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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 20:37:31 GMT
Hi,

That's interesting...What holds STS back from working on the other
scheduler backends, e.g. YARN or Mesos? I haven't spent much time with
it, but thought it's a mere Spark application.

The property is spark.deploy.spreadOut = Whether the standalone
cluster manager should spread applications out across nodes or try to
consolidate them onto as few nodes as possible. Spreading out is
usually better for data locality in HDFS, but consolidating is more
efficient for compute-intensive workloads.

See https://spark.apache.org/docs/latest/spark-standalone.html

Pozdrawiam,
Jacek Laskowski
----
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On Mon, Jul 25, 2016 at 9:24 PM, Mich Talebzadeh
<mich.talebzadeh@gmail.com> wrote:
> Thanks. As I understand STS only works in Standalone mode :(
>
> Dr Mich Talebzadeh
>
>
>
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> On 25 July 2016 at 19:34, Jacek Laskowski <jacek@japila.pl> wrote:
>>
>> 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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>>> Disclaimer: Use it at your own risk. Any and all responsibility for any
>>> loss, damage or destruction of data or any other property which may arise
>>> from relying on this email's technical content is explicitly disclaimed. The
>>> author will in no case be liable for any monetary damages arising from such
>>> loss, damage or destruction.
>>>
>>>
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