Re: Mesos Spark Fine Grained Execution - CPU count
This depends if you have isolation enabled, i.e., cgroups. If you do not have isolation enabled, so just the posix isolator, it will just run fine. In this case when the Spark Executor is idle, Mesos should show zero allocated resources whilst in fact the SparkExecutor process is fact still taking resources. However, expect this to fail horribly if you *do* have an isolator like cgroups configured on the mesos-slaves. If I remember correctly, I actually tried this and the SparkExecutor process actually grinds to a halt because cgroups makes sure it gets very little resources. Or it just crashed. On Tue, Dec 27, 2016 at 3:30 AM, Chawla,Sumit <sumitkcha...@gmail.com> wrote: > What is the expected effect of reducing the mesosExecutor.cores to zero? > What functionality of executor is impacted? Is the impact is just that it > just behaves like a regular process? > > Regards > Sumit Chawla > > > On Mon, Dec 26, 2016 at 9:25 AM, Michael Gummelt <mgumm...@mesosphere.io> > wrote: > >> > Using 0 for spark.mesos.mesosExecutor.cores is better than dynamic >> allocation >> >> Maybe for CPU, but definitely not for memory. Executors never shut down >> in fine-grained mode, which means you only elastically grow and shrink CPU >> usage, not memory. >> >> On Sat, Dec 24, 2016 at 10:14 PM, Davies Liu <davies@gmail.com> >> wrote: >> >>> Using 0 for spark.mesos.mesosExecutor.cores is better than dynamic >>> allocation, but have to pay a little more overhead for launching a >>> task, which should be OK if the task is not trivial. >>> >>> Since the direct result (up to 1M by default) will also go through >>> mesos, it's better to tune it lower, otherwise mesos could become the >>> bottleneck. >>> >>> spark.task.maxDirectResultSize >>> >>> On Mon, Dec 19, 2016 at 3:23 PM, Chawla,Sumit <sumitkcha...@gmail.com> >>> wrote: >>> > Tim, >>> > >>> > We will try to run the application in coarse grain mode, and share the >>> > findings with you. >>> > >>> > Regards >>> > Sumit Chawla >>> > >>> > >>> > On Mon, Dec 19, 2016 at 3:11 PM, Timothy Chen <tnac...@gmail.com> >>> wrote: >>> > >>> >> Dynamic allocation works with Coarse grain mode only, we wasn't aware >>> >> a need for Fine grain mode after we enabled dynamic allocation support >>> >> on the coarse grain mode. >>> >> >>> >> What's the reason you're running fine grain mode instead of coarse >>> >> grain + dynamic allocation? >>> >> >>> >> Tim >>> >> >>> >> On Mon, Dec 19, 2016 at 2:45 PM, Mehdi Meziane >>> >> <mehdi.mezi...@ldmobile.net> wrote: >>> >> > We will be interested by the results if you give a try to Dynamic >>> >> allocation >>> >> > with mesos ! >>> >> > >>> >> > >>> >> > - Mail Original - >>> >> > De: "Michael Gummelt" <mgumm...@mesosphere.io> >>> >> > À: "Sumit Chawla" <sumitkcha...@gmail.com> >>> >> > Cc: user@mesos.apache.org, d...@mesos.apache.org, "User" >>> >> > <u...@spark.apache.org>, d...@spark.apache.org >>> >> > Envoyé: Lundi 19 Décembre 2016 22h42:55 GMT +01:00 Amsterdam / >>> Berlin / >>> >> > Berne / Rome / Stockholm / Vienne >>> >> > Objet: Re: Mesos Spark Fine Grained Execution - CPU count >>> >> > >>> >> > >>> >> >> Is this problem of idle executors sticking around solved in Dynamic >>> >> >> Resource Allocation? Is there some timeout after which Idle >>> executors >>> >> can >>> >> >> just shutdown and cleanup its resources. >>> >> > >>> >> > Yes, that's exactly what dynamic allocation does. But again I have >>> no >>> >> idea >>> >> > what the state of dynamic allocation + mesos is. >>> >> > >>> >> > On Mon, Dec 19, 2016 at 1:32 PM, Chawla,Sumit < >>> sumitkcha...@gmail.com> >>> >> > wrote: >>> >> >> >>> >> >> Great. Makes much better sense now. What will be reason to have >>> >> >> spark.mesos.mesosExecutor.cores more than 1, as this number >>> doesn't >>> &g
Re: Mesos Spark Fine Grained Execution - CPU count
