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Posted to user@hadoop.apache.org by rammohan ganapavarapu <ra...@gmail.com> on 2016/08/19 00:06:52 UTC
ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
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
Re: ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
Posted by rammohan ganapavarapu <ra...@gmail.com>.
Guys,
I was able to fix this issue but trail and error not sure which property
made it work :) but its working and i have to use 8032 as jobtracker. I
also restarted all the components before i was only restarting nodemanager
and resource manager after property update.
You get this error if you use wrong port
Socket Reader #1 for port 8030: readAndProcess from client 10.16.3.51 threw
exception [org.apache.hadoop.security.AccessControlException: SIMPLE
authentication is not enabled. Available:[TOKEN]]
org.apache.hadoop.security.AccessControlException: SIMPLE authentication is
not enabled. Available:[TOKEN]
Thanks a lot for all your help,
Ram
On Mon, Aug 22, 2016 at 10:27 AM, rammohan ganapavarapu <
rammohanganap@gmail.com> wrote:
> Thank you all, I have updated my oozie job.properties to use 8030 and now
> i am getting below error
>
>
>
>
>
> 2016-08-22 17:22:02,893 INFO org.apache.hadoop.yarn.server.
> resourcemanager.ResourceTrackerService: NodeManager from node
> slave03(cmPort: 40511 httpPort: 8042) registered with capability:
> <memory:8192, vCores:8>, assigned nodeId slave03:40511
> 2016-08-22 17:22:02,893 INFO org.apache.hadoop.yarn.server.
> resourcemanager.rmnode.RMNodeImpl: slave03:40511 Node Transitioned from
> NEW to RUNNING
> 2016-08-22 17:22:02,893 INFO org.apache.hadoop.yarn.server.
> resourcemanager.scheduler.capacity.CapacityScheduler: Added node
> slave03:40511 clusterResource: <memory:24576, vCores:24>
> 2016-08-22 17:23:14,258 INFO org.apache.hadoop.ipc.Server: Socket Reader
> #1 for port 8030: readAndProcess from client 10.16.3.51 threw exception
> [org.apache.hadoop.security.AccessControlException: SIMPLE authentication
> is not enabled. Available:[TOKEN]]
> org.apache.hadoop.security.AccessControlException: SIMPLE authentication
> is not enabled. Available:[TOKEN]
> at org.apache.hadoop.ipc.Server$Connection.
> initializeAuthContext(Server.java:1564)
> at org.apache.hadoop.ipc.Server$Connection.readAndProcess(
> Server.java:1520)
> at org.apache.hadoop.ipc.Server$Listener.doRead(Server.java:771)
> at org.apache.hadoop.ipc.Server$Listener$Reader.doRunLoop(
> Server.java:637)
> at org.apache.hadoop.ipc.Server$Listener$Reader.run(Server.
> java:608)
>
>
> So i did enabled simple auth by below config in core-site.xml and
> restarted namenode,datanode,rm and nm but still getting same error do i
> have to do any thing else to enable simple auth?
>
>
> <property>
> <name>hadoop.security.authentication</name>
> <value>simple</value>
> </property>
>
>
> Ram
>
> On Mon, Aug 22, 2016 at 9:43 AM, Sunil Govind <su...@gmail.com>
> wrote:
>
>> HI Ram
>>
>> RM logs looks fine and as per config it looks like RM is running on 8030
>> itself.
>> I am not very sure about the oozie end config which you mentioned. I
>> suggest you could check the config end more and debug there.
>> Also will let other community folks to pitch in if they have some other
>> opinion.
>>
>> Thanks
>> Sunil
>>
>> On Mon, Aug 22, 2016 at 8:57 PM rammohan ganapavarapu <
>> rammohanganap@gmail.com> wrote:
>>
>>> any thoughts from the logs and config I have shared?
>>>
>>> On Aug 21, 2016 8:32 AM, "rammohan ganapavarapu" <
>>> rammohanganap@gmail.com> wrote:
>>>
>>>> so in job.properties what is the jobtracker property, is it RM ip: port
>>>> or scheduler port which is 8030, if I use 8030 I am getting unknown
>>>> protocol proto buffer error.
>>>>
>>>> On Aug 21, 2016 7:37 AM, "Sunil Govind" <su...@gmail.com> wrote:
>>>>
>>>>> Hi.
>>>>>
>>>>> It seems its an oozie issue. From conf, RM scheduler is running at
>>>>> port 8030.
>>>>> But your job.properties is taking 8032. I suggest you could double
>>>>> confirm your oozie configuration and see the configurations are intact to
>>>>> contact RM. Sharing a link also
>>>>> https://discuss.zendesk.com/hc/en-us/articles/203355837-How-
>>>>> to-run-a-MapReduce-jar-using-Oozie-workflow
>>>>>
>>>>> Thanks
>>>>> Sunil
>>>>>
>>>>>
>>>>> On Sun, Aug 21, 2016 at 8:41 AM rammohan ganapavarapu <
>>>>> rammohanganap@gmail.com> wrote:
>>>>>
>>>>>> Please find the attached config that i got from yarn ui and AM,RM
>>>>>> logs. I only see that connecting to 0.0.0.0:8030 when i submit job
>>>>>> using oozie, but if i submit as yarn jar its working fine as i posted in my
>>>>>> previous posts.
