命令行部署#
前提#
YARN需要依赖HDFS集群, HDFS安装部署请参考：HDFS 安装。
HDFS服务地址假定为hdfs1:9000,hdfs2:9000,hdfs3:9000
ZooKeeper安装部署请参考：ZooKeeper 安装。
ZooKeeper服务地址假定为zookeeper1:2181,zookeeper2:2181,zookeeper3:2181
Kerberos认证依赖(可选)#
若开启Kerberos认证，Kerberos安装部署请参考：Kerberos 安装。
KDC服务地址假定为kdc1
Ranger认证依赖(可选)#
若开启Ranger认证，Ranger安装部署请参考：Ranger 安装。
Ranger 服务地址假定为ranger1
配置yum源并安装lava#
登录到yarn1，然后切换到root用户
ssh yarn1
su - root
配置yum源，安装lava命令行管理工具
# 从yum源所在机器(假设为192.168.1.10)获取repo文件
scp root@192.168.1.10:/etc/yum.repos.d/oushu.repo /etc/yum.repos.d/oushu.repo
# 追加yum源所在机器信息到/etc/hosts文件
# 安装lava命令行管理工具
yum clean all
yum makecache
yum install -y lava
创建一个yarnhost文件
touch yarnhost
配置yarnhost内容为YARN的所有hostname
yarn1
yarn2
yarn3
在首台机器上和集群内其他节点交换公钥，以便ssh免密码登陆和分发配置文件
# 和集群内其他机器交换公钥
lava ssh-exkeys -f ${HOME}/yarnhost -p ********
# 将repo文件分发给集群内其他机器
lava scp -f ${HOME}/yarnhost /etc/yum.repos.d/oushu.repo =:/etc/yum.repos.d
安装#
准备#
创建rmhost文件
touch rmhost
配置rmhost内容为YARN的ResourceManager节点hostname：
yarn1
yarn2
安装YARN
lava ssh -f ${HOME}/yarnhost -e 'sudo yum install -y yarn'
lava ssh -f ${HOME}/yarnhost -e 'mkdir -p /data1/yarn/nodemanager'
lava ssh -f ${HOME}/yarnhost -e 'chown -R yarn:hadoop /data1/yarn/'
lava ssh -f ${HOME}/yarnhost -e 'chmod -R 755 yarn:hadoop /data1/yarn/'
安装MapReduce（默认计算引擎，可选）
lava ssh -f ${HOME}/yarnhost -e 'sudo yum install -y mapreduce'
如需需在非YARN节点安装使用客户端，遵守HDFS章节客户端安装的步骤即可。
Kerberos准备(可选)#
如果开启Kerberos，则需要在所有YARN节点安装Kerberos客户端。
lava ssh -f ${HOME}/yarnhost -e "yum install -y krb5-libs krb5-workstation"
在yarn1节点执行下面命令进入Kerberos控制台
ssh kdc1
kadmin.loacl
进入控制台后执行下列操作
# 根据角色规划信息为每个节点的对应角色生成实例
addprinc -randkey resourcemanager/yarn1@OUSHU.COM
addprinc -randkey resourcemanager/yarn2@OUSHU.COM
addprinc -randkey nodemanager/yarn1@OUSHU.COM
addprinc -randkey nodemanager/yarn2@OUSHU.COM
addprinc -randkey nodemanager/yarn3@OUSHU.COM
addprinc -randkey HTTP/yarn1@OUSHU.COM
addprinc -randkey HTTP/yarn2@OUSHU.COM
addprinc -randkey HTTP/yarn3@OUSHU.COM
addprinc -randkey yarn@OUSHU.COM
# 为每个实例生成keytab文件
ktadd -k /etc/security/keytabs/yarn.keytab resourcemanager/yarn1@OUSHU.COM
ktadd -k /etc/security/keytabs/yarn.keytab resourcemanager/yarn2@OUSHU.COM
ktadd -k /etc/security/keytabs/yarn.keytab nodemanager/yarn1@OUSHU.COM
ktadd -k /etc/security/keytabs/yarn.keytab nodemanager/yarn2@OUSHU.COM
ktadd -k /etc/security/keytabs/yarn.keytab nodemanager/yarn3@OUSHU.COM
ktadd -k /etc/security/keytabs/yarn.keytab yarn@OUSHU.COM
# 为每个keytab文件追加HTTP实例的密钥
ktadd -k /etc/security/keytabs/yarn.keytab HTTP/yarn1@OUSHU.COM
ktadd -k /etc/security/keytabs/yarn.keytab HTTP/yarn2@OUSHU.COM
ktadd -k /etc/security/keytabs/yarn.keytab HTTP/yarn3@OUSHU.COM
# 如果使用mapreduce引擎添加下边步骤
addprinc -randkey mapreduce@OUSHU.COM
ktadd -k /etc/security/keytabs/yarn.keytab mapreduce@OUSHU.COM
# 完成退出
quit
回到yarn1并将生成的keytab进行分发：
ssh yarn1
lava ssh -f ${HOME}/yarnhost -e 'mkdir -p /etc/security/keytabs/'
scp root@kdc1:/etc/krb5.conf /etc/krb5.conf
scp root@kdc1:/etc/security/keytabs/yarn.keytab /etc/security/keytabs/yarn.keytab
