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分类: LINUX

2019-07-10 10:45:17

flume的特点:

flume是一个分布式、可靠、和高可用的海量日志采集、聚合和传输的系统。支持在日志系统中定制各类数据发送方,用于收集数据;同时,Flume提供对数据进行简单处理,并写到各种数据接受方(比如文本、HDFS、Hbase等)的能力 。

flume的数据流由事件(Event)贯穿始终。事件是Flume的基本数据单位,它携带日志数据(字节数组形式)并且携带有头信息,这些Event由Agent外部的Source生成,当Source捕获事件后会进行特定的格式化,然后Source会把事件推入(单个或多个)Channel中。你可以把Channel看作是一个缓冲区,它将保存事件直到Sink处理完该事件。Sink负责持久化日志或者把事件推向另一个Source。


 flume的可靠性 :

 当节点出现故障时,日志能够被传送到其他节点上而不会丢失。Flume提供了三种级别的可靠性保障,从强到弱依次分别为:end-to-end(收到数据agent首先将event写到磁盘上,当数据传送成功后,再删除;如果数据发送失败,可以重新发送。),Store on failure(这也是scribe采用的策略,当数据接收方crash时,将数据写到本地,待恢复后,继续发送),Besteffort(数据发送到接收方后,不会进行确认)。


flume的可恢复性:

还是靠Channel。推荐使用FileChannel,事件持久化在本地文件系统里(性能较差)。 


flume的一些核心概念:

Agent使用JVM 运行Flume。每台机器运行一个agent,但是可以在一个agent中包含多个sources和sinks。


Client生产数据,运行在一个独立的线程。

Source从Client收集数据,传递给Channel。

Sink从Channel收集数据,运行在一个独立线程。

Channel连接 sources 和 sinks ,这个有点像一个队列。

Events可以是日志记录、 avro 对象等。

Flume以agent为最小的独立运行单位。一个agent就是一个JVM。单agent由Source、Sink和Channel三大组件构成,如下图:


  值得注意的是,Flume提供了大量内置的Source、Channel和Sink类型。不同类型的Source,Channel和Sink可以自由组合。组合方式基于用户设置的配置文件,非常灵活。比如:Channel可以把事件暂存在内存里,也可以持久化到本地硬盘上。Sink可以把日志写入HDFS, HBase,甚至是另外一个Source等等。Flume支持用户建立多级流,也就是说,多个agent可以协同工作,并且支持Fan-in、Fan-out、Contextual Routing、Backup Routes,这也正是NB之处。如下图所示:



二、如何安装?
    1.下载安装包

           2.配置环境变量

           3.修改配置文件(案例给出)

           4.启动服务(案例给出)

           5.验证   flume-ng -version

三、flume的案例
案例1:Avro 可以发送一个给定的文件给Flume,Avro 源使用AVRO RPC机制


(a)创建agent配置文件

 

vi /home/hadoop/flume-1.5.0-bin/conf/avro.conf


a1.sources = r1

a1.sinks = k1

a1.channels = c1

# Describe/configure the source

a1.sources.r1.type= avro

a1.sources.r1.channels = c1

a1.sources.r1.bind = 0.0.0.0

a1.sources.r1.port = 4141

# 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)启动服务 flume agent a1

 

flume-ng agent -c .-f /home/hadoop/flume-1.5.0-bin/conf/avro.conf -n a1 -Dflume.root.logger=INFO,console

  (c)创建指定文件

 

echo "hello world" > /home/hadoop/flume-1.5.0-bin/log.00


(d)使用avro-client发送文件

 

flume-ng avro-client -c . -H m1 -p 4141 -F /home/hadoop/flume-1.5.0-bin/log.00


(f)在m1的控制台,可以看到以下信息,注意最后一行:  hello world



案例2:Spool 监测配置的目录下新增的文件,并将文件中的数据读取出来。需要注意两点:

