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)
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
