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From "Matei Zaharia (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HADOOP-3136) Assign multiple tasks per TaskTracker heartbeat
Date Wed, 17 Sep 2008 06:07:44 GMT

    [ https://issues.apache.org/jira/browse/HADOOP-3136?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12631663#action_12631663
] 

Matei Zaharia commented on HADOOP-3136:
---------------------------------------

At the very least, it would help to schedule at least one reduce and one map per heartbeat,
because those are pretty independent. This seemed to make a difference in my tests using Gridmix
at Facebook because Gridmix would some periods of time when it wouldn't launch reduces at
all. Beyond that, for maps, maybe it would also help to limit the number you launch per heartbeat
(say to 2), or to ask the job for tasks until it stops giving you local tasks? The current
patch seems to go all out asking for tasks from the top job in the queue.

> Assign multiple tasks per TaskTracker heartbeat
> -----------------------------------------------
>
>                 Key: HADOOP-3136
>                 URL: https://issues.apache.org/jira/browse/HADOOP-3136
>             Project: Hadoop Core
>          Issue Type: Improvement
>          Components: mapred
>            Reporter: Devaraj Das
>            Assignee: Arun C Murthy
>             Fix For: 0.19.0
>
>         Attachments: HADOOP-3136_0_20080805.patch, HADOOP-3136_1_20080809.patch, HADOOP-3136_2_20080911.patch
>
>
> In today's logic of finding a new task, we assign only one task per heartbeat.
> We probably could give the tasktracker multiple tasks subject to the max number of free
slots it has - for maps we could assign it data local tasks. We could probably run some logic
to decide what to give it if we run out of data local tasks (e.g., tasks from overloaded racks,
tasks that have least locality, etc.). In addition to maps, if it has reduce slots free, we
could give it reduce task(s) as well. Again for reduces we could probably run some logic to
give more tasks to nodes that are closer to nodes running most maps (assuming data generated
is proportional to the number of maps). For e.g., if rack1 has 70% of the input splits, and
we know that most maps are data/rack local, we try to schedule ~70% of the reducers there.
> Thoughts?

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