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From Ji Yan <ji...@drive.ai>
Subject Re: Dynamic resource allocation to Spark on Mesos
Date Thu, 02 Feb 2017 22:42:42 GMT
I tried setting spark.executor.cores per executor, but Spark seems to be
spinning up as many executors as possible up to spark.cores.max or however
many cpu cores available on the cluster, and this may be undesirable
because the number of executors in rdd.parallelize(collection, # of
partitions) is being overriden

On Thu, Feb 2, 2017 at 1:30 PM, Michael Gummelt <mgummelt@mesosphere.io>
wrote:

> As of Spark 2.0, Mesos mode does support setting cores on the executor
> level, but you might need to set the property directly (--conf
> spark.executor.cores=<cores>).  I've written about this here:
> https://docs.mesosphere.com/1.8/usage/service-guides/spark/job-scheduling/.
> That doc is for DC/OS, but the configuration is the same.
>
> On Thu, Feb 2, 2017 at 1:06 PM, Ji Yan <jiyan@drive.ai> wrote:
>
>> I was mainly confused why this is the case with memory, but with cpu
>> cores, it is not specified on per executor level
>>
>> On Thu, Feb 2, 2017 at 1:02 PM, Michael Gummelt <mgummelt@mesosphere.io>
>> wrote:
>>
>>> It sounds like you've answered your own question, right?
>>> --executor-memory means the memory per executor.  If you have no executor
>>> w/ 200GB memory, then the driver will accept no offers.
>>>
>>> On Thu, Feb 2, 2017 at 1:01 PM, Ji Yan <jiyan@drive.ai> wrote:
>>>
>>>> sorry, to clarify, i was using --executor-memory for memory,
>>>> and --total-executor-cores for cpu cores
>>>>
>>>> On Thu, Feb 2, 2017 at 12:56 PM, Michael Gummelt <
>>>> mgummelt@mesosphere.io> wrote:
>>>>
>>>>> What CLI args are your referring to?  I'm aware of spark-submit's
>>>>> arguments (--executor-memory, --total-executor-cores, and --executor-cores)
>>>>>
>>>>> On Thu, Feb 2, 2017 at 12:41 PM, Ji Yan <jiyan@drive.ai> wrote:
>>>>>
>>>>>> I have done a experiment on this today. It shows that only CPUs are
>>>>>> tolerant of insufficient cluster size when a job starts. On my cluster,
I
>>>>>> have 180Gb of memory and 64 cores, when I run spark-submit ( on mesos
)
>>>>>> with --cpu_cores set to 1000, the job starts up with 64 cores. but
when I
>>>>>> set --memory to 200Gb, the job fails to start with "Initial job has
>>>>>> not accepted any resources; check your cluster UI to ensure that
workers
>>>>>> are registered and have sufficient resources"
>>>>>>
>>>>>> Also it is confusing to me that --cpu_cores specifies the number
of
>>>>>> cpu cores across all executors, but --memory specifies per executor
memory
>>>>>> requirement.
>>>>>>
>>>>>> On Mon, Jan 30, 2017 at 11:34 AM, Michael Gummelt <
>>>>>> mgummelt@mesosphere.io> wrote:
>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Mon, Jan 30, 2017 at 9:47 AM, Ji Yan <jiyan@drive.ai>
wrote:
>>>>>>>
>>>>>>>> Tasks begin scheduling as soon as the first executor comes
up
>>>>>>>>
>>>>>>>>
>>>>>>>> Thanks all for the clarification. Is this the default behavior
of
>>>>>>>> Spark on Mesos today? I think this is what we are looking
for because
>>>>>>>> sometimes a job can take up lots of resources and later jobs
could not get
>>>>>>>> all the resources that it asks for. If a Spark job starts
with only a
>>>>>>>> subset of resources that it asks for, does it know to expand
its resources
>>>>>>>> later when more resources become available?
>>>>>>>>
>>>>>>>
>>>>>>> Yes.
>>>>>>>
>>>>>>>
>>>>>>>>
>>>>>>>> Launch each executor with at least 1GB RAM, but if mesos
offers 2GB
>>>>>>>>> at some moment, then launch an executor with 2GB RAM
>>>>>>>>
>>>>>>>>
>>>>>>>> This is less useful in our use case. But I am also quite
interested
>>>>>>>> in cases in which this could be helpful. I think this will
also help with
>>>>>>>> overall resource utilization on the cluster if when another
job starts up
>>>>>>>> that has a hard requirement on resources, the extra resources
to the first
>>>>>>>> job can be flexibly re-allocated to the second job.
>>>>>>>>
>>>>>>>> On Sat, Jan 28, 2017 at 2:32 PM, Michael Gummelt <
>>>>>>>> mgummelt@mesosphere.io> wrote:
>>>>>>>>
>>>>>>>>> We've talked about that, but it hasn't become a priority
because
>>>>>>>>> we haven't had a driving use case.  If anyone has a good
argument for
>>>>>>>>> "variable" resource allocation like this, please let
me know.
>>>>>>>>>
>>>>>>>>> On Sat, Jan 28, 2017 at 9:17 AM, Shuai Lin <linshuai2012@gmail.com