What is the expected effect of reducing the mesosExecutor.cores to zero? What functionality of executor is impacted? Is the impact is just that it just behaves like a regular process? Regards Sumit Chawla On Mon, Dec 26, 2016 at 9:25 AM, Michael Gummelt <mgumm...@mesosphere.io> wrote: > > Using 0 for spark.mesos.mesosExecutor.cores is better than dynamic > allocation > > Maybe for CPU, but definitely not for memory. Executors never shut down > in fine-grained mode, which means you only elastically grow and shrink CPU > usage, not memory. > > On Sat, Dec 24, 2016 at 10:14 PM, Davies Liu <davies@gmail.com> wrote: > >> Using 0 for spark.mesos.mesosExecutor.cores is better than dynamic >> allocation, but have to pay a little more overhead for launching a >> task, which should be OK if the task is not trivial. >> >> Since the direct result (up to 1M by default) will also go through >> mesos, it's better to tune it lower, otherwise mesos could become the >> bottleneck. >> >> spark.task.maxDirectResultSize >> >> On Mon, Dec 19, 2016 at 3:23 PM, Chawla,Sumit <sumitkcha...@gmail.com> >> wrote: >> > Tim, >> > >> > We will try to run the application in coarse grain mode, and share the >> > findings with you. >> > >> > Regards >> > Sumit Chawla >> > >> > >> > On Mon, Dec 19, 2016 at 3:11 PM, Timothy Chen <tnac...@gmail.com> >> wrote: >> > >> >> Dynamic allocation works with Coarse grain mode only, we wasn't aware >> >> a need for Fine grain mode after we enabled dynamic allocation support >> >> on the coarse grain mode. >> >> >> >> What's the reason you're running fine grain mode instead of coarse >> >> grain + dynamic allocation? >> >> >> >> Tim >> >> >> >> On Mon, Dec 19, 2016 at 2:45 PM, Mehdi Meziane >> >> <mehdi.mezi...@ldmobile.net> wrote: >> >> > We will be interested by the results if you give a try to Dynamic >> >> allocation >> >> > with mesos ! >> >> > >> >> > >> >> > - Mail Original - >> >> > De: "Michael Gummelt" <mgumm...@mesosphere.io> >> >> > À: "Sumit Chawla" <sumitkcha...@gmail.com> >> >> > Cc: user@mesos.apache.org, d...@mesos.apache.org, "User" >> >> > <u...@spark.apache.org>, d...@spark.apache.org >> >> > Envoyé: Lundi 19 Décembre 2016 22h42:55 GMT +01:00 Amsterdam / >> Berlin / >> >> > Berne / Rome / Stockholm / Vienne >> >> > Objet: Re: Mesos Spark Fine Grained Execution - CPU count >> >> > >> >> > >> >> >> Is this problem of idle executors sticking around solved in Dynamic >> >> >> Resource Allocation? Is there some timeout after which Idle >> executors >> >> can >> >> >> just shutdown and cleanup its resources. >> >> > >> >> > Yes, that's exactly what dynamic allocation does. But again I have >> no >> >> idea >> >> > what the state of dynamic allocation + mesos is. >> >> > >> >> > On Mon, Dec 19, 2016 at 1:32 PM, Chawla,Sumit < >> sumitkcha...@gmail.com> >> >> > wrote: >> >> >> >> >> >> Great. Makes much better sense now. What will be reason to have >> >> >> spark.mesos.mesosExecutor.cores more than 1, as this number doesn't >> >> include >> >> >> the number of cores for tasks. >> >> >> >> >> >> So in my case it seems like 30 CPUs are allocated to executors. And >> >> there >> >> >> are 48 tasks so 48 + 30 = 78 CPUs. And i am noticing this gap of >> 30 is >> >> >> maintained till the last task exits. This explains the gap. >> Thanks >> >> >> everyone. I am still not sure how this number 30 is calculated. ( >> Is >> >> it >> >> >> dynamic based on current resources, or is it some configuration. I >> >> have 32 >> >> >> nodes in my cluster). >> >> >> >> >> >> Is this problem of idle executors sticking around solved in Dynamic >> >> >> Resource Allocation? Is there some timeout after which Idle >> executors >> >> can >> >> >> just shutdown and cleanup its resources. >> >>
Re: Mesos Spark Fine Grained Execution - CPU count