>>>>>>
>>>>>> Here is my oozie job.properties file, i have a java class that just
>>>>>> prints
>>>>>>
>>>>>> nameNode=hdfs://master01:8020
>>>>>> jobTracker=master01:8032
>>>>>> workflowName=EchoJavaJob
>>>>>> oozie.use.system.libpath=true
>>>>>>
>>>>>> queueName=default
>>>>>> hdfsWorkflowHome=/user/uap/oozieWorkflows
>>>>>>
>>>>>> workflowPath=${nameNode}${hdfsWorkflowHome}/${workflowName}
>>>>>> oozie.wf.application.path=${workflowPath}
>>>>>>
>>>>>> Please let me know if you guys find any clue why its trying to
>>>>>> connect to 0.0.0.:8030.
>>>>>>
>>>>>> Thanks,
>>>>>> Ram
>>>>>>
>>>>>>
>>>>>> On Fri, Aug 19, 2016 at 11:54 PM, Sunil Govind <
>>>>>> sunil.govind@gmail.com> wrote:
>>>>>>
>>>>>>> Hi Ram
>>>>>>>
>>>>>>> From the console log, as Rohith said, AM is looking for AM at 8030.
>>>>>>> So pls confirm the RM port once.
>>>>>>> Could you please share AM and RM logs.
>>>>>>>
>>>>>>> Thanks
>>>>>>> Sunil
>>>>>>>
>>>>>>> On Sat, Aug 20, 2016 at 10:36 AM rammohan ganapavarapu <
>>>>>>> rammohanganap@gmail.com> wrote:
>>>>>>>
>>>>>>>> yes, I did configured.
>>>>>>>>
>>>>>>>> On Aug 19, 2016 7:22 PM, "Rohith Sharma K S" <
>>>>>>>> ksrohithsharma@gmail.com> wrote:
>>>>>>>>
>>>>>>>>> Hi
>>>>>>>>>
>>>>>>>>> From below discussion and AM logs, I see that AM container has
>>>>>>>>> launched but not able to connect to RM.
>>>>>>>>>
>>>>>>>>> This looks like your configuration issue. Would you check your
>>>>>>>>> job.xml jar that does *yarn.resourcemanager.scheduler.address *has
>>>>>>>>> been configured?
>>>>>>>>>
>>>>>>>>> Essentially, this address required by MRAppMaster for connecting
>>>>>>>>> to RM for heartbeats. If you don’t not configure, default value will be
>>>>>>>>> taken i.e 8030.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Thanks & Regards
>>>>>>>>> Rohith Sharma K S
>>>>>>>>>
>>>>>>>>> On Aug 20, 2016, at 7:02 AM, rammohan ganapavarapu <
>>>>>>>>> rammohanganap@gmail.com> wrote:
>>>>>>>>>
>>>>>>>>> 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/mapre
>>>>>>>>>> duce/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
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> ---------------------------------------------------------------------
>>>>>>>>>>>>>> To unsubscribe, e-mail: user-unsubscribe@hadoop.apache.org
>>>>>>>>>>>>>> For additional commands, e-mail: user-help@hadoop.apache.org
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>
>
Re: ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
Posted by rammohan ganapavarapu <ra...@gmail.com>.
Thank you all, I have updated my oozie job.properties to use 8030 and now i
am getting below error
2016-08-22 17:22:02,893 INFO
org.apache.hadoop.yarn.server.resourcemanager.ResourceTrackerService:
NodeManager from node slave03(cmPort: 40511 httpPort: 8042) registered with
capability: <memory:8192, vCores:8>, assigned nodeId slave03:40511
2016-08-22 17:22:02,893 INFO
org.apache.hadoop.yarn.server.resourcemanager.rmnode.RMNodeImpl:
slave03:40511 Node Transitioned from NEW to RUNNING
2016-08-22 17:22:02,893 INFO
org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler:
Added node slave03:40511 clusterResource: <memory:24576, vCores:24>
2016-08-22 17:23:14,258 INFO org.apache.hadoop.ipc.Server: Socket Reader #1
for port 8030: readAndProcess from client 10.16.3.51 threw exception
[org.apache.hadoop.security.AccessControlException: SIMPLE authentication
is not enabled. Available:[TOKEN]]
org.apache.hadoop.security.AccessControlException: SIMPLE authentication is
not enabled. Available:[TOKEN]
at
org.apache.hadoop.ipc.Server$Connection.initializeAuthContext(Server.java:1564)
at
org.apache.hadoop.ipc.Server$Connection.readAndProcess(Server.java:1520)
at org.apache.hadoop.ipc.Server$Listener.doRead(Server.java:771)
at
org.apache.hadoop.ipc.Server$Listener$Reader.doRunLoop(Server.java:637)
at org.apache.hadoop.ipc.Server$Listener$Reader.run(Server.java:608)
So i did enabled simple auth by below config in core-site.xml and restarted
namenode,datanode,rm and nm but still getting same error do i have to do
any thing else to enable simple auth?
