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chirag aggarwal commented on HIVE-8956: --------------------------------------- Does this take care of instances like: ERROR util.Utils: Uncaught exception in thread Result resolver thread-1 java.lang.OutOfMemoryError: Java heap space at java.nio.HeapByteBuffer.<init>(Unknown Source) at java.nio.ByteBuffer.allocate(Unknown Source) at org.apache.spark.storage.BlockMessage.set(BlockMessage.scala:94) at org.apache.spark.storage.BlockMessage$.fromByteBuffer(BlockMessage.scala:176) at org.apache.spark.storage.BlockMessageArray.set(BlockMessageArray.scala:63) at org.apache.spark.storage.BlockMessageArray$.fromBufferMessage(BlockMessageArray.scala:109) at org.apache.spark.storage.BlockManagerWorker$.syncGetBlock(BlockManagerWorker.scala:138) at org.apache.spark.storage.BlockManager$$anonfun$doGetRemote$2.apply(BlockManager.scala:530) at org.apache.spark.storage.BlockManager$$anonfun$doGetRemote$2.apply(BlockManager.scala:528) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at org.apache.spark.storage.BlockManager.doGetRemote(BlockManager.scala:528) at org.apache.spark.storage.BlockManager.getRemoteBytes(BlockManager.scala:522) at org.apache.spark.scheduler.TaskResultGetter$$anon$2$$anonfun$run$1.apply$mcV$sp(TaskResultGetter.scala:53) at org.apache.spark.scheduler.TaskResultGetter$$anon$2$$anonfun$run$1.apply(TaskResultGetter.scala:47) at org.apache.spark.scheduler.TaskResultGetter$$anon$2$$anonfun$run$1.apply(TaskResultGetter.scala:47) at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1311) at org.apache.spark.scheduler.TaskResultGetter$$anon$2.run(TaskResultGetter.scala:46) at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source) at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source) at java.lang.Thread.run(Unknown Source) > Hive hangs while some error/exception happens beyond job execution [Spark > Branch] > --------------------------------------------------------------------------------- > > Key: HIVE-8956 > URL: https://issues.apache.org/jira/browse/HIVE-8956 > Project: Hive > Issue Type: Sub-task > Components: Spark > Reporter: Chengxiang Li > Assignee: Rui Li > Labels: Spark-M3 > Fix For: spark-branch > > Attachments: HIVE-8956.1-spark.patch > > > Remote spark client communicate with remote spark context asynchronously, if > error/exception is throw out during job execution in remote spark context, it > would be wrapped and send back to remote spark client, but if error/exception > is throw out beyond job execution, such as job serialized failed, remote > spark client would never know what's going on in remote spark context, and it > would hangs there. > Set a timeout in remote spark client side may not a great idea, as we are not > sure how long the query executed in spark cluster. we need find a way to > check whether job has failed(whole life cycle) in remote spark context. -- This message was sent by Atlassian JIRA (v6.3.4#6332)