Thanks a LOT, Michael! Pozdrawiam, Jacek Laskowski https://medium.com/@jaceklaskowski/ Mastering Apache Spark 2.0 https://bit.ly/mastering-apache-spark Follow me at https://twitter.com/jaceklaskowski On Mon, Dec 26, 2016 at 10:04 PM, Michael Gummelt <mgumm...@mesosphere.io> wrote: > In fine-grained mode (which is deprecated), Spark tasks (which are threads) > were implemented as Mesos tasks. When a Mesos task starts and stops, its > underlying cgroup, and therefore the resources its consuming on the cluster, > grows or shrinks based on the resources allocated to the tasks, which in > Spark is just CPU. This is what I mean by CPU usage "elastically growing". > > However, all Mesos tasks are run by an "executor", which has its own > resource allocation. In Spark, the executor is the JVM, and all memory is > allocated to the executor, because JVMs can't relinquish memory. If memory > were allocated to the tasks, then the cgroup's memory allocation would > shrink when the task terminated, but the JVM's memory consumption would stay > constant, and the JVM would OOM. > > And, without dynamic allocation, executors never terminate during the > duration of a Spark job, because even if they're idle (no tasks), they still > may be hosting shuffle files. That's why dynamic allocation depends on an > external shuffle service. Since executors never terminate, and all memory > is allocated to the executors, Spark jobs even in fine-grained mode only > grow in memory allocation, they don't shrink. > > On Mon, Dec 26, 2016 at 12:39 PM, Jacek Laskowski <ja...@japila.pl> wrote: >> >> Hi Michael, >> >> That caught my attention... >> >> Could you please elaborate on "elastically grow and shrink CPU usage" >> and how it really works under the covers? It seems that CPU usage is >> just a "label" for an executor on Mesos. Where's this in the code? >> >> Pozdrawiam, >> Jacek Laskowski >> >> https://medium.com/@jaceklaskowski/ >> Mastering Apache Spark 2.0 https://bit.ly/mastering-apache-spark >> Follow me at https://twitter.com/jaceklaskowski >> >> >> On Mon, Dec 26, 2016 at 6:25 PM, Michael Gummelt <mgumm...@mesosphere.io> >> wrote: >> >> Using 0 for spark.mesos.mesosExecutor.cores is better than dynamic >> >> allocation >> > >> > Maybe for CPU, but definitely not for memory. Executors never shut down >> > in >> > fine-grained mode, which means you only elastically grow and shrink CPU >> > usage, not memory. >> > >> > On Sat, Dec 24, 2016 at 10:14 PM, Davies Liu <davies@gmail.com> >> > wrote: >> >> >> >> Using 0 for spark.mesos.mesosExecutor.cores is better than dynamic >> >> allocation, but have to pay a little more overhead for launching a >> >> task, which should be OK if the task is not trivial. >> >> >> >> Since the direct result (up to 1M by default) will also go through >> >> mesos, it's better to tune it lower, otherwise mesos could become the >> >> bottleneck. >> >> >> >> spark.task.maxDirectResultSize >> >> >> >> On Mon, Dec 19, 2016 at 3:23 PM, Chawla,Sumit <sumitkcha...@gmail.com> >> >> wrote: >> >> > Tim, >> >> > >> >> > We will try to run the application in coarse grain mode, and share >> >> > the >> >> > findings with you. >> >> > >> >> > Regards >> >> > Sumit Chawla >> >> > >> >> > >> >> > On Mon, Dec 19, 2016 at 3:11 PM, Timothy Chen <tnac...@gmail.com> >> >> > wrote: >> >> > >> >> >> Dynamic allocation works with Coarse grain mode only, we wasn't >> >> >> aware >> >> >> a need for Fine grain mode after we enabled dynamic allocation >> >> >> support >> >> >> on the coarse grain mode. >> >> >> >> >> >> What's the reason you're running fine grain mode instead of coarse >> >> >> grain + dynamic allocation? >> >> >> >> >> >> Tim >> >> >> >> >> >> On Mon, Dec 19, 2016 at 2:45 PM, Mehdi Meziane >> >> >> <mehdi.mezi...@ldmobile.net> wrote: >> >> >> > We will be interested by the results if you give a try to Dynamic >> >> >> allocation >> >> >> > with mesos ! >> >> >> > >> >&g
Re: Mesos Spark Fine Grained Execution - CPU count