<property>
<name>hadoop.security.authentication</name>
<value>simple</value>
</property>
Ram
On Mon, Aug 22, 2016 at 9:43 AM, Sunil Govind <su...@gmail.com>
wrote:
> HI Ram
>
> RM logs looks fine and as per config it looks like RM is running on 8030
> itself.
> I am not very sure about the oozie end config which you mentioned. I
> suggest you could check the config end more and debug there.
> Also will let other community folks to pitch in if they have some other
> opinion.
>
> Thanks
> Sunil
>
> On Mon, Aug 22, 2016 at 8:57 PM rammohan ganapavarapu <
> rammohanganap@gmail.com> wrote:
>
>> any thoughts from the logs and config I have shared?
>>
>> On Aug 21, 2016 8:32 AM, "rammohan ganapavarapu" <ra...@gmail.com>
>> wrote:
>>
>>> so in job.properties what is the jobtracker property, is it RM ip: port
>>> or scheduler port which is 8030, if I use 8030 I am getting unknown
>>> protocol proto buffer error.
>>>
>>> On Aug 21, 2016 7:37 AM, "Sunil Govind" <su...@gmail.com> wrote:
>>>
>>>> Hi.
>>>>
>>>> It seems its an oozie issue. From conf, RM scheduler is running at port
>>>> 8030.
>>>> But your job.properties is taking 8032. I suggest you could double
>>>> confirm your oozie configuration and see the configurations are intact to
>>>> contact RM. Sharing a link also
>>>> https://discuss.zendesk.com/hc/en-us/articles/203355837-
>>>> How-to-run-a-MapReduce-jar-using-Oozie-workflow
>>>>
>>>> Thanks
>>>> Sunil
>>>>
>>>>
>>>> On Sun, Aug 21, 2016 at 8:41 AM rammohan ganapavarapu <
>>>> rammohanganap@gmail.com> wrote:
>>>>
>>>>> Please find the attached config that i got from yarn ui and AM,RM
>>>>> logs. I only see that connecting to 0.0.0.0:8030 when i submit job
>>>>> using oozie, but if i submit as yarn jar its working fine as i posted in my
>>>>> previous posts.
>>>>>
>>>>> Here is my oozie job.properties file, i have a java class that just
>>>>> prints
>>>>>
>>>>> nameNode=hdfs://master01:8020
>>>>> jobTracker=master01:8032
>>>>> workflowName=EchoJavaJob
>>>>> oozie.use.system.libpath=true
>>>>>
>>>>> queueName=default
>>>>> hdfsWorkflowHome=/user/uap/oozieWorkflows
>>>>>
>>>>> workflowPath=${nameNode}${hdfsWorkflowHome}/${workflowName}
>>>>> oozie.wf.application.path=${workflowPath}
>>>>>
>>>>> Please let me know if you guys find any clue why its trying to connect
>>>>> to 0.0.0.:8030.
>>>>>
>>>>> Thanks,
>>>>> Ram
>>>>>
>>>>>
>>>>> On Fri, Aug 19, 2016 at 11:54 PM, Sunil Govind <sunil.govind@gmail.com
>>>>> > wrote:
>>>>>
>>>>>> Hi Ram
>>>>>>
>>>>>> From the console log, as Rohith said, AM is looking for AM at 8030.
>>>>>> So pls confirm the RM port once.
>>>>>> Could you please share AM and RM logs.
>>>>>>
>>>>>> Thanks
>>>>>> Sunil
>>>>>>
>>>>>> On Sat, Aug 20, 2016 at 10:36 AM rammohan ganapavarapu <
>>>>>> rammohanganap@gmail.com> wrote:
>>>>>>
>>>>>>> yes, I did configured.
>>>>>>>
>>>>>>> On Aug 19, 2016 7:22 PM, "Rohith Sharma K S" <
>>>>>>> ksrohithsharma@gmail.com> wrote:
>>>>>>>
>>>>>>>> Hi
>>>>>>>>
>>>>>>>> From below discussion and AM logs, I see that AM container has
>>>>>>>> launched but not able to connect to RM.
>>>>>>>>
>>>>>>>> This looks like your configuration issue. Would you check your
>>>>>>>> job.xml jar that does *yarn.resourcemanager.scheduler.address *has
>>>>>>>> been configured?
>>>>>>>>
>>>>>>>> Essentially, this address required by MRAppMaster for connecting to
>>>>>>>> RM for heartbeats. If you don’t not configure, default value will be taken
>>>>>>>> i.e 8030.