lava scp -r -f ${HOME}/yarnhost /etc/krb5.conf =:/etc/krb5.conf
lava scp -r -f ${HOME}/yarnhost /etc/security/keytabs/yarn.keytab =:/etc/security/keytabs/yarn.keytab
lava ssh -f ${HOME}/yarnhost -e 'chown yarn:hadoop /etc/security/keytabs/yarn.keytab'
配置#
HA配置#
修改 YARN 配置
vim /usr/local/oushu/conf/common/yarn-site.xml
<!-- 添加下面配置 -->
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
<!-- 根据实际情况配置cpu和memory --> <property>
<name>yarn.nodemanager.resource.memory-mb</name>
<value>8192</value>
</property>
<property>
<name>yarn.nodemanager.resource.cpu-vcores</name>
<value>2</value>
</property>
<property>
<name>yarn.scheduler.minimum-allocation-mb</name>
<value>1024</value>
</property>
<property>
<name>yarn.scheduler.maximum-allocation-mb</name>
<value>4096</value>
</property>
<!-- 配置实际zookeeper集群地址 --> <property>
<name>yarn.resourcemanager.zk-address</name>
<value>yarn1:2181,yarn2:2181,yarn3:2181</value>
</property>
<property>
<name>yarn.resourcemanager.ha.enabled</name>
<value>true</value>
</property>
<property>
<name>yarn.resourcemanager.cluster-id</name>
<value>yarn1</value>
</property>
<property>
<name>yarn.resourcemanager.ha.rm-ids</name>
<value>rm1,rm2</value>
</property>
<property>
<name>yarn.resourcemanager.hostname.rm1</name>
<value>yarn1</value>
</property>
<property>
<name>yarn.resourcemanager.hostname.rm2</name>
<value>yarn2</value>
</property>
<property>
<name>yarn.resourcemanager.webapp.address.rm1</name>
<value>yarn1:8088</value>
</property>
<property>
<name>yarn.resourcemanager.webapp.address.rm2</name>
<value>yarn2:8088</value>
</property>
<property>
<name>yarn.resourcemanager.webapp.https.address.rm1</name>
<value>yarn1:8090</value>
</property>
<property>
<name>yarn.resourcemanager.webapp.https.address.rm2</name>
<value>yarn2:8090</value>
</property>
<property>
<name>yarn.resourcemanager.recovery.enabled</name>
<value>true</value>
</property>
<property>
<name>yarn.resourcemanager.store.class</name>
<value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value>
</property>
<property>
<name>yarn.nodemanager.pmem-check-enabled</name>
<value>false</value>
</property>
<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
</property>
<property>
<name>yarn.scheduler.capacity.maximum-am-resource-percent</name>
<value>0.6</value>
</property>
<property>
<name>yarn.nodemanager.local-dirs</name>
<value>/data1/yarn/nodemanager</value>
</property>
<property>
<name>yarn.nodemanager.log-dirs</name>
<value>/usr/local/oushu/log/hadoop</value>
</property>
<property>
<name>yarn.resourcemanager.recovery.enabled</name>
<value>true</value>
</property>
<property>
<name>yarn.resourcemanager.store.class</name>
<value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value>
</property>
同步配置文件
# 拷贝配置文件到其他节点
lava scp -r -f ${HOME}/yarnhost /usr/local/oushu/conf/common/* =:/usr/local/oushu/conf/common/
Kerberos配置(可选)#
vim /usr/local/oushu/conf/common/yarn-site.xml
