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From rammohan ganapavarapu <rammohanga...@gmail.com>
Subject Re: ACCEPTED: waiting for AM container to be allocated, launched and register with RM
Date Sat, 20 Aug 2016 01:32:38 GMT
Even if  the cluster dont have enough resources it should connect to "

/0.0.0.0:8030" right? it should connect to my <RM_HOST:8030>, not sure
why its trying to connect to 0.0.0.0:8030.

I have verified the config and i removed traces of 0.0.0.0 still no luck.

org.apache.hadoop.yarn.client.RMProxy: Connecting to ResourceManager
at /0.0.0.0:8030

If an one has any clue please share.

Thanks,

Ram



On Fri, Aug 19, 2016 at 2:32 PM, rammohan ganapavarapu <
rammohanganap@gmail.com> wrote:

> When i submit a job using yarn its seems working only with oozie its
> failing i guess, not sure what is missing.
>
> yarn jar /uap/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.1.jar
> pi 20 1000
> Number of Maps  = 20
> Samples per Map = 1000
> .
> .
> .
> Job Finished in 19.622 seconds
> Estimated value of Pi is 3.14280000000000000000
>
> Ram
>
> On Fri, Aug 19, 2016 at 11:46 AM, rammohan ganapavarapu <
> rammohanganap@gmail.com> wrote:
>
>> Ok, i have used yarn-utils.py to get the correct values for my cluster
>> and update those properties and restarted RM and NM but still no luck not
>> sure what i am missing, any other insights will help me.
>>
>> Below are my properties from yarn-site.xml and map-site.xml.
>>
>> python yarn-utils.py -c 24 -m 63 -d 3 -k False
>>  Using cores=24 memory=63GB disks=3 hbase=False
>>  Profile: cores=24 memory=63488MB reserved=1GB usableMem=62GB disks=3
>>  Num Container=6
>>  Container Ram=10240MB
>>  Used Ram=60GB
>>  Unused Ram=1GB
>>  yarn.scheduler.minimum-allocation-mb=10240
>>  yarn.scheduler.maximum-allocation-mb=61440
>>  yarn.nodemanager.resource.memory-mb=61440
>>  mapreduce.map.memory.mb=5120
>>  mapreduce.map.java.opts=-Xmx4096m
>>  mapreduce.reduce.memory.mb=10240
>>  mapreduce.reduce.java.opts=-Xmx8192m
>>  yarn.app.mapreduce.am.resource.mb=5120
>>  yarn.app.mapreduce.am.command-opts=-Xmx4096m
>>  mapreduce.task.io.sort.mb=1024
>>
>>
>>     <property>
>>       <name>mapreduce.map.memory.mb</name>
>>       <value>5120</value>
>>     </property>
>>     <property>
>>       <name>mapreduce.map.java.opts</name>
>>       <value>-Xmx4096m</value>
>>     </property>
>>     <property>
>>       <name>mapreduce.reduce.memory.mb</name>
>>       <value>10240</value>
>>     </property>
>>     <property>
>>       <name>mapreduce.reduce.java.opts</name>
>>       <value>-Xmx8192m</value>
>>     </property>
>>     <property>
>>       <name>yarn.app.mapreduce.am.resource.mb</name>
>>       <value>5120</value>
>>     </property>
>>     <property>
>>       <name>yarn.app.mapreduce.am.command-opts</name>
>>       <value>-Xmx4096m</value>
>>     </property>
>>     <property>
>>       <name>mapreduce.task.io.sort.mb</name>
>>       <value>1024</value>
>>     </property>
>>
>>
>>
>>      <property>
>>       <name>yarn.scheduler.minimum-allocation-mb</name>
>>       <value>10240</value>
>>     </property>
>>
>>      <property>
>>       <name>yarn.scheduler.maximum-allocation-mb</name>
>>       <value>61440</value>
>>     </property>
>>
>>      <property>
>>       <name>yarn.nodemanager.resource.memory-mb</name>
>>       <value>61440</value>
>>     </property>
>>
>>
>> Ram
>>
>> On Thu, Aug 18, 2016 at 11:14 PM, tkg_cangkul <yuza.rasfar@gmail.com>
>> wrote:
>>
>>> maybe this link can be some reference to tune up the cluster:
>>>
>>> http://jason4zhu.blogspot.co.id/2014/10/memory-configuration
>>> -in-hadoop.html
>>>
>>>
>>> On 19/08/16 11:13, rammohan ganapavarapu wrote:
>>>
>>> Do you know what properties to tune?
>>>
>>> Thanks,
>>> Ram
>>>
>>> On Thu, Aug 18, 2016 at 9:11 PM, tkg_cangkul <yuza.rasfar@gmail.com>
>>> wrote:
>>>
>>>> i think that's because you don't have enough resource.  u can tune your
