This looks to be fixed in master:
>>> from pyspark.sql import SQLContext>>> sqlContext = SQLContext(sc)>>>
>>> sc.parallelize(['{"foo":[[1,2,3], [4,5,6]]}', '{"foo":[[1,2,3], [4,5,6]]}'])
ParallelCollectionRDD[5] at parallelize at PythonRDD.scala:315>>>
sqlContext.jsonRDD(sc.parallelize(['{"foo":[[1,2,3], [4,5,6]]}',
'{"foo":[[1,2,3], [4,5,6]]}']))
MapPartitionsRDD[14] at mapPartitions at SchemaRDD.scala:408>>>
sqlContext.jsonRDD(sc.parallelize(['{"foo":[[1,2,3], [4,5,6]]}',
'{"foo":[[1,2,3], [4,5,6]]}'])).printSchema()
root
|-- foo: array (nullable = true)
| |-- element: array (containsNull = false)
| | |-- element: integer (containsNull = false)
>>>
Nick
On Tue, Aug 5, 2014 at 7:12 PM, Brad Miller <bmill...@eecs.berkeley.edu>
wrote:
> Hi All,
>
> I've built and deployed the current head of branch-1.0, but it seems to
> have only partly fixed the bug.
>
> This code now runs as expected with the indicated output:
> > srdd = sqlCtx.jsonRDD(sc.parallelize(['{"foo":[1,2,3]}',
> '{"foo":[4,5,6]}']))
> > srdd.printSchema()
> root
> |-- foo: ArrayType[IntegerType]
> > srdd.collect()
> [{u'foo': [1, 2, 3]}, {u'foo': [4, 5, 6]}]
>
> This code still crashes:
> > srdd = sqlCtx.jsonRDD(sc.parallelize(['{"foo":[[1,2,3], [4,5,6]]}',
> '{"foo":[[1,2,3], [4,5,6]]}']))
> > srdd.printSchema()
> root
> |-- foo: ArrayType[ArrayType(IntegerType)]
> > srdd.collect()
> Py4JJavaError: An error occurred while calling o63.collect.
> : org.apache.spark.SparkException: Job aborted due to stage failure: Task
> 3.0:29 failed 4 times, most recent failure: Exception failure in TID 67 on
> host kunitz.research.intel-research.net:
> net.razorvine.pickle.PickleException: couldn't introspect javabean:
> java.lang.IllegalArgumentException: wrong number of arguments
>
> I may be able to see if this is fixed in master, but since it's not fixed
> in 1.0.3 it seems unlikely to be fixed in master either. I previously tried
> master as well, but ran into a build problem that did not occur with the
> 1.0 branch.
>
> Can anybody else verify that the second example still crashes (and is
> meant to work)? If so, would it be best to modify JIRA-2376 or start a new
> bug?
> https://issues.apache.org/jira/browse/SPARK-2376
>
> best,
> -Brad
>
>
>
>
>
> On Tue, Aug 5, 2014 at 12:10 PM, Brad Miller <bmill...@eecs.berkeley.edu>
> wrote:
>
>> Nick: Thanks for both the original JIRA bug report and the link.
>>
>> Michael: This is on the 1.0.1 release. I'll update to master and
>> follow-up if I have any problems.
>>
>> best,
>> -Brad
>>
>>
>> On Tue, Aug 5, 2014 at 12:04 PM, Michael Armbrust <mich...@databricks.com
>> > wrote:
>>
>>> Is this on 1.0.1? I'd suggest running this on master or the 1.1-RC
>>> which should be coming out this week. Pyspark did not have good support
>>> for nested data previously. If you still encounter issues using a more
>>> recent version, please file a JIRA. Thanks!
>>>
>>>
>>> On Tue, Aug 5, 2014 at 11:55 AM, Brad Miller <bmill...@eecs.berkeley.edu
>>> > wrote:
>>>
>>>> Hi All,
>>>>
>>>> I am interested to use jsonRDD and jsonFile to create a SchemaRDD out
>>>> of some JSON data I have, but I've run into some instability involving the
>>>> following java exception:
>>>>
>>>> An error occurred while calling o1326.collect.
