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From Michael Gummelt <mgumm...@mesosphere.io>
Subject Re: Dynamic resource allocation to Spark on Mesos
Date Thu, 02 Feb 2017 20:56:31 GMT
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
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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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