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
>>> >> >> >>>>>>>
>>> >> >> >>>>>>
>>> >> >> >>>>
>>> >> >> >>
>>> >> >> >
>>>
>>
>
