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From "Hemanth Yamijala (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HADOOP-5170) Set max map/reduce tasks on a per-job basis, either per-node or cluster-wide
Date Thu, 07 May 2009 16:51:30 GMT

    [ https://issues.apache.org/jira/browse/HADOOP-5170?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12706990#action_12706990
] 

Hemanth Yamijala commented on HADOOP-5170:
------------------------------------------

Matei, wanted to understand this a bit more..

Another use case that I heard of is the following: If I have a CPU bound job, I may want to
restrict the number of tasks that run on a node, so I can use the cores all for myself. So,
if I set mapred.max.maps.per.node for this job appropriately, the patch makes sure that only
those many tasks of this job are scheduled on the node, even if slots are free.

However, another job that has no limits set can get scheduled on the free slots. Then, the
exclusiveness is not met, right ? So, is the above use case not handled by this patch, or
can it be accomplished in some way still.

> Set max map/reduce tasks on a per-job basis, either per-node or cluster-wide
> ----------------------------------------------------------------------------
>
>                 Key: HADOOP-5170
>                 URL: https://issues.apache.org/jira/browse/HADOOP-5170
>             Project: Hadoop Core
>          Issue Type: New Feature
>          Components: mapred
>            Reporter: Jonathan Gray
>         Attachments: tasklimits.patch
>
>
> There are a number of use cases for being able to do this.  The focus of this jira should
be on finding what would be the simplest to implement that would satisfy the most use cases.
> This could be implemented as either a per-node maximum or a cluster-wide maximum.  It
seems that for most uses, the former is preferable however either would fulfill the requirements
of this jira.
> Some of the reasons for allowing this feature (mine and from others on list):
> - I have some very large CPU-bound jobs.  I am forced to keep the max map/node limit
at 2 or 3 (on a 4 core node) so that I do not starve the Datanode and Regionserver.  I have
other jobs that are network latency bound and would like to be able to run high numbers of
them concurrently on each node.  Though I can thread some jobs, there are some use cases that
are difficult to thread (scanning from hbase) and there's significant complexity added to
the job rather than letting hadoop handle the concurrency.
> - Poor assignment of tasks to nodes creates some situations where you have multiple reducers
on a single node but other nodes that received none.  A limit of 1 reducer per node for that
job would prevent that from happening. (only works with per-node limit)
> - Poor mans MR job virtualization.  Since we can limit a jobs resources, this gives much
more control in allocating and dividing up resources of a large cluster.  (makes most sense
w/ cluster-wide limit)

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