We're using the capacity scheduler, to the best of my knowledge. Unsure if multi resource scheduling is used, but if you know of an easy way to figure that out, then let me know.
Thanks, Anders On Sat, Feb 21, 2015 at 12:05 AM, Sandy Ryza <sandy.r...@cloudera.com> wrote: > Are you using the capacity scheduler or fifo scheduler without multi > resource scheduling by any chance? > > On Thu, Feb 12, 2015 at 1:51 PM, Anders Arpteg <arp...@spotify.com> wrote: > >> The nm logs only seems to contain similar to the following. Nothing else >> in the same time range. Any help? >> >> 2015-02-12 20:47:31,245 WARN >> org.apache.hadoop.yarn.server.nodemanager.containermanager.ContainerManagerImpl: >> Event EventType: KILL_CONTAINER sent to absent container >> container_1422406067005_0053_01_000002 >> 2015-02-12 20:47:31,246 WARN >> org.apache.hadoop.yarn.server.nodemanager.containermanager.ContainerManagerImpl: >> Event EventType: KILL_CONTAINER sent to absent container >> container_1422406067005_0053_01_000012 >> 2015-02-12 20:47:31,246 WARN >> org.apache.hadoop.yarn.server.nodemanager.containermanager.ContainerManagerImpl: >> Event EventType: KILL_CONTAINER sent to absent container >> container_1422406067005_0053_01_000022 >> 2015-02-12 20:47:31,246 WARN >> org.apache.hadoop.yarn.server.nodemanager.containermanager.ContainerManagerImpl: >> Event EventType: KILL_CONTAINER sent to absent container >> container_1422406067005_0053_01_000032 >> 2015-02-12 20:47:31,246 WARN >> org.apache.hadoop.yarn.server.nodemanager.containermanager.ContainerManagerImpl: >> Event EventType: KILL_CONTAINER sent to absent container >> container_1422406067005_0053_01_000042 >> 2015-02-12 21:24:30,515 WARN >> org.apache.hadoop.yarn.server.nodemanager.containermanager.ContainerManagerImpl: >> Event EventType: FINISH_APPLICATION sent to absent application >> application_1422406067005_0053 >> >> On Thu, Feb 12, 2015 at 10:38 PM, Sandy Ryza <sandy.r...@cloudera.com> >> wrote: >> >>> It seems unlikely to me that it would be a 2.2 issue, though not >>> entirely impossible. Are you able to find any of the container logs? Is >>> the NodeManager launching containers and reporting some exit code? >>> >>> -Sandy >>> >>> On Thu, Feb 12, 2015 at 1:21 PM, Anders Arpteg <arp...@spotify.com> >>> wrote: >>> >>>> No, not submitting from windows, from a debian distribution. Had a >>>> quick look at the rm logs, and it seems some containers are allocated but >>>> then released again for some reason. Not easy to make sense of the logs, >>>> but here is a snippet from the logs (from a test in our small test cluster) >>>> if you'd like to have a closer look: http://pastebin.com/8WU9ivqC >>>> >>>> Sandy, sounds like it could possible be a 2.2 issue then, or what do >>>> you think? >>>> >>>> Thanks, >>>> Anders >>>> >>>> On Thu, Feb 12, 2015 at 3:11 PM, Aniket Bhatnagar < >>>> aniket.bhatna...@gmail.com> wrote: >>>> >>>>> This is tricky to debug. Check logs of node and resource manager of >>>>> YARN to see if you can trace the error. In the past I have to closely look >>>>> at arguments getting passed to YARN container (they get logged before >>>>> attempting to launch containers). If I still don't get a clue, I had to >>>>> check the script generated by YARN to execute the container and even run >>>>> manually to trace at what line the error has occurred. >>>>> >>>>> BTW are you submitting the job from windows? >>>>> >>>>> On Thu, Feb 12, 2015, 3:34 PM Anders Arpteg <arp...@spotify.com> >>>>> wrote: >>>>> >>>>>> Interesting to hear that it works for you. Are you using Yarn 2.2 as >>>>>> well? No strange log message during startup, and can't see any other log >>>>>> messages since no executer gets launched. Does not seems to work in >>>>>> yarn-client mode either, failing with the exception below. >>>>>> >>>>>> Exception in thread "main" org.apache.spark.SparkException: Yarn >>>>>> application has already ended! It might have been killed or unable to >>>>>> launch application master. >>>>>> at >>>>>> org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.waitForApplication(YarnClientSchedulerBackend.scala:119) >>>>>> at >>>>>> org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:59) >>>>>> at >>>>>> org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:141) >>>>>> at >>>>>> org.apache.spark.SparkContext.<init>(SparkContext.scala:370) >>>>>> at >>>>>> com.spotify.analytics.AnalyticsSparkContext.<init>(AnalyticsSparkContext.scala:8) >>>>>> at >>>>>> com.spotify.analytics.DataSampler$.main(DataSampler.scala:42) >>>>>> at com.spotify.analytics.DataSampler.main(DataSampler.scala) >>>>>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) >>>>>> at >>>>>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) >>>>>> at >>>>>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) >>>>>> at java.lang.reflect.Method.invoke(Method.java:597) >>>>>> at >>>>>> org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:551) >>>>>> at >>>>>> org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:155) >>>>>> at >>>>>> org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:178) >>>>>> at >>>>>> org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:99) >>>>>> at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) >>>>>> >>>>>> /Anders >>>>>> >>>>>> >>>>>> On Thu, Feb 12, 2015 at 1:33 AM, Sandy Ryza <sandy.r...@cloudera.com> >>>>>> wrote: >>>>>> >>>>>>> Hi Anders, >>>>>>> >>>>>>> I just tried this out and was able to successfully acquire >>>>>>> executors. Any strange log messages or additional color you can >>>>>>> provide on >>>>>>> your setup? Does yarn-client mode work? >>>>>>> >>>>>>> -Sandy >>>>>>> >>>>>>> On Wed, Feb 11, 2015 at 1:28 PM, Anders Arpteg <arp...@spotify.com> >>>>>>> wrote: >>>>>>> >>>>>>>> Hi, >>>>>>>> >>>>>>>> Compiled the latest master of Spark yesterday (2015-02-10) for >>>>>>>> Hadoop 2.2 and failed executing jobs in yarn-cluster mode for that >>>>>>>> build. Works successfully with spark 1.2 (and also master from >>>>>>>> 2015-01-16), >>>>>>>> so something has changed since then that prevents the job from >>>>>>>> receiving >>>>>>>> any executors on the cluster. >>>>>>>> >>>>>>>> Basic symptoms are that the jobs fires up the AM, but after >>>>>>>> examining the "executors" page in the web ui, only the driver is >>>>>>>> listed, no executors are ever received, and the driver keep waiting >>>>>>>> forever. Has anyone seemed similar problems? >>>>>>>> >>>>>>>> Thanks for any insights, >>>>>>>> Anders >>>>>>>> >>>>>>> >>>>>>> >>>>>> >>>> >>> >> >
