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From Varun Saxena <vsaxena.va...@gmail.com>
Subject Re: yarn.nodemanager.resource.cpu-vcores vs yarn.scheduler.maximum-allocation-vcores
Date Sun, 23 Aug 2015 20:29:53 GMT
So how does hadoop get this property if it is per node? Does it get the
minimum of all nodes?

--> No its not minimum of all nodes. Each nodemanager reads this
configuration from its respective configuration file(yarn-site.xml).
Nodemanager is like an agent which manages the lifecycle of containers and
installed on each node where you want to run containers.
It communicates with resource manager and that is how resource manager
comes to know about capability of each node. At the time of registration
with RM, Nodemanager tells about that node's capability to RM(for
scheduling) by reading above 2 configuration items(one for memory and one
for vcores).

By capability of node I meant you may have some nodes which has 8 cores and
some which have 16 cores, for instance. Some may have 16 GB memory and some
24 GB.
So above 2 configurations can be configured accordingly because till Hadoop
2.7 we were not getting a node's hardware capability from operating system.
This will be automatically read from OS(Linux/Windows), if configured to do
so, from 2.8 onwards.

This is a nodemanager configuration and is not required to be configured at
the client side while submitting the job.

Regards,
Varun Saxena


On Mon, Aug 24, 2015 at 1:26 AM, Varun Saxena <vsaxena.varun@gmail.com>
wrote:

> This configuration is read and used by NodeManager, on whichever node its
> running.
> If it is not configured, default value will be taken.
>
> Regards,
> Varun Saxena.
>
> On Mon, Aug 24, 2015 at 1:21 AM, Pedro Magalhaes <pedrorjbr@gmail.com>
> wrote:
>
>> Thanks Varun! Like we say in Brazil.  "U are the guy!" (Você é o cara!)
>>
>> I have another question. You said that:
>> "yarn.nodemanager.resource.cpu-vcores on the other hand will have to be
>> configured as per resource capability of that particular node. "
>>
>> I get the configuration from my job and printed it:
>> yarn.nodemanager.resource.cpu-vcores 8
>> yarn.nodemanager.resource.memory-mb 8192
>>
>> So how does hadoop get this property if it is per node? Does it get the
>> minimum of all nodes? Thanks again!
>>
>>
>>
>> On Sun, Aug 23, 2015 at 4:40 PM, Varun Saxena <vsaxena.varun@gmail.com>
>> wrote:
>>
>>> The fix would be released in next version(2.8.0).
>>> I had checked the code to find out the default value and then found it
>>> fixed in documentation(configuration list).
>>>
>>> As this is an unreleased version, a URL link (of the form
>>> https://hadoop.apache.org/docs/r2.7.1/hadoop-yarn/hadoop-yarn-common/yarn-default.xml)
>>> may not be available AFAIK,
>>> However, this XML(yarn-default.xml) can be checked online in git
>>> repository.
>>>
>>> Associated JIRA which fixes this is
>>> https://issues.apache.org/jira/browse/YARN-3823
>>>
>>> Regards,
>>> Varun Saxena.
>>>
>>> On Mon, Aug 24, 2015 at 12:53 AM, Pedro Magalhaes <pedrorjbr@gmail.com>
>>> wrote:
>>>
>>>> Thanks Varun!
>>>> Could plz send me the link with the fixed?
>>>>
>>>> On Sun, Aug 23, 2015 at 2:20 PM, Varun Saxena <vsaxena.varun@gmail.com>
>>>> wrote:
>>>>
>>>>> Hi Pedro,
>>>>>
>>>>> Real default value of yarn.scheduler.maximum-allocation-vcores is 4.
>>>>> The value of 32 is actually a documentation issue and has been fixed
>>>>> recently.
>>>>>
>>>>> Regards,
>>>>> Varun Saxena.
>>>>>
>>>>>
>>>>> On Sun, Aug 23, 2015 at 10:39 PM, Pedro Magalhaes <pedrorjbr@gmail.com
>>>>> > wrote:
>>>>>
>>>>>> Varun,
>>>>>> Thanks for the reply. I undestand the arn.scheduler.maximum-
>>>>>> allocation-vcores parameter. I just asking why the default parameter
>>>>>> is yarn.scheduler.maximum-allocation-vcores=32. And
>>>>>> yarn.nodemanager.resource.cpu-vcores=8.
>>>>>>
>>>>>> In my opinion, if the yarn.scheduler.maximun-allocation-vcore is
32
>>>>>> tby default the yarn.nodemanager.resource.cpu-vcores  would be equal
or
>>>>>> greater than 32, by default.
>>>>>> Is this make sense?
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Sun, Aug 23, 2015 at 2:00 PM, Varun Saxena <
>>>>>> vsaxena.varun@gmail.com> wrote:
>>>>>>
>>>>>>> Hi Pedro,
>>>>>>>
>>>>>>> Actual allocation would depend on the total resource capability
>>>>>>> advertised by NM while registering with RM.
>>>>>>>
>>>>>>> yarn.scheduler.maximum-allocation-vcores merely puts an upper
cap on number of vcores which can be allocated by RM i.e. any Resource request/ask from AM
which asks for vcores > 32(default value) for a container, will be normalized back to 32.
>>>>>>>
>>>>>>> If there is no such node available, this allocation will not
be fulfilled.
>>>>>>>
>>>>>>> yarn.scheduler.maximum-allocation-vcores will be configured in
>>>>>>> resource manager and hence will be common for a cluster which
can possibly
>>>>>>> have multiple nodes with heterogeneous resource capabilities
>>>>>>>
>>>>>>> yarn.nodemanager.resource.cpu-vcores on the other hand will have
to
>>>>>>> be configured as per resource capability of that particular node.
>>>>>>>
>>>>>>> Recently there has been work done to automatically get memory
and
>>>>>>> CPU information from underlying OS(supported OS being Linux and
Windows) if
>>>>>>> configured to do so. This change would be available in 2.8
>>>>>>> I hope this answers your question.
>>>>>>>
>>>>>>> Regards,
>>>>>>> Varun Saxena.
>>>>>>>
>>>>>>> On Sun, Aug 23, 2015 at 9:40 PM, Pedro Magalhaes <
>>>>>>> pedrorjbr@gmail.com> wrote:
>>>>>>>
>>>>>>>> I was looking at default parameters for:
>>>>>>>>
>>>>>>>> yarn.nodemanager.resource.cpu-vcores = 8
>>>>>>>> yarn.scheduler.maximum-allocation-vcores = 32
>>>>>>>>
>>>>>>>> For me this two parameters as default doesnt make any sense.
>>>>>>>>
>>>>>>>> The first one say "the number of CPU cores that can be allocated
>>>>>>>> for containers." (I imagine that is vcore) The seconds says:
"The maximum
>>>>>>>> allocation for every container request at the RM". In my
opinion, the
>>>>>>>> second one must be equal or less than the first one.
>>>>>>>>
>>>>>>>> How can allocate 32 vcores for a container if i have only
8 cores
>>>>>>>> available per container?
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>

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