Using TestSQLContext from multiple tests leads to:
SparkException: : Task not serializable
ERROR ContextCleaner: Error cleaning broadcast 10
java.lang.NullPointerException
at
org.apache.spark.broadcast.TorrentBroadcast$.unpersist(TorrentBroadcast.scala:246)
at
org.apache.spark.broadcast.TorrentBroadcastFactory.unbroadcast(TorrentBroadcastFactory.scala:46)
at
org.apache.spark.broadcast.BroadcastManager.unbroadcast(BroadcastManager.scala:66)
at
org.apache.spark.ContextCleaner.doCleanupBroadcast(ContextCleaner.scala:185)
at
org.apache.spark.ContextCleaner$$anonfun$org$apache$spark$ContextCleaner$$keepCleaning$1$$anonfun$apply$mcV$sp$2.apply(ContextCleaner.scala:147)
at
org.apache.spark.ContextCleaner$$anonfun$org$apache$spark$ContextCleaner$$keepCleaning$1$$anonfun$apply$mcV$sp$2.apply(ContextCleaner.scala:138)
at scala.Option.foreach(Option.scala:236)
On 15.12.2014, at 22:36, Marius Soutier <mps....@gmail.com> wrote:
> Ok, maybe these test versions will help me then. I’ll check it out.
>
> On 15.12.2014, at 22:33, Michael Armbrust <mich...@databricks.com> wrote:
>
>> Using a single SparkContext should not cause this problem. In the SQL tests
>> we use TestSQLContext and TestHive which are global singletons for all of
>> our unit testing.
>>
>> On Mon, Dec 15, 2014 at 1:27 PM, Marius Soutier <mps....@gmail.com> wrote:
>> Possible, yes, although I’m trying everything I can to prevent it, i.e. fork
>> in Test := true and isolated. Can you confirm that reusing a single
>> SparkContext for multiple tests poses a problem as well?
>>
>> Other than that, just switching from SQLContext to HiveContext also provoked
>> the error.
>>
>>
>> On 15.12.2014, at 20:22, Michael Armbrust <mich...@databricks.com> wrote:
>>
>>> Is it possible that you are starting more than one SparkContext in a single
>>> JVM with out stopping previous ones? I'd try testing with Spark 1.2, which
>>> will throw an exception in this case.
>>>
>>> On Mon, Dec 15, 2014 at 8:48 AM, Marius Soutier <mps....@gmail.com> wrote:
>>> Hi,
>>>
>>> I’m seeing strange, random errors when running unit tests for my Spark
>>> jobs. In this particular case I’m using Spark SQL to read and write Parquet
>>> files, and one error that I keep running into is this one:
>>>
>>> org.apache.spark.SparkException: Job aborted due to stage failure: Task 19
>>> in stage 6.0 failed 1 times, most recent failure: Lost task 19.0 in stage
>>> 6.0 (TID 223, localhost): java.io.IOException: PARSING_ERROR(2)
>>> org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
>>> org.xerial.snappy.SnappyNative.uncompressedLength(Native Method)
>>> org.xerial.snappy.Snappy.uncompressedLength(Snappy.java:545)
>>>
>>> I can only prevent this from happening by using isolated Specs tests thats
>>> always create a new SparkContext that is not shared between tests (but
>>> there can also be only a single SparkContext per test), and also by using
>>> standard SQLContext instead of HiveContext. It does not seem to have
>>> anything to do with the actual files that I also create during the test run
>>> with SQLContext.saveAsParquetFile.
>>>
>>>
>>> Cheers
>>> - Marius
>>>
>>>
>>> PS The full trace:
>>>
>>> org.apache.spark.SparkException: Job aborted due to stage failure: Task 19
>>> in stage 6.0 failed 1 times, most recent failure: Lost task 19.0 in stage
>>> 6.0 (TID 223, localhost): java.io.IOException: PARSING_ERROR(2)
>>> org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
>>> org.xerial.snappy.SnappyNative.uncompressedLength(Native Method)
>>> org.xerial.snappy.Snappy.uncompressedLength(Snappy.java:545)
>>>
>>> org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:125)
>>>
>>> org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
>>>
>>> org.xerial.snappy.SnappyInputStream.<init>(SnappyInputStream.java:58)
>>>
>>> org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
>>>
>>> org.apache.spark.broadcast.TorrentBroadcast$.unBlockifyObject(TorrentBroadcast.scala:232)
>>>
>>> org.apache.spark.broadcast.TorrentBroadcast$$anonfun$readObject$1.apply$mcV$sp(TorrentBroadcast.scala:169)
>>> org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:927)
>>>
>>> org.apache.spark.broadcast.TorrentBroadcast.readObject(TorrentBroadcast.scala:155)
>>> sun.reflect.GeneratedMethodAccessor5.invoke(Unknown Source)
>>>
>>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
>>> java.lang.reflect.Method.invoke(Method.java:606)
>>>
>>> java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1017)
>>>
>>> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1893)
>>>
>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
>>> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
>>>
>>> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990)
>>>
>>> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915)
>>>
>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
>>> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
>>> java.io.ObjectInputStream.readObject(ObjectInputStream.java:370)
>>>
>>> org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:62)
>>>
>>> org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:87)
>>>
>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:160)
>>>
>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>>>
>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>>> java.lang.Thread.run(Thread.java:745)
>>> Driver stacktrace:
>>> at
>>> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1185)
>>> ~[spark-core_2.10-1.1.1.jar:1.1.1]
>>> at
>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1174)
>>> ~[spark-core_2.10-1.1.1.jar:1.1.1]
>>> at
>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1173)
>>> ~[spark-core_2.10-1.1.1.jar:1.1.1]
>>> at
>>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>>> ~[scala-library.jar:na]
>>> at
>>> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
>>> ~[scala-library.jar:na]
>>> at
>>> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1173)
>>> ~[spark-core_2.10-1.1.1.jar:1.1.1]
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>>
>
