Include stack trace for exceptions thrown by user code. #409
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Updated with suggestions from @aarondav. |
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thanks. merged. |
asfgit
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Apr 17, 2014
It is very confusing when your code throws an exception, but the only stack trace show is in the DAGScheduler. This is a simple patch to include the stack trace for the actual failure in the error message. Suggestions on formatting welcome.
Before:
```
scala> sc.parallelize(1 :: Nil).map(_ => sys.error("Ahh!")).collect()
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0:3 failed 1 times (most recent failure: Exception failure in TID 3 on host localhost: java.lang.RuntimeException: Ahh!)
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1055)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1039)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1037)
...
```
After:
```
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0:3 failed 1 times, most recent failure: Exception failure in TID 3 on host localhost: java.lang.RuntimeException: Ahh!
scala.sys.package$.error(package.scala:27)
$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:13)
$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:13)
scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
scala.collection.Iterator$class.foreach(Iterator.scala:727)
scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
scala.collection.AbstractIterator.to(Iterator.scala:1157)
scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
org.apache.spark.rdd.RDD$$anonfun$6.apply(RDD.scala:676)
org.apache.spark.rdd.RDD$$anonfun$6.apply(RDD.scala:676)
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1048)
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1048)
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:110)
org.apache.spark.scheduler.Task.run(Task.scala:50)
org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:211)
org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:46)
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:176)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
java.lang.Thread.run(Thread.java:744)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1055)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1039)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1037)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1037)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:614)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:614)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:614)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse(DAGScheduler.scala:143)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
at akka.actor.ActorCell.invoke(ActorCell.scala:456)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
at akka.dispatch.Mailbox.run(Mailbox.scala:219)
at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
```
Author: Michael Armbrust <[email protected]>
Closes #409 from marmbrus/stacktraces and squashes the following commits:
3e4eb65 [Michael Armbrust] indent. include header for driver stack trace.
018b06b [Michael Armbrust] Include stack trace for exceptions in user code.
(cherry picked from commit d4916a8)
Signed-off-by: Reynold Xin <[email protected]>
pwendell
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May 12, 2014
Automatically unpersisting RDDs that have been cleaned up from DStreams Earlier RDDs generated by DStreams were forgotten but not unpersisted. The system relied on the natural BlockManager LRU to drop the data. The cleaner.ttl was a hammer to clean up RDDs but it is something that needs to be set separately and need to be set very conservatively (at best, few minutes). This automatic unpersisting allows the system to handle this automatically, which reduces memory usage. As a side effect it will also improve GC performance as there are less number of objects stored in memory. In fact, for some workloads, it may allow RDDs to be cached as deserialized, which speeds up processing without too much GC overheads. This is disabled by default. To enable it set configuration spark.streaming.unpersist to true. In future release, this will be set to true by default. Also, reduced sleep time in TaskSchedulerImpl.stop() from 5 second to 1 second. From my conversation with Matei, there does not seem to be any good reason for the sleep for letting messages be sent out be so long.
pdeyhim
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that referenced
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Jun 25, 2014
It is very confusing when your code throws an exception, but the only stack trace show is in the DAGScheduler. This is a simple patch to include the stack trace for the actual failure in the error message. Suggestions on formatting welcome.
Before:
```
scala> sc.parallelize(1 :: Nil).map(_ => sys.error("Ahh!")).collect()
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0:3 failed 1 times (most recent failure: Exception failure in TID 3 on host localhost: java.lang.RuntimeException: Ahh!)
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1055)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1039)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1037)
...
```
After:
```
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0:3 failed 1 times, most recent failure: Exception failure in TID 3 on host localhost: java.lang.RuntimeException: Ahh!
scala.sys.package$.error(package.scala:27)
$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:13)
$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:13)
scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
scala.collection.Iterator$class.foreach(Iterator.scala:727)
scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
scala.collection.AbstractIterator.to(Iterator.scala:1157)
scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
org.apache.spark.rdd.RDD$$anonfun$6.apply(RDD.scala:676)
org.apache.spark.rdd.RDD$$anonfun$6.apply(RDD.scala:676)
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1048)
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1048)
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:110)
org.apache.spark.scheduler.Task.run(Task.scala:50)
org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:211)
org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:46)
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:176)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
java.lang.Thread.run(Thread.java:744)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1055)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1039)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1037)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1037)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:614)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:614)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:614)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse(DAGScheduler.scala:143)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
at akka.actor.ActorCell.invoke(ActorCell.scala:456)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
at akka.dispatch.Mailbox.run(Mailbox.scala:219)
at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
```
Author: Michael Armbrust <[email protected]>
Closes apache#409 from marmbrus/stacktraces and squashes the following commits:
3e4eb65 [Michael Armbrust] indent. include header for driver stack trace.
018b06b [Michael Armbrust] Include stack trace for exceptions in user code.
mccheah
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bzhaoopenstack
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Change to run Packer jobs on v1.3.3
arjunshroff
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RolatZhang
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It is very confusing when your code throws an exception, but the only stack trace show is in the DAGScheduler. This is a simple patch to include the stack trace for the actual failure in the error message. Suggestions on formatting welcome.
Before:
After: