I am not familiar with your code.
bq. and then create the rdd
I assume you call ObjectOutputStream.close() prior to the above step.
Cheers
On Mon, Aug 31, 2015 at 9:42 AM, Ashish Shrowty <ashish.shro...@gmail.com>
wrote:
> Sure .. here it is (scroll below to see the NotSerializableException).
> Note that upstream, I do load up the (user,item,ratings) data from a file
> using ObjectInputStream, do some calculations that I put in a map and then
> create the rdd used in the code above from that map. I even tried
> checkpointing the rdd and persisting it to break any lineage to the
> original ObjectInputStream (if that was what was happening) -
>
> org.apache.spark.SparkException: Task not serializable
>
> at
> org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:166)
>
> at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:158)
>
> at org.apache.spark.SparkContext.clean(SparkContext.scala:1478)
>
> at org.apache.spark.rdd.RDD.flatMap(RDD.scala:295)
>
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:38)
>
> at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:46)
>
> at $iwC$$iwC$$iwC$$iwC.<init>(<console>:48)
>
> at $iwC$$iwC$$iwC.<init>(<console>:50)
>
> at $iwC$$iwC.<init>(<console>:52)
>
> at $iwC.<init>(<console>:54)
>
> at <init>(<console>:56)
>
> at .<init>(<console>:60)
>
> at .<clinit>(<console>)
>
> at .<init>(<console>:7)
>
> at .<clinit>(<console>)
>
> at $print(<console>)
>
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
>
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
>
> at java.lang.reflect.Method.invoke(Method.java:606)
>
> at
> org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)
>
> at
> org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)
>
> at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)
>
> at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)
>
> at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)
>
> at org.apache.spark.repl.SparkILoop.pasteCommand(SparkILoop.scala:796)
>
> at
> org.apache.spark.repl.SparkILoop$$anonfun$standardCommands$8.apply(SparkILoop.scala:321)
>
> at
> org.apache.spark.repl.SparkILoop$$anonfun$standardCommands$8.apply(SparkILoop.scala:321)
>
> at
> scala.tools.nsc.interpreter.LoopCommands$LoopCommand$$anonfun$nullary$1.apply(LoopCommands.scala:65)
>
> at
> scala.tools.nsc.interpreter.LoopCommands$LoopCommand$$anonfun$nullary$1.apply(LoopCommands.scala:65)
>
> at
> scala.tools.nsc.interpreter.LoopCommands$NullaryCmd.apply(LoopCommands.scala:76)
>
> at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:780)
>
> at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:628)
>
> at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:636)
>
> at org.apache.spark.repl.SparkILoop.loop(SparkILoop.scala:641)
>
> at
> org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply$mcZ$sp(SparkILoop.scala:968)
>
> at
> org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:916)
>
> at
> org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:916)
>
> at
> scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
>
> at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:916)
>
> at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1011)
>
> at org.apache.spark.repl.Main$.main(Main.scala:31)
>
> at org.apache.spark.repl.Main.main(Main.scala)
>
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
>
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
>
> at java.lang.reflect.Method.invoke(Method.java:606)
>
> at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:358)
>
> at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:75)
>
> at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
>
> *Caused by: java.io.NotSerializableException: java.io.ObjectInputStream*
>
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1183)
>
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
>
> at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
>
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
>
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
>
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
>
> at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
>
> ...
>
> ...
>
> On Mon, Aug 31, 2015 at 12:23 PM Ted Yu <yuzhih...@gmail.com> wrote:
>
>> Ashish:
>> Can you post the complete stack trace for NotSerializableException ?
>>
>> Cheers
>>
>> On Mon, Aug 31, 2015 at 8:49 AM, Ashish Shrowty <ashish.shro...@gmail.com
>> > wrote:
>>
>>> bcItemsIdx is just a broadcast variable constructed out of
>>> Array[(String)] .. it holds the item ids and I use it for indexing the
>>> MatrixEntry objects
>>>
>>>
>>> On Mon, Aug 31, 2015 at 10:41 AM Sean Owen <so...@cloudera.com> wrote:
>>>
>>>> It's not clear; that error is different still and somehow suggests
>>>> you're serializing a stream somewhere. I'd look at what's inside
>>>> bcItemsIdx as that is not shown here.
