Actually after some experiments we figured out that spark.max.cores / spark.executor.cores is the upper bound for the executors. Spark apps will run even only if one executor can be launched.
Is there any way to specify also the lower bound? It is a bit annoying that seems that we can’t control the resource usage of an application. By the way, we are not using dynamic allocation. - Thodoris > On 10 Jul 2018, at 14:35, Pavel Plotnikov <pavel.plotni...@team.wrike.com> > wrote: > > Hello Thodoris! > Have you checked this: > - does mesos cluster have available resources? > - if spark have waiting tasks in queue more than > spark.dynamicAllocation.schedulerBacklogTimeout configuration value? > - And then, have you checked that mesos send offers to spark app mesos > framework at least with 10 cores and 2GB RAM? > > If mesos have not available offers with 10 cores, for example, but have with > 8 or 9, so you can use smaller executers for better fit for available > resources on nodes for example with 4 cores and 1 GB RAM, for example > > Cheers, > Pavel > >> On Mon, Jul 9, 2018 at 9:05 PM Thodoris Zois <z...@ics.forth.gr> wrote: >> Hello list, >> >> We are running Apache Spark on a Mesos cluster and we face a weird behavior >> of executors. When we submit an app with e.g 10 cores and 2GB of memory and >> max cores 30, we expect to see 3 executors running on the cluster. However, >> sometimes there are only 2... Spark applications are not the only one that >> run on the cluster. I guess that Spark starts executors on the available >> offers even if it does not satisfy our needs. Is there any configuration >> that we can use in order to prevent Spark from starting when there are no >> resource offers for the total number of executors? >> >> Thank you >> - Thodoris >> >> --------------------------------------------------------------------- >> To unsubscribe e-mail: user-unsubscr...@spark.apache.org >>