>>>>>>>>
>>>>>>>>
>>>>>>>> Thanks & Regards
>>>>>>>> Rohith Sharma K S
>>>>>>>>
>>>>>>>> On Aug 20, 2016, at 7:02 AM, rammohan ganapavarapu <
>>>>>>>> rammohanganap@gmail.com> wrote:
>>>>>>>>
>>>>>>>> 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=ResourceManager,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
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> ---------------------------------------------------------------------
>>>>>>>>>>>>> To unsubscribe, e-mail: user-unsubscribe@hadoop.apache.org
>>>>>>>>>>>>> For additional commands, e-mail: user-help@hadoop.apache.org
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>
Re: ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
Posted by Sunil Govind <su...@gmail.com>.
HI Ram
RM logs looks fine and as per config it looks like RM is running on 8030
itself.
I am not very sure about the oozie end config which you mentioned. I
suggest you could check the config end more and debug there.
Also will let other community folks to pitch in if they have some other
opinion.
Thanks
Sunil
On Mon, Aug 22, 2016 at 8:57 PM rammohan ganapavarapu <
rammohanganap@gmail.com> wrote:
> any thoughts from the logs and config I have shared?
>
> On Aug 21, 2016 8:32 AM, "rammohan ganapavarapu" <ra...@gmail.com>
> wrote:
>
>> so in job.properties what is the jobtracker property, is it RM ip: port
>> or scheduler port which is 8030, if I use 8030 I am getting unknown
>> protocol proto buffer error.
>>
>> On Aug 21, 2016 7:37 AM, "Sunil Govind" <su...@gmail.com> wrote:
>>
>>> Hi.
>>>
>>> It seems its an oozie issue. From conf, RM scheduler is running at port
>>> 8030.
>>> But your job.properties is taking 8032. I suggest you could double
>>> confirm your oozie configuration and see the configurations are intact to
>>> contact RM. Sharing a link also
>>>
>>> https://discuss.zendesk.com/hc/en-us/articles/203355837-How-to-run-a-MapReduce-jar-using-Oozie-workflow
>>>
>>> Thanks
>>> Sunil
>>>
>>>
>>> On Sun, Aug 21, 2016 at 8:41 AM rammohan ganapavarapu <
>>> rammohanganap@gmail.com> wrote:
>>>
>>>> Please find the attached config that i got from yarn ui and AM,RM
>>>> logs. I only see that connecting to 0.0.0.0:8030 when i submit job
>>>> using oozie, but if i submit as yarn jar its working fine as i posted in my
>>>> previous posts.
>>>>
>>>> Here is my oozie job.properties file, i have a java class that just
>>>> prints
>>>>
>>>> nameNode=hdfs://master01:8020
>>>> jobTracker=master01:8032
>>>> workflowName=EchoJavaJob
>>>> oozie.use.system.libpath=true
>>>>
>>>> queueName=default
>>>> hdfsWorkflowHome=/user/uap/oozieWorkflows
>>>>
>>>> workflowPath=${nameNode}${hdfsWorkflowHome}/${workflowName}
>>>> oozie.wf.application.path=${workflowPath}
>>>>
>>>> Please let me know if you guys find any clue why its trying to connect
>>>> to 0.0.0.:8030.
>>>>
>>>> Thanks,
>>>> Ram
>>>>
>>>>
>>>> On Fri, Aug 19, 2016 at 11:54 PM, Sunil Govind <su...@gmail.com>
>>>> wrote:
>>>>
>>>>> Hi Ram
>>>>>
>>>>> From the console log, as Rohith said, AM is looking for AM at 8030. So
>>>>> pls confirm the RM port once.
>>>>> Could you please share AM and RM logs.
>>>>>
>>>>> Thanks
>>>>> Sunil
>>>>>
>>>>> On Sat, Aug 20, 2016 at 10:36 AM rammohan ganapavarapu <
>>>>> rammohanganap@gmail.com> wrote:
>>>>>
>>>>>> yes, I did configured.
>>>>>>
>>>>>> On Aug 19, 2016 7:22 PM, "Rohith Sharma K S" <
>>>>>> ksrohithsharma@gmail.com> wrote:
>>>>>>
>>>>>>> Hi
>>>>>>>
>>>>>>> From below discussion and AM logs, I see that AM container has
>>>>>>> launched but not able to connect to RM.
>>>>>>>
>>>>>>> This looks like your configuration issue. Would you check your
>>>>>>> job.xml jar that does *yarn.resourcemanager.scheduler.address *has
>>>>>>> been configured?
>>>>>>>
>>>>>>> Essentially, this address required by MRAppMaster for connecting to
>>>>>>> RM for heartbeats. If you don’t not configure, default value will be taken
>>>>>>> i.e 8030.
>>>>>>>
>>>>>>>
>>>>>>> Thanks & Regards
>>>>>>> Rohith Sharma K S
>>>>>>>
>>>>>>> On Aug 20, 2016, at 7:02 AM, rammohan ganapavarapu <
>>>>>>> rammohanganap@gmail.com> wrote:
>>>>>>>
>>>>>>> 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=ResourceManager,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
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> ---------------------------------------------------------------------
>>>>>>>>>>>> To unsubscribe, e-mail: user-unsubscribe@hadoop.apache.org
>>>>>>>>>>>> For additional commands, e-mail: user-help@hadoop.apache.org
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>
Re: ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
Posted by rammohan ganapavarapu <ra...@gmail.com>.
any thoughts from the logs and config I have shared?
