a3 -f jobs/group1/flume-flume-dir.conf
   
   [codecat@hadoop102 flume-1.9.0]$ flume-ng agent -c conf/ -n a2 -f jobs/group1/flume-flume-hdfs.conf 
   
   [codecat@hadoop102 flume-1.9.0]$ flume-ng agent -c conf/ -n a1 -f jobs/group1/flume-file-flume.conf 
   

6. 启动hive，检查 HDFS 上数据
   

7. 检查/opt/module/datas/flume3 目录中数据
   

Flume支持使用将多个sink逻辑上分到一个sink组，sink组配合不同的SinkProcessor可以实现负载均衡和错误恢复的功能

使用 Flume1 监控一个端口，其 sink 组中的 sink 分别对接 Flume2 和 Flume3，采用FailoverSinkProcessor，实现故障转移的功能。

1. 创建 flume-netcat-flume.conf

   配置 1 个 netcat source 和 1 个 channel、1 个 sink group（2 个 sink），分别输送给flume-flume-console1 和 flume-flume-console2。添加如下内容：

   # Name the components on this agent
   a1.sources = r1
   a1.channels = c1
   a1.sinkgroups = g1
   a1.sinks = k1 k2
   # Describe/configure the source
   a1.sources.r1.type = netcat
   a1.sources.r1.bind = localhost
   a1.sources.r1.port = 44444
   a1.sinkgroups.g1.processor.type = failover
   a1.sinkgroups.g1.processor.priority.k1 = 5
   a1.sinkgroups.g1.processor.priority.k2 = 10
   a1.sinkgroups.g1.processor.maxpenalty = 10000
   # Describe the sink
   a1.sinks.k1.type = avro
   a1.sinks.k1.hostname = hadoop102
   a1.sinks.k1.port = 4141
   a1.sinks.k2.type = avro
   a1.sinks.k2.hostname = hadoop102
   a1.sinks.k2.port = 4142
   # Describe the channel
   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.sinkgroups.g1.sinks = k1 k2
   a1.sinks.k1.channel = c1
   a1.sinks.k2.channel = c1
   

2. 创建 flume-flume-console1.conf

   配置上级Flume 输出的Source，输出是到本地控制台。添加如下内容：

   # Name the components on this agent
   a2.sources = r1
   a2.sinks = k1
   a2.channels = c1
   # Describe/configure the source
   a2.sources.r1.type = avro
   a2.sources.r1.bind = hadoop102
   a2.sources.r1.port = 4141
   # Describe the sink
   a2.sinks.k1.type = logger
   # Describe the channel
   a2.channels.c1.type = memory
   a2.channels.c1.capacity = 1000
   a2.channels.c1.transactionCapacity = 100
   # Bind the source and sink to the channel
   a2.sources.r1.channels = c1
   a2.sinks.k1.channel = c1
   

3. 创建 flume-flume-console2.conf

   配置上级 Flume 输出的 Source，输出是到本地控制台。添加如下内容：

   # Name the components on this agent
   a3.sources = r1
   a3.sinks = k1
   a3.channels = c2
   # Describe/configure the source
   a3.sources.r1.type = avro
   a3.sources.r1.bind = hadoop102
   a3.sources.r1.port = 4142
   # Describe the sink
   a3.sinks.k1.type = logger
   # Describe the channel
   a3.channels.c2.type = memory
   a3.channels.c2.capacity = 1000
   a3.channels.c2.transactionCapacity = 100
   # Bind the source and sink to the channel
   a3.sources.r1.channels = c2
   a3.sinks.k1.channel = c2
   

4. 执行配置文件

   [codecat@hadoop102 flume-1.9.0]$ flume-ng agent -c conf/ -n a3 -f jobs/group2/flume-flume-console2.conf -Dflume.root.logger=INFO,console
   
   [codecat@hadoop102 flume-1.9.0]$ flume-ng agent -c conf/ -n a2 -f jobs/group2/flume-flume-console1.conf -Dflume.root.logger=INFO,console
   
