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https://issues.apache.org/jira/browse/MAPREDUCE-961?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12772390#action_12772390
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Matei Zaharia commented on MAPREDUCE-961:
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Hi Scott and Dhruba,
I've looked at the patch a little bit and have a few comments:
# I agree with Dhruba that it would be good to have the option of running
multiple Hadoop clusters in parallel. It's also good design to allow the
metrics data to be consumed by multiple sources.
# In MemBasedLoadManager.canLaunchTask, you are returning true in some cases
and saying that this is "equivalent to the case of using only
CapBasedLoadManager". How is that happening? I think you would need to return
super.canLaunchTask(...), not true. The Fair Scheduler itself doesn't look at
slot counts.
# It might be useful to use the max map slots / max reduce slots settings as
upper bounds on the total number of tasks on each node, to limit the number of
processes launched. In this case an administrator could configure the slots
higher (e.g. 20 map slots and 10 reduce slots), and the node utilization would
be used to determine when fewer than this number of tasks should be launched.
Otherwise, a job with very low-utilization tasks could cause hundreds of
processes to be launched on each node.
# Have you thought in detail about how the MemBasedLoadManager will work when
the scheduler tries to launch multiple tasks per heartbeat (part of
MAPREDUCE-706)? I think there are two questions:
#* First, you will need to cap the number of tasks launched per heartbeat based
on free memory on the node, so that we don't end up launching too many tasks
and overcommitting memory. One way to do this might be to count tasks we
schedule against the free memory on the node, and conservatively estimate them
to each use 2 GB or something (admin-configurable).
#* Second, it's important to launch both reduces and maps if both types of
tasks are available. The current multiple-task-per-heartbeat code in
MAPREDUCE-706 (and in all the other schedulers as far as I know) will first try
to launch map tasks until canLaunchTask(TaskType.MAP) returns false (or until
there are no pending map tasks), and will the look for pending reduce tasks.
With the current MemBasedLoadManager, this would starve reduces whenever there
are pending maps. It would be better to alternate between the two task types if
both are available.
> ResourceAwareLoadManager to dynamically decide new tasks based on current
> CPU/memory load on TaskTracker(s)
> -----------------------------------------------------------------------------------------------------------
>
> Key: MAPREDUCE-961
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-961
> Project: Hadoop Map/Reduce
> Issue Type: New Feature
> Components: contrib/fair-share
> Reporter: dhruba borthakur
> Assignee: dhruba borthakur
> Attachments: HIVE-961.patch, MAPREDUCE-961-v2.patch
>
>
> Design and develop a ResouceAwareLoadManager for the FairShare scheduler that
> dynamically decides how many maps/reduces to run on a particular machine
> based on the CPU/Memory/diskIO/network usage in that machine. The amount of
> resources currently used on each task tracker is being fed into the
> ResourceAwareLoadManager in real-time via an entity that is external to
> Hadoop.
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