<!-- resourcemanager security config -->
<property>
<name>yarn.resourcemanager.keytab</name>
<value>/etc/security/keytabs/yarn.keytab</value>
</property>
<property>
<name>yarn.resourcemanager.principal</name>
<value>resourcemanager/_HOST@OUSHU.COM</value>
</property>
<property>
<name>yarn.resourcemanager.webapp.spnego-principal</name>
<value>HTTP/_HOST@OUSHU.COM</value>
</property>
<property>
<name>yarn.nodemanager.keytab</name>
<value>/etc/security/keytabs/yarn.keytab</value>
</property>
<!-- nodemanager security config -->
<property>
<name>yarn.nodemanager.principal</name>
<value>nodemanager/_HOST@OUSHU.COM</value>
</property>
<!-- 如果YARN 开启 SSL -->
<property>
<name>yarn.http.policy</name>
<value>HTTPS_ONLY</value>
</property>
<property>
<name>yarn.nodemanager.container-executor.class</name>
<value>org.apache.hadoop.yarn.server.nodemanager.LinuxContainerExecutor</value>
</property>
<property>
<name>yarn.nodemanager.linux-container-executor.group</name>
<value>hadoop</value>
</property>
<!-- LinuxContainerExecutor脚本路径 -->
<property>
<name>yarn.nodemanager.linux-container-executor.path</name>
<value>/usr/local/oushu/yarn/bin/container-executor</value>
</property>
修改container-executor和container-executor.cfg权限#
节点的container-executor所有者和权限，要求其所有者为root，所有组为hadoop。
节点的container-executor.cfg文件的所有者和权限，要求该文件及其所有的上级目录的所有者均为root，所有组为hadoop
lava ssh -f ${HOME}/yarnhost -e 'chown root:hadoop /usr/local/oushu/yarn/bin/container-executor'
lava ssh -f ${HOME}/yarnhost -e 'chmod 6050 /usr/local/oushu/yarn/bin/container-executor'
lava ssh -f ${HOME}/yarnhost -e 'mkdir -p /etc/hadoop/'
scp /usr/local/oushu/conf/common/container-executor.cfg /etc/hadoop/
vim /etc/hadoop/container-executor.cfg
->
yarn.nodemanager.local-dirs=/data1/yarn/nodemanager
yarn.nodemanager.log-dirs=/usr/local/oushu/log/hadoop
yarn.nodemanager.linux-container-executor.group=hadoop
banned.users=
allowed.system.users=
min.user.id=1000
lava scp -r -f ${HOME}/yarnhost /etc/hadoop/container-executor.cfg =:/etc/hadoop/
lava ssh -f ${HOME}/yarnhost -e 'chown root:hadoop /etc/hadoop/container-executor.cfg'
lava ssh -f ${HOME}/yarnhost -e 'chmod 400 /etc/hadoop/container-executor.cfg'
如果使用MapReduce计算引擎
vim /usr/local/oushu/conf/common/mapred-site.xml
<configuration>
<property>
<!-- 使用yarn运行mapreduce，非本地运行 -->
<name>mapreduce.framework.name</name>
<value>yarn</value> <!-- 使用yarn运行mapred，无论是否kerberos都需要 -->
</property>
<!-- 如果mapred 开启 SSL -->
<property>
<name>mapreduce.jobhistory.http.policy</name>
<value>HTTPS_ONLY</value>
</property>
</configuration>
分发修改的配置
lava scp -r -f ${HOME}/yarnhost /usr/local/oushu/conf/common/mapred-site.xml =:/usr/local/oushu/conf/common/
lava scp -r -f ${HOME}/yarnhost /usr/local/oushu/conf/common/yarn-site.xml =:/usr/local/oushu/conf/common/
配置调优(可选)#
除默认配置外，我们在实际使用过程中可能会出现性能差，实际运行效果不理想的情况，此时我们需要考虑对YARN和Mapreduce进行调优。
一般我们需要考虑结合实际内存和CPU性能进行调整，查看内存和CPU使用情况，一般来讲资源分配的CPU核数为物理实际核数的两到三倍。
# 查看内存使用情况
$ free -h
total used free shared buff/cache available
