Jörn, thanks for the post...
Unfortunately, I am stuck with the hardware I have and might not be
able to get budget allocated for a new stack of servers when I've
already got so many "ok" servers on hand... And even more
unfortunately, a large subset of these machines are... shall we say...
extremely humble in their cpus and ram. My group has exclusive access
to the machine and rarely do we need to run concurrent jobs-- What I
really want is max capacity per-job. The applications are massive
machine-learning experiments, so I'm not sure about the feasibility of
breaking it up into concurrent jobs. At this point, I am seriously
considering dropping down to Akka-level programming. Why, oh why,
doesn't spark allow for allocating variable worker threads per host?
this would seem to be the correct point of abstraction that would
allow the construction of massive clusters using "on-hand" hardware?
(the scheduler probably wouldn't have to change at all)
On Thu, Aug 21, 2014 at 9:25 AM, Jörn Franke [via Apache Spark User
List] <[hidden email]> wrote:> Le 21 août 2014 16:55, "[hidden email]" <[hidden email]> a écrit :
> Well, you could use Mesos or Yarn2 to define resources per Job - you can
> give only as much resources (cores, memory etc.) per machine as your "worst"
> machine has. The rest is done by Mesos or Yarn. By doing this you avoid a
> per machine resource assignment without any disadvantages. You can run
> without any problems run other jobs in parallel and older machines won't get
> however, you should take care that your cluster does not get too
> Best regards,
>>>>> To unsubscribe, e-mail: [hidden email]
>> I've got a stack of Dell Commodity servers-- Ram~>(8 to 32Gb) single or
>> quad core processor cores per machine. I think I will have them loaded
>> CentOS. Eventually, I may want to add GPUs on the nodes to handle linear
>> alg. operations...
>> My Idea has been:
>> 1) to find a way to configure Spark to allocate different resources
>> per-machine, per-job. -- at least have a "standard executor"... and allow
>> different machines to have different numbers of executors.
>> 2) make (using vanilla spark) a pre-run optimization phase which
>> the throughput of each node (per hardware), and repartition the dataset to
>> more efficiently use the hardware rather than rely on Spark Speculation--
>> which has always seemed a dis-optimal way to balance the load across
>> differing machines.
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