Flume架构以及应用介绍

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

 # Describe/configure the source a1.sources.r1.type = netcat a1.sources.r1.bind = localhost a1.sources.r1.port = 44444 # Describe the sink a1.sinks.k1.type = logger # Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100

3>通过channel将source与sink连接起来

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

启动agent的shell操作：

 flume-ng agent -n a1 -c ../conf -f ../conf/example.file -Dflume.root.logger=DEBUG,console 

Property Name Default Description channels – type – The component type name, needs to be netcat bind – 日志需要发送到的主机名或者Ip地址，该主机运行着netcat类型的source在监听 port – 日志需要发送到的端口号，该端口号要有netcat类型的source在监听 

a) 编写配置文件：

# Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = netcat a1.sources.r1.bind = 192.168.80.80 a1.sources.r1.port = 44444 # Describe the sink a1.sinks.k1.type = logger # Use a channel which buffers events in memory 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

b) 启动flume agent a1 服务端

flume-ng agent -n a1 -c ../conf -f ../conf/netcat.conf -Dflume.root.logger=DEBUG,console

c) 使用telnet发送数据

telnet 192.168.80.80 44444 big data world！（windows中运行的）

# Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = netcat a1.sources.r1.bind = 192.168.80.80 a1.sources.r1.port = 44444 # Describe the sink a1.sinks.k1.type = hdfs a1.sinks.k1.hdfs.path = hdfs://hadoop80:9000/dataoutput a1.sinks.k1.hdfs.writeFormat = Text a1.sinks.k1.hdfs.fileType = DataStream a1.sinks.k1.hdfs.rollInterval = 10 a1.sinks.k1.hdfs.rollSize = 0 a1.sinks.k1.hdfs.rollCount = 0 a1.sinks.k1.hdfs.filePrefix = %Y-%m-%d-%H-%M-%S a1.sinks.k1.hdfs.useLocalTimeStamp = true # Use a channel which buffers events in file a1.channels.c1.type = file a1.channels.c1.checkpointDir = /usr/flume/checkpoint a1.channels.c1.dataDirs = /usr/flume/data # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1

b) 启动flume agent a1 服务端

flume-ng agent -n a1 -c ../conf -f ../conf/netcat.conf -Dflume.root.logger=DEBUG,console

c) 使用telnet发送数据

telnet 192.168.80.80 44444 big data world！（windows中运行的）

Property Name Default Description channels – type – The component type name, needs to be spooldir. spoolDir – Spooling Directory Source监听的目录 fileSuffix .COMPLETED 文件内容写入到channel之后，标记该文件 deletePolicy never 文件内容写入到channel之后的删除策略: never or immediate fileHeader false Whether to add a header storing the absolute path filename. ignorePattern ^$ Regular expression specifying which files to ignore (skip) interceptors – 指定传输中event的head(头信息)，常用timestamp

Spooling Directory Source的两个注意事项：

①If a file is written to after being placed into the spooling directory, Flume will print an error to its log file and stop processing. 即：拷贝到spool目录下的文件不可以再打开编辑 ②If a file name is reused at a later time, Flume will print an error to its log file and stop processing. 即：不能将具有相同文件名字的文件拷贝到这个目录下

a) 编写配置文件：

# Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = spooldir a1.sources.r1.spoolDir = /usr/local/datainput a1.sources.r1.fileHeader = true a1.sources.r1.interceptors = i1 a1.sources.r1.interceptors.i1.type = timestamp # Describe the sink a1.sinks.k1.type = logger # Use a channel which buffers events in memory 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

b) 启动flume agent a1 服务端

flume-ng agent -n a1 -c ../conf -f ../conf/spool.conf -Dflume.root.logger=DEBUG,console

c) 使用cp命令向Spooling Directory 中发送数据

 cp datafile /usr/local/datainput (注：datafile中的内容为：big data world！)

[root@hadoop80 datainput]# ls datafile.COMPLETED

案例4：Spooling Directory Source：监听一个指定的目录，即只要应用程序向这个指定的目录中添加新的文件，source组件就可以获取到该信息，并解析该文件的内容，然后写入到channle。写入完成后，标记该文件已完成或者删除该文件。 其中 Sink：hdfs Channel：file (相比于案例3的两个变化)