In fine-grained mode (which is deprecated), Spark tasks (which are threads) were implemented as Mesos tasks. When a Mesos task starts and stops, its underlying cgroup, and therefore the resources its consuming on the cluster, grows or shrinks based on the resources allocated to the tasks, which in Spark is just CPU. This is what I mean by CPU usage "elastically growing". However, all Mesos tasks are run by an "executor", which has its own resource allocation. In Spark, the executor is the JVM, and all memory is allocated to the executor, because JVMs can't relinquish memory. If memory were allocated to the tasks, then the cgroup's memory allocation would shrink when the task terminated, but the JVM's memory consumption would stay constant, and the JVM would OOM. And, without dynamic allocation, executors never terminate during the duration of a Spark job, because even if they're idle (no tasks), they still may be hosting shuffle files. That's why dynamic allocation depends on an external shuffle service. Since executors never terminate, and all memory is allocated to the executors, Spark jobs even in fine-grained mode only grow in memory allocation, they don't shrink. On Mon, Dec 26, 2016 at 12:39 PM, Jacek Laskowski <ja...@japila.pl> wrote: > Hi Michael, > > That caught my attention... > > Could you please elaborate on "elastically grow and shrink CPU usage" > and how it really works under the covers? It seems that CPU usage is > just a "label" for an executor on Mesos. Where's this in the code? > > Pozdrawiam, > Jacek Laskowski > > https://medium.com/@jaceklaskowski/ > Mastering Apache Spark 2.0 https://bit.ly/mastering-apache-spark > Follow me at https://twitter.com/jaceklaskowski > > > On Mon, Dec 26, 2016 at 6:25 PM, Michael Gummelt <mgumm...@mesosphere.io> > wrote: > >> Using 0 for spark.mesos.mesosExecutor.cores is better than dynamic > >> allocation > > > > Maybe for CPU, but definitely not for memory. Executors never shut down > in > > fine-grained mode, which means you only elastically grow and shrink CPU > > usage, not memory. > > > > On Sat, Dec 24, 2016 at 10:14 PM, Davies Liu <davies@gmail.com> > wrote: > >> > >> Using 0 for spark.mesos.mesosExecutor.cores is better than dynamic > >> allocation, but have to pay a little more overhead for launching a > >> task, which should be OK if the task is not trivial. > >> > >> Since the direct result (up to 1M by default) will also go through > >> mesos, it's better to tune it lower, otherwise mesos could become the > >> bottleneck. > >> > >> spark.task.maxDirectResultSize > >> > >> On Mon, Dec 19, 2016 at 3:23 PM, Chawla,Sumit <sumitkcha...@gmail.com> > >> wrote: > >> > Tim, > >> > > >> > We will try to run the application in coarse grain mode, and share the > >> > findings with you. > >> > > >> > Regards > >> > Sumit Chawla > >> > > >> > > >> > On Mon, Dec 19, 2016 at 3:11 PM, Timothy Chen <tnac...@gmail.com> > wrote: > >> > > >> >> Dynamic allocation works with Coarse grain mode only, we wasn't aware > >> >> a need for Fine grain mode after we enabled dynamic allocation > support > >> >> on the coarse grain mode. > >> >> > >> >> What's the reason you're running fine grain mode instead of coarse > >> >> grain + dynamic allocation? > >> >> > >> >> Tim > >> >> > >> >> On Mon, Dec 19, 2016 at 2:45 PM, Mehdi Meziane > >> >> <mehdi.mezi...@ldmobile.net> wrote: > >> >> > We will be interested by the results if you give a try to Dynamic > >> >> allocation > >> >> > with mesos ! > >> >> > > >> >> > > >> >> > - Mail Original - > >> >> > De: "Michael Gummelt" <mgumm...@mesosphere.io> > >> >> > À: "Sumit Chawla" <sumitkcha...@gmail.com> > >> >> > Cc: user@mesos.apache.org, d...@mesos.apache.org, "User" > >> >> > <u...@spark.apache.org>, d...@spark.apache.org > >> >> > Envoyé: Lundi 19 Décembre 2016 22h42:55 GMT +01:00 Amsterdam / > Berlin > >> >> > / > >> >> > Berne / Rome / Stockholm / Vienne > >> >> > Objet: Re: Mesos Spark Fine Grained Execution - CPU count > >> >> > > >> >> > > &g