1) 拷贝到spool目录下的文件不可以再打开编辑。
2) spool目录下不可包含相应的子目录

(a)创建agent配置文件

 

vi /home/hadoop/flume-1.5.0-bin/conf/spool.conf


a1.sources = r1

a1.sinks = k1

a1.channels = c1

# Describe/configure the source

a1.sources.r1.type= spooldir

a1.sources.r1.channels = c1

a1.sources.r1.spoolDir = /home/hadoop/flume-1.5.0-bin/logs

a1.sources.r1.fileHeader = true

# 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)启动服务flume agent a1

 

flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/spool.conf -n a1 -Dflume.root.logger=INFO,console

(c)追加文件到/home/hadoop/flume-1.5.0-bin/logs目录

 

echo "spool test1" > /home/hadoop/flume-1.5.0-bin/logs/spool_text.log

(d)在m1的控制台,可以看到以下相关信息:

 Event: { headers:{file=/home/hadoop/flume-1.5.0-bin/logs/spool_text.log} body: 73 70 6F 6F 6C 20 74 65 73 74 31        spool test1 }


案例3:Exec 执行一个给定的命令获得输出的源,如果要使用tail命令,必选使得file足够大才能看到输出内容

(a)创建agent配置文件

 

vi /home/hadoop/flume-1.5.0-bin/conf/exec_tail.conf


a1.sources = r1

a1.sinks = k1

a1.channels = c1

# Describe/configure the source

a1.sources.r1.type= exec

a1.sources.r1.channels = c1

a1.sources.r1.command= tail-F /home/hadoop/flume-1.5.0-bin/log_exec_tail

# 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)启动服务flume agent a1

 

flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/exec_tail.conf -n a1 -Dflume.root.logger=INFO,console

(c)生成足够多的内容在文件里

 

for i in {1..100};do echo "exec tail$i" >> /home/hadoop/flume-1.5.0-bin/log_exec_tail;echo $i;sleep 0.1;done

(e)在m1的控制台,可以看到以下信息:

 

Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 20 74 65 73 74 exec tail test }

Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 20 74 65 73 74 exec tail test }


案例4:Syslogtcp 监听TCP的端口做为数据源
(a)创建agent配置文件

 

vi /home/hadoop/flume-1.5.0-bin/conf/syslog_tcp.conf


a1.sources = r1

a1.sinks = k1

a1.channels = c1

# Describe/configure the source

a1.sources.r1.type= syslogtcp

a1.sources.r1.port = 5140

a1.sources.r1.host = localhost

a1.sources.r1.channels = c1

# 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

 

flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/syslog_tcp.conf -n a1 -Dflume.root.logger=INFO,console

 (c)测试产生syslog

 

echo "hello idoall.org syslog" | nc localhost 5140

 (d)在m1的控制台,可以看到以下信息:

 

Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 68 65 6C 6C 6F 20 69 64 6F 61 6C 6C 2E 6F 72 67 hello idoall.org }


案例5:JSONHandler

(a)创建agent配置文件


 

vi /home/hadoop/flume-1.5.0-bin/conf/post_json.conf


a1.sources = r1

a1.sinks = k1

a1.channels = c1

# Describe/configure the source

a1.sources.r1.type= org.apache.flume.source.http.HTTPSource

a1.sources.r1.port = 8888

a1.sources.r1.channels = c1

# 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


 

flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/post_json.conf -n a1 -Dflume.root.logger=INFO,console

(c)生成JSON 格式的POST request

 

curl -X POST -d '[{ "headers" :{"a" : "a1","b" : "b1"},"body" : "idoall.org_body"}]' http://localhost:8888

(d)在m1的控制台,可以看到以下信息:

 

Event: { headers:{b=b1, a=a1}

body: 69 64 6F 61 6C 6C 2E 6F 72 67 5F 62 6F 64 79  idoall.org_body }


案例6:Hadoop sink
(a)创建agent配置文件

 

vi /home/hadoop/flume-1.5.0-bin/conf/hdfs_sink.conf


a1.sources = r1

a1.sinks = k1

a1.channels = c1

# Describe/configure the source

a1.sources.r1.type= syslogtcp

a1.sources.r1.port = 5140

a1.sources.r1.host = localhost

a1.sources.r1.channels = c1

# Describe the sink

a1.sinks.k1.type= hdfs

a1.sinks.k1.channel = c1

a1.sinks.k1.hdfs.path = hdfs://m1:9000/user/flume/syslogtcp

a1.sinks.k1.hdfs.filePrefix = Syslog

a1.sinks.k1.hdfs.round = true

a1.sinks.k1.hdfs.roundValue = 10

a1.sinks.k1.hdfs.roundUnit = minute

# 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


flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/hdfs_sink.conf -n a1 -Dflume.root.logger=INFO,console