Mem: 61G 15G 32G 273M 13G 45G
Swap: 0B 0B 0B
修改配置文件/usr/local/oushu/conf/common/mapred-site.xml，设置使用Job的内存。
<property>
<name>mapreduce.am.max-attempts</name>
<value>2</value>
</property>
<property>
<name>mapreduce.job.counters.max</name>
<value>130</value>
</property>
<property>
<name>mapreduce.job.reduce.slowstart.completedmaps</name>
<value>0.05</value>
</property>
<property>
<name>mapreduce.map.java.opts</name>
<value>-Xmx1024m</value>
</property>
<property>
<name>mapreduce.map.memory.mb</name>
<value>2048</value>
</property>
<property>
<name>mapreduce.map.sort.spill.percent</name>
<value>0.7</value>
</property>
<property>
<name>mapreduce.reduce.java.opts</name>
<value>-Xmx1024m</value>
</property>
<property>
<name>mapreduce.reduce.memory.mb</name>
<value>2048</value>
</property>
<property>
<name>mapreduce.reduce.shuffle.input.buffer.percent</name>
<value>0.8</value>
</property>
<property>
<name>mapreduce.reduce.shuffle.merge.percent</name>
<value>0.75</value>
</property>
<property>
<name>mapreduce.reduce.shuffle.parallelcopies</name>
<value>30</value>
</property>
<property>
<name>mapreduce.reduce.speculative</name>
<value>false</value>
</property>
<property>
<name>mapreduce.task.io.sort.factor</name>
<value>100</value>
</property>
<property>
<name>mapreduce.task.io.sort.mb</name>
<value>358</value>
</property>
<property>
<name>yarn.app.mapreduce.am.command-opts</name>
<value>-Xmx1024m</value>
</property>
<property>
<name>yarn.app.mapreduce.am.resource.mb</name>
<value>2048</value>
</property>
<property>
<name>mapreduce.map.cpu.vcores</name>
<value>2</value>
</property>
<property>
<name>mapreduce.reduce.cpu.vcores</name>
<value>2</value>
</property>
<property>
<name>mapred.job.reuse.jvm.num.tasks</name>
<value>10</value>
</property>
修改配置文件/usr/local/oushu/conf/common/yarn-site.xml
//container
<property>
<name>yarn.scheduler.maximum-allocation-mb</name>
<value>12800</value>
</property>
<property>
<name>yarn.scheduler.maximum-allocation-vcores</name>
<value>8</value>
</property>
<property>
<name>yarn.scheduler.minimum-allocation-mb</name>
<value>512</value>
</property>
// nodemanger
<property>
<name>yarn.nodemanager.container-metrics.unregister-delay-ms</name>
<value>60000</value>
</property>
<property>
<name>yarn.nodemanager.container-monitor.interval-ms</name>
<value>3000</value>
</property>
<property>
<name>yarn.nodemanager.log-aggregation.compression-type</name>
<value>gz</value>
</property>
<property>
<name>yarn.nodemanager.resource.cpu-vcores</name>
<value>8</value>
</property>
<property>
<name>yarn.nodemanager.resource.memory-mb</name>
<value>123904</value>
</property>
<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
</property>
<property>
<name>yarn.nodemanager.vmem-pmem-ratio</name>
<value>2.1</value>
</property>
//rm
<property>
<name>yarn.resourcemanager.am.max-attempts</name>
<value>2</value>
</property>
<property>
<name>yarn.resourcemanager.monitor.capacity.preemption.natural_termination_factor</name>
<value>1</value>