Re: Mesos Spark Fine Grained Execution - CPU count
Hi Michael, That caught my attention... Could you please elaborate on "elastically grow and shrink CPU usage" and how it really works under the covers? It seems that CPU usage is just a "label" for an executor on Mesos. Where's this in the code? Pozdrawiam, Jacek Laskowski https://medium.com/@jaceklaskowski/ Mastering Apache Spark 2.0 https://bit.ly/mastering-apache-spark Follow me at https://twitter.com/jaceklaskowski On Mon, Dec 26, 2016 at 6:25 PM, Michael Gummelt <mgumm...@mesosphere.io> wrote: >> Using 0 for spark.mesos.mesosExecutor.cores is better than dynamic >> allocation > > Maybe for CPU, but definitely not for memory. Executors never shut down in > fine-grained mode, which means you only elastically grow and shrink CPU > usage, not memory. > > On Sat, Dec 24, 2016 at 10:14 PM, Davies Liu <davies@gmail.com> wrote: >> >> Using 0 for spark.mesos.mesosExecutor.cores is better than dynamic >> allocation, but have to pay a little more overhead for launching a >> task, which should be OK if the task is not trivial. >> >> Since the direct result (up to 1M by default) will also go through >> mesos, it's better to tune it lower, otherwise mesos could become the >> bottleneck. >> >> spark.task.maxDirectResultSize >> >> On Mon, Dec 19, 2016 at 3:23 PM, Chawla,Sumit <sumitkcha...@gmail.com> >> wrote: >> > Tim, >> > >> > We will try to run the application in coarse grain mode, and share the >> > findings with you. >> > >> > Regards >> > Sumit Chawla >> > >> > >> > On Mon, Dec 19, 2016 at 3:11 PM, Timothy Chen <tnac...@gmail.com> wrote: >> > >> >> Dynamic allocation works with Coarse grain mode only, we wasn't aware >> >> a need for Fine grain mode after we enabled dynamic allocation support >> >> on the coarse grain mode. >> >> >> >> What's the reason you're running fine grain mode instead of coarse >> >> grain + dynamic allocation? >> >> >> >> Tim >> >> >> >> On Mon, Dec 19, 2016 at 2:45 PM, Mehdi Meziane >> >> <mehdi.mezi...@ldmobile.net> wrote: >> >> > We will be interested by the results if you give a try to Dynamic >> >> allocation >> >> > with mesos ! >> >> > >> >> > >> >> > - Mail Original - >> >> > De: "Michael Gummelt" <mgumm...@mesosphere.io> >> >> > À: "Sumit Chawla" <sumitkcha...@gmail.com> >> >> > Cc: user@mesos.apache.org, d...@mesos.apache.org, "User" >> >> > <u...@spark.apache.org>, d...@spark.apache.org >> >> > Envoyé: Lundi 19 Décembre 2016 22h42:55 GMT +01:00 Amsterdam / Berlin >> >> > / >> >> > Berne / Rome / Stockholm / Vienne >> >> > Objet: Re: Mesos Spark Fine Grained Execution - CPU count >> >> > >> >> > >> >> >> Is this problem of idle executors sticking around solved in Dynamic >> >> >> Resource Allocation? Is there some timeout after which Idle >> >> >> executors >> >> can >> >> >> just shutdown and cleanup its resources. >> >> > >> >> > Yes, that's exactly what dynamic allocation does. But again I have >> >> > no >> >> idea >> >> > what the state of dynamic allocation + mesos is. >> >> > >> >> > On Mon, Dec 19, 2016 at 1:32 PM, Chawla,Sumit >> >> > <sumitkcha...@gmail.com> >> >> > wrote: >> >> >> >> >> >> Great. Makes much better sense now. What will be reason to have >> >> >> spark.mesos.mesosExecutor.cores more than 1, as this number doesn't >> >> include >> >> >> the number of cores for tasks. >> >> >> >> >> >> So in my case it seems like 30 CPUs are allocated to executors. And >> >> there >> >> >> are 48 tasks so 48 + 30 = 78 CPUs. And i am noticing this gap of >> >> >> 30 is >> >> >> maintained till the last task exits. This explains the gap. >> >> >> Thanks >> >> >> everyone. I am still not sure how this number 30 is calculated. ( >> >> >> Is >> >> it >> >> >> dynamic based on current resources, or is it some configuration. I >> >> have 32 >> >> >> nodes in my cluster