(c)测试产生syslog

 

echo "hello idoall flume -> hadoop testing one" | nc localhost 5140

(d) 在m1上再打开一个窗口,去hadoop上检查文件是否生成

 

hadoop fs -ls /user/flume/syslogtcp

hadoop fs -cat /user/flume/syslogtcp/Syslog.1407644509504


案例7:File Roll Sink
(a)创建agent配置文件

 

vi /home/hadoop/flume-1.5.0-bin/conf/file_roll.conf


a1.sources = r1

a1.sinks = k1

a1.channels = c1

# Describe/configure the source

a1.sources.r1.type= syslogtcp

a1.sources.r1.port = 5555

a1.sources.r1.host = localhost

a1.sources.r1.channels = c1

# Describe the sink

a1.sinks.k1.type= file_roll

a1.sinks.k1.sink.directory = /home/hadoop/flume-1.5.0-bin/logs

# 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

 

flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/file_roll.conf -n a1 -Dflume.root.logger=INFO,console

(c)测试产生log

 

echo "hello idoall.org syslog" | nc localhost 5555

echo "hello idoall.org syslog 2" | nc localhost 5555

(d)查看/home/hadoop/flume-1.5.0-bin/logs下是否生成文件,默认每30秒生成一个新文件

 

ll /home/hadoop/flume-1.5.0-bin/logs

cat /home/hadoop/flume-1.5.0-bin/logs/1407646164782-1

cat /home/hadoop/flume-1.5.0-bin/logs/1407646164782-2

hello idoall.org syslog

hello idoall.org syslog 2



案例8:Replicating Channel Selector Flume支持Fan out流从一个源到多个通道。有两种模式的Fan out,分别是复制和复用。在复制的情况下,流的事件被发送到所有的配置通道。在复用的情况下,事件被发送到可用的渠道中的一个子集。Fan out流需要指定源和Fan out通道的规则。这次我们需要用到m1,m2两台机器
(a)在m1创建replicating_Channel_Selector配置文件

 

vi /home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector.conf


a1.sources = r1

a1.sinks = k1 k2

a1.channels = c1 c2

# Describe/configure the source

a1.sources.r1.type= syslogtcp

a1.sources.r1.port = 5140

a1.sources.r1.host = localhost

a1.sources.r1.channels = c1 c2

a1.sources.r1.selector.type= replicating

# Describe the sink

a1.sinks.k1.type= avro

a1.sinks.k1.channel = c1

a1.sinks.k1.hostname= m1

a1.sinks.k1.port = 5555

a1.sinks.k2.type= avro

a1.sinks.k2.channel = c2

a1.sinks.k2.hostname= m2

a1.sinks.k2.port = 5555

# Use a channel which buffers events in memory

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

(b)在m1创建replicating_Channel_Selector_avro配置文件

 

vi /home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector_avro.conf


a1.sources = r1

a1.sinks = k1

a1.channels = c1

# Describe/configure the source

a1.sources.r1.type= avro

a1.sources.r1.channels = c1

a1.sources.r1.bind = 0.0.0.0

a1.sources.r1.port = 5555

# 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

(c)在m1上将2个配置文件复制到m2上一份

 

scp -r /home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector.conf

 scp -r /home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector_avro.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector_avro.conf

(d)打开4个窗口,在m1和m2上同时启动两个flume agent

 

flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector_avro.conf -n a1 -Dflume.root.logger=INFO,console

flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector.conf -n a1 -Dflume.root.logger=INFO,console

(e)然后在m1或m2的任意一台机器上,测试产生syslog

 

echo "hello idoall.org syslog" | nc localhost 5140

(f)在m1和m2的sink窗口,分别可以看到以下信息,这说明信息得到了同步:

 

Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 68 65 6C 6C 6F 20 69 64 6F 61 6C 6C 2E 6F 72 67 hello idoall.org }