On Aug 21, 2016 8:32 AM, "rammohan ganapavarapu" <ra...@gmail.com>
wrote:
> so in job.properties what is the jobtracker property, is it RM ip: port or
> scheduler port which is 8030, if I use 8030 I am getting unknown protocol
> proto buffer error.
>
> On Aug 21, 2016 7:37 AM, "Sunil Govind" <su...@gmail.com> wrote:
>
>> Hi.
>>
>> It seems its an oozie issue. From conf, RM scheduler is running at port
>> 8030.
>> But your job.properties is taking 8032. I suggest you could double
>> confirm your oozie configuration and see the configurations are intact to
>> contact RM. Sharing a link also
>> https://discuss.zendesk.com/hc/en-us/articles/203355837-How-
>> to-run-a-MapReduce-jar-using-Oozie-workflow
>>
>> Thanks
>> Sunil
>>
>>
>> On Sun, Aug 21, 2016 at 8:41 AM rammohan ganapavarapu <
>> rammohanganap@gmail.com> wrote:
>>
>>> Please find the attached config that i got from yarn ui and AM,RM logs.
>>> I only see that connecting to 0.0.0.0:8030 when i submit job using
>>> oozie, but if i submit as yarn jar its working fine as i posted in my
>>> previous posts.
>>>
>>> Here is my oozie job.properties file, i have a java class that just
>>> prints
>>>
>>> nameNode=hdfs://master01:8020
>>> jobTracker=master01:8032
>>> workflowName=EchoJavaJob
>>> oozie.use.system.libpath=true
>>>
>>> queueName=default
>>> hdfsWorkflowHome=/user/uap/oozieWorkflows
>>>
>>> workflowPath=${nameNode}${hdfsWorkflowHome}/${workflowName}
>>> oozie.wf.application.path=${workflowPath}
>>>
>>> Please let me know if you guys find any clue why its trying to connect
>>> to 0.0.0.:8030.
>>>
>>> Thanks,
>>> Ram
>>>
>>>
>>> On Fri, Aug 19, 2016 at 11:54 PM, Sunil Govind <su...@gmail.com>
>>> wrote:
>>>
>>>> Hi Ram
>>>>
>>>> From the console log, as Rohith said, AM is looking for AM at 8030. So
>>>> pls confirm the RM port once.
>>>> Could you please share AM and RM logs.
>>>>
>>>> Thanks
>>>> Sunil
>>>>
>>>> On Sat, Aug 20, 2016 at 10:36 AM rammohan ganapavarapu <
>>>> rammohanganap@gmail.com> wrote:
>>>>
>>>>> yes, I did configured.
>>>>>
>>>>> On Aug 19, 2016 7:22 PM, "Rohith Sharma K S" <ks...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi
>>>>>>
>>>>>> From below discussion and AM logs, I see that AM container has
>>>>>> launched but not able to connect to RM.
>>>>>>
>>>>>> This looks like your configuration issue. Would you check your
>>>>>> job.xml jar that does *yarn.resourcemanager.scheduler.address *has
>>>>>> been configured?
>>>>>>
>>>>>> Essentially, this address required by MRAppMaster for connecting to
>>>>>> RM for heartbeats. If you don’t not configure, default value will be taken
>>>>>> i.e 8030.
>>>>>>
>>>>>>
>>>>>> Thanks & Regards
>>>>>> Rohith Sharma K S
>>>>>>
>>>>>> On Aug 20, 2016, at 7:02 AM, rammohan ganapavarapu <
>>>>>> rammohanganap@gmail.com> wrote:
>>>>>>
>>>>>> 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/mapre
>>>>>>> duce/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
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> ---------------------------------------------------------------------
>>>>>>>>>>> To unsubscribe, e-mail: user-unsubscribe@hadoop.apache.org
>>>>>>>>>>> For additional commands, e-mail: user-help@hadoop.apache.org
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>
Re: ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
Posted by rammohan ganapavarapu <ra...@gmail.com>.
so in job.properties what is the jobtracker property, is it RM ip: port or
scheduler port which is 8030, if I use 8030 I am getting unknown protocol
proto buffer error.
On Aug 21, 2016 7:37 AM, "Sunil Govind" <su...@gmail.com> wrote:
> Hi.
>
> It seems its an oozie issue. From conf, RM scheduler is running at port
> 8030.
> But your job.properties is taking 8032. I suggest you could double confirm
> your oozie configuration and see the configurations are intact to contact
> RM. Sharing a link also
> https://discuss.zendesk.com/hc/en-us/articles/203355837-
> How-to-run-a-MapReduce-jar-using-Oozie-workflow
>
> Thanks
> Sunil
>
>
> On Sun, Aug 21, 2016 at 8:41 AM rammohan ganapavarapu <
> rammohanganap@gmail.com> wrote:
>
>> Please find the attached config that i got from yarn ui and AM,RM logs.
