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Posted to user@spark.apache.org by Padmanabhan,
"Mahesh (contractor)" <ma...@twc-contractor.com> on 2014/08/07 18:03:59 UTC
Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Hello all,
I am not sure what is going on – I am getting a NotSerializedException and initially I thought it was due to not registering one of my classes with Kryo but that doesn’t seem to be the case. I am essentially eliminating duplicates in a spark streaming application by using a “window” to eliminate duplicates from the current batch and sending de-dup stats.
The strange thing is that my application is not affected by this error at all.
I tried registering with Kryo like this (though it was more out of desperation):
class MyRegistrator extends KryoRegistrator {
override def registerClasses(kryo: Kryo) {
kryo.register(classOf[Long])
kryo.register(classOf[Tuple2[Int, Tuple2[Long, Long]]])
}
}
Immediately before the exception, I have a piece of code that seems to be executed (at rdd.foreach) though I am not sure if that is the culprit:
Here is the code:
msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
rdd.foreach(item => item match {
case (key, (oc, dc)) => {
DebugLogger.log("Original event count = " + oc)
DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+" events")
if (enableOpStat) {
try {
val statBody = Array(("batchCount", oc.toString()),
("duplicateCount", (oc-dc).toString()))
OperationalStatProducer.produce(statBody)
} catch { case e: Exception => DebugLogger.report(e) }
}
}
})
})
Here is the exception:
ERROR akka.actor.OneForOneStrategy - org.apache.spark.streaming.StreamingContext
java.io.NotSerializableException: org.apache.spark.streaming.StreamingContext
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)
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)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
at java.io.ObjectOutputStream.writeArray(ObjectOutputStream.java:1377)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1173)
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.defaultWriteObject(ObjectOutputStream.java:440)
at org.apache.spark.streaming.DStreamGraph.writeObject(DStreamGraph.scala:168)
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 java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:988)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1495)
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)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
at org.apache.spark.streaming.CheckpointWriter.write(Checkpoint.scala:185)
at org.apache.spark.streaming.scheduler.JobGenerator.doCheckpoint(JobGenerator.scala:259)
at org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:167)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$start$1$$anon$1$$anonfun$receive$1.applyOrElse(JobGenerator.scala:76)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
at akka.actor.ActorCell.invoke(ActorCell.scala:456)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
at akka.dispatch.Mailbox.run(Mailbox.scala:219)
at akka.dispatch.ForkJoinExecutorConfigurator$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)
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Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Posted by Tathagata Das <ta...@gmail.com>.
LOL! Glad it solved it.
TD
On Thu, Aug 7, 2014 at 2:23 PM, Padmanabhan, Mahesh (contractor) <
mahesh.padmanabhan@twc-contractor.com> wrote:
> Slap my head moment – using rdd.context solved it!
>
> Thanks TD,
>
> Mahesh
>
> From: Tathagata Das <ta...@gmail.com>
> Date: Thursday, August 7, 2014 at 3:06 PM
> To: Mahesh Padmanabhan <ma...@twc-contractor.com>
> Cc: amit <am...@gmail.com>, "user@spark.incubator.apache.org" <
> user@spark.incubator.apache.org>
>
> Subject: Re: Spark 1.0.1 NotSerialized exception (a bit of a head
> scratcher)
>
> Well I dont see the rdd in the foreachRDD being passed into the A.func1()
> so I am not sure what is purpose of the function. Assuming that you do want
> to pass on that RDD into that function, and also want to have access to the
> sparkContext, you can only pass on the RDD and then access the associated
> context as rdd.context. This will avoid referring of the ssc inside the
> foreachRDD.
>
> See if that helps.
>
> TD
>
>
> On Thu, Aug 7, 2014 at 12:47 PM, Padmanabhan, Mahesh (contractor) <
> mahesh.padmanabhan@twc-contractor.com> wrote:
>
>> Thanks TD, Amit.
>>
>> I think I figured out where the problem is through the process of
>> commenting out individual lines of code one at a time :(
>>
>> Can either of you help me find the right solution? I tried creating the
>> SparkContext outside the foreachRDD but that didn’t help.
>>
>> I have an object (let’s say A) that is passed a SparkContext like this:
>>
>> object A {
>> def func1(sc: SparkContext) {
>> //Do something with sc
>> }
>>
>> In my main object that creates the StreamingContext, I call object A’s
>> func1 method like this:
>>
>> val ssc = new StreamingContext(spark, Seconds(batchTime))
>>
>> ssc.checkpoint(checkPointDir)
>>
>> val messageStream = KafkaConsumer.messageStream(ssc)
>>
>> messageStream.foreachRDD(rdd => {
>> A.func1(ssc.sparkContext)
>> }
>>
>> Seems like the call A.func1(ssc.sparkContext) above is the cause of the
>> exception.
