There is a PR to fix this: https://github.com/apache/spark/pull/1802
On Tue, Aug 5, 2014 at 10:11 PM, Brad Miller <bmill...@eecs.berkeley.edu> wrote: > I concur that printSchema works; it just seems to be operations that use the > data where trouble happens. > > Thanks for posting the bug. > > -Brad > > > On Tue, Aug 5, 2014 at 10:05 PM, Yin Huai <yh...@databricks.com> wrote: >> >> I tried jsonRDD(...).printSchema() and it worked. Seems the problem is >> when we take the data back to the Python side, SchemaRDD#javaToPython failed >> on your cases. I have created >> https://issues.apache.org/jira/browse/SPARK-2875 to track it. >> >> Thanks, >> >> Yin >> >> >> On Tue, Aug 5, 2014 at 9:20 PM, Brad Miller <bmill...@eecs.berkeley.edu> >> wrote: >>> >>> Hi All, >>> >>> I checked out and built master. Note that Maven had a problem building >>> Kafka (in my case, at least); I was unable to fix this easily so I moved on >>> since it seemed unlikely to have any influence on the problem at hand. >>> >>> Master improves functionality (including the example Nicholas just >>> demonstrated) but unfortunately there still seems to be a bug related to >>> using dictionaries as values. I've put some code below to illustrate the >>> bug. >>> >>> # dictionary as value works fine >>> > print sqlCtx.jsonRDD(sc.parallelize(['{"key0": {"key1": >>> > "value"}}'])).collect() >>> [Row(key0=Row(key1=u'value'))] >>> >>> # dictionary as value works fine, even when inner keys are varied >>> > print sqlCtx.jsonRDD(sc.parallelize(['{"key0": {"key1": "value1"}}', >>> > '{"key0": {"key2": "value2"}}'])).collect() >>> [Row(key0=Row(key1=u'value1', key2=None)), Row(key0=Row(key1=None, >>> key2=u'value2'))] >>> >>> # dictionary as value works fine when inner keys are missing and outer >>> key is present >>> > print sqlCtx.jsonRDD(sc.parallelize(['{"key0": {}}', '{"key0": {"key1": >>> > "value1"}}'])).collect() >>> [Row(key0=Row(key1=None)), Row(key0=Row(key1=u'value1'))] >>> >>> # dictionary as value FAILS when outer key is missing >>> > print sqlCtx.jsonRDD(sc.parallelize(['{}', '{"key0": {"key1": >>> > "value1"}}'])).collect() >>> Py4JJavaError: An error occurred while calling o84.collect. >>> : org.apache.spark.SparkException: Job aborted due to stage failure: Task >>> 14 in stage 7.0 failed 4 times, most recent failure: Lost task 14.3 in stage >>> 7.0 (TID 242, engelland.research.intel-research.net): >>> java.lang.NullPointerException... >>> >>> # dictionary as value FAILS when outer key is present with null value >>> > print sqlCtx.jsonRDD(sc.parallelize(['{"key0": null}', '{"key0": >>> > {"key1": "value1"}}'])).collect() >>> Py4JJavaError: An error occurred while calling o98.collect. >>> : org.apache.spark.SparkException: Job aborted due to stage failure: Task >>> 14 in stage 9.0 failed 4 times, most recent failure: Lost task 14.3 in stage >>> 9.0 (TID 305, kunitz.research.intel-research.net): >>> java.lang.NullPointerException... >>> >>> # nested lists work even when outer key is missing >>> > print sqlCtx.jsonRDD(sc.parallelize(['{}', '{"key0": [["item0", >>> > "item1"], ["item2", "item3"]]}'])).collect() >>> [Row(key0=None), Row(key0=[[u'item0', u'item1'], [u'item2', u'item3']])] >>> >>> Is anyone able to replicate this behavior? >>> >>> -Brad >>> >>> >>> >>> >>> On Tue, Aug 5, 2014 at 6:11 PM, Michael Armbrust <mich...@databricks.com> >>> wrote: >>>> >>>> We try to keep master very stable, but this is where active development >>>> happens. YMMV, but a lot of people do run very close to master without >>>> incident (myself included). >>>> >>>> branch-1.0 has been cut for a while and we only merge bug fixes into it >>>> (this is more strict for non-alpha components like spark core.). For Spark >>>> SQL, this branch is pretty far behind as the project is very young and we >>>> are fixing bugs / adding features very rapidly compared with Spark core. >>>> >>>> branch-1.1 was just cut and is being QAed for a release, at this point >>>> its likely the same as master, but that will change as features start >>>> getting added to master in the coming weeks. >>>> >>>> >>>> >>>> On Tue, Aug 5, 2014 at 5:38 PM, Nicholas Chammas >>>> <nicholas.cham...@gmail.com> wrote: >>>>> >>>>> collect() works, too. >>>>> >>>>> >>> sqlContext.jsonRDD(sc.parallelize(['{"foo":[[1,2,3], [4,5,6]]}', >>>>> >>> '{"foo":[[1,2,3], [4,5,6]]}'])).collect() >>>>> [Row(foo=[[1, 2, 3], [4, 5, 6]]), Row(foo=[[1, 2, 3], [4, 5, 6]])] >>>>> >>>>> Can’t answer your question about branch stability, though. Spark is a >>>>> very active project, so stuff is happening all the time. >>>>> >>>>> Nick >>>>> >>>>> >>>>> >>>>> On Tue, Aug 5, 2014 at 7:20 PM, Brad Miller >>>>> <bmill...@eecs.berkeley.edu> wrote: >>>>>> >>>>>> Hi Nick, >>>>>> >>>>>> Can you check that the call to "collect()" works as well as >>>>>> "printSchema()"? I actually experience that "printSchema()" works fine, >>>>>> but >>>>>> then it crashes on "collect()". >>>>>> >>>>>> In general, should I expect the master (which seems to be on >>>>>> branch-1.1) to be any more/less stable than branch-1.0? While it would >>>>>> be >>>>>> great to have this fixed, it would be good to know if I should expect >>>>>> lots >>>>>> of other instability. >>>>>> >>>>>> best, >>>>>> -Brad >>>>>> >>>>>> >>>>>> On Tue, Aug 5, 2014 at 4:15 PM, Nicholas Chammas >>>>>> <nicholas.cham...@gmail.com> wrote: >>>>>>> >>>>>>> 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) >>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
