IT博客汇 | [原]Oozie4.2.0配置安装实战

[原]Oozie4.2.0配置安装实战

fansy1990发表于 2016-01-23 23:44:39

软件版本：

Oozie4.2.0，Hadoop2.6.0，Spark1.4.1，Hive0.14，Pig0.15.0，Maven3.2，JDK1.7，zookeeper3.4.6，HBase1.1.2，MySQL5.6

集群部署：

node1~4.centos.com node1~4 192.168.0.31~34 1G*4 内存 1核*4 虚拟机

node1:NameNode 、ResourceManager；

node2：SecondaryNameNode、Master、HMaster、HistoryServer、JobHistoryServer

node3：oozie-server（tomcat）、DataNode、NodeManager、HRegionServer、Worker、QuorumPeerMain

node4：DataNode、NodeManager、HRegionServer、Worker、Pig client、Hive Client、HiveServer2、QuorumPeerMain、mysql

1. 编译Oozie4.2.0

此篇参考 http://oozie.apache.org/docs/4.2.0/DG_QuickStart.html#Building_Oozie 、 http://blog.csdn.net/u014729236/article/details/47188631

1.1 编译环境准备

使用tomcat7，而不是tomcat6的下载地址：

1）下载压缩包oozie-4.2.0.tar.gz,并解压缩到/usr/local/oozie目录
2）修改pom.xml
/usr/local/oozie/oozie-4.2.0/distro/pom.xml
<get src="http://archive.apache.org/dist/tomcat/tomcat-6 ==>
<get src="http://archive.apache.org/dist/tomcat/tomcat-7

3）修改maven setting.xml ，使用开源中国的库
<mirror>
<id>nexus-osc</id>
<name>OSChina Central</name>
<url>http://maven.oschina.net/content/groups/public/</url>
<mirrorOf>*</mirrorOf>
</mirror>

1.2 编译

进入oozie解压缩目录，使用下面的命令：

bin/mkdistro.sh -DskipTests -Phadoop-2 -Dhadoop.auth.version=2.6.0 -Ddistcp.version=2.6.0 -Dspark.version=1.4.1 -Dpig.version=0.15.0 -Dtomcat.version=7.0.52

如果加入了hbase或者hive，并且指定到较高版本，则会出错，如：

#bin/mkdistro.sh -DskipTests -Phadoop-2 -Dhadoop.auth.version=2.6.0 -Ddistcp.version=2.6.0 -Dspark.version=1.4.1 -Dpig.version=0.15.0 -Dtomcat.version=7.0.52 #-Dhive.version=0.14.0 -Dhbase.version=1.1.2 ## 指定hive和hbase到较高版本编译通不过

1.3 修改HDFS配置：

修改hadoop core-site.xml，内容如下：

<property>
    <name>hadoop.proxyuser.[USER].hosts</name>
    <value>*</value>
  </property>
  <property>
    <name>hadoop.proxyuser.[USER].groups</name>
    <value>*</value>
  </property>

其中，[USER]需要改为后面启动oozie tomcat的用户

不重启hadoop集群，而使配置生效

hdfs dfsadmin -refreshSuperUserGroupsConfiguration
  yarn rmadmin -refreshSuperUserGroupsConfiguration

1.4 配置Oozie

（由于是在node3上部署oozie，所以把下面的压缩包拷贝到node3上）

1）取得压缩包:
oozie-4.2.0/distro/target/oozie-4.2.0-distro.tar.gz
2）解压缩：
tar -zxf oozie-4.2.0-distro.tar.gz

3）在oozie-4.2.0目录下新建libext目录，并把
ext-2.2.zip 拷贝到该目录下；
并拷贝hadoop相关jar包到该目录下
cp $HADOOP_HOME/share/hadoop/*/*.jar libext/
cp $HADOOP_HOME/share/hadoop/*/lib/*.jar libext/

把hadoop与tomcat冲突jar包去掉
mv servlet-api-2.5.jar servlet-api-2.5.jar.bak
mv jsp-api-2.1.jar jsp-api-2.1.jar.bak
mv jasper-compiler-5.5.23.jar jasper-compiler-5.5.23.jar.bak
mv jasper-runtime-5.5.23.jar jasper-runtime-5.5.23.jar.bak

