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https://issues.apache.org/jira/browse/MESOS-1688?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14109779#comment-14109779
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ASF GitHub Bot commented on MESOS-1688:
---------------------------------------
Github user MartinWeindel closed the pull request at:
https://github.com/apache/mesos/pull/24
> 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.
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