Thank you, Dr Mich Talebzadeh, I will capture the error messages, but currently, my cluster is running to do the other job. After it finished, I will try your suggestions
On Sun, May 29, 2016 at 7:55 AM, Mich Talebzadeh <mich.talebza...@gmail.com> wrote: > You should have errors in yarn-nodemanager and yarn-resourcemanager logs. > > Something like below for heathy container > > 2016-05-29 00:50:50,496 INFO > org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl: > Memory usage of ProcessTree 29769 for container-id > container_1464210869844_0061_01_000001: 372.6 MB of 4 GB physical memory > used; 2.7 GB of 8.4 GB virtual memory used > > It appears that you are running out of memory. Have you also checked with > jps and jmonitor for SparkSubmit (the driver process) for the failing job? > It will show you the resource usage= like memory/heap/cpu etc > > HTH > > Dr Mich Talebzadeh > > > > LinkedIn * > https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw > <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* > > > > http://talebzadehmich.wordpress.com > > > > On 29 May 2016 at 00:26, heri wijayanto <heri0...@gmail.com> wrote: > >> I implement spark with join function for processing in around 250 million >> rows of text. >> >> When I just used several hundred of rows, it could run, but when I use >> the large data, it is failed. >> >> My spark version in 1.6.1, run above yarn-cluster mode, and we have 5 >> node computers. >> >> Thank you very much, Ted Yu >> >> On Sun, May 29, 2016 at 6:48 AM, Ted Yu <yuzhih...@gmail.com> wrote: >> >>> Can you let us know your case ? >>> >>> When the join failed, what was the error (consider pastebin) ? >>> >>> Which release of Spark are you using ? >>> >>> Thanks >>> >>> > On May 28, 2016, at 3:27 PM, heri wijayanto <heri0...@gmail.com> >>> wrote: >>> > >>> > Hi everyone, >>> > I perform join function in a loop, and it is failed. I found a >>> tutorial from the web, it says that I should use a broadcast variable but >>> it is not a good choice for doing it on the loop. >>> > I need your suggestion to address this problem, thank you very much. >>> > and I am sorry, I am a beginner in Spark programming >>> >> >> >