拷贝mysql驱动到该目录下（使用mysql数据库，默认是derby）
scp mysql-connector-java-5.1.25-bin.jar node3:/usr/oozie/oozie-4.2.0/libext/

4）配置数据库连接，文件是conf/oozie-site.xml

<property>
    <name>oozie.service.JPAService.create.db.schema</name>
    <value>true</value>
</property>
<property>
    <name>oozie.service.JPAService.jdbc.driver</name>
    <value>com.mysql.jdbc.Driver</value>
</property>
<property>
    <name>oozie.service.JPAService.jdbc.url</name>
    <value>jdbc:mysql://node4:3306/oozie?createDatabaseIfNotExist=true</value>
</property>

<property>
    <name>oozie.service.JPAService.jdbc.username</name>
    <value>root</value>
</property>

<property>
    <name>oozie.service.JPAService.jdbc.password</name>
    <value>root</value>
</property>
<property>
    <name>oozie.service.HadoopAccessorService.hadoop.configurations</name>
    <value>*=/usr/hadoop/hadoop-2.6.0/etc/hadoop</value>
</property>

最后一个配置，是需要配置的，不然后面运行调度的时候，任务会报File /user/root/share/lib does not exist 的错误

5）启动前的初始化

a. 打war包
bin/oozie-setup.sh prepare-war

b. 初始化数据库
bin/ooziedb.sh create -sqlfile oozie.sql -run

c. 修改oozie-4.2.0/oozie-server/conf/server.xml文件，注释掉下面的记录


d. 上传jar包
bin/oozie-setup.sh sharelib create -fs hdfs://node1:8020

1.5 启动

bin/oozied.sh start

2. 流程实例

数据为：bank.csv ,并已经上传到hdfs://node1:8020/user/root/bank.csv ,可以在http://zeppelin-project.org/docs/tutorial/tutorial.html页面下载该数据

(当执行Hive、Pig任务的时候需要把第一行数据删除)

默认所有操作用户都是root，如果是其他用户，可能需要修改对应的目录

配置环境变量：export OOZIE_URL=http://node3:11000/oozie

2.1 MR任务流程

1. job.properties :

oozie.wf.application.path=hdfs://node1:8020/user/root/workflow/mr_demo/wf
#Hadoop"R
jobTracker=node1:8032
#Hadoop"fs.default.name
nameNode=hdfs://node1:8020/
#Hadoop"mapred.queue.name
queueName=default

2. workflow.xml

<workflow-app xmlns="uri:oozie:workflow:0.2" name="map-reduce-wf">
    <start to="mr-node"/>
    <action name="mr-node">
        <map-reduce>
            <job-tracker>${jobTracker}</job-tracker>
            <name-node>${nameNode}</name-node>
            <prepare>
                <delete path="${nameNode}/user/${wf:user()}/workflow/mr_demo/output"/>
            </prepare>
            <configuration>
                <property>
                    <name>mapred.job.queue.name</name>
                    <value>${queueName}</value>
                </property>
                <property>
                    <name>mapreduce.mapper.class</name>
                    <value>org.apache.hadoop.examples.WordCount$TokenizerMapper</value>
                </property>
                <property>
                    <name>mapreduce.reducer.class</name>
                    <value>org.apache.hadoop.examples.WordCount$IntSumReducer</value>
                </property>
                <property>
                    <name>mapred.map.tasks</name>
                    <value>1</value>
                </property>
                <property>
                    <name>mapred.input.dir</name>
                    <value>/user/${wf:user()}/bank.csv</value>
                </property>
                <property>
                    <name>mapred.output.dir</name>
                    <value>/user/${wf:user()}/workflow/mr_demo/output</value>
                </property>
            </configuration>
        </map-reduce>
        <ok to="end"/>
        <error to="fail"/>
    </action>
    <kill name="fail">
        <message>Map/Reduce failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
    </kill>
    <end name="end"/>
</workflow-app>

