GoldenGate实时投递数据到大数据平台（3）- Apache Flume

Apache Flume

    Flume NG是一个分布式、可靠、可用的系统，它能够将不同数据源的海量日志数据进行高效收集、聚合，最后存储到一个中心化数据存储系统中,方便进行数据分析。事实上flume也可以收集其他信息，不仅限于日志。包括端口数据、JMS、命令行等输出数据。

架构

Flume主要的组件包括source（数据源），数据中间存储（channel），sink数据目标存储。

可实现多种拓扑架构，如级联数据传输。

可以多对一做数据集中

也可以一对多做数据分发

Flume支持的组件

Source

Channel

sink

安装及简单测试

从flume.apache.org下载安装包之后，解压到/u02/flume1.8

编辑一个示例文件 conf/a1.conf，内容如下

# Name the components on this agent

a1.sources = r1

a1.sinks = k1

a1.channels = c1

# Describe/configure the source

a1.sources.r1.type = 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

# Bind the source and sink to the channel

a1.sources.r1.channels = c1

a1.sinks.k1.channel = c1

以上配置监听44444端口的netcat source。

启动代理

bin/flume-ng agent --conf conf --conf-file conf/a1.conf --name a1 -Dflume.root.logger=INFO,console

输出日志如下

info: Including Hive libraries found via () for Hive access

+ exec /u01/jdk1.8.0_111/bin/java -Xmx20m -Dflume.root.logger=INFO,console -cp '/u02/flume1.8/conf:/u02/flume1.8/lib/*:/lib/*' -Djava.library.path= org.apache.flume.node.Application --conf-file conf/a1.conf --name a1

2018-01-02 09:11:28,634 (lifecycleSupervisor-1-0) [INFO - org.apache.flume.node.PollingPropertiesFileConfigurationProvider.start(PollingPropertiesFileConfigurationProvider.java:62)] Configuration provider starting

2018-01-02 09:11:28,640 (conf-file-poller-0) [INFO - org.apache.flume.node.PollingPropertiesFileConfigurationProvider$FileWatcherRunnable.run(PollingPropertiesFileConfigurationProvider.java:134)] Reloading configuration file:conf/a1.conf

............................

2018-01-02 09:11:28,814 (lifecycleSupervisor-1-0) [INFO - org.apache.flume.source.NetcatSource.start(NetcatSource.java:166)] Created serverSocket:sun.nio.ch.ServerSocketChannelImpl[/127.0.0.1:44444]

可以看到监听的IP和端口已经正常启动。

在另一个终端上执行telnet，输入相关的文字。

$telnet localhost 44444

Trying 127.0.0.1...

Connected to localhost.

Escape character is '^]'.

hello

OK

that's ok

OK

quit

OK

^]

telnet> quit

Connection closed.

可以看到flume agent窗口输出的日志包含有刚才输入的文字，flume agent可以正常运行。

2018-01-02 09:12:07,928 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:95)] Event: { headers:{} body: 68 65 6C 6C 6F 0D hello. }

2018-01-02 09:12:16,932 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:95)] Event: { headers:{} body: 74 68 61 74 27 73 20 6F 6B 0D that's ok. }

2018-01-02 09:12:20,935 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:95)] Event: { headers:{} body: 71 75 69 74 0D quit. }

OGG与flume集成的架构

GoldenGate在源端捕获关系型数据库的增量数据，OGG通过网络将数据传输到flume配置的节点，在此节点上配置flume agent，并启动一个监听端口，然后OGG Java adapter解析增量数据，实时将数据写入到Flume agent的source端口中，最后由flume通过channel, sink将数据传输到下一个目标存储节点。

采用GoldenGate与flume的集成，可以实现RDBMS的增量数据，实时写入到flume支持的任意目标类型节点。

针对OGG的投递，只需要在a1.conf中添加一段接收avro的监听配置即可，如下：

a1.sources = r1 r2

a1.sources.r2.channels = c1

a1.sources.r2.type = avro

a1.sources.r2.bind = 192.168.89.132

a1.sources.r2.port = 41414

重新启动flume agent，可以看到输出日志中包含了AVRO相关的日志信息:

2018-01-02 09:39:21,785 (lifecycleSupervisor-1-0) [INFO - org.apache.flume.instrumentation.MonitoredCounterGroup.register(MonitoredCounterGroup.java:119)] Monitored counter group for type: SOURCE, name: r2: Successfully registered new MBean.