a) 编写配置文件：

# Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = spooldir a1.sources.r1.spoolDir = /usr/local/datainput a1.sources.r1.fileHeader = true a1.sources.r1.interceptors = i1 a1.sources.r1.interceptors.i1.type = timestamp # Describe the sink # Describe the sink a1.sinks.k1.type = hdfs a1.sinks.k1.hdfs.path = hdfs://hadoop80:9000/dataoutput a1.sinks.k1.hdfs.writeFormat = Text a1.sinks.k1.hdfs.fileType = DataStream a1.sinks.k1.hdfs.rollInterval = 10 a1.sinks.k1.hdfs.rollSize = 0 a1.sinks.k1.hdfs.rollCount = 0 a1.sinks.k1.hdfs.filePrefix = %Y-%m-%d-%H-%M-%S a1.sinks.k1.hdfs.useLocalTimeStamp = true # Use a channel which buffers events in file a1.channels.c1.type = file a1.channels.c1.checkpointDir = /usr/flume/checkpoint a1.channels.c1.dataDirs = /usr/flume/data # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1

b) 启动flume agent a1 服务端

flume-ng agent -n a1 -c ../conf -f ../conf/spool.conf -Dflume.root.logger=DEBUG,console

c) 使用cp命令向Spooling Directory 中发送数据

 cp datafile /usr/local/datainput (注：datafile中的内容为：big data world！)

a) 编写配置文件：

# 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 /usr/local/log.file # Describe the sink a1.sinks.k1.type = hdfs a1.sinks.k1.hdfs.path = hdfs://hadoop80:9000/dataoutput a1.sinks.k1.hdfs.writeFormat = Text a1.sinks.k1.hdfs.fileType = DataStream a1.sinks.k1.hdfs.rollInterval = 10 a1.sinks.k1.hdfs.rollSize = 0 a1.sinks.k1.hdfs.rollCount = 0 a1.sinks.k1.hdfs.filePrefix = %Y-%m-%d-%H-%M-%S a1.sinks.k1.hdfs.useLocalTimeStamp = true # Use a channel which buffers events in file a1.channels.c1.type = file a1.channels.c1.checkpointDir = /usr/flume/checkpoint a1.channels.c1.dataDirs = /usr/flume/data # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1

b)在hive中建立外部表—–hdfs://hadoop80:9000/dataoutput的目录，方便查看日志捕获内容

hive> create external table t1(infor string) > row format delimited > fields terminated by '\t' > location '/dataoutput/'; OK Time taken: 0.284 seconds

c) 启动flume agent a1 服务端

flume-ng agent -n a1 -c ../conf -f ../conf/exec.conf -Dflume.root.logger=DEBUG,console

d) 使用echo命令向/usr/local/datainput 中发送数据

 echo big data > log.file

hive> select * from t1; OK big data Time taken: 0.086 seconds

e)使用echo命令向/usr/local/datainput 中在追加一条数据

echo big data world! >> log.file

hive> select * from t1; OK big data big data world! Time taken: 0.511 seconds

总结Exec source：Exec source和Spooling Directory Source是两种常用的日志采集的方式，其中Exec source可以实现对日志的实时采集，Spooling Directory Source在对日志的实时采集上稍有欠缺，尽管Exec source可以实现对日志的实时采集，但是当Flume不运行或者指令执行出错时，Exec source将无法收集到日志数据，日志会出现丢失，从而无法保证收集日志的完整性。

Property Name Default Description channels – type – The component type name, needs to be avro bind – 日志需要发送到的主机名或者ip，该主机运行着ARVO类型的source port – 日志需要发送到的端口号，该端口要有ARVO类型的source在监听

1)编写配置文件

# Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = avro a1.sources.r1.bind = 192.168.80.80 a1.sources.r1.port = 4141 # Describe the sink a1.sinks.k1.type = hdfs a1.sinks.k1.hdfs.path = hdfs://hadoop80:9000/dataoutput a1.sinks.k1.hdfs.writeFormat = Text a1.sinks.k1.hdfs.fileType = DataStream a1.sinks.k1.hdfs.rollInterval = 10 a1.sinks.k1.hdfs.rollSize = 0 a1.sinks.k1.hdfs.rollCount = 0 a1.sinks.k1.hdfs.filePrefix = %Y-%m-%d-%H-%M-%S a1.sinks.k1.hdfs.useLocalTimeStamp = true # Use a channel which buffers events in file a1.channels.c1.type = file a1.channels.c1.checkpointDir = /usr/flume/checkpoint a1.channels.c1.dataDirs = /usr/flume/data # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1

b) 启动flume agent a1 服务端

flume-ng agent -n a1 -c ../conf -f ../conf/avro.conf -Dflume.root.logger=DEBUG,console

c)使用avro-client发送文件

flume-ng avro-client -c ../conf -H 192.168.80.80 -p 4141 -F /usr/local/log.file

注：log.file文件中的内容为：

[root@hadoop80 local]# more log.file big data big data world!

通过上面的几个案例，我们可以发现：flume配置文件的书写是相当灵活的—-不同类型的Source、Channel和Sink可以自由组合！

如有问题，欢迎留言指正！