>> I only see that connecting to 0.0.0.0:8030 when i submit job using
>> oozie, but if i submit as yarn jar its working fine as i posted in my
>> previous posts.
>>
>> Here is my oozie job.properties file, i have a java class that just
>> prints
>>
>> nameNode=hdfs://master01:8020
>> jobTracker=master01:8032
>> workflowName=EchoJavaJob
>> oozie.use.system.libpath=true
>>
>> queueName=default
>> hdfsWorkflowHome=/user/uap/oozieWorkflows
>>
>> workflowPath=${nameNode}${hdfsWorkflowHome}/${workflowName}
>> oozie.wf.application.path=${workflowPath}
>>
>> Please let me know if you guys find any clue why its trying to connect to
>> 0.0.0.:8030.
>>
>> Thanks,
>> Ram
>>
>>
>> On Fri, Aug 19, 2016 at 11:54 PM, Sunil Govind <su...@gmail.com>
>> wrote:
>>
>>> Hi Ram
>>>
>>> From the console log, as Rohith said, AM is looking for AM at 8030. So
>>> pls confirm the RM port once.
>>> Could you please share AM and RM logs.
>>>
>>> Thanks
>>> Sunil
>>>
>>> On Sat, Aug 20, 2016 at 10:36 AM rammohan ganapavarapu <
>>> rammohanganap@gmail.com> wrote:
>>>
>>>> yes, I did configured.
>>>>
>>>> On Aug 19, 2016 7:22 PM, "Rohith Sharma K S" <ks...@gmail.com>
>>>> wrote:
>>>>
>>>>> Hi
>>>>>
>>>>> From below discussion and AM logs, I see that AM container has
>>>>> launched but not able to connect to RM.
>>>>>
>>>>> This looks like your configuration issue. Would you check your job.xml
>>>>> jar that does *yarn.resourcemanager.scheduler.address *has been
>>>>> configured?
>>>>>
>>>>> Essentially, this address required by MRAppMaster for connecting to RM
>>>>> for heartbeats. If you don’t not configure, default value will be taken i.e
>>>>> 8030.
>>>>>
>>>>>
>>>>> Thanks & Regards
>>>>> Rohith Sharma K S
>>>>>
>>>>> On Aug 20, 2016, at 7:02 AM, rammohan ganapavarapu <
>>>>> rammohanganap@gmail.com> wrote:
>>>>>
>>>>> 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=ResourceManager,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
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> ---------------------------------------------------------------------
>>>>>>>>>> To unsubscribe, e-mail: user-unsubscribe@hadoop.apache.org
>>>>>>>>>> For additional commands, e-mail: user-help@hadoop.apache.org
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>
Re: ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
Posted by Sunil Govind <su...@gmail.com>.
Hi.
It seems its an oozie issue. From conf, RM scheduler is running at port
8030.
But your job.properties is taking 8032. I suggest you could double confirm
your oozie configuration and see the configurations are intact to contact
RM. Sharing a link also
https://discuss.zendesk.com/hc/en-us/articles/203355837-How-to-run-a-MapReduce-jar-using-Oozie-workflow
Thanks
Sunil
On Sun, Aug 21, 2016 at 8:41 AM rammohan ganapavarapu <
rammohanganap@gmail.com> wrote:
> Please find the attached config that i got from yarn ui and AM,RM logs. I
> only see that connecting to 0.0.0.0:8030 when i submit job using oozie,
> but if i submit as yarn jar its working fine as i posted in my previous
> posts.
>
> Here is my oozie job.properties file, i have a java class that just prints
>
> nameNode=hdfs://master01:8020
> jobTracker=master01:8032
> workflowName=EchoJavaJob
> oozie.use.system.libpath=true
>
> queueName=default
> hdfsWorkflowHome=/user/uap/oozieWorkflows
>
> workflowPath=${nameNode}${hdfsWorkflowHome}/${workflowName}
> oozie.wf.application.path=${workflowPath}
>
> Please let me know if you guys find any clue why its trying to connect to
> 0.0.0.:8030.
>
> Thanks,
> Ram
>
>
> On Fri, Aug 19, 2016 at 11:54 PM, Sunil Govind <su...@gmail.com>
> wrote:
>
>> Hi Ram
>>
>> From the console log, as Rohith said, AM is looking for AM at 8030. So
>> pls confirm the RM port once.
>> Could you please share AM and RM logs.
>>
>> Thanks
>> Sunil
>>
>> On Sat, Aug 20, 2016 at 10:36 AM rammohan ganapavarapu <
>> rammohanganap@gmail.com> wrote:
>>
>>> yes, I did configured.
>>>
>>> On Aug 19, 2016 7:22 PM, "Rohith Sharma K S" <ks...@gmail.com>
>>> wrote:
>>>
>>>> Hi
>>>>
>>>> From below discussion and AM logs, I see that AM container has launched
>>>> but not able to connect to RM.
>>>>
>>>> This looks like your configuration issue. Would you check your job.xml
>>>> jar that does *yarn.resourcemanager.scheduler.address *has been
>>>> configured?
