> I should preassume that No of executors should be less than number of tasks.
No. Each executor runs 0 or more tasks. Each executor consumes 1 CPU, and each task running on that executor consumes another CPU. You can customize this via spark.mesos.mesosExecutor.cores ( https://github.com/apache/spark/blob/v1.6.3/docs/running-on-mesos.md) and spark.task.cpus ( https://github.com/apache/spark/blob/v1.6.3/docs/configuration.md) On Mon, Dec 19, 2016 at 12:09 PM, Chawla,Sumit <sumitkcha...@gmail.com> wrote: > Ah thanks. looks like i skipped reading this *"Neither will executors > terminate when they’re idle."* > > So in my job scenario, I should preassume that No of executors should be > less than number of tasks. Ideally one executor should execute 1 or more > tasks. But i am observing something strange instead. I start my job with > 48 partitions for a spark job. In mesos ui i see that number of tasks is > 48, but no. of CPUs is 78 which is way more than 48. Here i am assuming > that 1 CPU is 1 executor. I am not specifying any configuration to set > number of cores per executor. > > Regards > Sumit Chawla > > > On Mon, Dec 19, 2016 at 11:35 AM, Joris Van Remoortere < > jo...@mesosphere.io> wrote: > >> That makes sense. From the documentation it looks like the executors are >> not supposed to terminate: >> http://spark.apache.org/docs/latest/running-on-mesos.html#fi >> ne-grained-deprecated >> >>> Note that while Spark tasks in fine-grained will relinquish cores as >>> they terminate, they will not relinquish memory, as the JVM does not give >>> memory back to the Operating System. Neither will executors terminate when >>> they’re idle. >> >> >> I suppose your task to executor CPU ratio is low enough that it looks >> like most of the resources are not being reclaimed. If your tasks were >> using significantly more CPU the amortized cost of the idle executors would >> not be such a big deal. >> >> >> — >> *Joris Van Remoortere* >> Mesosphere >> >> On Mon, Dec 19, 2016 at 11:26 AM, Timothy Chen <tnac...@gmail.com> wrote: >> >>> Hi Chawla, >>> >>> One possible reason is that Mesos fine grain mode also takes up cores >>> to run the executor per host, so if you have 20 agents running Fine >>> grained executor it will take up 20 cores while it's still running. >>> >>> Tim >>> >>> On Fri, Dec 16, 2016 at 8:41 AM, Chawla,Sumit <sumitkcha...@gmail.com> >>> wrote: >>> > Hi >>> > >>> > I am using Spark 1.6. I have one query about Fine Grained model in >>> Spark. >>> > I have a simple Spark application which transforms A -> B. Its a >>> single >>> > stage application. To begin the program, It starts with 48 partitions. >>> > When the program starts running, in mesos UI it shows 48 tasks and 48 >>> CPUs >>> > allocated to job. Now as the tasks get done, the number of active >>> tasks >>> > number starts decreasing. How ever, the number of CPUs does not >>> decrease >>> > propotionally. When the job was about to finish, there was a single >>> > remaininig task, however CPU count was still 20. >>> > >>> > My questions, is why there is no one to one mapping between tasks and >>> cpus >>> > in Fine grained? How can these CPUs be released when the job is done, >>> so >>> > that other jobs can start. >>> > >>> > >>> > Regards >>> > Sumit Chawla >>> >> >> > -- Michael Gummelt Software Engineer Mesosphere