>>>>>>>>> > wrote:
>>>>>>>>>
>>>>>>>>>> An alternative behavior is to launch the job with
the best
>>>>>>>>>>> resource offer Mesos is able to give
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> Michael has just made an excellent explanation about
dynamic
>>>>>>>>>> allocation support in mesos. But IIUC, what you want
to achieve is
>>>>>>>>>> something like (using RAM as an example) : "Launch
each executor with at
>>>>>>>>>> least 1GB RAM, but if mesos offers 2GB at some moment,
then launch an
>>>>>>>>>> executor with 2GB RAM".
>>>>>>>>>>
>>>>>>>>>> I wonder what's benefit of that? To reduce the "resource
>>>>>>>>>> fragmentation"?
>>>>>>>>>>
>>>>>>>>>> Anyway, that is not supported at this moment. In
all the
>>>>>>>>>> supported cluster managers of spark (mesos, yarn,
standalone, and the
>>>>>>>>>> up-to-coming spark on kubernetes), you have to specify
the cores and memory
>>>>>>>>>> of each executor.
>>>>>>>>>>
>>>>>>>>>> It may not be supported in the future, because only
mesos has the
>>>>>>>>>> concepts of offers because of its two-level scheduling
model.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Sat, Jan 28, 2017 at 1:35 AM, Ji Yan <jiyan@drive.ai>
wrote:
>>>>>>>>>>
>>>>>>>>>>> Dear Spark Users,
>>>>>>>>>>>
>>>>>>>>>>> Currently is there a way to dynamically allocate
resources to
>>>>>>>>>>> Spark on Mesos? Within Spark we can specify the
CPU cores, memory before
>>>>>>>>>>> running job. The way I understand is that the
Spark job will not run if the
>>>>>>>>>>> CPU/Mem requirement is not met. This may lead
to decrease in overall
>>>>>>>>>>> utilization of the cluster. An alternative behavior
is to launch the job
>>>>>>>>>>> with the best resource offer Mesos is able to
give. Is this possible with
>>>>>>>>>>> the current implementation?
>>>>>>>>>>>
>>>>>>>>>>> Thanks
>>>>>>>>>>> Ji
>>>>>>>>>>>
>>>>>>>>>>> The information in this email is confidential
and may be legally
>>>>>>>>>>> privileged. It is intended solely for the addressee.
Access to this email
>>>>>>>>>>> by anyone else is unauthorized. If you are not
the intended recipient, any
>>>>>>>>>>> disclosure, copying, distribution or any action
taken or omitted to be
>>>>>>>>>>> taken in reliance on it, is prohibited and may
be unlawful.
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> --
>>>>>>>>> Michael Gummelt
>>>>>>>>> Software Engineer
>>>>>>>>> Mesosphere
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> The information in this email is confidential and may be
legally
>>>>>>>> privileged. It is intended solely for the addressee. Access
to this email
>>>>>>>> by anyone else is unauthorized. If you are not the intended
recipient, any
>>>>>>>> disclosure, copying, distribution or any action taken or
omitted to be
>>>>>>>> taken in reliance on it, is prohibited and may be unlawful.
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>>> Michael Gummelt
>>>>>>> Software Engineer
>>>>>>> Mesosphere
>>>>>>>
>>>>>>
>>>>>>
>>>>>> The information in this email is confidential and may be legally
>>>>>> privileged. It is intended solely for the addressee. Access to this
email
>>>>>> by anyone else is unauthorized. If you are not the intended recipient,
any
>>>>>> disclosure, copying, distribution or any action taken or omitted
to be
>>>>>> taken in reliance on it, is prohibited and may be unlawful.
>>>>>>
>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Michael Gummelt
>>>>> Software Engineer
>>>>> Mesosphere
>>>>>
>>>>
>>>>
>>>> The information in this email is confidential and may be legally
>>>> privileged. It is intended solely for the addressee. Access to this email
>>>> by anyone else is unauthorized. If you are not the intended recipient, any
>>>> disclosure, copying, distribution or any action taken or omitted to be
>>>> taken in reliance on it, is prohibited and may be unlawful.
>>>>
>>>
>>>
>>>
>>> --
>>> Michael Gummelt
>>> Software Engineer
>>> Mesosphere
>>>
>>
>>
>> The information in this email is confidential and may be legally
>> privileged. It is intended solely for the addressee. Access to this email
>> by anyone else is unauthorized. If you are not the intended recipient, any
>> disclosure, copying, distribution or any action taken or omitted to be
>> taken in reliance on it, is prohibited and may be unlawful.
>>
>
>
>
> --
> Michael Gummelt
> Software Engineer
> Mesosphere
>

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