Prabhu,
I reformulate my question:
I successfully run following job: yarn jar
/opt/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.1.jar pi 3
10
and noticed that highest node physical memory usage was alway <512MB during
job duration; else job completed (see details below)
quote....
Every 2.0s: yarn node -status hadoop-1.mydomain.local:44718
19/08/15 19:10:54 INFO client.RMProxy: Connecting to ResourceManager at
hadoop-1.mydomain.local/192.168.100.11:8032
Node Report :
Node-Id : hadoop-1.mydomain.local:44718
Rack : /default-rack
Node-State : RUNNING
Node-Http-Address : hadoop-1.mydomain.local:8042
Last-Health-Update : Thu 15/Aug/19 07:10:22:75CEST
Health-Report :
Containers : 2
Memory-Used : 2048MB
Memory-Capacity : 5120MB
CPU-Used : 2 vcores
CPU-Capacity : 6 vcores
Node-Labels :
Resource Utilization by Node : *PMem:471 MB*, VMem:1413 MB,
VCores:0.80463576
Resource Utilization by Containers : PMem:110 MB, VMem:4014 MB,
VCores:0.97300005
...unquote
My question is : which job setting may I use to force a node physical
memory usage >512MB and force a job kill due (or thanks) pmem check.
Hope above better explain my question ;)
thanks/Guido
On Thu, Aug 15, 2019 at 5:09 PM Prabhu Josephraj
<[email protected]> wrote:
> Jeff, Available node size for YARN is the value of
> yarn.nodemanager.resource.memory-mb which is set ten times of 512MB.
>
> Guido, Did not get the below question, can you explain the same.
>
> Are you aware of any job syntax to tune the 'container physical
> memory usage' to 'force' job kill/log?
>
>
> On Thu, Aug 15, 2019 at 7:20 PM . . <[email protected]>
> wrote:
>
>> Hi Prabhu,
>>
>> thanks for your explanation. It makes sense, but I wonder YARN allows you
>> to define 'yarn.nodemanager.resource.memory-mb' higher then node physical
>> memory w/out logging any entry under resourcemanager log.
>>
>> Are you aware of any job syntax to tune the 'container physical memory
>> usage' to 'force' job kill/log?
>>
>> thanks/Guido
>>
>>
>>
>> On Thu, Aug 15, 2019 at 1:50 PM Prabhu Josephraj
>> <[email protected]> wrote:
>>
>>> YARN allocates based on the configuration
>>> (yarn.nodemanager.resource.memory-mb) user has configured. It has allocated
>>> the AM Container of size 1536MB as it can fit in 5120MB Available Node
>>> Size.
>>>
>>> yarn.nodemanager.pmem-check-enabled will kill the container if the
>>> physical memory usage of the container process is above
>>> 1536MB. MR ApplicationMaster for a pi job is light weight and it won't
>>> require that much memory and so not got killed.
>>>
>>>
>>>
>>> On Thu, Aug 15, 2019 at 4:02 PM . . <[email protected]>
>>> wrote:
>>>
>>>> Correct: I set 'yarn.nodemanager.resource.memory-mb' ten times the
>>>> node physical memory (512MB) and I was able to successfully execute a 'pi
>>>> 1 10' mapreduce job.
>>>>
>>>> Since default 'yarn.app.mapreduce.am.resource.mb' value is 1536MB I
>>>> expected the job to never start / be allocated and I have no valid
>>>> explanation.
>>>>
>>>>
>>>> On Wed, Aug 14, 2019 at 10:32 PM . . <[email protected]> wrote:
>>>>
>>>>> Correct: I set 'yarn.nodemanager.resource.memory-mb' ten times the
>>>>> node physical memory (512MB) and I was able to successfully execute a 'pi
>>>>> 1 10' mapreduce job.
>>>>>
>>>>> Since default 'yarn.app.mapreduce.am.resource.mb' value is 1536MB I
>>>>> expected the job to never start / be allocated and I have no valid
>>>>> explanation.
>>>>>
>>>>>
>>>>>
>>>>> On Wed, Aug 14, 2019 at 8:31 PM Jeff Hubbs <[email protected]> wrote:
>>>>>
>>>>>> To make sure I understand...you've allocated *ten times* your
>>>>>> physical RAM for containers? If so, I think that's your issue.
>>>>>>
>>>>>> For reference, under Hadoop 3.x I didn't have a cluster that would
>>>>>> really do anything until its worker nodes had at least 8GiB.
>>>>>>
>>>>>> On 8/14/19 12:10 PM, . . wrote:
>>>>>>
>>>>>> Hi all,
>>>>>>
>>>>>> I installed a basic 3 nodes Hadoop 2.9.1 cluster and playing with
>>>>>> YARN settings.
>>>>>> The 3 nodes has following configuration:
>>>>>> 1 cpu / 1 core?? / 512MB RAM
>>>>>>
>>>>>> I wonder I was able to configure yarn-site.xml with following
>>>>>> settings (higher than node physical limits) and successfully run a
>>>>>> mapreduce 'pi 1 10' job
>>>>>>
>>>>>> quote...
>>>>>> ?? <property>
>>>>>> ?? ?? ??
>>>>>> <name>yarn.resourcemanager.scheduler.class</name><value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value>
>>>>>> </property>
>>>>>>
>>>>>> ?? ?? <property>
>>>>>> ?? ?? ?? ?? <name>yarn.nodemanager.resource.memory-mb</name>
>>>>>> ?? ?? ?? ?? <value>5120</value>
>>>>>> ?? ?? ?? ?? <description>Amount of physical memory, in MB, that can
>>>>>> be allocated for containers. If set to -1 and
>>>>>> yarn.nodemanager.resource.detect-hardware-capabilities is true, it is
>>>>>> automatically calculated. In other cases, the default is
>>>>>> 8192MB</description>
>>>>>> ?? ?? </property>
>>>>>>
>>>>>> ?? ?? <property>
>>>>>> ?? ?? ?? ?? <name>yarn.nodemanager.resource.cpu-vcores</name>
>>>>>> ?? ?? ?? ?? <value>6</value>
>>>>>> ?? ?? ?? ?? <description>Number of CPU cores that can be allocated
>>>>>> for containers.</description>
>>>>>> ?? ?? </property>
>>>>>> ...unquote
>>>>>>
>>>>>> Can anyone provide an explanation please?
>>>>>>
>>>>>> Should 'yarn.nodemanager.vmem-check-enabled' and
>>>>>> 'yarn.nodemanager.pmem-check-enabled' properties (set to 'true' as
>>>>>> default)
>>>>>> check that my YARN settings are higher than physical limits?
>>>>>>
>>>>>> Which mapreduce 'pi' job settings can I use, to 'force' containers to
>>>>>> use more than node physical resources?
>>>>>>
>>>>>> Many thanks in advance!
>>>>>> Guido
>>>>>>
>>>>>>
>>>>>>
