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

　　　　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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