   [codecat@hadoop102 flume-1.9.0]$ flume-ng agent -c conf/ -n a1 -f jobs/group2/flume-netcat-flume.conf 
   

5. 使用 netcat 工具向本机的 44444 端口发送内容
   

6. 查看 Flume2及 Flume3 的控制台打印日志
   

hadoop102 上的 Flume-1 监控文件/opt/module/group.log

hadoop103 上的 Flume-2 监控某一个端口的数据流

Flume-1 与 Flume-2 将数据发送给 hadoop104 上的 Flume-3，Flume-3 将最终数据打印到控制台

1. 准备工作

   // 分发 Flume
   [codecat@hadoop102 module]$ xsync flume-1.9.0/
   
   [codecat@hadoop102 jobs]$ mkdir group3
   [codecat@hadoop103 jobs]$ mkdir group3
   [codecat@hadoop104 jobs]$ mkdir group3
   

2. 在 hadoop102 上创建 flume1-logger-flume.conf

   [codecat@hadoop102 group3]$ vim flume1-logger-flume.conf 
   

   添加如下内容

   # Name the components on this agent
   a1.sources = r1
   a1.sinks = k1
   a1.channels = c1
   # Describe/configure the source
   a1.sources.r1.type = exec
   a1.sources.r1.command = tail -F /opt/module/group.log
   a1.sources.r1.shell = /bin/bash -c
   # Describe the sink
   a1.sinks.k1.type = avro
   a1.sinks.k1.hostname = hadoop104
   a1.sinks.k1.port = 4141
   
   # Describe the channel
   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
   

3. 在 hadoop103 上创建 flume2-netcat-flume.conf

   [codecat@hadoop103 group3]$ vim flume2-netcat-flume.conf
   

   添加如下内容

   # Name the components on this agent
   a2.sources = r1
   a2.sinks = k1
   a2.channels = c1
   # Describe/configure the source
   a2.sources.r1.type = netcat
   a2.sources.r1.bind = hadoop103
   a2.sources.r1.port = 44444
   # Describe the sink
   a2.sinks.k1.type = avro
   a2.sinks.k1.hostname = hadoop104
   a2.sinks.k1.port = 4141
   # Use a channel which buffers events in memory
   a2.channels.c1.type = memory
   a2.channels.c1.capacity = 1000
   a2.channels.c1.transactionCapacity = 100
   # Bind the source and sink to the channel
   a2.sources.r1.channels = c1
   a2.sinks.k1.channel = c1
   

4. 在 hadoop104 上创建 flume3-flume-logger.conf

   [codecat@hadoop104 group3]$ vim flume3-flume-logger.conf
   

   添加如下内容

   # Name the components on this agent
   a3.sources = r1
   a3.sinks = k1
   a3.channels = c1
   # Describe/configure the source
   a3.sources.r1.type = avro
   a3.sources.r1.bind = hadoop104
   a3.sources.r1.port = 4141
   # Describe the sink
   # Describe the sink
   a3.sinks.k1.type = logger
   # Describe the channel
   a3.channels.c1.type = memory
   a3.channels.c1.capacity = 1000
   a3.channels.c1.transactionCapacity = 100
   # Bind the source and sink to the channel
   a3.sources.r1.channels = c1
   a3.sinks.k1.channel = c1
   

5. 执行配置文件

   [codecat@hadoop104 flume-1.9.0]$ bin/flume-ng agent -c conf/ -n a3 -f jobs/group3/flume3-flume-logger.conf -Dflume.root.logger=INFO,console
   
   [codecat@hadoop102 flume-1.9.0]$ bin/flume-ng agent -c conf/ -n a2 -f jobs/group3/flume1-logger-flume.conf 
   
   [codecat@hadoop103 flume-1.9.0]$ bin/flume-ng agent -c conf/ -n a2 -f jobs/group3/flume2-netcat-flume.conf 
   

6. 在 hadoop102 上向/opt/module 目录下的 group.log 追加内容

   [codecat@hadoop102 module]$ echo 'hello' > group.log
   

7. 在 hadoop103 上向 44444 端口发送数据
   

8. 检查 hadoop104 上数据