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https://issues.apache.org/jira/browse/SPARK-3321?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Matthew Farrellee closed SPARK-3321.
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Resolution: Not a Problem
> Defining a class within python main script
> ------------------------------------------
>
> Key: SPARK-3321
> URL: https://issues.apache.org/jira/browse/SPARK-3321
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 1.0.1
> Environment: Python version 2.6.6
> Spark version version 1.0.1
> jdk1.6.0_43
> Reporter: Shawn Guo
> Priority: Minor
>
> *leftOuterJoin(self, other, numPartitions=None)*
> Perform a left outer join of self and other.
> For each element (k, v) in self, the resulting RDD will either contain all
> pairs (k, (v, w)) for w in other, or the pair (k, (v, None)) if no elements
> in other have key k.
> *Background*: leftOuterJoin will produce None element in result dataset.
> I define a new class 'Null' in the main script to replace all python native
> None to new 'Null' object. 'Null' object overload the [] operator.
> {code:title=Class Null|borderStyle=solid}
> class Null(object):
> def __getitem__(self,key): return None;
> def __getstate__(self): pass;
> def __setstate__(self, dict): pass;
> def convert_to_null(x):
> return Null() if x is None else x
> X = A.leftOuterJoin(B)
> X.mapValues(lambda line: (line[0],convert_to_null(line[1]))
> {code}
> The code seems running good in pyspark console, however spark-submit failed
> with below error messages:
> /spark-1.0.1-bin-hadoop1/bin/spark-submit --master local[2]
> /tmp/python_test.py
> {noformat}
> File "/data/work/spark-1.0.1-bin-hadoop1/python/pyspark/worker.py", line
> 77, in main
> serializer.dump_stream(func(split_index, iterator), outfile)
> File "/data/work/spark-1.0.1-bin-hadoop1/python/pyspark/serializers.py",
> line 191, in dump_stream
> self.serializer.dump_stream(self._batched(iterator), stream)
> File "/data/work/spark-1.0.1-bin-hadoop1/python/pyspark/serializers.py",
> line 124, in dump_stream
> self._write_with_length(obj, stream)
> File "/data/work/spark-1.0.1-bin-hadoop1/python/pyspark/serializers.py",
> line 134, in _write_with_length
> serialized = self.dumps(obj)
> File "/data/work/spark-1.0.1-bin-hadoop1/python/pyspark/serializers.py",
> line 279, in dumps
> def dumps(self, obj): return cPickle.dumps(obj, 2)
> PicklingError: Can't pickle <class '__main__.Null'>: attribute lookup
> __main__.Null failed
>
> org.apache.spark.api.python.PythonRDD$$anon$1.read(PythonRDD.scala:115)
>
> org.apache.spark.api.python.PythonRDD$$anon$1.<init>(PythonRDD.scala:145)
> org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:78)
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> org.apache.spark.rdd.UnionPartition.iterator(UnionRDD.scala:33)
> org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:74)
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>
> org.apache.spark.api.python.PythonRDD$WriterThread$$anonfun$run$1.apply$mcV$sp(PythonRDD.scala:200)
>
> org.apache.spark.api.python.PythonRDD$WriterThread$$anonfun$run$1.apply(PythonRDD.scala:175)
>
> org.apache.spark.api.python.PythonRDD$WriterThread$$anonfun$run$1.apply(PythonRDD.scala:175)
> org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1160)
>
> org.apache.spark.api.python.PythonRDD$WriterThread.run(PythonRDD.scala:174)
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1044)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1028)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1026)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1026)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:634)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:634)
> at scala.Option.foreach(Option.scala:236)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:634)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1229)
> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
> at akka.actor.ActorCell.invoke(ActorCell.scala:456)
> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
> at akka.dispatch.Mailbox.run(Mailbox.scala:219)
> at
> akka.dispatch.ForkJoinExecutorConfigurator$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)
> {noformat}
> I do google search a lot and fount it may caused by pickling problem in
> python pickle module. Please refer to the link below:
> [Python: defining a class and pickling an instance in the same
> file|http://stefaanlippens.net/pickleproblem]
> One workaround is to define the class in another module seperately, and
> import it in main script, meanwhile add .py file to be distributed with main
> script by --py-files parameter.
> /spark-1.0.1-bin-hadoop1/bin/spark-submit *--py-files Null.py*
> However I'm thinking if it is possible to define the new class within main
> script by improving the code?
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