2018-01-02 09:39:21,786 (lifecycleSupervisor-1-0) [INFO - org.apache.flume.instrumentation.MonitoredCounterGroup.start(MonitoredCounterGroup.java:95)] Component type: SOURCE, name: r2 started

2018-01-02 09:39:21,788 (lifecycleSupervisor-1-0) [INFO - org.apache.flume.source.AvroSource.start(AvroSource.java:260)] Avro source r2 started.

OGG配置

解压goldengate for bigdata 12.3到/u01/ogg4bd_12.3目录。

设置环境变量

export LD_LIBRARY_PATH=/u01/jdk1.8.0_111/jre/lib/amd64/server

拷贝Adapter-Examples/big-data/flume中的所有文件到dirprm/目录下。

修改flume.props文件中flume对应的lib路径。

修改custom-flume-rpc.properties中flume监听avro的主机和端口。

flume.props文件内容如下：

[oracle@ol73 dirprm]$ more flume.props

gg.handlerlist = flumehandler

gg.handler.flumehandler.type=flume

gg.handler.flumehandler.RpcClientPropertiesFile=custom-flume-rpc.properties

gg.handler.flumehandler.format=avro_op

gg.handler.flumehandler.mode=tx

#gg.handler.flumehandler.maxGroupSize=100, 1Mb

#gg.handler.flumehandler.minGroupSize=50, 500 Kb

gg.handler.flumehandler.EventMapsTo=tx

gg.handler.flumehandler.PropagateSchema=true

gg.handler.flumehandler.includeTokens=false

gg.handler.flumehandler.format.WrapMessageInGenericAvroMessage=true

goldengate.userexit.timestamp=utc

goldengate.userexit.writers=javawriter

javawriter.stats.display=TRUE

javawriter.stats.full=TRUE

gg.log=log4j

gg.log.level=INFO

gg.report.time=30sec

#Sample gg.classpath for Apache Flume

gg.classpath=dirprm/:/u02/flume1.8/lib/*:

javawriter.bootoptions=-Xmx512m -Xms32m -Djava.class.path=ggjava/ggjava.jar

以上文件内容从示例文件中直接拷贝即可，唯一要修改的是gg.classpath。

custom-flume-rpc.properties内容如下

[oracle@ol73 dirprm]$ more custom-flume-rpc.properties

client.type=default

hosts=h1

hosts.h1=192.168.89.132:41414

batch-size=100

connect-timeout=20000

request-timeout=20000

唯一要修改的是flume监听的主机IP和端口，如果有多个flume组成的集群，可以设置多个host。

最后，是ogg投递的进程参数rflume.prm

[oracle@ol73 dirprm]$ more rflume.prm

REPLICAT rflume

-- Trail file for this example is located in "AdapterExamples/trail" directory

-- Command to add REPLICAT

-- add replicat rflume, exttrail AdapterExamples/trail/tr

TARGETDB LIBFILE libggjava.so SET property=dirprm/flume.props

REPORTCOUNT EVERY 1 MINUTES, RATE

GROUPTRANSOPS 10000

MAP QASOURCE.*, TARGET QASOURCE.*;

OGG投递测试及验证

进入ggsci，添加进程，使用自带的示例数据

GGSCI> add replicat rflume, exttrail AdapterExamples/trail/tr

启动投递进程

GGSCI (ol73) 5> start rflume

Sending START request to MANAGER ...

