Yeah, I am not sure what is going on. The only way to figure to take a look
at the disassembled bytecodes using javap.
TD
On Tue, Apr 21, 2015 at 1:53 PM, Jean-Pascal Billaud <j...@tellapart.com>
wrote:
> At this point I am assuming that nobody has an idea... I am still going to
> give it a last shot just in case it was missed by some people :)
>
> Thanks,
>
> On Mon, Apr 20, 2015 at 2:20 PM, Jean-Pascal Billaud <j...@tellapart.com>
> wrote:
>
>> Hey, so I start the context at the very end when all the piping is done.
>> BTW a foreachRDD will be called on the resulting dstream.map() right after
>> that.
>>
>> The puzzling thing is why removing the context bounds solve the
>> problem... What does this exception mean in general?
>>
>> On Mon, Apr 20, 2015 at 1:33 PM, Tathagata Das <t...@databricks.com>
>> wrote:
>>
>>> When are you getting this exception? After starting the context?
>>>
>>> TD
>>>
>>> On Mon, Apr 20, 2015 at 10:44 AM, Jean-Pascal Billaud <j...@tellapart.com>
>>> wrote:
>>>
>>>> Hi,
>>>>
>>>> I am getting this serialization exception and I am not too sure what
>>>> "Graph is unexpectedly null when DStream is being serialized" means?
>>>>
>>>> 15/04/20 06:12:38 INFO yarn.ApplicationMaster: Final app status:
>>>> FAILED, exitCode: 15, (reason: User class threw exception: Task not
>>>> serializable)
>>>> Exception in thread "Driver" 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:1435)
>>>> at org.apache.spark.streaming.dstream.DStream.map(DStream.
>>>> scala:438)
>>>> [...]
>>>> Caused by: java.io.NotSerializableException: Graph is unexpectedly
>>>> null when DStream is being serialized.
>>>> at org.apache.spark.streaming.dstream.DStream$anonfun$
>>>> writeObject$1.apply$mcV$sp(DStream.scala:420)
>>>> at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:
>>>> 985)
>>>> at org.apache.spark.streaming.dstream.DStream.writeObject(
>>>> DStream.scala:403)
>>>>
>>>> The operation comes down to something like this:
>>>>
>>>> dstream.map(tuple => {
>>>> val w = StreamState.fetch[K,W](state.prefixKey, tuple._1)
>>>> (tuple._1, (tuple._2, w)) })
>>>>
>>>> And StreamState being a very simple standalone object:
>>>>
>>>> object StreamState {
>>>> def fetch[K : ClassTag : Ordering, V : ClassTag](prefixKey: String,
>>>> key: K) : Option[V] = None
>>>> }
>>>>
>>>> However if I remove the context bounds from K in fetch e.g. removing
>>>> ClassTag and Ordering then everything is fine.
>>>>
>>>> If anyone has some pointers, I'd really appreciate it.
>>>>
>>>> Thanks,
>>>>
>>>
>>>
>>
>
