We were able to reproduce it with a minimal example. I've opened a jira issue:
https://issues.apache.org/jira/browse/SPARK-15825 On Wed, Jun 8, 2016 at 12:43 PM, Koert Kuipers <ko...@tresata.com> wrote: > great! > > we weren't able to reproduce it because the unit tests use a > broadcast-join while on the cluster it uses sort-merge-join. so the issue > is in sort-merge-join. > > we are now able to reproduce it in tests using > spark.sql.autoBroadcastJoinThreshold=-1 > it produces weird looking garbled results in the join. > hoping to get a minimal reproducible example soon. > > On Wed, Jun 8, 2016 at 10:24 AM, Pete Robbins <robbin...@gmail.com> wrote: > >> I just raised https://issues.apache.org/jira/browse/SPARK-15822 for a >> similar looking issue. Analyzing the core dump from the segv with Memory >> Analyzer it looks very much like a UTF8String is very corrupt. >> >> Cheers, >> >> >> On Fri, 27 May 2016 at 21:00 Koert Kuipers <ko...@tresata.com> wrote: >> >>> hello all, >>> after getting our unit tests to pass on spark 2.0.0-SNAPSHOT we are now >>> trying to run some algorithms at scale on our cluster. >>> unfortunately this means that when i see errors i am having a harder >>> time boiling it down to a small reproducible example. >>> >>> today we are running an iterative algo using the dataset api and we are >>> seeing tasks fail with errors which seem to related to unsafe operations. >>> the same tasks succeed without issues in our unit tests. >>> >>> i see either: >>> >>> 16/05/27 12:54:46 ERROR executor.Executor: Exception in task 31.0 in >>> stage 21.0 (TID 1073) >>> java.lang.NegativeArraySizeException >>> at >>> org.apache.spark.unsafe.types.UTF8String.getBytes(UTF8String.java:229) >>> at >>> org.apache.spark.unsafe.types.UTF8String.toString(UTF8String.java:821) >>> at >>> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificSafeProjection.apply(Unknown >>> Source) >>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409) >>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409) >>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409) >>> at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434) >>> at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440) >>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408) >>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408) >>> at >>> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.sort_addToSorter$(Unknown >>> Source) >>> at >>> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown >>> Source) >>> at >>> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) >>> at >>> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$7$$anon$1.hasNext(WholeStageCodegenExec.scala:359) >>> at >>> org.apache.spark.sql.execution.aggregate.SortBasedAggregateExec$$anonfun$doExecute$1$$anonfun$3.apply(SortBasedAggregateExec.scala:74) >>> at >>> org.apache.spark.sql.execution.aggregate.SortBasedAggregateExec$$anonfun$doExecute$1$$anonfun$3.apply(SortBasedAggregateExec.scala:71) >>> at >>> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:775) >>> at >>> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:775) >>> at >>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>> at >>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:318) >>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:282) >>> at >>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>> at >>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:318) >>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:282) >>> at >>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79) >>> at >>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47) >>> at org.apache.spark.scheduler.Task.run(Task.scala:85) >>> at >>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) >>> at >>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) >>> at >>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) >>> >>> or alternatively: >>> >>> # A fatal error has been detected by the Java Runtime Environment: >>> # >>> # SIGSEGV (0xb) at pc=0x00007fe571041cba, pid=2450, tid=140622965913344 >>> # >>> # JRE version: Java(TM) SE Runtime Environment (7.0_75-b13) (build >>> 1.7.0_75-b13) >>> # Java VM: Java HotSpot(TM) 64-Bit Server VM (24.75-b04 mixed mode >>> linux-amd64 compressed oops) >>> # Problematic frame: >>> # v ~StubRoutines::jbyte_disjoint_arraycopy >>> >>> i assume the best thing would be to try to get it to print out the >>> generated code that is causing this? >>> what switch do i need to use again to do so? >>> thanks, >>> koert >>> >> >