REPLICAT RFLUME starting

查看进程状态，发现已经投递完成

GGSCI (ol73) 6> info rflume

REPLICAT RFLUME Last Started 2018-01-02 09:41 Status RUNNING

Checkpoint Lag 00:00:00 (updated 00:00:00 ago)

Process ID 66856

Log Read Checkpoint File /u01/ogg4bd_12.3/AdapterExamples/trail/tr000000000

2015-11-06 02:45:39.000000 RBA 5660

检查投递的数据

GGSCI (ol73) 7> stats rflume, total

Sending STATS request to REPLICAT RFLUME ...

Start of Statistics at 2018-01-02 09:41:51.

Replicating from QASOURCE.TCUSTMER to QASOURCE.TCUSTMER:

*** Total statistics since 2018-01-02 09:41:45 ***

Total inserts 5.00

Total updates 1.00

Total deletes 0.00

Total discards 0.00

Total operations 6.00

Replicating from QASOURCE.TCUSTORD to QASOURCE.TCUSTORD:

*** Total statistics since 2018-01-02 09:41:45 ***

Total inserts 5.00

Total updates 3.00

Total deletes 2.00

Total discards 0.00

Total operations 10.00

End of Statistics.

在flume agent的日志上可以看到

2018-01-02 09:41:45,296 (New I/O server boss #5) [INFO - org.apache.avro.ipc.NettyServer$NettyServerAvroHandler.handleUpstream(NettyServer.java:171)] [id: 0x41866afa, /192.168.89.132:17935 => /192.168.89.132:41414] OPEN

2018-01-02 09:41:45,299 (New I/O worker #1) [INFO - org.apache.avro.ipc.NettyServer$NettyServerAvroHandler.handleUpstream(NettyServer.java:171)] [id: 0x41866afa, /192.168.89.132:17935 => /192.168.89.132:41414] BOUND: /192.168.89.132:41414

2018-01-02 09:41:45,300 (New I/O worker #1) [INFO - org.apache.avro.ipc.NettyServer$NettyServerAvroHandler.handleUpstream(NettyServer.java:171)] [id: 0x41866afa, /192.168.89.132:17935 => /192.168.89.132:41414] CONNECTED: /192.168.89.132:17935

2018-01-02 09:41:45,700 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:95)] Event: { headers:{SCHEMA_EVENT=TRUE, SCHEMA_NAME=QASOURCE, TABLE_NAME=TCUSTMER, GENERIC_WRAPPER=false, SCHEMA_FINGERPRINT=-1783711649} body: 7B 0A 20 20 22 74 79 70 65 22 20 3A 20 22 72 65 {. "type" : "re }

2018-01-02 09:41:45,701 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:95)] Event: { headers:{SCHEMA_EVENT=TRUE, SCHEMA_NAME=QASOURCE, TABLE_NAME=TCUSTMER, GENERIC_WRAPPER=true, SCHEMA_FINGERPRINT=1472787928} body: 7B 0A 20 20 22 74 79 70 65 22 20 3A 20 22 72 65 {. "type" : "re }

2018-01-02 09:41:45,701 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:95)] Event: { headers:{SCHEMA_EVENT=TRUE, SCHEMA_NAME=QASOURCE, TABLE_NAME=TCUSTORD, GENERIC_WRAPPER=false, SCHEMA_FINGERPRINT=495754722} body: 7B 0A 20 20 22 74 79 70 65 22 20 3A 20 22 72 65 {. "type" : "re }

2018-01-02 09:41:45,701 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:95)] Event: { headers:{OP_COUNT=4, GG_TRANID=00000000000000001956} body: 22 51 41 53 4F 55 52 43 45 2E 54 43 55 53 54 4D "QASOURCE.TCUSTM }

2018-01-02 09:41:45,748 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:95)] Event: { headers:{OP_COUNT=12, GG_TRANID=00000000000000003286} body: 22 51 41 53 4F 55 52 43 45 2E 54 43 55 53 54 4D "QASOURCE.TCUSTM }

可以看到，在flume输出的日志中，已经有对应的表及源端DB操作等数据，console输出信息中，flume-ng针对logger是只显示16个字节的，剩下的都被sink截了。

如果有需求，也可以使用flume sink将数据输出到HDFS、HIVE, kafka等目标端，从而实现RDBMS数据与其它半结构化数据的实时整合。