>>>> cluster config to maximize your resource.
>>>>
>>>>
>>>> On 19/08/16 11:03, rammohan ganapavarapu wrote:
>>>>
>>>> I dont see any thing odd except this not sure if i have to worry about
>>>> it or not.
>>>>
>>>> 2016-08-19 03:29:26,621 INFO [main] org.apache.hadoop.yarn.client.RMProxy:
>>>> Connecting to ResourceManager at /0.0.0.0:8030
>>>> 2016-08-19 03:29:27,646 INFO [main] org.apache.hadoop.ipc.Client:
>>>> Retrying connect to server: 0.0.0.0/0.0.0.0:8030. Already tried 0
>>>> time(s); retry policy is RetryUpToMaximumCo
>>>> untWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS)
>>>> 2016-08-19 03:29:28,647 INFO [main] org.apache.hadoop.ipc.Client:
>>>> Retrying connect to server: 0.0.0.0/0.0.0.0:8030. Already tried 1
>>>> time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10,
>>>> sleepTime=1000 MILLISECONDS)
>>>>
>>>>
>>>> its keep printing this log ..in app container logs.
>>>>
>>>> On Thu, Aug 18, 2016 at 8:20 PM, tkg_cangkul <yuza.rasfar@gmail.com>
>>>> wrote:
>>>>
>>>>> maybe u can check the logs from port 8088 on your browser. that was RM
>>>>> UI. just choose your job id and then check the logs.
>>>>>
>>>>> On 19/08/16 10:14, rammohan ganapavarapu wrote:
>>>>>
>>>>> Sunil,
>>>>>
>>>>> Thanks you for your input, below are my server metrics for RM. Also
>>>>> attached RM UI for capacity scheduler resources. How else i can find?
>>>>>
>>>>> {
>>>>>       "name": "Hadoop:service=ResourceManage
>>>>> r,name=QueueMetrics,q0=root",
>>>>>       "modelerType": "QueueMetrics,q0=root",
>>>>>       "tag.Queue": "root",
>>>>>       "tag.Context": "yarn",
>>>>>       "tag.Hostname": "hadoop001",
>>>>>       "running_0": 0,
>>>>>       "running_60": 0,
>>>>>       "running_300": 0,
>>>>>       "running_1440": 0,
>>>>>       "AppsSubmitted": 1,
>>>>>       "AppsRunning": 0,
>>>>>       "AppsPending": 0,
>>>>>       "AppsCompleted": 0,
>>>>>       "AppsKilled": 0,
>>>>>       "AppsFailed": 1,
>>>>>       "AllocatedMB": 0,
>>>>>       "AllocatedVCores": 0,
>>>>>       "AllocatedContainers": 0,
>>>>>       "AggregateContainersAllocated": 2,
>>>>>       "AggregateContainersReleased": 2,
>>>>>       "AvailableMB": 64512,
>>>>>       "AvailableVCores": 24,
>>>>>       "PendingMB": 0,
>>>>>       "PendingVCores": 0,
>>>>>       "PendingContainers": 0,
>>>>>       "ReservedMB": 0,
>>>>>       "ReservedVCores": 0,
>>>>>       "ReservedContainers": 0,
>>>>>       "ActiveUsers": 0,
>>>>>       "ActiveApplications": 0
>>>>>     },
>>>>>
>>>>> On Thu, Aug 18, 2016 at 6:49 PM, Sunil Govind <sunil.govind@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi
>>>>>>
>>>>>> It could be because of many of reasons. Also I am not sure about
>>>>>> which scheduler your are using, pls share more details such as RM
log etc.
>>>>>>
>>>>>> I could point out few reasons
>>>>>>  - Such as "Not enough resource is cluster" can cause this
>>>>>>  - If using Capacity Scheduler, if queue capacity is maxed out, such
>>>>>> case can happen.
>>>>>>  - Similarly if max-am-resource-percent is crossed per queue level,
>>>>>> then also AM container may not be launched.
>>>>>>
>>>>>> you could check RM log to get more information if AM container is
>>>>>> laucnhed.
>>>>>>
>>>>>> Thanks
>>>>>> Sunil
>>>>>>
>>>>>> On Fri, Aug 19, 2016 at 5:37 AM rammohan ganapavarapu <
>>>>>> rammohanganap@gmail.com> wrote:
>>>>>>
>>>>>>> Hi,
>>>>>>>
>>>>>>> When i submit a MR job, i am getting this from AM UI but it never
>>>>>>> get finished, what am i missing ?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ram
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>> ---------------------------------------------------------------------
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>>>>> For additional commands, e-mail: user-help@hadoop.apache.org
>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>

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