Re: Mesos Spark Fine Grained Execution - CPU count
> Using 0 for spark.mesos.mesosExecutor.cores is better than dynamic allocation Maybe for CPU, but definitely not for memory. Executors never shut down in fine-grained mode, which means you only elastically grow and shrink CPU usage, not memory. On Sat, Dec 24, 2016 at 10:14 PM, Davies Liu <davies@gmail.com> wrote: > Using 0 for spark.mesos.mesosExecutor.cores is better than dynamic > allocation, but have to pay a little more overhead for launching a > task, which should be OK if the task is not trivial. > > Since the direct result (up to 1M by default) will also go through > mesos, it's better to tune it lower, otherwise mesos could become the > bottleneck. > > spark.task.maxDirectResultSize > > On Mon, Dec 19, 2016 at 3:23 PM, Chawla,Sumit <sumitkcha...@gmail.com> > wrote: > > Tim, > > > > We will try to run the application in coarse grain mode, and share the > > findings with you. > > > > Regards > > Sumit Chawla > > > > > > On Mon, Dec 19, 2016 at 3:11 PM, Timothy Chen <tnac...@gmail.com> wrote: > > > >> Dynamic allocation works with Coarse grain mode only, we wasn't aware > >> a need for Fine grain mode after we enabled dynamic allocation support > >> on the coarse grain mode. > >> > >> What's the reason you're running fine grain mode instead of coarse > >> grain + dynamic allocation? > >> > >> Tim > >> > >> On Mon, Dec 19, 2016 at 2:45 PM, Mehdi Meziane > >> <mehdi.mezi...@ldmobile.net> wrote: > >> > We will be interested by the results if you give a try to Dynamic > >> allocation > >> > with mesos ! > >> > > >> > > >> > - Mail Original - > >> > De: "Michael Gummelt" <mgumm...@mesosphere.io> > >> > À: "Sumit Chawla" <sumitkcha...@gmail.com> > >> > Cc: user@mesos.apache.org, d...@mesos.apache.org, "User" > >> > <u...@spark.apache.org>, d...@spark.apache.org > >> > Envoyé: Lundi 19 Décembre 2016 22h42:55 GMT +01:00 Amsterdam / Berlin > / > >> > Berne / Rome / Stockholm / Vienne > >> > Objet: Re: Mesos Spark Fine Grained Execution - CPU count > >> > > >> > > >> >> Is this problem of idle executors sticking around solved in Dynamic > >> >> Resource Allocation? Is there some timeout after which Idle > executors > >> can > >> >> just shutdown and cleanup its resources. > >> > > >> > Yes, that's exactly what dynamic allocation does. But again I have no > >> idea > >> > what the state of dynamic allocation + mesos is. > >> > > >> > On Mon, Dec 19, 2016 at 1:32 PM, Chawla,Sumit <sumitkcha...@gmail.com > > > >> > wrote: > >> >> > >> >> Great. Makes much better sense now. What will be reason to have > >> >> spark.mesos.mesosExecutor.cores more than 1, as this number doesn't > >> include > >> >> the number of cores for tasks. > >> >> > >> >> So in my case it seems like 30 CPUs are allocated to executors. And > >> there > >> >> are 48 tasks so 48 + 30 = 78 CPUs. And i am noticing this gap of > 30 is > >> >> maintained till the last task exits. This explains the gap. Thanks > >> >> everyone. I am still not sure how this number 30 is calculated. ( > Is > >> it > >> >> dynamic based on current resources, or is it some configuration. I > >> have 32 > >> >> nodes in my cluster). > >> >> > >> >> Is this problem of idle executors sticking around solved in Dynamic > >> >> Resource Allocation? Is there some timeout after which Idle > executors > >> can > >> >> just shutdown and cleanup its resources. > >> >> > >> >> > >> >> Regards > >> >> Sumit Chawla > >> >> > >> >> > >> >> On Mon, Dec 19, 2016 at 12:45 PM, Michael Gummelt < > >> mgumm...@mesosphere.io> > >> >> wrote: > >> >>> > >> >>> > 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 > >>
Re: Mesos Spark Fine Grained Execution - CPU count