>>>>
>>>> On Mon, Aug 31, 2015 at 3:34 PM, Ashish Shrowty
>>>>
>>>> <ashish.shro...@gmail.com> wrote:
>>>> > Sean,
>>>> >
>>>> > Thanks for your comments. What I was really trying to do was to
>>>> transform a
>>>> > RDD[(userid,itemid,ratings)] into a RowMatrix so that I can do some
>>>> column
>>>> > similarity calculations while exploring the data before building some
>>>> > models. But to do that I need to first convert the user and item ids
>>>> into
>>>> > respective indexes where I intended on passing in an array into the
>>>> closure,
>>>> > which is where I got stuck with this overflowerror trying to figure
>>>> out
>>>> > where it is happening. The actual error I got was slightly different
>>>> (Caused
>>>> > by: java.io.NotSerializableException: java.io.ObjectInputStream). I
>>>> started
>>>> > investigating this issue which led me to the earlier code snippet
>>>> that I had
>>>> > posted. This is again because of the bcItemsIdx variable being passed
>>>> into
>>>> > the closure. Below code works if I don't pass in the variable and use
>>>> simply
>>>> > a constant like 10 in its place .. The code thus far -
>>>> >
>>>> > // rdd below is RDD[(String,String,Double)]
>>>> > // bcItemsIdx below is Broadcast[Array[String]] which is an array of
>>>> item
>>>> > ids
>>>> > val gRdd = rdd.map{case(user,item,rating) =>
>>>> > ((user),(item,rating))}.groupByKey
>>>> > val idxRdd = gRdd.zipWithIndex
>>>> > val cm = new CoordinateMatrix(
>>>> > idxRdd.flatMap[MatrixEntry](e => {
>>>> > e._1._2.map(item=> {
>>>> > MatrixEntry(e._2, bcItemsIdx.value.indexOf(item._1),
>>>> > item._2) // <- This is where I get the Serialization error passing in
>>>> the
>>>> > index
>>>> > // MatrixEntry(e._2, 10, item._2) // <- This works
>>>> > })
>>>> > })
>>>> > )
>>>> > val rm = cm.toRowMatrix
>>>> > val simMatrix = rm.columnSimilarities()
>>>> >
>>>> > I would like to make this work in the Spark shell as I am still
>>>> exploring
>>>> > the data. Let me know if there is an alternate way of constructing the
>>>> > RowMatrix.
>>>> >
>>>> > Thanks and appreciate all the help!
>>>> >
>>>> > Ashish
>>>> >
>>>> > On Mon, Aug 31, 2015 at 3:41 AM Sean Owen <so...@cloudera.com> wrote:
>>>> >>
>>>> >> Yeah I see that now. I think it fails immediately because the map
>>>> >> operation does try to clean and/or verify the serialization of the
>>>> >> closure upfront.
>>>> >>
>>>> >> I'm not quite sure what is going on, but I think it's some strange
>>>> >> interaction between how you're building up the list and what the
>>>> >> resulting representation happens to be like, and how the closure
>>>> >> cleaner works, which can't be perfect. The shell also introduces an
>>>> >> extra layer of issues.
>>>> >>
>>>> >> For example, the slightly more canonical approaches work fine:
>>>> >>
>>>> >> import scala.collection.mutable.MutableList
>>>> >> val lst = MutableList[(String,String,Double)]()
>>>> >> (0 to 10000).foreach(i => lst :+ ("10", "10", i.toDouble))
>>>> >>
>>>> >> or just
>>>> >>
>>>> >> val lst = (0 to 10000).map(i => ("10", "10", i.toDouble))
>>>> >>
>>>> >> If you just need this to work, maybe those are better alternatives
>>>> anyway.
