Mailing-List: contact yarn-dev-help@hadoop.apache.org; run by ezmlm
Precedence: bulk
Reply-To: yarn-dev@hadoop.apache.org
Date: Wed, 24 Feb 2016 07:07:18 +0000 (UTC)
From: "Prabhu Joseph (JIRA)" <jira@apache.org>
To: yarn-dev@hadoop.apache.org
Message-ID: <JIRA.12941655.1456297583000.130902.1456297638130@Atlassian.JIRA>
In-Reply-To: <JIRA.12941655.1456297583000@Atlassian.JIRA>
References: <JIRA.12941655.1456297583000@Atlassian.JIRA>
 <JIRA.12941655.1456297583082@arcas>
Subject: [jira] [Created] (YARN-4730) YARN preemption based on instantaneous
 fair share
MIME-Version: 1.0
Content-Type: text/plain; charset=utf-8
Content-Transfer-Encoding: 7bit

Prabhu Joseph created YARN-4730:
-----------------------------------

             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.

          
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)