flume将数据发送到kafka、hdfs、hive、http、netcat等模式的使用总结

1、source为http模式，sink为logger模式，将数据在控制台打印出来。

conf配置文件如下：

# Name the components on this agent

a1.sources = r1

a1.sinks = k1

a1.channels = c1

 

# Describe/configure the source

a1.sources.r1.type = http #该设置表示接收通过http方式发送过来的数据

a1.sources.r1.bind = hadoop-master #运行flume的主机或IP地址都可以

a1.sources.r1.port = 9000#端口

#a1.sources.r1.fileHeader = true

 

# Describe the sink

a1.sinks.k1.type = logger#该设置表示将数据在控制台打印出来

 

# 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

启动flume命令为：

bin/flume-ng agent -c conf -f conf/http.conf -n a1 -Dflume.root.logger=INFO,console。

显示如下的信息表示启动flume成功。

895 (lifecycleSupervisor-1-3) [INFO -org.apache.flume.instrumentation.MonitoredCounterGroup.start(MonitoredCounterGroup.java:96)] Component type: SOURCE, name: r1 started

打开另外一个终端，通过http post的方式发送数据：

curl -X POST -d '[{"headers":{"timestampe":"1234567","host":"master"},"body":"badou flume"}]' hadoop-master:9000。

hadoop-master就是flume配置文件绑定的主机名，9000就是绑定的端口。

然后在运行flume的窗口就是看到如下的内容：

2018-06-12 08:24:04,472 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:94)] Event: { headers:{timestampe=1234567, host=master} body: 62 61 64 6F 75 20 66 6C 75 6D 65 badou flume }

 

2、source为netcat（udp、tcp模式），sink为logger模式，将数据打印在控制台

conf配置文件如下：

a1.sources = r1

a1.sinks = k1

a1.channels = c1

 

a1.sources.r1.type = netcat

a1.sources.r1.bind = hadoop-master#绑定的主机名或IP地址

a1.sources.r1.port = 44444

 

a1.sinks.k1.type = logger

 

a1.channels.c1.type = memory

a1.channels.c1.capacity = 1000

a1.channels.c1.transcationCapacity = 100

 

a1.sources.r1.channels = c1

a1.sinks.k1.channel = c1

启动flume

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

 

然后在另外一个终端，使用telnet发送数据：

命令为：telnet hadoop-maser 44444

 

[root@hadoop-master ~]# telnet hadoop-master 44444

Trying 192.168.194.6...

Connected to hadoop-master.

Escape character is '^]'.

显示上面的信息表示连接flume成功，然后输入：

12213213213

OK

12321313

OK

在flume就会收到相应的信息：

2018-06-12 08:38:51,129 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:94)] Event: { headers:{} body: 31 32 32 31 33 32 31 33 32 31 33 0D 12213213213. }

2018-06-12 08:38:51,130 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:94)] Event: { headers:{} body: 31 32 33 32 31 33 31 33 0D 12321313. }

 

3、source为netcat/http模式，sink为hdfs模式，将数据存储在hdfs中。

conf配置文件如下，文件名为hdfs.conf：

# 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 = hadoop-master

a1.sources.r1.port = 44444

 

a1.sources.r1.interceptors = i1

a1.sources.r1.interceptors.i1.type =regex_filter

a1.sources.r1.interceptors.i1.regex =^[0-9]*$

a1.sources.r1.interceptors.i1.excludeEvents =true

 

# Describe the sink

#a1.sinks.k1.type = logger

a1.channels = c1

a1.sinks = k1

a1.sinks.k1.type = hdfs

a1.sinks.k1.channel = c1

a1.sinks.k1.hdfs.path = hdfs:/flume/events #文件在hdfs文件系统中存放的位置

a1.sinks.k1.hdfs.filePrefix = events- #文件的前缀

a1.sinks.k1.hdfs.round = true

a1.sinks.k1.hdfs.roundValue = 10

a1.sinks.k1.hdfs.roundUnit = minute

a1.sinks.k1.hdfs.fileType = DataStream #制定文件的存放格式，这个设置是以text的格式存放从flume传输过来的数据。

# 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

 

在hdfs文件系统中创建文件存放的路径：

hadoop fs -mkdir /flume/event1。

 

启动flume:

bin/flume-ng agent -c conf -f conf/hdfs.conf -n a1 -Dflume.root.logger=INFO,console

 

通过telnet模式向flume中发送文件：

telnet hadoop-master 44444

然后输入：

aaaaaaaa

bbbbbbb

ccccccccc

dddddddddd

 

通过如下的命令hadoop fs -ls /flume/events/查看hdfs中的文件，可以看到hdfs中有/flume/events有如下文件：

-rw-r--r-- 3 root supergroup 16 2018-06-05 06:02 /flume/events/events-.1528203709070

-rw-r--r-- 3 root supergroup 5 2018-06-05 06:02 /flume/events/events-.1528203755556

-rw-r--r-- 3 root supergroup 11 2018-06-05 06:03 /flume/events/events-.1528203755557

-rw-r--r-- 3 root supergroup 26 2018-06-13 07:28 /flume/events/events-.1528900112215

