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From Ted Yu <yuzhih...@gmail.com>
Subject Re: How to take user jars precedence over Spark jars
Date Mon, 19 Oct 2015 14:23:41 GMT
Have you tried the following options ?

--conf spark.driver.userClassPathFirst=true --conf spark.executor.
userClassPathFirst=true

Cheers

On Mon, Oct 19, 2015 at 5:07 AM, YiZhi Liu <javelinjs@gmail.com> wrote:

> I'm trying to read a Thrift object from SequenceFile, using
> elephant-bird's ThriftWritable. My code looks like
>
> val rawData = sc.sequenceFile[BooleanWritable,
> ThriftWritable[TrainingSample]](input)
> val samples = rawData.map { case (key, value) => {
>   value.setConverter(classOf[TrainingSample])
>   val conversion = if (key.get) 1 else 0
>   val sample = value.get
>   (conversion, sample)
> }}
>
> When I spark-submit in local mode, it failed with
>
> (Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times,
> most recent failure: Lost task 0.0 in stage 1.0 (TID 2, localhost):
> java.lang.AbstractMethodError:
>
> org.apache.thrift.TUnion.standardSchemeReadValue(Lorg/apache/thrift/protocol/TProtocol;Lorg/apache/thrift/protocol/TField;)Ljava/lang/Object;
> ... ...
>
> I'm pretty sure it is caused by the conflict of libthrift, I use
> thrift-0.6.1 while spark uses 0.9.2, which requires TUnion object to
> implement the abstract 'standardSchemeReadValue' method.
>
> But when I set spark.files.userClassPathFirst=true, it failed even earlier:
>
> (Job aborted due to stage failure: Task 1 in stage 0.0 failed 1 times,
> most recent failure: Lost task 1.0 in stage 0.0 (TID 1, localhost):
> java.lang.ClassCastException: cannot assign instance of scala.None$ to
> field org.apache.spark.scheduler.Task.metrics of type scala.Option in
> instance of org.apache.spark.scheduler.ResultTask
> at
> java.io.ObjectStreamClass$FieldReflector.setObjFieldValues(ObjectStreamClass.java:2089)
> at java.io.ObjectStreamClass.setObjFieldValues(ObjectStreamClass.java:1261)
> at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2006)
> at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
> at
> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:371)
> at
> org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:69)
> at
> org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:95)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:194)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
>
> It seems I introduced more conflict, but I couldn't figure out which
> one caused this failure.
>
> Interestingly, when I ran mvn test in my project, which test spark job
> in locally mode, all worked fine.
>
> So what is the right way to take user jars precedence over Spark jars?
>
> --
> Yizhi Liu
> Senior Software Engineer / Data Mining
> www.mvad.com, Shanghai, China
>
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