Hi Jakub,

In standalone mode Spark does the resource management. Which version of
Spark are you running?

How do you define your SparkConf() parameters for example setMaster etc.

From

spark-submit --driver-class-path spark/sqljdbc4.jar --class DemoApp
SparkPOC.jar 10 4.3

I did not see any executor, memory allocation, so I assume you are
allocating them somewhere else?

HTH



Dr Mich Talebzadeh



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On 4 July 2016 at 16:31, Jakub Stransky <stransky...@gmail.com> wrote:

> Hello,
>
> I have a spark cluster consisting of 4 nodes in a standalone mode, master
> + 3 workers nodes with configured available memory and cpus etc.
>
> I have an spark application which is essentially a MLlib pipeline for
> training a classifier, in this case RandomForest  but could be a
> DecesionTree just for the sake of simplicity.
>
> But when I submit the spark application to the cluster via spark submit it
> is running out of memory. Even though the executors are "taken"/created in
> the cluster they are esentially doing nothing ( poor cpu, nor memory
> utilization) while the master seems to do all the work which finally
> results in OOM.
>
> My submission is following:
> spark-submit --driver-class-path spark/sqljdbc4.jar --class DemoApp
> SparkPOC.jar 10 4.3
>
> I am submitting from the master node.
>
> By default it is running in client mode which the driver process is
> attached to spark-shell.
>
> Do I need to set up some settings to make MLlib algos parallelized and
> distributed as well or all is driven by parallel factor set on dataframe
> with input data?
>
> Essentially it seems that all work is just done on master and the rest is
> idle.
> Any hints what to check?
>
> Thx
> Jakub
>
>
>
>

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