</property>
<property>
<name>yarn.resourcemanager.monitor.capacity.preemption.total_preemption_per_round</name>
<value>0.33</value>
</property>
<property>
<name>yarn.resourcemanager.placement-constraints.handler</name>
<value>scheduler</value>
</property>
<property>
<name>yarn.resourcemanager.scheduler.class</name>
<value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler</value>
</property>
<property>
<name>yarn.scheduler.maximum-allocation-mb</name>
<value>12800</value>
</property>
<property>
<name>yarn.scheduler.maximum-allocation-vcores</name>
<value>8</value>
</property>
<property>
<name>yarn.scheduler.minimum-allocation-mb</name>
<value>512</value>
</property>
<!--
<property>
<name>yarn.nodemanager.container-executor.class</name>
<value>org.apache.hadoop.yarn.server.nodemanager.LinuxContainerExecutor</value>
</property>
<property>
<name>yarn.nodemanager.linux-container-executor.group</name>
<value>hadoop</value>
</property>
<property>
<name>yarn.nodemanager.linux-container-executor.nonsecure-mode.limit-users</name>
<value>false</value>
</property>
<property>
<name>yarn.nodemanager.linux-container-executor.nonsecure-mode.local-user</name>
<value>yarn</value>
</property>
-->
同时可以适当增加NameNode使用内存来提升效率，查看usr/local/oushu/conf/common/hadoop-env.xml
export HADOOP_NAMENODE_OPTS="-Xmx6144m -XX:+UseCMSInitiatingOccupancyOnly -XX:CMSInitiatingOccupancyFraction=70"
export HADOOP_DATANODE_OPTS="-Xmx2048m -Xss256k"
增加”-Xmx” 后的数值来增加分配内存
启动#
在yarn1节点 如果开启了Kerberos认证(可选)：
su - yarn
kinit -kt /etc/security/keytabs/yarn.keytab yarn@OUSHU.COM
# 一般情况下,没有报错提示就认为成功,也可以通过：
echo $?
0
# 返回值为0则是成功
启动YARN服务
# 启动ResourceManager
lava ssh -f ${HOME}/rmhost -e 'sudo -E -u yarn yarn --daemon start resourcemanager'
# 启动NodeManager
lava ssh -f ${HOME}/yarnhost -e 'sudo -E -u yarn yarn --daemon start nodemanager'
检查状态#
YARN集群情况检查
ResourceManager整体情况
yarn rmadmin -getAllServiceState
yarn1:8033 active
yarn2:8033 standby
查看NodeManager信息
yarn node -list -all
Total Nodes:3
Node-Id Node-State Node-Http-Address Number-of-Running-Containers
yarn1:45477 RUNNING yarn1:8042 0
yarn2:38203 RUNNING yarn2:8042 0
yarn3:44035 RUNNING yarn3:8042 0
常用命令#
停止所有ResourceManager
lava ssh -f ${HOME}/rmhost -e 'sudo -u yarn yarn --daemon stop resourcemanager'
停止所有NodeManager
lava ssh -f ${HOME}/yarnhost -e 'sudo -u yarn yarn --daemon stop nodemanager'
注册到Skylab(可选)#
将要安装的机器需要通过机器管理添加到skylab中，如果您尚未添加，请参考注册机器。
在yarn1上修改/usr/local/oushu/lava/conf配置server.json，替换localhost为skylab的服务器ip，具体skylab的基础服务lava安装步骤请参考：lava安装。
然后创建~/yarn.json文件，文件内容参考如下:
{
"data": {
"name": "YARNCluster",
"group_roles": [
{
"role": "yarn.resourcemanager",
"cluster_name": "resourcemanager",
"group_name": "resourcemanager-id",
"machines": [
{
"id": 1,
"name": "ResourceManager",
"subnet": "lava",
"data_ip": "192.168.1.11",
"manage_ip": "",
"assist_port": 1622,
"ssh_port": 22
},{
"id": 2,
"name": "ResourceManager2",
"subnet": "lava",
"data_ip": "192.168.1.12",
"manage_ip": "",
"assist_port": 1622,
