I decided to do YARN over-commit and add 896 to yarn.nodemanager.resource.memory-mb it was 54,272 now I set it to 54,272+896 = 55,168
Kelly, can I ask you couple questions 1. it is possible to add yarn label to particular instance group boxes on EMR? 2. in addition to maximizeResourceAllocation it would be nice if we have executorsPerBox setting in EMR. I have a case when I need to run 2 or 4 executors on r3.2xlarge On Tue, Feb 9, 2016 at 9:56 AM, Alexander Pivovarov <apivova...@gmail.com> wrote: > I use hadoop 2.7.1 > On Feb 9, 2016 9:54 AM, "Marcelo Vanzin" <van...@cloudera.com> wrote: > >> You should be able to use spark.yarn.am.nodeLabelExpression if your >> version of YARN supports node labels (and you've added a label to the >> node where you want the AM to run). >> >> On Tue, Feb 9, 2016 at 9:51 AM, Alexander Pivovarov >> <apivova...@gmail.com> wrote: >> > Am container starts first and yarn selects random computer to run it. >> > >> > Is it possible to configure yarn so that it selects small computer for >> am >> > container. >> > >> > On Feb 9, 2016 12:40 AM, "Sean Owen" <so...@cloudera.com> wrote: >> >> >> >> If it's too small to run an executor, I'd think it would be chosen for >> >> the AM as the only way to satisfy the request. >> >> >> >> On Tue, Feb 9, 2016 at 8:35 AM, Alexander Pivovarov >> >> <apivova...@gmail.com> wrote: >> >> > If I add additional small box to the cluster can I configure yarn to >> >> > select >> >> > small box to run am container? >> >> > >> >> > >> >> > On Mon, Feb 8, 2016 at 10:53 PM, Sean Owen <so...@cloudera.com> >> wrote: >> >> >> >> >> >> Typically YARN is there because you're mediating resource requests >> >> >> from things besides Spark, so yeah using every bit of the cluster >> is a >> >> >> little bit of a corner case. There's not a good answer if all your >> >> >> nodes are the same size. >> >> >> >> >> >> I think you can let YARN over-commit RAM though, and allocate more >> >> >> memory than it actually has. It may be beneficial to let them all >> >> >> think they have an extra GB, and let one node running the AM >> >> >> technically be overcommitted, a state which won't hurt at all unless >> >> >> you're really really tight on memory, in which case something might >> >> >> get killed. >> >> >> >> >> >> On Tue, Feb 9, 2016 at 6:49 AM, Jonathan Kelly < >> jonathaka...@gmail.com> >> >> >> wrote: >> >> >> > Alex, >> >> >> > >> >> >> > That's a very good question that I've been trying to answer myself >> >> >> > recently >> >> >> > too. Since you've mentioned before that you're using EMR, I assume >> >> >> > you're >> >> >> > asking this because you've noticed this behavior on emr-4.3.0. >> >> >> > >> >> >> > In this release, we made some changes to the >> >> >> > maximizeResourceAllocation >> >> >> > (which you may or may not be using, but either way this issue is >> >> >> > present), >> >> >> > including the accidental inclusion of somewhat of a bug that >> makes it >> >> >> > not >> >> >> > reserve any space for the AM, which ultimately results in one of >> the >> >> >> > nodes >> >> >> > being utilized only by the AM and not an executor. >> >> >> > >> >> >> > However, as you point out, the only viable fix seems to be to >> reserve >> >> >> > enough >> >> >> > memory for the AM on *every single node*, which in some cases >> might >> >> >> > actually >> >> >> > be worse than wasting a lot of memory on a single node. >> >> >> > >> >> >> > So yeah, I also don't like either option. Is this just the price >> you >> >> >> > pay >> >> >> > for >> >> >> > running on YARN? >> >> >> > >> >> >> > >> >> >> > ~ Jonathan >> >> >> > >> >> >> > On Mon, Feb 8, 2016 at 9:03 PM Alexander Pivovarov >> >> >> > <apivova...@gmail.com> >> >> >> > wrote: >> >> >> >> >> >> >> >> Lets say that yarn has 53GB memory available on each slave >> >> >> >> >> >> >> >> spark.am container needs 896MB. (512 + 384) >> >> >> >> >> >> >> >> I see two options to configure spark: >> >> >> >> >> >> >> >> 1. configure spark executors to use 52GB and leave 1 GB on each >> box. >> >> >> >> So, >> >> >> >> some box will also run am container. So, 1GB memory will not be >> used >> >> >> >> on >> >> >> >> all >> >> >> >> slaves but one. >> >> >> >> >> >> >> >> 2. configure spark to use all 53GB and add additional 53GB box >> which >> >> >> >> will >> >> >> >> run only am container. So, 52GB on this additional box will do >> >> >> >> nothing >> >> >> >> >> >> >> >> I do not like both options. Is there a better way to configure >> >> >> >> yarn/spark? >> >> >> >> >> >> >> >> >> >> >> >> Alex >> >> > >> >> > >> >> >> >> -- >> Marcelo >> >