Using 0 for spark.mesos.mesosExecutor.cores is better than dynamic allocation, but have to pay a little more overhead for launching a task, which should be OK if the task is not trivial. Since the direct result (up to 1M by default) will also go through mesos, it's better to tune it lower, otherwise mesos could become the bottleneck. spark.task.maxDirectResultSize On Mon, Dec 19, 2016 at 3:23 PM, Chawla,Sumit <sumitkcha...@gmail.com> wrote: > Tim, > > We will try to run the application in coarse grain mode, and share the > findings with you. > > Regards > Sumit Chawla > > > On Mon, Dec 19, 2016 at 3:11 PM, Timothy Chen <tnac...@gmail.com> wrote: > >> Dynamic allocation works with Coarse grain mode only, we wasn't aware >> a need for Fine grain mode after we enabled dynamic allocation support >> on the coarse grain mode. >> >> What's the reason you're running fine grain mode instead of coarse >> grain + dynamic allocation? >> >> Tim >> >> On Mon, Dec 19, 2016 at 2:45 PM, Mehdi Meziane >> <mehdi.mezi...@ldmobile.net> wrote: >> > We will be interested by the results if you give a try to Dynamic >> allocation >> > with mesos ! >> > >> > >> > - Mail Original - >> > De: "Michael Gummelt" <mgumm...@mesosphere.io> >> > À: "Sumit Chawla" <sumitkcha...@gmail.com> >> > Cc: user@mesos.apache.org, d...@mesos.apache.org, "User" >> > <u...@spark.apache.org>, d...@spark.apache.org >> > Envoyé: Lundi 19 Décembre 2016 22h42:55 GMT +01:00 Amsterdam / Berlin / >> > Berne / Rome / Stockholm / Vienne >> > Objet: Re: Mesos Spark Fine Grained Execution - CPU count >> > >> > >> >> Is this problem of idle executors sticking around solved in Dynamic >> >> Resource Allocation? Is there some timeout after which Idle executors >> can >> >> just shutdown and cleanup its resources. >> > >> > Yes, that's exactly what dynamic allocation does. But again I have no >> idea >> > what the state of dynamic allocation + mesos is. >> > >> > On Mon, Dec 19, 2016 at 1:32 PM, Chawla,Sumit <sumitkcha...@gmail.com> >> > wrote: >> >> >> >> Great. Makes much better sense now. What will be reason to have >> >> spark.mesos.mesosExecutor.cores more than 1, as this number doesn't >> include >> >> the number of cores for tasks. >> >> >> >> So in my case it seems like 30 CPUs are allocated to executors. And >> there >> >> are 48 tasks so 48 + 30 = 78 CPUs. And i am noticing this gap of 30 is >> >> maintained till the last task exits. This explains the gap. Thanks >> >> everyone. I am still not sure how this number 30 is calculated. ( Is >> it >> >> dynamic based on current resources, or is it some configuration. I >> have 32 >> >> nodes in my cluster). >> >> >> >> Is this problem of idle executors sticking around solved in Dynamic >> >> Resource Allocation? Is there some timeout after which Idle executors >> can >> >> just shutdown and cleanup its resources. >> >> >> >> >> >> Regards >> >> Sumit Chawla >> >> >> >> >> >> On Mon, Dec 19, 2016 at 12:45 PM, Michael Gummelt < >> mgumm...@mesosphere.io> >> >> wrote: >> >>> >> >>> > 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 n
Re: Mesos Spark Fine Grained Execution - CPU count
Tim, We will try to run the application in coarse grain mode, and share the findings with you. Regards Sumit Chawla On Mon, Dec 19, 2016 at 3:11 PM, Timothy Chen <tnac...@gmail.com> wrote: > Dynamic allocation works with Coarse grain mode only, we wasn't aware > a need for Fine grain mode after we enabled dynamic allocation support > on the coarse grain mode. > > What's the reason you're running fine grain mode instead of coarse > grain + dynamic allocation? > > Tim > > On Mon, Dec 19, 2016 at 2:45 PM, Mehdi Meziane > <mehdi.mezi...@ldmobile.net> wrote: > > We will be interested by the results if you give a try to Dynamic > allocation > > with mesos ! > > > > > > - Mail Original - > > De: "Michael Gummelt" <mgumm...@mesosphere.io> > > À: "Sumit Chawla" <sumitkcha...@gmail.com> > > Cc: user@mesos.apache.org, d...@mesos.apache.org, "User" > > <u...@spark.apache.org>, d...