Prabhu Joseph created YARN-4730:
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Summary: YARN preemption based on instantaneous fair share
Key: YARN-4730
URL: https://issues.apache.org/jira/browse/YARN-4730
Project: Hadoop YARN
Issue Type: Bug
Reporter: Prabhu Joseph
On a big cluster with Total Cluster Resource of 10TB, 3000 cores and Fair
Sheduler having 230 queues and total 60000 jobs run a day. [ all 230 queues are
very critical and hence the minResource is same for all]. On this case, when a
Spark Job is run on queue A and which occupies the entire cluster resource and
does not release any resource, another job submitted into queue B and
preemption is getting only the Fair Share which is <10TB , 3000> / 230 = <45 GB
, 13 cores> which is very less fair share for a queue.shared by many
applications.
The Preemption should get the instantaneous fair Share, that is <10TB, 3000> /
2 (active queues) = 5TB and 1500 cores, so that the first job won't hog the
entire cluster resource and also the subsequent jobs run fine.
This issue is only when the number of queues are very high. In case of less
number of queues, Preemption getting Fair Share would be suffice as the fair
share will be high. But in case of too many number of queues, Preemption should
try to get the instantaneous Fair Share.
Note: Configuring optimal maxResources to 230 queues is difficult and also
putting constraint for the queues using maxResource will leave cluster
resource idle most of the time.
There are 1000s of Spark Jobs, so asking each user to restrict the
number of executors is also difficult.
Preempting Instantaneous Fair Share will help to overcome the above issues.
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