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From "Paul Sutter" <psut...@quantcast.com>
Subject RE: Job scheduling (Re: Unable to run more than one job concurrently)
Date Fri, 19 May 2006 18:01:05 GMT

A few suggestions to allow for a very simple extension to the current

(1) Allow submission times in the future, enabling the creation of
"background" jobs. My understanding is that job submission times are used to
prioritize scheduling. All tasks from a job submitted early run to
completion before those of a job submitted later. If we could submit any
days-long jobs with a submission time in the future, say the year 2010, and
any short hours-long jobs with the current time, that short job would be
able to interrupt the long job. Hack? Yes. Useful? I think so.

(2) Have a per-job total task count limit. Currently, we establish the
number of tasks each node runs, and how many map or reduce tasks we have
total in a given job. But it would be great if we could set a ceiling on the
number of tasks that run concurrently for a given job. This may help with
Andrzej's fetcher (since it is bandwidth constrained, maybe fewer concurrent
jobs would be fine?).

(3) Don't start the reducers until a certain number of mappers have
completed (25%? 75%? 90%?). This optimization of starting early will be less
important when we've solved the map output copy problems.

Just a few ideas.

-----Original Message-----
From: bpendleton@gmail.com [mailto:bpendleton@gmail.com] On Behalf Of Bryan
A. Pendleton
Sent: Friday, May 19, 2006 10:44 AM
To: hadoop-dev@lucene.apache.org
Subject: Re: Job scheduling (Re: Unable to run more than one job

There are some additional risks to running simultaneous jobs. Right now,
Hadoop does a very bad job dealing with out-of-space conditions. If you run
two jobs, where the total amount of temporary space (for map outputs)
between both jobs is greater than the amount of space available on the
cluster, then they will both fail. If you run them serially, they should
both succeed.

In the very least, it's probably wise to take into account more than just
scheduling priority in any scheduler. (Expected) temporary space demands,
bandwidth limits, and size of jobs should be some of the criteria available
to the scheduler.

On 5/19/06, Andrzej Bialecki <ab@getopt.org> wrote:
> Andrzej Bialecki wrote:
> > Hi all,
> >
> > I'm running Hadoop on a relatively small cluster (5 nodes) with
> > growing datasets.
> >
> > I noticed that if I start a job that is configured to run more map
> > tasks than is the cluster capacity (mapred.tasktracker.tasks.maximum *
> > number of nodes, 20 in this case), of course only that many map tasks
> > will run, and when they are finished the next map tasks from that job
> > will be scheduled.
> >
> > However, when I try to start another job in parallel, only its reduce
> > tasks will be scheduled (uselessly spin-waiting for map output, and
> > only reducing the number of available tasks in the cluster...), and no
> > map tasks from this job will be scheduled - until the first job
> > completes. This feels wrong - not only I'm not making progress on the
> > second job, but I'm also taking the slots away from the first job!
> >
> > I'm somewhat miffed about this - I'd think that jobtracker should
> > split the available resources evenly between these two jobs, i.e. it
> > should schedule some map tasks from the first job and some from the
> > second one. This is not what is happening, though ...
> >
> > Is this a configuration error, a bug, or a feature? :)
> >
> It seems it's a feature - I found the code in
> JobTracker.pollForNewTask(), and I'm not too happy about it.
> Let's consider the following example: if I'm running a Nutch fetcher,
> the main limitation is the available bandwidth to fetch pages, and not
> the capacity of the cluster. I'd love to be able to execute other jobs
> in parallel, so that I don't have to wait until fetcher completes. I
> could sacrifice some of the task slots on tasktrackers for that other
> job, because the fetcher job wouldn't suffer from this anyway (at least
> not too much).
> So, I'd like to change this code to pick up a random job from the list
> jobsByArrival, and take job.obtainNewMapTask from that randomly selected
> job. Would that work? Additionally, if no map tasks from that job have
> been allocated I'd like to skip adding reduce tasks from that job, later
> in lines 721-750.
> Perhaps we should extend JobInProgress to include a priority, and
> implement something a la Unix scheduler.
> --
> Best regards,
> Andrzej Bialecki     <><
> ___. ___ ___ ___ _ _   __________________________________
> [__ || __|__/|__||\/|  Information Retrieval, Semantic Web
> ___|||__||  \|  ||  |  Embedded Unix, System Integration
> http://www.sigram.com  Contact: info at sigram dot com

Bryan A. Pendleton
Ph: (877) geek-1-bp

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