3. 运行：

1）拷贝workflow.xml文件到HDFS的 hdfs://node1:8020/user/root/workflow/mr_demo/wf/workflow.xml 目录；

2）在node3（node3既作为oozie的server也作为client）上运行 bin/oozie job -config job.properties -run ，即可提交任务，提交任务后会返回一个jobId ,例如：

0000004-160123180442501-oozie-root-W

3）使用 bin/oozie job -info 0000004-160123180442501-oozie-root-W 即可查看流程状态；

4）流程结束后，查看流程状态以及在对应的目录即可查看输出结果；

2.2 Pig任务流程

1. job.properties

oozie.wf.application.path=hdfs://node1:8020/user/root/workflow/pig_demo/wf
oozie.use.system.libpath=true #pig流程必须配置此选项
#Hadoop"ResourceManager
resourceManager=node1:8032
#Hadoop"fs.default.name
nameNode=hdfs://node1:8020/
#Hadoop"mapred.queue.name
queueName=default

2. workflow.xml

<workflow-app xmlns="uri:oozie:workflow:0.2"
name="whitehouse-workflow">
<start to="transform_job"/>
	<action name="transform_job">
		<pig>
			<job-tracker>${resourceManager}</job-tracker>
			<name-node>${nameNode}</name-node>
			<prepare>
				<delete path="/user/root/workflow/pig_demo/output"/>
			</prepare>
			<script>transform_job.pig</script>
		</pig>
		<ok to="end"/>
		<error to="fail"/>
	</action>
	<kill name="fail">
		<message>Job failed, error
			message[${wf:errorMessage(wf:lastErrorNode())}]
		</message>
	</kill>
	<end name="end"/>
</workflow-app>

3 . transform_job.pig pig任务用到的脚本

bank_data= LOAD '/user/root/bank.csv' USING PigStorage(';') AS
(age:int, job:chararray, marital:chararray,education:chararray,
 default:chararray,balance:int,housing:chararray,loan:chararray,
contact:chararray,day:int,month:chararray,duration:int,campaign:int,
pdays:int,previous:int,poutcom:chararray,y:chararray);

age_gt_30 = FILTER bank_data BY age >= 30;

store age_gt_30 into '/user/root/workflow/pig_demo/output' using PigStorage(',');

4. 运行

1）把 transform_job.pig ，workflow.xml 文件拷贝到 hdfs://node1:8020/user/root/workflow/pig_demo/wf/ 目录下面

2）运行 bin/oozie job -config job.properties -run

3）运行 bin/oozie job -info jobId 查看对应任务的进度状态，或者在浏览器中的node3:11000 URL中查看所有任务；

2.3 Hive任务流程

注意：hive 任务运行完成后，bank.csv文件会被删除（应该是移动到hive的warehouse目录下），所以进行其他或者再次运行时需要重新上传文件

1. job.properties

nameNode=hdfs://node1:8020
jobTracker=node1:8032
queueName=default
maxAge=30
input=/user/root/bank.csv
output=/user/root/workflow/hive_demo/output
oozie.use.system.libpath=true

oozie.wf.application.path=${nameNode}/user/${user.name}/workflow/hive_demo/wf

2. workflow.xml

<workflow-app xmlns="uri:oozie:workflow:0.2" name="hive-wf">
    <start to="hive-node"/>

    <action name="hive-node">
        <hive xmlns="uri:oozie:hive-action:0.2">
            <job-tracker>${jobTracker}</job-tracker>
            <name-node>${nameNode}</name-node>
            <prepare>
                <delete path="${output}/hive"/>
                <mkdir path="${output}"/>
            </prepare>
            <configuration>
                <property>
                    <name>mapred.job.queue.name</name>
                    <value>${queueName}</value>
                </property>
            </configuration>
            <script>script.hive</script>
            <param>INPUT=${input}</param>
            <param>OUTPUT=${output}/hive</param>
       		<param>maxAge=${maxAge}</param>
	 </hive>
        <ok to="end"/>
        <error to="fail"/>
    </action>