>>>> >> You can also check whether it works without the shell, as I suspect
>>>> >> that's a factor.
>>>> >>
>>>> >> It's not an error in Spark per se but saying that something's default
>>>> >> Java serialization graph is very deep, so it's like the code you
>>>> wrote
>>>> >> plus the closure cleaner ends up pulling in some huge linked list and
>>>> >> serializing it the direct and unuseful way.
>>>> >>
>>>> >> If you have an idea about exactly why it's happening you can open a
>>>> >> JIRA, but arguably it's something that's nice to just work but isn't
>>>> >> to do with Spark per se. Or, have a look at others related to the
>>>> >> closure and shell and you may find this is related to other known
>>>> >> behavior.
>>>> >>
>>>> >>
>>>> >> On Sun, Aug 30, 2015 at 8:08 PM, Ashish Shrowty
>>>> >> <ashish.shro...@gmail.com> wrote:
>>>> >> > Sean .. does the code below work for you in the Spark shell? Ted
>>>> got the
>>>> >> > same error -
>>>> >> >
>>>> >> > val a=10
>>>> >> > val lst = MutableList[(String,String,Double)]()
>>>> >> > Range(0,10000).foreach(i=>lst+=(("10","10",i:Double)))
>>>> >> > sc.makeRDD(lst).map(i=> if(a==10) 1 else 0)
>>>> >> >
>>>> >> > -Ashish
>>>> >> >
>>>> >> >
>>>> >> > On Sun, Aug 30, 2015 at 2:52 PM Sean Owen <so...@cloudera.com>
>>>> wrote:
>>>> >> >>
>>>> >> >> I'm not sure how to reproduce it? this code does not produce an
>>>> error
>>>> >> >> in
>>>> >> >> master.
>>>> >> >>
>>>> >> >> On Sun, Aug 30, 2015 at 7:26 PM, Ashish Shrowty
>>>> >> >> <ashish.shro...@gmail.com> wrote:
>>>> >> >> > Do you think I should create a JIRA?
>>>> >> >> >
>>>> >> >> >
>>>> >> >> > On Sun, Aug 30, 2015 at 12:56 PM Ted Yu <yuzhih...@gmail.com>
>>>> wrote:
>>>> >> >> >>
>>>> >> >> >> I got StackOverFlowError as well :-(
>>>> >> >> >>
>>>> >> >> >> On Sun, Aug 30, 2015 at 9:47 AM, Ashish Shrowty
>>>> >> >> >> <ashish.shro...@gmail.com>
>>>> >> >> >> wrote:
>>>> >> >> >>>
>>>> >> >> >>> Yep .. I tried that too earlier. Doesn't make a difference.
>>>> Are you
>>>> >> >> >>> able
>>>> >> >> >>> to replicate on your side?
>>>> >> >> >>>
>>>> >> >> >>>
>>>> >> >> >>> On Sun, Aug 30, 2015 at 12:08 PM Ted Yu <yuzhih...@gmail.com>
>>>> >> >> >>> wrote:
>>>> >> >> >>>>
>>>> >> >> >>>> I see.
>>>> >> >> >>>>
>>>> >> >> >>>> What about using the following in place of variable a ?
>>>> >> >> >>>>
>>>> >> >> >>>>
>>>> >> >> >>>>
>>>> >> >> >>>>
>>>> http://spark.apache.org/docs/latest/programming-guide.html#broadcast-variables
>>>> >> >> >>>>
>>>> >> >> >>>> Cheers
>>>> >> >> >>>>
>>>> >> >> >>>> On Sun, Aug 30, 2015 at 8:54 AM, Ashish Shrowty
>>>> >> >> >>>> <ashish.shro...@gmail.com> wrote:
>>>> >> >> >>>>>
>>>> >> >> >>>>> @Sean - Agree that there is no action, but I still get the
>>>> >> >> >>>>> stackoverflowerror, its very weird
>>>> >> >> >>>>>
>>>> >> >> >>>>> @Ted - Variable a is just an int - val a = 10 ... The error
>>>> >> >> >>>>> happens
>>>> >> >> >>>>> when I try to pass a variable into the closure. The example
>>>> you
>>>> >> >> >>>>> have
>>>> >> >> >>>>> above
>>>> >> >> >>>>> works fine since there is no variable being passed into the
>>>> >> >> >>>>> closure
>>>> >> >> >>>>> from the
>>>> >> >> >>>>> shell.
