hadoop版本为cloudera hadoop cdh3u3
配置步骤为
1. 将$HADOOP_HOME/contrib/fairscheduler/hadoop-fairscheduler-0.20.2-cdh3u3.jar拷贝到$HADOOP_HOME/lib文件夹中
2. 修改$HADOOP_HOME/conf/mapred-site.xml配置文件
<property>
<name>mapred.jobtracker.taskScheduler</name>
<value>org.apache.hadoop.mapred.FairScheduler</value>
</property>
<property>
<name>mapred.fairscheduler.allocation.file</name>
<value>/home/hadoop/hadoop-0.20.2-cdh3u3/conf/fair-scheduler.xml</value>
</property>
<property>
<name>mapred.fairscheduler.preemption</name>
<value>true</value>
</property>
<property>
<name>mapred.fairscheduler.assignmultiple</name>
<value>true</value>
</property>
<property>
<name>mapred.fairscheduler.poolnameproperty</name>
<value>mapred.queue.name</value>
<description>job.set("mapred.queue.name",pool); // pool is set to either 'high' or 'low' </description>
</property>
<property>
<name>mapred.fairscheduler.preemption.only.log</name>
<value>true</value>
</property>
<property>
<name>mapred.fairscheduler.preemption.interval</name>
<value>15000</value>
</property>
<property>
<name>mapred.queue.names</name>
<value>default,hadoop,hive</value>
</property>
3. 在$HADOOP_HOME/conf/新建配置文件fair-scheduler.xml
<?xml version="1.0"?>
<allocations>
<pool name="hive">
<minMaps>90</minMaps>
<minReduces>20</minReduces>
<maxRunningJobs>20</maxRunningJobs>
<weight>2.0</weight>
<minSharePreemptionTimeout>30</minSharePreemptionTimeout>
</pool>
<pool name="hadoop">
<minMaps>9</minMaps>
<minReduces>2</minReduces>
<maxRunningJobs>20</maxRunningJobs>
<weight>1.0</weight>
<minSharePreemptionTimeout>30</minSharePreemptionTimeout>
</pool>
<user name="hadoop">
<maxRunningJobs>6</maxRunningJobs>
</user>
<poolMaxJobsDefault>10</poolMaxJobsDefault>
<userMaxJobsDefault>8</userMaxJobsDefault>
<defaultMinSharePreemptionTimeout>600</defaultMinSharePreemptionTimeout>
<fairSharePreemptionTimeout>600</fairSharePreemptionTimeout>
</allocations>
4. 在集群的各个节点执行以上步骤，然后重启集群，在http://namenode:50030/scheduler 即可查看到调度器运行状态，如果修改调度器配置的话，只需要修改文件fair-scheduler.xml ，不需重启配置即可生效。
5. 在执行hive任务时，设置hive属于的队列set mapred.queue.name=hadoop; (set mapred.job.queue.name=hadoop;)
设置hive的任务名称set mapred.job.name=goldts;
设置任务的优先级别set mapred.job.priority=HIGH;
6. 如果在执行MR JOB的时候出现XX用户访问不了YY队列的话，就需要在mapred-queue-acls.xml里配置相应的属性，来对访问权限进行控制，比如：
<property>
<name>mapred.queue.default.acl-submit-job</name>
<value>*</value>
<description> Comma separated list of user and group names that are allowed
to submit jobs to the 'default' queue. The user list and the group list
are separated by a blank. For e.g. user1,user2 group1,group2.
If set to the special value '*', it means all users are allowed to
submit jobs. If set to ' '(i.e. space), no user will be allowed to submit
jobs.
It is only used if authorization is enabled in Map/Reduce by setting the
configuration property mapred.acls.enabled to true.
Irrespective of this ACL configuration, the user who started the cluster and
cluster administrators configured via
mapreduce.cluster.administrators can submit jobs.
</description>
</property>
<property>
<name>mapred.queue.default.acl-administer-jobs</name>
<value>*</value>
<description> Comma separated list of user and group names that are allowed
to view job details, kill jobs or modify job's priority for all the jobs
in the 'default' queue. The user list and the group list
are separated by a blank. For e.g. user1,user2 group1,group2.
If set to the special value '*', it means all users are allowed to do
this operation. If set to ' '(i.e. space), no user will be allowed to do
this operation.
It is only used if authorization is enabled in Map/Reduce by setting the
configuration property mapred.acls.enabled to true.
Irrespective of this ACL configuration, the user who started the cluster and
cluster administrators configured via
mapreduce.cluster.administrators can do the above operations on all the jobs
in all the queues. The job owner can do all the above operations on his/her
job irrespective of this ACL configuration.
</description>
</property>