As such we do not open any files by ourselves. EventLoggingListener opens the file to write down the events in json format for history server. But it uses the same writer(PrintWriter object) and eventually the same output stream (which boils down to DFSOutputStream for us). It seems DFSOutputStream maintains an instance variable hasError in case of an error and even if hdfs comes back up, it throws exceptions.
On Wed, Jul 8, 2015 at 1:10 PM, Akhil Das <ak...@sigmoidanalytics.com> wrote: > Can you look in the datanode logs and see whats going on? Most likely, you > are hitting the ulimit on open file handles. > > Thanks > Best Regards > > On Wed, Jul 8, 2015 at 10:55 AM, Pankaj Arora <pankaj.ar...@guavus.com> > wrote: > >> Hi, >> >> I am running long running application over yarn using spark and I am >> facing issues while using spark’s history server when the events are >> written to hdfs. It seems to work fine for some time and in between I see >> following exception. >> >> 2015-06-01 00:00:03,247 [SparkListenerBus] ERROR >> org.apache.spark.scheduler.LiveListenerBus - Listener EventLoggingListener >> threw an exception >> >> java.lang.reflect.InvocationTargetException >> >> at sun.reflect.GeneratedMethodAccessor69.invoke(Unknown Source) >> >> at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source) >> >> at java.lang.reflect.Method.invoke(Unknown Source) >> >> at >> org.apache.spark.util.FileLogger$$anonfun$flush$2.apply(FileLogger.scala:203) >> >> at >> org.apache.spark.util.FileLogger$$anonfun$flush$2.apply(FileLogger.scala:203) >> >> at scala.Option.foreach(Option.scala:236) >> >> at org.apache.spark.util.FileLogger.flush(FileLogger.scala:203) >> >> at >> org.apache.spark.scheduler.EventLoggingListener.logEvent(EventLoggingListener.scala:90) >> >> at >> org.apache.spark.scheduler.EventLoggingListener.onUnpersistRDD(EventLoggingListener.scala:121) >> >> at >> org.apache.spark.scheduler.SparkListenerBus$$anonfun$postToAll$11.apply(SparkListenerBus.scala:66) >> >> at >> org.apache.spark.scheduler.SparkListenerBus$$anonfun$postToAll$11.apply(SparkListenerBus.scala:66) >> >> at >> org.apache.spark.scheduler.SparkListenerBus$$anonfun$foreachListener$1.apply(SparkListenerBus.scala:83) >> >> at >> org.apache.spark.scheduler.SparkListenerBus$$anonfun$foreachListener$1.apply(SparkListenerBus.scala:81) >> >> at >> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) >> >> at >> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) >> >> at >> org.apache.spark.scheduler.SparkListenerBus$class.foreachListener(SparkListenerBus.scala:81) >> >> at >> org.apache.spark.scheduler.SparkListenerBus$class.postToAll(SparkListenerBus.scala:66) >> >> at >> org.apache.spark.scheduler.LiveListenerBus.postToAll(LiveListenerBus.scala:32) >> >> at >> org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(LiveListenerBus.scala:56) >> >> at >> org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(LiveListenerBus.scala:56) >> >> at scala.Option.foreach(Option.scala:236) >> >> at >> org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1.apply$mcV$sp(LiveListenerBus.scala:56) >> >> at >> org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1.apply(LiveListenerBus.scala:47) >> >> at >> org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1.apply(LiveListenerBus.scala:47) >> >> at >> org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1545) >> >> at >> org.apache.spark.scheduler.LiveListenerBus$$anon$1.run(LiveListenerBus.scala:46) >> >> Caused by: java.io.IOException: All datanodes 192.168.162.54:50010 are >> bad. Aborting... >> >> at >> org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.setupPipelineForAppendOrRecovery(DFSOutputStream.java:1128) >> >> at >> org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.processDatanodeError(DFSOutputStream.java:924) >> >> at >> org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:486) >> >> >> >> And after that this error continue to come and spark reaches into >> unstable stage where no job is able to progress. >> >> FYI. >> HDFS was up and running before and after this error and on restarting >> application it runs fine for some hours and again same error comes. >> Enough disk space was available on each data node. >> >> Any suggestion or help would be appreciated. >> >> Regards >> Pankaj >> >> >