    <kill name="fail">
        <message>Hive failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
    </kill>
    <end name="end"/>
</workflow-app>

3. hive任务用到的脚本 script.hive

DROP TABLE IF EXISTS bank;

CREATE TABLE bank(
	age int,
	job string,
	marital string,education string,
 default string,balance int,housing string,loan string,
contact string,day int,month string,duration int,campaign int,
pdays int,previous int,poutcom string,y string
) 
 ROW FORMAT DELIMITED FIELDS TERMINATED BY '\073'
 STORED AS TEXTFILE;

 LOAD DATA INPATH '${INPUT}' INTO TABLE bank;

INSERT OVERWRITE DIRECTORY '${OUTPUT}' SELECT * FROM bank where age > '${maxAge}';

注意：‘\073’ 代表分号；
4. 运行，参考上面

2.4 Hive 2 任务流程

1. job.properties

nameNode=hdfs://node1:8020
jobTracker=node1:8032
queueName=default
jdbcURL=jdbc:hive2://node4:10000/default # hiveserver2 时，配置此选项
maxAge=30
input=/user/root/bank.csv
output=/user/root/workflow/hive2_demo/output
oozie.use.system.libpath=true

oozie.wf.application.path=${nameNode}/user/${user.name}/workflow/hive2_demo/wf

2. workflow.xml

<workflow-app xmlns="uri:oozie:workflow:0.5" name="hive2-wf">
    <start to="hive2-node"/>

    <action name="hive2-node">
        <hive2 xmlns="uri:oozie:hive2-action:0.1">
            <job-tracker>${jobTracker}</job-tracker>
            <name-node>${nameNode}</name-node>
            <prepare>
                <delete path="${output}/hive"/>
                <mkdir path="${output}"/>
            </prepare>
            <configuration>
                <property>
                    <name>mapred.job.queue.name</name>
                    <value>${queueName}</value>
                </property>
            </configuration>

	    <jdbc-url>${jdbcURL}</jdbc-url>
            <script>script2.hive</script>
            <param>INPUT=${input}</param>
            <param>OUTPUT=${output}/hive</param>
       		<param>maxAge=${maxAge}</param>
	 </hive2>
        <ok to="end"/>
        <error to="fail"/>
    </action>

    <kill name="fail">
        <message>Hive2 failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
    </kill>
    <end name="end"/>
</workflow-app>

3. hive2用到的脚本： script2.hive

DROP TABLE IF EXISTS bank2;

CREATE TABLE bank2(
	age int,
	job string,
	marital string,education string,
 default string,balance int,housing string,loan string,
contact string,day int,month string,duration int,campaign int,
pdays int,previous int,poutcom string,y string
) 
 ROW FORMAT DELIMITED FIELDS TERMINATED BY '\073'
 STORED AS TEXTFILE;

 LOAD DATA INPATH '${INPUT}' INTO TABLE bank2;

INSERT OVERWRITE DIRECTORY '${OUTPUT}' SELECT * FROM bank2 where age > '${maxAge}';

4. 运行，参考上面

2.5 Spark 任务流程

1. job.properties :

nameNode=hdfs://node1:8020
jobTracker=node1:8032
#master=spark://node2:7077 
master=spark://node2:6066
sparkMode=cluster
queueName=default
oozie.use.system.libpath=true
input=/user/root/bank.csv
output=/user/root/workflow/spark_demo/output
# the jar file must be local
jarPath=${nameNode}/user/root/workflow/spark_demo/lib/oozie-examples.jar
oozie.wf.application.path=${nameNode}/user/${user.name}/workflow/spark_demo/wf

由于sparkMode采用cluster，所以master的链接需要是下面的6066,：

sparkMode使用client没有试验成功；

2. workflow.xml

<workflow-app xmlns='uri:oozie:workflow:0.5' name='SparkFileCopy'>
    <start to='spark-node' />

