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From Gary Malouf <malouf.g...@gmail.com>
Subject Re: Mesos/Spark Deadlock
Date Sat, 23 Aug 2014 23:30:27 GMT
Hi Matei,

We have an analytics team that uses the cluster on a daily basis.  They use
two types of 'run modes':

1) For running actual queries, they set the spark.executor.memory to
something between 4 and 8GB of RAM/worker.

2) A shell that takes a minimal amount of memory on workers (128MB) for
prototyping out a larger query.  This allows them to not take up RAM on the
cluster when they do not really need it.

We see the deadlocks when there are a few shells in either case.  From the
usage patterns we have, coarse-grained mode would be a challenge as we have
to constantly remind people to kill their shells as soon as their queries
finish.

Am I correct in viewing Mesos in coarse-grained mode as being similar to
Spark Standalone's cpu allocation behavior?




On Sat, Aug 23, 2014 at 7:16 PM, Matei Zaharia <matei.zaharia@gmail.com>
wrote:

> Hey Gary, just as a workaround, note that you can use Mesos in
> coarse-grained mode by setting spark.mesos.coarse=true. Then it will hold
> onto CPUs for the duration of the job.
>
> Matei
>
> On August 23, 2014 at 7:57:30 AM, Gary Malouf (malouf.gary@gmail.com)
> wrote:
>
> I just wanted to bring up a significant Mesos/Spark issue that makes the
> combo difficult to use for teams larger than 4-5 people. It's covered in
> https://issues.apache.org/jira/browse/MESOS-1688. My understanding is
> that
> Spark's use of executors in fine-grained mode is a very different behavior
> than many of the other common frameworks for Mesos.
>
>

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