案例9:Multiplexing Channel Selector
(a)在m1创建Multiplexing_Channel_Selector配置文件


 

vi /home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector.conf


a1.sources = r1

a1.sinks = k1 k2

a1.channels = c1 c2

# Describe/configure the source

a1.sources.r1.type= org.apache.flume.source.http.HTTPSource

a1.sources.r1.port = 5140

a1.sources.r1.channels = c1 c2

a1.sources.r1.selector.type= multiplexing

a1.sources.r1.selector.header = type

#映射允许每个值通道可以重叠。默认值可以包含任意数量的通道。

a1.sources.r1.selector.mapping.baidu = c1

a1.sources.r1.selector.mapping.ali = c2

a1.sources.r1.selector.default = c1

# Describe the sink

a1.sinks.k1.type= avro

a1.sinks.k1.channel = c1

a1.sinks.k1.hostname= m1

a1.sinks.k1.port = 5555

a1.sinks.k2.type= avro

a1.sinks.k2.channel = c2

a1.sinks.k2.hostname= m2

a1.sinks.k2.port = 5555

# Use a channel which buffers events in memory

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

(b)在m1创建Multiplexing_Channel_Selector_avro配置文件

 

vi /home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector_avro.conf

a1.sources = r1

a1.sinks = k1

a1.channels = c1

# Describe/configure the source

a1.sources.r1.type= avro

a1.sources.r1.channels = c1

a1.sources.r1.bind = 0.0.0.0

a1.sources.r1.port = 5555

# 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

(c)将2个配置文件复制到m2上一份

 

scp -r /home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector.conf

scp -r /home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector_avro.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector_avro.conf

(d)打开4个窗口,在m1和m2上同时启动两个flume agent

 

flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector_avro.conf -n a1 -Dflume.root.logger=INFO,console


flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector.conf -n a1 -Dflume.root.logger=INFO,console

(e)然后在m1或m2的任意一台机器上,测试产生syslog

 

curl -X POST -d '[{ "headers" :{"type" : "baidu"},"body" : "idoall_TEST1"}]' http://localhost:5140 &&

curl -X POST -d '[{ "headers" :{"type" : "ali"},"body" : "idoall_TEST2"}]' http://localhost:5140 &&

curl -X POST -d '[{ "headers" :{"type" : "qq"},"body" : "idoall_TEST3"}]' http://localhost:5140

(f)在m1的sink窗口,可以看到以下信息:

 

Event: { headers:{type=baidu} body: 69 64 6F 61 6C 6C 5F 54 45 53 54 31} 

Event: { headers:{type=qq} body: 69 64 6F 61 6C 6C 5F 54 45 53 54 33}

(g)在m2的sink窗口,可以看到以下信息:

 

Event: { headers:{type=ali} body: 69 64 6F 61 6C 6C 5F 54 45 53 54 32}

可以看到,根据header中不同的条件分布到不同的channel上


案例10:Flume Sink Processors failover的机器是一直发送给其中一个sink,当这个sink不可用的时候,自动发送到下一个sink。

(a)在m1创建Flume_Sink_Processors配置文件

 

vi /home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors.conf


a1.sources = r1

a1.sinks = k1 k2

a1.channels = c1 c2

#这个是配置failover的关键,需要有一个sink group

a1.sinkgroups = g1

a1.sinkgroups.g1.sinks = k1 k2

#处理的类型是failover

a1.sinkgroups.g1.processor.type= failover

#优先级,数字越大优先级越高,每个sink的优先级必须不相同

a1.sinkgroups.g1.processor.priority.k1 = 5

a1.sinkgroups.g1.processor.priority.k2 = 10

#设置为10秒,当然可以根据你的实际状况更改成更快或者很慢

a1.sinkgroups.g1.processor.maxpenalty = 10000

# Describe/configure the source

a1.sources.r1.type= syslogtcp

a1.sources.r1.port = 5140

a1.sources.r1.channels = c1 c2

a1.sources.r1.selector.type= replicating

# Describe the sink

a1.sinks.k1.type= avro

a1.sinks.k1.channel = c1

a1.sinks.k1.hostname= m1

a1.sinks.k1.port = 5555

a1.sinks.k2.type= avro

a1.sinks.k2.channel = c2

a1.sinks.k2.hostname= m2

a1.sinks.k2.port = 5555

# Use a channel which buffers events in memory

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

(b)在m1创建Flume_Sink_Processors_avro配置文件

 