>>>> >> >> >>>>>
>>>> >> >> >>>>> -Ashish
>>>> >> >> >>>>>
>>>> >> >> >>>>> On Sun, Aug 30, 2015 at 9:55 AM Ted Yu <yuzhih...@gmail.com
>>>> >
>>>> >> >> >>>>> wrote:
>>>> >> >> >>>>>>
>>>> >> >> >>>>>> Using Spark shell :
>>>> >> >> >>>>>>
>>>> >> >> >>>>>> scala> import scala.collection.mutable.MutableList
>>>> >> >> >>>>>> import scala.collection.mutable.MutableList
>>>> >> >> >>>>>>
>>>> >> >> >>>>>> scala> val lst = MutableList[(String,String,Double)]()
>>>> >> >> >>>>>> lst: scala.collection.mutable.MutableList[(String, String,
>>>> >> >> >>>>>> Double)]
>>>> >> >> >>>>>> =
>>>> >> >> >>>>>> MutableList()
>>>> >> >> >>>>>>
>>>> >> >> >>>>>> scala>
>>>> Range(0,10000).foreach(i=>lst+=(("10","10",i:Double)))
>>>> >> >> >>>>>>
>>>> >> >> >>>>>> scala> val rdd=sc.makeRDD(lst).map(i=> if(a==10) 1 else 0)
>>>> >> >> >>>>>> <console>:27: error: not found: value a
>>>> >> >> >>>>>> val rdd=sc.makeRDD(lst).map(i=> if(a==10) 1 else 0)
>>>> >> >> >>>>>> ^
>>>> >> >> >>>>>>
>>>> >> >> >>>>>> scala> val rdd=sc.makeRDD(lst).map(i=> if(i._1==10) 1 else
>>>> 0)
>>>> >> >> >>>>>> rdd: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[1]
>>>> at map
>>>> >> >> >>>>>> at
>>>> >> >> >>>>>> <console>:27
>>>> >> >> >>>>>>
>>>> >> >> >>>>>> scala> rdd.count()
>>>> >> >> >>>>>> ...
>>>> >> >> >>>>>> 15/08/30 06:53:40 INFO DAGScheduler: Job 0 finished: count
>>>> at
>>>> >> >> >>>>>> <console>:30, took 0.478350 s
>>>> >> >> >>>>>> res1: Long = 10000
>>>> >> >> >>>>>>
>>>> >> >> >>>>>> Ashish:
>>>> >> >> >>>>>> Please refine your example to mimic more closely what your
>>>> code
>>>> >> >> >>>>>> actually did.
>>>> >> >> >>>>>>
>>>> >> >> >>>>>> Thanks
>>>> >> >> >>>>>>
>>>> >> >> >>>>>> On Sun, Aug 30, 2015 at 12:24 AM, Sean Owen <
>>>> so...@cloudera.com>
>>>> >> >> >>>>>> wrote:
>>>> >> >> >>>>>>>
>>>> >> >> >>>>>>> That can't cause any error, since there is no action in
>>>> your
>>>> >> >> >>>>>>> first
>>>> >> >> >>>>>>> snippet. Even calling count on the result doesn't cause an
>>>> >> >> >>>>>>> error.
>>>> >> >> >>>>>>> You
>>>> >> >> >>>>>>> must be executing something different.
