How to Configure Scheduling
Change the automatic setting where all jobs submitted to the cluster are under one user account called
datameer or are into one queue.
To take advantage from scheduling or capacity queues in your cluster back end, you need to define a queue for each user or job within the
To address different execution frameworks you can set two parameters:
mapreduce.job.queuename=<Queue_X> tez.queue.name=<Queue_X> spark.yarn.queue=<Queue_X>
If you use Impersonation or Secure Impersonation, no configuration in Datameer is necessary because jobs are submitted to the cluster under the owner of the artifact
<Person_X>. The configuration is done on the cluster only.