Thanks a lot.
After reading Mesos-1688, I still don't understand how/why a job will hoard
and hold on to so many resources even in the presence of that bug.
Looking at the release notes, I think this ticket could be relevant to
preventing the behavior we're seeing:
[MESOS-186] - Resource offers
Hi Gerard,
As others has mentioned I believe you're hitting Mesos-1688, can you
upgrade to the latest Mesos release (0.21.1) and let us know if it resolves
your problem?
Thanks,
Tim
On Tue, Jan 27, 2015 at 10:39 AM, Sam Bessalah samkiller@gmail.com
wrote:
Hi Geraard,
isn't this the same
Hi Geraard,
isn't this the same issueas this?
https://issues.apache.org/jira/browse/MESOS-1688
On Mon, Jan 26, 2015 at 9:17 PM, Gerard Maas gerard.m...@gmail.com wrote:
Hi,
We are observing with certain regularity that our Spark jobs, as Mesos
framework, are hoarding resources and not
Hopefully some very bad ugly bug that has been fixed already and that
will urge us to upgrade our infra?
Mesos 0.20 + Marathon 0.7.4 + Spark 1.1.0
Could be https://issues.apache.org/jira/browse/MESOS-1688 (fixed in Mesos
0.21)
On Mon, Jan 26, 2015 at 2:45 PM, Gerard Maas gerard.m...@gmail.com
Hi,
We are observing with certain regularity that our Spark jobs, as Mesos
framework, are hoarding resources and not releasing them, resulting in
resource starvation to all jobs running on the Mesos cluster.
For example:
This is a job that has spark.cores.max = 4 and spark.executor.memory=3g
(looks like the list didn't like a HTML table on the previous email. My
excuses for any duplicates)
Hi,
We are observing with certain regularity that our Spark jobs, as Mesos
framework, are hoarding resources and not releasing them, resulting in
resource starvation to all jobs running on the
Hi,
What do your jobs do? Ideally post source code, but some description would
already helpful to support you.
Memory leaks can have several reasons - it may not be Spark at all.
Thank you.
Le 26 janv. 2015 22:28, Gerard Maas gerard.m...@gmail.com a écrit :
(looks like the list didn't like
Hi Jörn,
A memory leak on the job would be contained within the resources reserved
for it, wouldn't it?
And the job holding resources is not always the same. Sometimes it's one of
the Streaming jobs, sometimes it's a heavy batch job that runs every hour.
Looks to me that whatever is causing the
