It sounds like you've answered your own question, right? --executor-memory means the memory per executor. If you have no executor w/ 200GB memory, then the driver will accept no offers.
On Thu, Feb 2, 2017 at 1:01 PM, Ji Yan <ji...@drive.ai> wrote: > sorry, to clarify, i was using --executor-memory for memory, > and --total-executor-cores for cpu cores > > On Thu, Feb 2, 2017 at 12:56 PM, Michael Gummelt <mgumm...@mesosphere.io> > wrote: > >> What CLI args are your referring to? I'm aware of spark-submit's >> arguments (--executor-memory, --total-executor-cores, and --executor-cores) >> >> On Thu, Feb 2, 2017 at 12:41 PM, Ji Yan <ji...@drive.ai> wrote: >> >>> I have done a experiment on this today. It shows that only CPUs are >>> tolerant of insufficient cluster size when a job starts. On my cluster, I >>> have 180Gb of memory and 64 cores, when I run spark-submit ( on mesos ) >>> with --cpu_cores set to 1000, the job starts up with 64 cores. but when I >>> set --memory to 200Gb, the job fails to start with "Initial job has not >>> accepted any resources; check your cluster UI to ensure that workers are >>> registered and have sufficient resources" >>> >>> Also it is confusing to me that --cpu_cores specifies the number of cpu >>> cores across all executors, but --memory specifies per executor memory >>> requirement. >>> >>> On Mon, Jan 30, 2017 at 11:34 AM, Michael Gummelt < >>> mgumm...@mesosphere.io> wrote: >>> >>>> >>>> >>>> On Mon, Jan 30, 2017 at 9:47 AM, Ji Yan <ji...@drive.ai> wrote: >>>> >>>>> Tasks begin scheduling as soon as the first executor comes up >>>>> >>>>> >>>>> Thanks all for the clarification. Is this the default behavior of >>>>> Spark on Mesos today? I think this is what we are looking for because >>>>> sometimes a job can take up lots of resources and later jobs could not get >>>>> all the resources that it asks for. If a Spark job starts with only a >>>>> subset of resources that it asks for, does it know to expand its resources >>>>> later when more resources become available? >>>>> >>>> >>>> Yes. >>>> >>>> >>>>> >>>>> Launch each executor with at least 1GB RAM, but if mesos offers 2GB at >>>>>> some moment, then launch an executor with 2GB RAM >>>>> >>>>> >>>>> This is less useful in our use case. But I am also quite interested in >>>>> cases in which this could be helpful. I think this will also help with >>>>> overall resource utilization on the cluster if when another job starts up >>>>> that has a hard requirement on resources, the extra resources to the first >>>>> job can be flexibly re-allocated to the second job. >>>>> >>>>> On Sat, Jan 28, 2017 at 2:32 PM, Michael Gummelt < >>>>> mgumm...@mesosphere.io> wrote: >>>>> >>>>>> We've talked about that, but it hasn't become a priority because we >>>>>> haven't had a driving use case. If anyone has a good argument for >>>>>> "variable" resource allocation like this, please let me know. >>>>>> >>>>>> On Sat, Jan 28, 2017 at 9:17 AM, Shuai Lin <linshuai2...@gmail.com> >>>>>> wrote: >>>>>> >>>>>>> An alternative behavior is to launch the job with the best resource >>>>>>>> offer Mesos is able to give >>>>>>> >>>>>>> >>>>>>> Michael has just made an excellent explanation about dynamic >>>>>>> allocation support in mesos. But IIUC, what you want to achieve is >>>>>>> something like (using RAM as an example) : "Launch each executor with at >>>>>>> least 1GB RAM, but if mesos offers 2GB at some moment, then launch an >>>>>>> executor with 2GB RAM". >>>>>>> >>>>>>> I wonder what's benefit of that? To reduce the "resource >>>>>>> fragmentation"? >>>>>>> >>>>>>> Anyway, that is not supported at this moment. In all the supported >>>>>>> cluster managers of spark (mesos, yarn, standalone, and the up-to-coming >>>>>>> spark on kubernetes), you have to specify the cores and memory of each >>>>>>> executor. >>>>>>> >>>>>>> It may not be supported in the future, because only mesos has the >>>>>>> concepts of offers because of its two-level scheduling model. >>>>>>> >>>>>>> >>>>>>> On Sat, Jan 28, 2017 at 1:35 AM, Ji Yan <ji...@drive.ai> wrote: >>>>>>> >>>>>>>> Dear Spark Users, >>>>>>>> >>>>>>>> Currently is there a way to dynamically allocate resources to Spark >>>>>>>> on Mesos? Within Spark we can specify the CPU cores, memory before >>>>>>>> running >>>>>>>> job. The way I understand is that the Spark job will not run if the >>>>>>>> CPU/Mem >>>>>>>> requirement is not met. This may lead to decrease in overall >>>>>>>> utilization of >>>>>>>> the cluster. An alternative behavior is to launch the job with the best >>>>>>>> resource offer Mesos is able to give. Is this possible with the current >>>>>>>> implementation? >>>>>>>> >>>>>>>> Thanks >>>>>>>> Ji >>>>>>>> >>>>>>>> The information in this email is confidential and may be legally >>>>>>>> privileged. It is intended solely for the addressee. Access to this >>>>>>>> email >>>>>>>> by anyone else is unauthorized. If you are not the intended recipient, >>>>>>>> any >>>>>>>> disclosure, copying, distribution or any action taken or omitted to be >>>>>>>> taken in reliance on it, is prohibited and may be unlawful. >>>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> Michael Gummelt >>>>>> Software Engineer >>>>>> Mesosphere >>>>>> >>>>> >>>>> >>>>> The information in this email is confidential and may be legally >>>>> privileged. It is intended solely for the addressee. Access to this email >>>>> by anyone else is unauthorized. If you are not the intended recipient, any >>>>> disclosure, copying, distribution or any action taken or omitted to be >>>>> taken in reliance on it, is prohibited and may be unlawful. >>>>> >>>> >>>> >>>> >>>> -- >>>> Michael Gummelt >>>> Software Engineer >>>> Mesosphere >>>> >>> >>> >>> The information in this email is confidential and may be legally >>> privileged. It is intended solely for the addressee. Access to this email >>> by anyone else is unauthorized. If you are not the intended recipient, any >>> disclosure, copying, distribution or any action taken or omitted to be >>> taken in reliance on it, is prohibited and may be unlawful. >>> >> >> >> >> -- >> Michael Gummelt >> Software Engineer >> Mesosphere >> > > > The information in this email is confidential and may be legally > privileged. It is intended solely for the addressee. Access to this email > by anyone else is unauthorized. If you are not the intended recipient, any > disclosure, copying, distribution or any action taken or omitted to be > taken in reliance on it, is prohibited and may be unlawful. > -- Michael Gummelt Software Engineer Mesosphere
