[
https://issues.apache.org/jira/browse/MESOS-1688?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14111358#comment-14111358
]
Timothy Chen commented on MESOS-1688:
-------------------------------------
Hi Vinod, currently the Spark scheduler creates executors with only Memory, and
later launch tasks with CPU resource. So if you do the update Spark will break
once it uses the new version.
> No offers if no memory is allocatable
> -------------------------------------
>
> Key: MESOS-1688
> URL: https://issues.apache.org/jira/browse/MESOS-1688
> Project: Mesos
> Issue Type: Bug
> Components: master
> Affects Versions: 0.18.1, 0.18.2, 0.19.0, 0.19.1
> Reporter: Martin Weindel
> Priority: Critical
>
> The [Spark
> scheduler|https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala]
> allocates memory only for the executor and cpu only for its tasks.
> So it can happen that all memory is nearly completely allocated by Spark
> executors, but all cpu resources are idle.
> In this case Mesos does not offer resources anymore, as less than MIN_MEM
> (=32MB) memory is allocatable.
> This effectively causes a dead lock in the Spark job, as it is not offered
> cpu resources needed for launching new tasks.
> see {{HierarchicalAllocatorProcess::allocatable(const Resources&)}} called in
> {{HierarchicalAllocatorProcess::allocate(const hashset<SlaveID>&)}}
> {code}
> template <class RoleSorter, class FrameworkSorter>
> bool
> HierarchicalAllocatorProcess<RoleSorter, FrameworkSorter>::allocatable(
> const Resources& resources)
> {
> ...
> Option<double> cpus = resources.cpus();
> Option<Bytes> mem = resources.mem();
> if (cpus.isSome() && mem.isSome()) {
> return cpus.get() >= MIN_CPUS && mem.get() > MIN_MEM;
> }
> return false;
> }
> {code}
> A possible solution may to completely drop the condition on allocatable
> memory.
--
This message was sent by Atlassian JIRA
(v6.2#6252)