-rw-r--r-- 3 root supergroup 209 2018-06-13 07:29 /flume/events/events-.1528900112216

-rw-r--r-- 3 root supergroup 72 2018-06-13 07:29 /flume/events/events-.1528900112217

通过hadoop fs -cat /flume/events/events-.1528900112216查看文件events-.1528900112216的内容：

aaaaaaaaaaaaaaaaa

bbbbbbbbbbbbbbbb

ccccccccccccccccccc

dddddddddddddddd

eeeeeeeeeeeeeeeeeee

fffffffffffffffffffffff

gggggggggggggggggg

hhhhhhhhhhhhhhhhhhhhhhh

iiiiiiiiiiiiiiiiiii

jjjjjjjjjjjjjjjjjjj

 

http模式就是把hdfs.conf文件中的netcat改为http，然后传输文件从telnet改为：

curl -X POST -d '[{"headers":{"timestampe":"1234567","host":"master"},"body":"badou flume"}]' hadoop-master:44444。

在hadoop文件中就会看到上面命令传输的内容：badou flume。

 

4、source为netcat/http模式，sink为hive模式，将数据存储在hive中，并分区存储。

conf配置如下，文件名为hive.conf：

# Describe/configure the source

a1.sources.r1.type = netcat

a1.sources.r1.bind = hadoop-master

a1.sources.r1.port = 44444

 

# Describe the sink

#a1.sinks.k1.type = logger

a1.channels = c1

a1.sinks = k1

a1.sinks.k1.type = hive

a1.sinks.k1.hive.metastore=thrift://hadoop-master:9083

a1.sinks.k1.hive.database=default#hive数据库名

a1.sinks.k1.hive.table=flume_user1

a1.sinks.k1.serializer=DELIMITED

a1.sinks.k1.hive.partition=3#如果以netcat模式，只能静态设置分区的值，因为netcat模式传输数据，无法传输某个字段的值，只能按照顺序来。这里设置age的分区值为3。

#a1.sinks.k1.hive.partition=%{age}#如果以http或json等模式，只能动态设置分区的值，因为http模式可以动态传输age的值。

a1.sinks.k1.serializer.delimiter=" "

a1.sinks.k1.serializer.serderSeparator=' '

a1.sinks.k1.serializer.fieldnames=user_id,user_name

a1.sinks.k1.hive.txnsPerBatchAsk = 10

a1.sinks.k1.hive.batchSize = 1500

 

# 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

 

在hive中创建表：

create table flume_user(

user_id int

,user_name string

)

partitioned by(age int)

clustered by (user_id) into 2 buckets

stored as orc

 

在hive-site.xml中添加如下内容：

<property>

<name>javax.jdo.option.ConnectionPassword</name>

<value>hive</value>

<description>password to use against metastore database</description>

</property>

 

<property>

<name>hive.support.concurrency</name>

<value>true</value>

</property>

<property>

<name>hive.exec.dynamic.partition.mode</name>

<value>nonstrict</value>

</property>

<property>

<name>hive.txn.manager</name>

<value>org.apache.hadoop.hive.ql.lockmgr.DbTxnManager</value>

</property>

<property>

<name>hive.compactor.initiator.on</name>

<value>true</value>

</property>

<property>

<name>hive.compactor.worker.threads</name>

<value>1</value>

</property>

 

将hive根目录下的/hcatalog/share/hcatalog文件夹中的如下三个文件夹添加到flume的lib目录下。

运行flume:

bin/flume-ng agent -c conf -f conf/hive.conf -n a1 -Dflume.root.logger=INFO,console。

 

重新打开一个窗口，

启动metastroe服务：

hive --service metastore &

重新打开一个客户端，通过telnet连接到flume

telnet hadoop-master 44444

然后输入：

1 1

3 3

就会在hive中看到如下两行数据：

flume_user1.user_id flume_user1.user_name flume_user1.age

1 1 3

3 3 3

age是在hive.conf中设置的值3。

 

现在将flume的source换成http模式，然后hive分区通过参数模式动态的传输分区值。

将hive.conf中的

a1.sources.r1.type = netcat改成a1.sources.r1.type = http

a1.sinks.k1.hive.partition=3改成a1.sinks.k1.hive.partition=%{age}。

然后启动flume：

bin/flume-ng agent -c conf -f conf/hive.conf -n a1 -Dflume.root.logger=INFO,console。

在重新打开的窗口中通过http的模式传输数据到flume

curl -X POST -d '[{"headers":{"age":"109"},"body":"11 ligongong"}]' hadoop-master:44444。

在hive中可以看到如下的数据：

flume_user1.user_id flume_user1.user_name flume_user1.age

11 ligongong 109

由此可以看出通过http模式传输数据到hive中时，分区字段的信息是在header中传输，而其他字段的信息是放在bady中传输，并且不同列之间以hive.conf文件定义好的分隔符分隔。

 