>>>> : org.apache.spark.SparkException: Job aborted due to stage failure:
>>>> Task 181.0:29 failed 4 times, most recent failure: Exception failure in TID
>>>> 1664 on host neal.research.intel-research.net:
>>>> net.razorvine.pickle.PickleException: couldn't introspect javabean:
>>>> java.lang.IllegalArgumentException: wrong number of arguments
>>>>
>>>> I've pasted code which produces the error as well as the full traceback
>>>> below. Note that I don't have any problem when I parse the JSON myself and
>>>> use inferSchema.
>>>>
>>>> Is anybody able to reproduce this bug?
>>>>
>>>> -Brad
>>>>
>>>> > srdd = sqlCtx.jsonRDD(sc.parallelize(['{"foo":"bar", "baz":[1,2,3]}',
>>>> '{"foo":"boom", "baz":[1,2,3]}']))
>>>> > srdd.printSchema()
>>>>
>>>> root
>>>> |-- baz: ArrayType[IntegerType]
>>>> |-- foo: StringType
>>>>
>>>> > srdd.collect()
>>>>
>>>>
>>>> ---------------------------------------------------------------------------
>>>> Py4JJavaError Traceback (most recent call
>>>> last)
>>>> <ipython-input-89-ec7e8e8c68c4> in <module>()
>>>> ----> 1 srdd.collect()
>>>>
>>>> /home/spark/spark-1.0.1-bin-hadoop1/python/pyspark/rdd.py in
>>>> collect(self)
>>>> 581 """
>>>> 582 with _JavaStackTrace(self.context) as st:
>>>> --> 583 bytesInJava = self._jrdd.collect().iterator()
>>>> 584 return
>>>> list(self._collect_iterator_through_file(bytesInJava))
>>>> 585
>>>>
>>>> /usr/local/lib/python2.7/dist-packages/py4j/java_gateway.pyc in
>>>> __call__(self, *args)
>>>> 535 answer = self.gateway_client.send_command(command)
>>>> 536 return_value = get_return_value(answer,
>>>> self.gateway_client,
>>>> --> 537 self.target_id, self.name)
>>>> 538
>>>> 539 for temp_arg in temp_args:
>>>>
>>>> /usr/local/lib/python2.7/dist-packages/py4j/protocol.pyc in
>>>> get_return_value(answer, gateway_client, target_id, name)
>>>> 298 raise Py4JJavaError(
>>>> 299 'An error occurred while calling
>>>> {0}{1}{2}.\n'.
>>>> --> 300 format(target_id, '.', name), value)
>>>> 301 else:
>>>> 302 raise Py4JError(
>>>>
>>>> Py4JJavaError: An error occurred while calling o1326.collect.
>>>> : org.apache.spark.SparkException: Job aborted due to stage failure:
>>>> Task 181.0:29 failed 4 times, most recent failure: Exception failure in TID
>>>> 1664 on host neal.research.intel-research.net:
>>>> net.razorvine.pickle.PickleException: couldn't introspect javabean:
>>>> java.lang.IllegalArgumentException: wrong number of arguments
>>>> net.razorvine.pickle.Pickler.put_javabean(Pickler.java:603)
>>>> net.razorvine.pickle.Pickler.dispatch(Pickler.java:299)
>>>> net.razorvine.pickle.Pickler.save(Pickler.java:125)
>>>> net.razorvine.pickle.Pickler.put_map(Pickler.java:322)
>>>> net.razorvine.pickle.Pickler.dispatch(Pickler.java:286)
>>>> net.razorvine.pickle.Pickler.save(Pickler.java:125)
>>>>
>>>> net.razorvine.pickle.Pickler.put_arrayOfObjects(Pickler.java:392)
>>>> net.razorvine.pickle.Pickler.dispatch(Pickler.java:195)
>>>> net.razorvine.pickle.Pickler.save(Pickler.java:125)
>>>> net.razorvine.pickle.Pickler.dump(Pickler.java:95)
>>>> net.razorvine.pickle.Pickler.dumps(Pickler.java:80)
>>>>
>>>> org.apache.spark.sql.SchemaRDD$anonfun$javaToPython$1$anonfun$apply$3.apply(SchemaRDD.scala:385)
>>>>
>>>> org.apache.spark.sql.SchemaRDD$anonfun$javaToPython$1$anonfun$apply$3.apply(SchemaRDD.scala:385)
>>>> scala.collection.Iterator$anon$11.next(Iterator.scala:328)
>>>>
>>>> org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:294)
>>>>
>>>> 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)
>>>>
>>>
>>>
>>
>
