yes, I did configured. On Aug 19, 2016 7:22 PM, "Rohith Sharma K S" <ksrohithsha...@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 < > rammohanga...@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 < > rammohanga...@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 < >> rammohanga...@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.ras...@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.ras...@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.ras...@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.gov...@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 < >>>>>>> rammohanga...@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-unsubscr...@hadoop.apache.org >>>>>> For additional commands, e-mail: user-h...@hadoop.apache.org >>>>>> >>>>>> >>>>>> >>>>> >>>>> >>>> >>>> >>> >> > >