vi /home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors_avro.conf


a1.sources = r1

a1.sinks = k1

a1.channels = c

# Describe/configure the source

a1.sources.r1.type= avro

a1.sources.r1.channels = c1

a1.sources.r1.bind = 0.0.0.0

a1.sources.r1.port = 5555

# 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

(c)将2个配置文件复制到m2上一份

 

scp -r /home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors.conf


scp -r /home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors_avro.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors_avro.conf

(d)打开4个窗口,在m1和m2上同时启动两个flume agent

 

flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors_avro.conf -n a1 -Dflume.root.logger=INFO,console

flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors.conf -n a1 -Dflume.root.logger=INFO,console

(e)然后在m1或m2的任意一台机器上,测试产生log

 

echo "idoall.org test1 failover" | nc localhost 5140

(f)因为m2的优先级高,所以在m2的sink窗口,可以看到以下信息,而m1没有:

 

Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 31 idoall.org test1 }

(g)这时我们停止掉m2机器上的sink(ctrl+c),再次输出测试数据:

 

echo "idoall.org test2 failover" | nc localhost 5140

(h)可以在m1的sink窗口,看到读取到了刚才发送的两条测试数据:

 

Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 31 idoall.org test1 }

Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 32 idoall.org test2 }

(i)我们再在m2的sink窗口中,启动sink:

 

flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors_avro.conf -n a1 -Dflume.root.logger=INFO,console

(j)输入两批测试数据:

 

echo "idoall.org test3 failover" | nc localhost 5140 && echo "idoall.org test4 failover" | nc localhost 5140

(k)在m2的sink窗口,我们可以看到以下信息,因为优先级的关系,log消息会再次落到m2上:

 

Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 33 idoall.org test3 }

Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 34 idoall.org test4 }



案例11:Load balancing Sink Processor load balance type和failover不同的地方是,load balance有两个配置,一个是轮询,一个是随机。两种情况下如果被选择的sink不可用,就会自动尝试发送到下一个可用的sink上面。

(a)在m1创建Load_balancing_Sink_Processors配置文件

 

vi /home/hadoop/flume-1.5.0-bin/conf/Load_balancing_Sink_Processors.conf


a1.sources = r1

a1.sinks = k1 k2

a1.channels = c1

#这个是配置Load balancing的关键,需要有一个sink group

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

# Describe/configure the source

a1.sources.r1.type= syslogtcp

a1.sources.r1.port = 5140

a1.sources.r1.channels = c1

# Describe the sink

a1.sinks.k1.type= avro

a1.sinks.k1.channel = c1

a1.sinks.k1.hostname= m1

a1.sinks.k1.port = 5555

a1.sinks.k2.type= avro

a1.sinks.k2.channel = c1

a1.sinks.k2.hostname= m2

a1.sinks.k2.port = 5555

# Use a channel which buffers events in memory

a1.channels.c1.type= memory

a1.channels.c1.capacity = 1000

a1.channels.c1.transactionCapacity = 100

(b)在m1创建Load_balancing_Sink_Processors_avro配置文件

 

vi /home/hadoop/flume-1.5.0-bin/conf/Load_balancing_Sink_Processors_avro.conf


a1.sources = r1

a1.sinks = k1

a1.channels = c1

# Describe/configure the source

a1.sources.r1.type= avro

a1.sources.r1.channels = c1

a1.sources.r1.bind = 0.0.0.0

a1.sources.r1.port = 5555

# 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

(c)将2个配置文件复制到m2上一份

 

scp -r /home/hadoop/flume-1.5.0-bin/conf/Load_balancing_Sink_Processors.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/Load_balancing_Sink_Processors.conf


scp -r /home/hadoop/flume-1.5.0-bin/conf/Load_balancing_Sink_Processors_avro.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/Load_balancing_Sink_Processors_avro.conf