5、使用avro模式，将数据在控制台打印出来。

不同的agent之间传输数据只能通过avro模式。

这里我们需要两台服务器来演示avro的使用，两台服务器分别是hadoop-master和hadoop-slave2

hadoop-master中运行agent2，然后指定agent2的sink为avro，并且将数据发送的主机名设置为hadoop-slave2。hadoop-master中flume的conf文件设置如下，名字为push.conf：

#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= hadoop-master

a2.sources.r1.port = 44444

a2.sources.r1.channels= c1

 

#Use a channel which buffers events in memory

a2.channels.c1.type= memory

a2.channels.c1.keep-alive= 10

a2.channels.c1.capacity= 100000

a2.channels.c1.transactionCapacity= 100000

 

#Describe/configure the source

a2.sinks.k1.type= avro#制定sink为avro

a2.sinks.k1.channel= c1

a2.sinks.k1.hostname= hadoop-slave2#指定sink要发送数据到的目的服务器名

a2.sinks.k1.port= 44444#目的服务器的端口

 

 

hadoop-slave2中运行的是agent1，agent1的source为avro。flume配置内容如下，文件名为pull.conf

#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.channels= c1

a1.sources.r1.bind= hadoop-slave2

a1.sources.r1.port= 44444

 

#Describe the sink

a1.sinks.k1.type= logger

a1.sinks.k1.channel = c1

 

#Use a channel which buffers events in memory

a1.channels.c1.type= memory

a1.channels.c1.keep-alive= 10

a1.channels.c1.capacity= 100000

a1.channels.c1.transactionCapacity= 100000。

现在hadoop-slave2中启动flume，然后在hadoop-master中启动flume，顺序一定要对，否则会报如下的错误：org.apache.flume.FlumeException: java.net.SocketException: Unresolved address

 

在hadoop-slave2中启动flume:

bin/flume-ng agent -c conf -f conf/pull.conf -n a1 -Dflume.root.logger=INFO,console

在hadoop-master中启动flume:

bin/flume-ng agent -c conf -f conf/push.conf -n a2 -Dflume.root.logger=INFO,console

 

重新打开一个窗口，通过telnet连接到hadoop-master

telnet hadoop-master 44444

然后发送11111aaaa

在hadoop-slave2的控制台中就会显示之前发送的，11111aaaa，如下所示：

2018-06-14 06:43:00,686 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:94)] Event: { headers:{} body: 31 31 31 31 31 61 61 61 61 0D 11111aaaa. }

 

 

6、通过flume将数据通传输到kafka，然后通过kafka将数据存储在hdfs和hive中。

首先要配置kafka。配置kafka请参考：https://blog.csdn.net/zxy987872674/article/details/72466504

在分别在hadoop-master、hadoop-slave1、hadoop-slave2上启动zookeeper。

命令为：

然后启动kafka，进入kafka的安装目录，执行命令：

./bin/kafka-server-start.sh config/server.properties &

在kafka中创建topic：

bin/kafka-topics.sh --create --zookeeper hadoop-master:2181,hadoop-slave1:2181,hadoop-slave2:2181 --replication-factor 1 --partitions 2 --topic flume_kafka

 

查看kafka中的topic：

bin/kafka-topics.sh --list --zookeeper hadoop-master:2181,hadoop-slave1:2181,hadoop-slave2:2181

 

启动kafka的消费者：

./kafka-console-consumer.sh --zookeeper hadoop-master:2181,hadoop-slave1:2181,hadoop-slave2:2181 --topic flume_kafka

 

配置flume中conf文件，设置source类型为exec，sink为org.apache.flume.sink.kafka.KafkaSink，设置kafka的topic为上面创建的flume_kafka，具体配置如下：

# Name the components on this agent

a1.sources = r1

a1.sinks = k1

a1.channels = c1

 

# Describe/configure the source

#设置sources的类型为exec，就是执行命令的意思

a1.sources.r1.type = exec

#设置sources要执行的命令

a1.sources.r1.command = tail -f /home/hadoop/flumeHomeWork/flumeCode/flume_exec_test.txt

 

# 设置kafka接收器

a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink

# 设置kafka的broker地址和端口号

a1.sinks.k1.brokerList=hadoop-master:9092

# 设置Kafka的topic

a1.sinks.k1.topic=flume_kafka

# 设置序列化的方式

a1.sinks.k1.serializer.class=kafka.serializer.StringEncoder

 

# use a channel which buffers events in memory

a1.channels.c1.type=memory

a1.channels.c1.capacity = 100000

a1.channels.c1.transactionCapacity = 1000

 

# Bind the source and sink to the channel

a1.sources.r1.channels=c1

a1.sinks.k1.channel=c1

 

启动flume:

只要/home/hadoop/flumeHomeWork/flumeCode/flume_exec_test.txt中有数据时flume就会加载kafka中，然后被上面启动的kafka消费者消费掉。

我们查看发现/home/hadoop/flumeHomeWork/flumeCode/flume_exec_test.txt文件中有如下的数据：

131,dry pasta

131,dry pasta

132,beauty

133,muscles joints pain relief

133,muscles joints pain relief

133,muscles joints pain relief

133,muscles joints pain relief

134,specialty wines champagnes

134,specialty wines champagnes

134,specialty wines champagnes