>>>> >> >> >>>>>>>
>>>> >> >> >>>>>>> On Sun, Aug 30, 2015 at 4:21 AM, ashrowty
>>>> >> >> >>>>>>> <ashish.shro...@gmail.com>
>>>> >> >> >>>>>>> wrote:
>>>> >> >> >>>>>>> > I am running the Spark shell (1.2.1) in local mode and
>>>> I have
>>>> >> >> >>>>>>> > a
>>>> >> >> >>>>>>> > simple
>>>> >> >> >>>>>>> > RDD[(String,String,Double)] with about 10,000 objects
>>>> in it.
>>>> >> >> >>>>>>> > I
>>>> >> >> >>>>>>> > get
>>>> >> >> >>>>>>> > a
>>>> >> >> >>>>>>> > StackOverFlowError each time I try to run the following
>>>> code
>>>> >> >> >>>>>>> > (the
>>>> >> >> >>>>>>> > code
>>>> >> >> >>>>>>> > itself is just representative of other logic where I
>>>> need to
>>>> >> >> >>>>>>> > pass
>>>> >> >> >>>>>>> > in a
>>>> >> >> >>>>>>> > variable). I tried broadcasting the variable too, but
>>>> no luck
>>>> >> >> >>>>>>> > ..
>>>> >> >> >>>>>>> > missing
>>>> >> >> >>>>>>> > something basic here -
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> > val rdd = sc.makeRDD(List(<Data read from file>)
>>>> >> >> >>>>>>> > val a=10
>>>> >> >> >>>>>>> > rdd.map(r => if (a==10) 1 else 0)
>>>> >> >> >>>>>>> > This throws -
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> > java.lang.StackOverflowError
>>>> >> >> >>>>>>> > at
>>>> >> >> >>>>>>> >
>>>> java.io.ObjectStreamClass.lookup(ObjectStreamClass.java:318)
>>>> >> >> >>>>>>> > at
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1133)
>>>> >> >> >>>>>>> > at
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
>>>> >> >> >>>>>>> > at
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
>>>> >> >> >>>>>>> > at
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
>>>> >> >> >>>>>>> > at
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
>>>> >> >> >>>>>>> > at
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
>>>> >> >> >>>>>>> > at
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
>>>> >> >> >>>>>>> > at
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
>>>> >> >> >>>>>>> > ...
>>>> >> >> >>>>>>> > ...
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> > More experiments .. this works -
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> > val lst =
>>>> Range(0,10000).map(i=>("10","10",i:Double)).toList
>>>> >> >> >>>>>>> > sc.makeRDD(lst).map(i=> if(a==10) 1 else 0)
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> > But below doesn't and throws the StackoverflowError -
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> > val lst = MutableList[(String,String,Double)]()
>>>> >> >> >>>>>>> > Range(0,10000).foreach(i=>lst+=(("10","10",i:Double)))
>>>> >> >> >>>>>>> > sc.makeRDD(lst).map(i=> if(a==10) 1 else 0)
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> > Any help appreciated!
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> > Thanks,
>>>> >> >> >>>>>>> > Ashish
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> > --
>>>> >> >> >>>>>>> > View this message in context:
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Spark-shell-and-StackOverFlowError-tp24508.html
>>>> >> >> >>>>>>> > Sent from the Apache Spark User List mailing list
>>>> archive at
>>>> >> >> >>>>>>> > Nabble.com.
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>> >
>>>> ---------------------------------------------------------------------
>>>> >> >> >>>>>>> > To unsubscribe, e-mail:
>>>> user-unsubscr...@spark.apache.org
>>>> >> >> >>>>>>> > For additional commands, e-mail:
>>>> user-h...@spark.apache.org
>>>> >> >> >>>>>>> >
>>>> >> >> >>>>>>>
>>>> >> >> >>>>>>>
>>>> >> >> >>>>>>>
>>>> >> >> >>>>>>>
>>>> ---------------------------------------------------------------------
>>>> >> >> >>>>>>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
>>>> >> >> >>>>>>> For additional commands, e-mail:
>>>> user-h...@spark.apache.org
>>>> >> >> >>>>>>>
>>>> >> >> >>>>>>
>>>> >> >> >>>>
>>>> >> >> >>
>>>> >> >> >
>>>>
>>>
>>