>>>>
>>>> Essentially, this address required by MRAppMaster for connecting to RM
>>>> for heartbeats. If you don’t not configure, default value will be taken i.e
>>>> 8030.
>>>>
>>>>
>>>> Thanks & Regards
>>>> Rohith Sharma K S
>>>>
>>>> On Aug 20, 2016, at 7:02 AM, rammohan ganapavarapu <
>>>> rammohanganap@gmail.com> wrote:
>>>>
>>>> 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 <yu...@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 <yu...@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=ResourceManager,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
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> ---------------------------------------------------------------------
>>>>>>>>> To unsubscribe, e-mail: user-unsubscribe@hadoop.apache.org
>>>>>>>>> For additional commands, e-mail: user-help@hadoop.apache.org
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>>
>
Re: ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
Posted by rammohan ganapavarapu <ra...@gmail.com>.
Please find the attached config that i got from yarn ui and AM,RM logs. I
only see that connecting to 0.0.0.0:8030 when i submit job using oozie, but
if i submit as yarn jar its working fine as i posted in my previous posts.
Here is my oozie job.properties file, i have a java class that just prints
nameNode=hdfs://master01:8020
jobTracker=master01:8032
workflowName=EchoJavaJob
oozie.use.system.libpath=true
queueName=default
hdfsWorkflowHome=/user/uap/oozieWorkflows
workflowPath=${nameNode}${hdfsWorkflowHome}/${workflowName}
oozie.wf.application.path=${workflowPath}
Please let me know if you guys find any clue why its trying to connect to
0.0.0.:8030.
Thanks,
Ram
On Fri, Aug 19, 2016 at 11:54 PM, Sunil Govind <su...@gmail.com>
wrote:
> Hi Ram
>
> From the console log, as Rohith said, AM is looking for AM at 8030. So pls
> confirm the RM port once.
> Could you please share AM and RM logs.
>
> Thanks
> Sunil
>
> On Sat, Aug 20, 2016 at 10:36 AM rammohan ganapavarapu <
> rammohanganap@gmail.com> wrote:
>
>> yes, I did configured.
>>
>> On Aug 19, 2016 7:22 PM, "Rohith Sharma K S" <ks...@gmail.com>
>> wrote:
>>
>>> Hi
>>>
>>> From below discussion and AM logs, I see that AM container has launched
>>> but not able to connect to RM.
>>>
>>> This looks like your configuration issue. Would you check your job.xml
>>> jar that does *yarn.resourcemanager.scheduler.address *has been
>>> configured?
>>>
>>> Essentially, this address required by MRAppMaster for connecting to RM
>>> for heartbeats. If you don’t not configure, default value will be taken i.e
>>> 8030.
>>>
>>>
>>> Thanks & Regards
>>> Rohith Sharma K S
>>>
>>> On Aug 20, 2016, at 7:02 AM, rammohan ganapavarapu <
>>> rammohanganap@gmail.com> wrote:
>>>
>>> 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 <yu...@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 <yu...@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 <yu...@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=ResourceManager,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
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> ---------------------------------------------------------------------
>>>>>>>> To unsubscribe, e-mail: user-unsubscribe@hadoop.apache.org
>>>>>>>> For additional commands, e-mail: user-help@hadoop.apache.org
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>>
Re: ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
Posted by Sunil Govind <su...@gmail.com>.
Hi Ram
From the console log, as Rohith said, AM is looking for AM at 8030. So pls
confirm the RM port once.
Could you please share AM and RM logs.
Thanks
Sunil
On Sat, Aug 20, 2016 at 10:36 AM rammohan ganapavarapu <
rammohanganap@gmail.com> wrote:
> yes, I did configured.
>
> On Aug 19, 2016 7:22 PM, "Rohith Sharma K S" <ks...@gmail.com>
> wrote:
>
>> Hi
>>
>> From below discussion and AM logs, I see that AM container has launched
>> but not able to connect to RM.
>>
>> This looks like your configuration issue. Would you check your job.xml
>> jar that does *yarn.resourcemanager.scheduler.address *has been
>> configured?
>>
>> Essentially, this address required by MRAppMaster for connecting to RM
>> for heartbeats. If you don’t not configure, default value will be taken i.e
>> 8030.
>>
>>
>> Thanks & Regards
>> Rohith Sharma K S
>>
>> On Aug 20, 2016, at 7:02 AM, rammohan ganapavarapu <
>> rammohanganap@gmail.com> wrote:
>>
>> 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 <yu...@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 <yu...@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 <yu...@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=ResourceManager,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
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> ---------------------------------------------------------------------
>>>>>>> To unsubscribe, e-mail: user-unsubscribe@hadoop.apache.org
>>>>>>> For additional commands, e-mail: user-help@hadoop.apache.org
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>
>>
>>
Re: ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
Posted by rammohan ganapavarapu <ra...@gmail.com>.
yes, I did configured.
