Re: Please reply if you use Mesos fine grained mode
Fine grain mode does reuse the same JVM but perhaps different placement or different allocated cores comparing to the same total memory allocation. Tim Sent from my iPhone > On Nov 3, 2015, at 6:00 PM, Reynold Xin wrote: > > Soren, > > If I understand how Mesos works correctly, even the fine grained mode keeps > the JVMs around? > > >> On Tue, Nov 3, 2015 at 4:22 PM, Soren Macbeth wrote: >> we use fine-grained mode. coarse-grained mode keeps JVMs around which often >> leads to OOMs, which in turn kill the entire executor, causing entire stages >> to be retried. In fine-grained mode, only the task fails and subsequently >> gets retried without taking out an entire stage or worse. >> >>> On Tue, Nov 3, 2015 at 3:54 PM, Reynold Xin wrote: >>> If you are using Spark with Mesos fine grained mode, can you please respond >>> to this email explaining why you use it over the coarse grained mode? >>> >>> Thanks. >
Re: Please reply if you use Mesos fine grained mode
Hi, We are using Mesos fine grained mode because we can have multiple instances of spark to share machines and each application get resources dynamically allocated. Thanks & Regards, Meethu M On Wednesday, 4 November 2015 5:24 AM, Reynold Xin wrote: If you are using Spark with Mesos fine grained mode, can you please respond to this email explaining why you use it over the coarse grained mode? Thanks.
Re: Please reply if you use Mesos fine grained mode
Soren, If I understand how Mesos works correctly, even the fine grained mode keeps the JVMs around? On Tue, Nov 3, 2015 at 4:22 PM, Soren Macbeth wrote: > we use fine-grained mode. coarse-grained mode keeps JVMs around which > often leads to OOMs, which in turn kill the entire executor, causing entire > stages to be retried. In fine-grained mode, only the task fails and > subsequently gets retried without taking out an entire stage or worse. > > On Tue, Nov 3, 2015 at 3:54 PM, Reynold Xin wrote: > >> If you are using Spark with Mesos fine grained mode, can you please >> respond to this email explaining why you use it over the coarse grained >> mode? >> >> Thanks. >> >> >
Re: Please reply if you use Mesos fine grained mode
We "used" Spark on Mesos to build interactive data analysis platform because the interactive session could be long and might not use Spark for the entire session. It is very wasteful of resources if we used the coarse-grained mode because it keeps resource for the entire session. Therefore, fine-grained mode was used. Knowing that Spark now supports dynamic resource allocation with coarse grained mode, we were thinking about using it. However, we decided to switch to Yarn because in addition to dynamic allocation, it has better supports on security. On Tue, Nov 3, 2015 at 7:22 PM, Soren Macbeth wrote: > we use fine-grained mode. coarse-grained mode keeps JVMs around which > often leads to OOMs, which in turn kill the entire executor, causing entire > stages to be retried. In fine-grained mode, only the task fails and > subsequently gets retried without taking out an entire stage or worse. > > On Tue, Nov 3, 2015 at 3:54 PM, Reynold Xin wrote: > >> If you are using Spark with Mesos fine grained mode, can you please >> respond to this email explaining why you use it over the coarse grained >> mode? >> >> Thanks. >> >> >
Re: Please reply if you use Mesos fine grained mode
we use fine-grained mode. coarse-grained mode keeps JVMs around which often leads to OOMs, which in turn kill the entire executor, causing entire stages to be retried. In fine-grained mode, only the task fails and subsequently gets retried without taking out an entire stage or worse. On Tue, Nov 3, 2015 at 3:54 PM, Reynold Xin wrote: > If you are using Spark with Mesos fine grained mode, can you please > respond to this email explaining why you use it over the coarse grained > mode? > > Thanks. > >
Please reply if you use Mesos fine grained mode
If you are using Spark with Mesos fine grained mode, can you please respond to this email explaining why you use it over the coarse grained mode? Thanks.