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From Jacek Laskowski <ja...@japila.pl>
Subject Re: Executors assigned to STS and number of workers in Stand Alone Mode
Date Mon, 25 Jul 2016 18:34:19 GMT
Hi,

My vague understanding of Spark Standalone is that it will take up all
available workers for a Spark application (despite the cmd options). There
was a property to disable it. Can't remember it now though.

Ps. Yet another reason for YARN ;-)

Jacek

On 25 Jul 2016 6:17 p.m., "Mich Talebzadeh" <mich.talebzadeh@gmail.com>
wrote:

> Hi,
>
>
> I am doing some tests
>
> I have started Spark in Standalone mode.
>
> For simplicity I am using one node only with 8 works and I have 12 cores
>
> In spark-env.sh I set this
>
> # Options for the daemons used in the standalone deploy mode
> export SPARK_WORKER_CORES=1 ##, total number of cores to be used by
> executors by each worker
> export SPARK_WORKER_MEMORY=1g ##, to set how much total memory workers
> have to give executors (e.g. 1000m, 2g)
> the worker
> export SPARK_WORKER_INSTANCES=8 ##, to set the number of worker processes
> per node
>
> So it is pretty straight forward with 8 works and each worker assigned one
> core
>
> jps|grep Worker
> 15297 Worker
> 14794 Worker
> 15374 Worker
> 14998 Worker
> 15198 Worker
> 15465 Worker
> 14897 Worker
> 15099 Worker
>
> I start Spark Thrift Server with the following parameters (using
> standalone mode)
>
> ${SPARK_HOME}/sbin/start-thriftserver.sh \
>                 --master spark://50.140.197.217:7077 \
>                 --hiveconf hive.server2.thrift.port=10055 \
>                 --driver-memory 1G \
>                 --num-executors 1 \
>                 --executor-cores 1 \
>                 --executor-memory 1G \
>                 --conf "spark.scheduler.mode=FIFO" \
>
> With one executor allocated 1 core
>
> However, I can see both in the OS and UI that it starts with 8 executors,
> the same number of workers on this node!
>
> jps|egrep 'SparkSubmit|CoarseGrainedExecutorBackend'|sort
> 32711 SparkSubmit
> 369 CoarseGrainedExecutorBackend
> 370 CoarseGrainedExecutorBackend
> 371 CoarseGrainedExecutorBackend
> 376 CoarseGrainedExecutorBackend
> 387 CoarseGrainedExecutorBackend
> 395 CoarseGrainedExecutorBackend
> 419 CoarseGrainedExecutorBackend
> 420 CoarseGrainedExecutorBackend
>
>
> I fail to see why this is happening. Nothing else is running Spark wise.
> The cause?
>
>  How can I stop STS going and using all available workers?
>
> Thanks
>
> Dr Mich Talebzadeh
>
>
>
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