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From Ji Yan <ji...@drive.ai>
Subject Re: Dynamic resource allocation to Spark on Mesos
Date Thu, 02 Feb 2017 21:06:19 GMT
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
>

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