    <action name='spark-node'>
        <spark xmlns="uri:oozie:spark-action:0.1">
            <job-tracker>${jobTracker}</job-tracker>
            <name-node>${nameNode}</name-node>
            <prepare>
                <delete path="${output}"/>
            </prepare>
            <master>${master}</master>
        <mode>${sparkMode}</mode>   
            <name>Spark-FileCopy</name>
	 <class>org.apache.oozie.example.SparkFileCopy</class>
            <jar>${jarPath}</jar>
            <arg>${input}</arg>
            <arg>${output}</arg>
        </spark>
        <ok to="end" />
        <error to="fail" />
    </action>

    <kill name="fail">
        <message>Workflow failed, error
            message[${wf:errorMessage(wf:lastErrorNode())}]
        </message>
    </kill>
    <end name='end' />
</workflow-app>

3. 运行：

1）这里用到的oozie-examples.jar 是在oozie-examples.tar.gz解压后的examples/apps/spark/lib目录下面

2）上传oozie-examples.jar 到hdfs://node1:8020/user/root/workflow/spark_demo/lib/oozie-examples.jar 目录；上传workflow.xml到hdfs://node1:8020/user/root/workflow/spark_demo/wf/workflow.xml文件；

3） bin/oozie job -config job.properties -run 即可运行；

4. 相关问题：

1）这种方式提交任务是通过yarn开启任务，然后提交到spark集群运行的，并不是直接由spark集群运行的，如下图：

首先在8088 界面看到yarn开启的任务：

接着去spark监控界面，同样可以看到监控界面：

但是这样时间就不对了，看日志：

可以看到连接到了yarn的resourcemanager后，直接就连接了spark的master了，然后提交了任务，接着就直接yarn的任务就successed了，然后yarn就返回了；

查看spark的日志，时间也是吻合的：

最后保存文件，关闭driver：

2.6 spark on yarn任务流程

参考官网的提示：

1. job.properties:

nameNode=hdfs://node1:8020
jobTracker=node1:8032
#master=spark://node2:7077
#master=spark://node2:6066
master=yarn-cluster
#sparkMode=cluster
queueName=default
oozie.use.system.libpath=true
input=/user/root/bank.csv
output=/user/root/workflow/sparkonyarn_demo/output

jarPath=${nameNode}/user/root/workflow/sparkonyarn_demo/lib/oozie-examples.jar
oozie.wf.application.path=${nameNode}/user/${user.name}/workflow/sparkonyarn_demo

2. workflow.xml:

<workflow-app xmlns='uri:oozie:workflow:0.5' name='SparkFileCopy_on_yarn'>
    <start to='spark-node' />

    <action name='spark-node'>
        <spark xmlns="uri:oozie:spark-action:0.1">
            <job-tracker>${jobTracker}</job-tracker>
            <name-node>${nameNode}</name-node>
            <prepare>
                <delete path="${output}"/>
            </prepare>
            <master>${master}</master>
            <name>Spark-FileCopy-on-yarn</name>
	 <class>org.apache.oozie.example.SparkFileCopy</class>
            <jar>${jarPath}</jar>
            <spark-opts>--conf spark.yarn.historyServer.address=http://node2:18080 --conf spark.eventLog.dir=hdfs://node1:8020/spark-log --conf spark.eventLog.enabled=true</spark-opts>
		<arg>${input}</arg>
            <arg>${output}</arg>
        </spark>
        <ok to="end" />
        <error to="fail" />
    </action>

    <kill name="fail">
        <message>Workflow failed, error
            message[${wf:errorMessage(wf:lastErrorNode())}]
        </message>
    </kill>
    <end name='end' />
</workflow-app>

3. 运行;

1）环境准备：拷贝workflow.xml 到hdfs;//node1:8020/user/root/workflow/sparkonyarn_demo/workflow.xml文件

2）拷贝oozie-exmaples.jar 到 hdfs;//node1:8020/user/root/workflow/sparkonyarn_demo/lib/oozie-examples.jar文件

3）拷贝$SPARK_HOME/lib/spark-assembly-1.4.1-hadoop2.6.0.jar文件到hdfs;//node1:8020/user/root/workflow/sparkonyarn_demo/lib/spark-assembly-1.4.1-hadoop2.6.0.jar

4） bin/oozie job -config job.properties -run

5）查看任务状态：