(d)打开4个窗口,在m1和m2上同时启动两个flume agent

 

flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/Load_balancing_Sink_Processors_avro.conf -n a1 -Dflume.root.logger=INFO,console

flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/Load_balancing_Sink_Processors.conf -n a1 -Dflume.root.logger=INFO,console

(e)然后在m1或m2的任意一台机器上,测试产生log,一行一行输入,输入太快,容易落到一台机器上

 

echo "idoall.org test1" | nc localhost 5140

echo "idoall.org test2" | nc localhost 5140

echo "idoall.org test3" | nc localhost 5140

echo "idoall.org test4" | nc localhost 5140

(f)在m1的sink窗口,可以看到以下信息:

 

Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 32 idoall.org test2 }

Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 34 idoall.org test4 }

(g)在m2的sink窗口,可以看到以下信息:

 

Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 31 idoall.org test1 }

Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 33 idoall.org test3 }

 说明轮询模式起到了作用。    



案例12:Hbase sink
 (a)在测试之前,请先将hbase启动

(b)然后将以下文件复制到flume中:

 

cp/home/hadoop/hbase-0.96.2-hadoop2/lib/protobuf-java-2.5.0.jar /home/hadoop/flume-1.5.0-bin/lib

cp/home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-client-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib

cp/home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-common-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib

cp/home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-protocol-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib

cp/home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-server-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib

cp/home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-hadoop2-compat-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib

cp/home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-hadoop-compat-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib

cp/home/hadoop/hbase-0.96.2-hadoop2/lib/htrace-core-2.04.jar /home/hadoop/flume-1.5.0-bin/lib

(c)确保test_idoall_org表在hbase中已经存在。

(d)在m1创建hbase_simple配置文件

 

vi /home/hadoop/flume-1.5.0-bin/conf/hbase_simple.conf


a1.sources = r1

a1.sinks = k1

a1.channels = c1

# Describe/configure the source

a1.sources.r1.type= syslogtcp

a1.sources.r1.port = 5140

a1.sources.r1.host = localhost

a1.sources.r1.channels = c1

# Describe the sink

a1.sinks.k1.type= logger

a1.sinks.k1.type= hbase

a1.sinks.k1.table = test_idoall_org

a1.sinks.k1.columnFamily = name

a1.sinks.k1.column = idoall

a1.sinks.k1.serializer = org.apache.flume.sink.hbase.RegexHbaseEventSerializer

a1.sinks.k1.channel = memoryChannel

# 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

(e)启动flume agent

 

flume-ngagent -c . –f /home/hadoop/flume-1.5.0-bin/conf/hbase_simple.conf -n a1 -Dflume.root.logger=INFO,console

(f)测试产生syslog

 

echo "hello idoall.org from flume" | nc localhost 5140

(g)这时登录到hbase中,可以发现新数据已经插入

 

hbase shell


hbase(main):001:0> list

TABLE                                                                                                        

hbase2hive_idoall                                                                                                  

hive2hbase_idoall                                                                                                  

test_idoall_org


=> ["hbase2hive_idoall","hive2hbase_idoall","test_idoall_org"]


hbase(main):002:0> scan "test_idoall_org"


hbase(main):004:0> quit


Flume常用的Type

Source:

名称 含义 注意点
avro avro协议的数据源
exec unix命令 可以命令监控文件 tail -F
spooldir 监控一个文件夹 不能含有子文件夹,不监控windows文件夹 
处理完文件不能再写数据到文件 
文件名不能冲突
TAILDIR 既可以监控文件也可以监控文件夹 支持断点续传功能, 重点使用这个
netcat 监听某个端口
kafka 监控卡夫卡数据


sink:

名称 含义 注意点
kafka 写到kafka中
HDFS 将数据写到HDFS中
logger 输出到控制台
avro avro协议 配合avro source使用

channel:


名称 含义 注意点
memory 存在内存中
kafka 将数据存到kafka中
file 存在本地磁盘文件中

经过这么多flume的例子测试,如果你全部做完后,会发现flume的功能真的很强大,可以进行各种搭配来完成你想要的工作,俗话说师傅领进门,修行在个人,如何能够结合你的产品业务,将flume更好的应用起来,快去动手实践吧。


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