"ssh_port": 22
}
]
},
{
"role": "yarn.nodemanager",
"cluster_name": "nodemanager",
"group_name": "nodemanager-id",
"machines": [
{
"id": 1,
"name": "nodemanager1",
"subnet": "lava",
"data_ip": "192.168.1.11",
"manage_ip": "",
"assist_port": 1622,
"ssh_port": 22
},{
"id": 2,
"name": "nodemanager2",
"subnet": "lava",
"data_ip": "192.168.1.12",
"manage_ip": "",
"assist_port": 1622,
"ssh_port": 22
},{
"id": 3,
"name": "nodemanager3",
"subnet": "lava",
"data_ip": "192.168.1.13",
"manage_ip": "",
"assist_port": 1622,
"ssh_port": 22
}
]
}
]
}
}
上述配置文件中，需要根据实际情况修改machines数组中的机器信息，在平台基础组件lava所安装的机器执行：
psql lavaadmin -p 4432 -U oushu -c "select m.id,m.name,s.name as subnet,m.private_ip as data_ip,m.public_ip as manage_ip,m.assist_port,m.ssh_port from machine as m,subnet as s where m.subnet_id=s.id;"
获取到所需的机器信息，根据服务角色对应的节点，将机器信息添加到machines数组中。
例如yarn1对应的YARN ResourceManager角色，yarn1的机器信息需要备添加到yarn.resourcemanager角色对应的machines数组中。
调用lava命令注册集群：
lava login -u oushu -p ********
lava onprem-register service -s YarnMapreduce -f ~/yarn.json
如果返回值为：
Add service by self success
则表示注册成功，如果有错误信息，请根据错误信息处理。
从页面登录后，在自动部署模块对应服务中可以查看到新添加的集群，同时列表中会实时监控yarn进程在机器上的状态。
YARN集成Ranger认证（可选）#
Ranger安装#
如果开启Ranger，则需要在所有YARN节点安装Ranger客户端。
lava ssh -f ${HOME}/yarnhost -e "yum install -y ranger-yarn-plugin"
lava ssh -f ${HOME}/yarnhost -e 'mkdir /usr/local/oushu/yarn/etc'
lava ssh -f ${HOME}/yarnhost -e "ln -s /usr/local/oushu/conf/yarn /usr/local/oushu/yarn/etc/hadoop"
Ranger配置#
在yarn1节点下
修改配置文件/usr/local/oushu/ranger-yarn-plugin_2.3.0/install.properties
POLICY_MGR_URL=http://ranger1:6080
REPOSITORY_NAME=yarndev
COMPONENT_INSTALL_DIR_NAME=/usr/local/oushu/yarn
同步YARN的Ranger配置，并执行初始化配置脚本
lava scp -r -f ${HOME}/yarnhost /usr/local/oushu/ranger-yarn-plugin_2.3.0/install.properties =:/usr/local/oushu/ranger-yarn-plugin_2.3.0/
lava ssh -f ${HOME}/yarnhost -e '/usr/local/oushu/ranger-yarn-plugin_2.3.0/enable-yarn-plugin.sh'
执行完初始化脚本后，看到如下信息说明成功，并按照要求重启服务。
Ranger Plugin for hive has been enabled. Please restart hive to ensure that changes are effective.
重新启动YARN
# 重启YARN集群
lava ssh -f ${HOME}/yarnhost -e 'sudo -E -u yarn yarn --daemon stop nodemanager'
lava ssh -f ${HOME}/rmhost -e 'sudo -E -u yarn yarn --daemon stop resourcemanager'
lava ssh -f ${HOME}/yarnhost -e 'sudo -E -u yarn yarn --daemon start nodemanager'
lava ssh -f ${HOME}/rmhost -e 'sudo -E -u yarn yarn --daemon start resourcemanager'
在rangerUI 上配置用户权限策略#
创建YARN Service服务#
登陆rangerUI http://192.168.1.14:6080，点击➕号添加
YARN Service
填写服务名，注意需要和
install.properties文件里的REPOSITORY_NAME名称保持一致
用户名、密码自定义，若开启HA则HA方式把ResourceManager链接方式全部写入，若kerberos开启了kerberos认证，则填写相应的keytab文件，否则使用默认配置
运行测试查看是否配置正确，正确后点击添加保存。
回到首页查看刚刚添加服务
创建访问测量#
找到刚刚创建的服务，点击名称
点击’Add New Policy’按钮
设置访问策略,使得yarn用户有提交资源队列的权限，同时，要确保recursive 滑块处于开启状态。
查看刚刚设置
Ranger + Kerberos 注意项#
当开启Kerberos配置时，需要对Ranger服务也开启Kerberos，同时在配置YARN repo时，加入参数如下：
参数值为配置的Kerberos实体用户名。
添加完Policy后，稍等约半分钟等待Policy生效。生效后使用yarn用户就可以向YARN的root.default队列中提交、删除、查询作业等操作。