@spark.apache.org > > Envoyé: Lundi 19 Décembre 2016 22h42:55 GMT +01:00 Amsterdam / Berlin / > > Berne / Rome / Stockholm / Vienne > > Objet: Re: Mesos Spark Fine Grained Execution - CPU count > > > > > >> Is this problem of idle executors sticking around solved in Dynamic > >> Resource Allocation? Is there some timeout after which Idle executors > can > >> just shutdown and cleanup its resources. > > > > Yes, that's exactly what dynamic allocation does. But again I have no > idea > > what the state of dynamic allocation + mesos is. > > > > On Mon, Dec 19, 2016 at 1:32 PM, Chawla,Sumit <sumitkcha...@gmail.com> > > wrote: > >> > >> Great. Makes much better sense now. What will be reason to have > >> spark.mesos.mesosExecutor.cores more than 1, as this number doesn't > include > >> the number of cores for tasks. > >> > >> So in my case it seems like 30 CPUs are allocated to executors. And > there > >> are 48 tasks so 48 + 30 = 78 CPUs. And i am noticing this gap of 30 is > >> maintained till the last task exits. This explains the gap. Thanks > >> everyone. I am still not sure how this number 30 is calculated. ( Is > it > >> dynamic based on current resources, or is it some configuration. I > have 32 > >> nodes in my cluster). > >> > >> Is this problem of idle executors sticking around solved in Dynamic > >> Resource Allocation? Is there some timeout after which Idle executors > can > >> just shutdown and cleanup its resources. > >> > >> > >> Regards > >> Sumit Chawla > >> > >> > >> On Mon, Dec 19, 2016 at 12:45 PM, Michael Gummelt < > mgumm...@mesosphere.io> > >> wrote: > >>> > >>> > 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 docum
Re: Mesos Spark Fine Grained Execution - CPU count
Dynamic allocation works with Coarse grain mode only, we wasn't aware a need for Fine grain mode after we enabled dynamic allocation support on the coarse grain mode. What's the reason you're running fine grain mode instead of coarse grain + dynamic allocation? Tim On Mon, Dec 19, 2016 at 2:45 PM, Mehdi Meziane <mehdi.mezi...@ldmobile.net> wrote: > We will be interested by the results if you give a try to Dynamic allocation > with mesos ! > > > - Mail Original - > De: "Michael Gummelt" <mgumm...@mesosphere.io> > À: "Sumit Chawla" <sumitkcha...@gmail.com> > Cc: user@mesos.apache.org, d...@mesos.apache.org, "User" > <u...@spark.apache.org>, d...@spark.apache.org > Envoyé: Lundi 19 Décembre 2016 22h42:55 GMT +01:00 Amsterdam / Berlin / > Berne / Rome / Stockholm / Vienne > Objet: Re: Mesos Spark Fine Grained Execution - CPU count > > >> Is this problem of idle executors sticking around solved in Dynamic >> Resource Allocation? Is there some timeout after which Idle executors can >> just shutdown and cleanup its resources. > > Yes, that's exactly what dynamic allocation does. But again I have no idea > what the state of dynamic allocation + mesos is. > > On Mon, Dec 19, 2016 at 1:32 PM, Chawla,Sumit <sumitkcha...@gmail.com> > wrote: >> >> Great. Makes much better sense now. What will be reason to have >> spark.mesos.mesosExecutor.cores more than 1, as this number doesn't include >> the number of cores for tasks. >> >> So in my case it seems like 30 CPUs are allocated to executors. And there >> are 48 tasks so 48 + 30 = 78 CPUs. And i am noticing this gap of 30 is >> maintained till the last task exits. This explains the gap. Thanks >> everyone. I am still not sure how this number 30 is calculated. ( Is it >> dynamic based on current resources, or is it some configuration. I have 32 >> nodes in my cluster). >> >> Is this problem of idle executors sticking around solved in Dynamic >> Resource Allocation? Is there some timeout after which Idle executors can >> just shutdown and cleanup its resources. >> >> >> Regards >> Sumit Chawla >> >> >> On Mon, Dec 19, 2016 at 12:45 PM, Michael Gummelt <mgumm...@mesosphere.io> >> wrote: >>> >>> > 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#fine-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 l