On Aug 19, 2016 7:22 PM, "Rohith Sharma K S" <ks...@gmail.com>
wrote:
> Hi
>
> From below discussion and AM logs, I see that AM container has launched
> but not able to connect to RM.
>
> This looks like your configuration issue. Would you check your job.xml jar
> that does *yarn.resourcemanager.scheduler.address *has been configured?
>
> Essentially, this address required by MRAppMaster for connecting to RM for
> heartbeats. If you don’t not configure, default value will be taken i.e
> 8030.
>
>
> Thanks & Regards
> Rohith Sharma K S
>
> On Aug 20, 2016, at 7:02 AM, rammohan ganapavarapu <
> rammohanganap@gmail.com> wrote:
>
> 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 <yu...@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 <yu...@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 <yu...@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
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>> ---------------------------------------------------------------------
>>>>>> To unsubscribe, e-mail: user-unsubscribe@hadoop.apache.org
>>>>>> For additional commands, e-mail: user-help@hadoop.apache.org
>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>
>
>
Re: ACCEPTED: waiting for AM container to be allocated, launched and register with RM
Posted by Rohith Sharma K S <ks...@gmail.com>.
Hi
From below discussion and AM logs, I see that AM container has launched but not able to connect to RM.
This looks like your configuration issue. Would you check your job.xml jar that does yarn.resourcemanager.scheduler.address has been configured?
Essentially, this address required by MRAppMaster for connecting to RM for heartbeats. If you don’t not configure, default value will be taken i.e 8030.
Thanks & Regards
Rohith Sharma K S
> On Aug 20, 2016, at 7:02 AM, rammohan ganapavarapu <ra...@gmail.com> wrote:
>
> Even if the cluster dont have enough resources it should connect to "
> /0.0.0.0:8030 <http://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 <http://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 <http://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 <ma...@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 <ma...@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 <http://yarn.app.mapreduce.am/>.resource.mb=5120
> yarn.app.mapreduce.am <http://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 <http://yarn.app.mapreduce.am/>.resource.mb</name>
> <value>5120</value>
> </property>
> <property>
> <name>yarn.app.mapreduce.am <http://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 <ma...@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 <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 <ma...@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 <http://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 <http://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 <http://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 <ma...@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=ResourceManager,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 <ma...@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 <ma...@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
>>>>
>>>>
>>>>
>>>> ---------------------------------------------------------------------
>>>> To unsubscribe, e-mail: user-unsubscribe@hadoop.apache.org <ma...@hadoop.apache.org>
>>>> For additional commands, e-mail: user-help@hadoop.apache.org <ma...@hadoop.apache.org>
>>>
>>
>>
>
>
>
>
Re: ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
Posted by rammohan ganapavarapu <ra...@gmail.com>.
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 <yu...@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 <yu...@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 <yu...@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 <su...@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
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>> ---------------------------------------------------------------------
>>>>> To unsubscribe, e-mail: user-unsubscribe@hadoop.apache.org
>>>>> For additional commands, e-mail: user-help@hadoop.apache.org
>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>
Re: ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
Posted by rammohan ganapavarapu <ra...@gmail.com>.
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 <yu...@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 <yu...@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 <yu...@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 <su...@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
>>>>>>
>>>>>
>>>>
>>>>
>>>> ---------------------------------------------------------------------
>>>> To unsubscribe, e-mail: user-unsubscribe@hadoop.apache.org
>>>> For additional commands, e-mail: user-help@hadoop.apache.org
>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
Re: ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
Posted by rammohan ganapavarapu <ra...@gmail.com>.
Do you know what properties to tune?
Thanks,
Ram
On Thu, Aug 18, 2016 at 9:11 PM, tkg_cangkul <yu...@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 <yu...@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=ResourceManager,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 <su...@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
>>>>
>>>
>>
>>
>> ---------------------------------------------------------------------
>> To unsubscribe, e-mail: user-unsubscribe@hadoop.apache.org
>> For additional commands, e-mail: user-help@hadoop.apache.org
>>
>>
>>
>
>
Re: ACCEPTED: waiting for AM container to be allocated, launched
and register with RM
Posted by tkg_cangkul <yu...@gmail.com>.
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 <http://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
> <http://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
> <http://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
> <ma...@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=ResourceManager,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 <ma...@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 <ma...@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
>>
>>
>>
>>
>> ---------------------------------------------------------------------
>> To unsubscribe, e-mail:user-unsubscribe@hadoop.apache.org <ma...@hadoop.apache.org>
>> For additional commands, e-mail:user-help@hadoop.apache.org <ma...@hadoop.apache.org>
>
>
Re: ACCEPTED: waiting for AM container to be allocated, launched
and register with RM
Posted by tkg_cangkul <yu...@gmail.com>.
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=ResourceManager,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
> <ma...@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 <ma...@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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Re: ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
Posted by rammohan ganapavarapu <ra...@gmail.com>.
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=ResourceManager,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 <su...@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
>>
>
Re: ACCEPTED: waiting for AM container to be allocated, launched and
register with RM
Posted by Sunil Govind <su...@gmail.com>.
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
>