>>
>> Thanks,
>> Mahesh
>>
>> From: Tathagata Das <ta...@gmail.com>
>> Date: Thursday, August 7, 2014 at 1:11 PM
>> To: amit <am...@gmail.com>
>> Cc: "user@spark.incubator.apache.org" <us...@spark.incubator.apache.org>
>>
>> Subject: Re: Spark 1.0.1 NotSerialized exception (a bit of a head
>> scratcher)
>>
>> From the extended info, I see that you have a function called
>> createStreamingContext() in your code. Somehow that is getting referenced
>> in in the foreach function. Is the whole foreachRDD code inside the
>> createStreamingContext() function? Did you try marking the ssc field as
>> transient?
>>
>> Here is a significantly different approach. Put the whole function to
>> apply on each item in an object.
>>
>> object MyFunctions {
>> def processItem(enable: Boolean)(item: (Int, (Long, Long)) = {
>> val (key, (oc, dc)) = item
>> DebugLogger.log("Original event count = " + oc)
>> DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+"
>> events")
>> if (enableOpStat) {
>> try {
>> val statBody = Array(("batchCount", oc.toString()),
>> ("duplicateCount", (oc-dc).toString()))
>> OperationalStatProducer.produce(statBody)
>> } catch { case e: Exception => DebugLogger.report(e) }
>> }
>> }
>> }
>> }
>>
>>
>>
>> And then use that
>>
>> msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
>> val enable = enableOptStat
>> rdd.foreach(MyFunction.processItem(enable) _ )
>> }
>>
>>
>>
>> On Thu, Aug 7, 2014 at 11:52 AM, amit <am...@gmail.com> wrote:
>>
>>> There is one more configuration option called spark.closure.serializer
>>> that
>>> can be used to specify serializer for closures.
>>>
>>> Maybe in the the class you have Streaming Context as a field, so when
>>> spark
>>> tries to serialize the whole class it uses the spark.closure.serializer
>>> to
>>> serialize even the streaming context. Classes like StreamingContext may
>>> not
>>> work if serialized and deserialized in a different JVM(?).
>>>
>>> So I see two solutions one is to somehow avoid serializing
>>> StreamingContext,
>>> other is to override the default serialization method to serialize only
>>> the
>>> params required by streaming context and recreate it in the serialization
>>> step from the params
>>>
>>>
>>>
>>> --
>>> View this message in context:
>>> http://apache-spark-user-list.1001560.n3.nabble.com/Spark-1-0-1-NotSerialized-exception-a-bit-of-a-head-scratcher-tp11666p11703.html
>>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>>
>>> ---------------------------------------------------------------------
>>> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
>>> For additional commands, e-mail: user-help@spark.apache.org
>>>
>>>
>>
>> ------------------------------
>> This E-mail and any of its attachments may contain Time Warner Cable
>> proprietary information, which is privileged, confidential, or subject to
>> copyright belonging to Time Warner Cable. This E-mail is intended solely
>> for the use of the individual or entity to which it is addressed. If you
>> are not the intended recipient of this E-mail, you are hereby notified that
>> any dissemination, distribution, copying, or action taken in relation to
>> the contents of and attachments to this E-mail is strictly prohibited and
>> may be unlawful. If you have received this E-mail in error, please notify
>> the sender immediately and permanently delete the original and any copy of
>> this E-mail and any printout.
>>
>
>
Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Posted by Padmanabhan,
"Mahesh (contractor)" <ma...@twc-contractor.com>.
Slap my head moment – using rdd.context solved it!
Thanks TD,
Mahesh
From: Tathagata Das <ta...@gmail.com>>
Date: Thursday, August 7, 2014 at 3:06 PM
To: Mahesh Padmanabhan <ma...@twc-contractor.com>>
Cc: amit <am...@gmail.com>>, "user@spark.incubator.apache.org<ma...@spark.incubator.apache.org>" <us...@spark.incubator.apache.org>>
Subject: Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Well I dont see the rdd in the foreachRDD being passed into the A.func1() so I am not sure what is purpose of the function. Assuming that you do want to pass on that RDD into that function, and also want to have access to the sparkContext, you can only pass on the RDD and then access the associated context as rdd.context. This will avoid referring of the ssc inside the foreachRDD.
See if that helps.
TD
On Thu, Aug 7, 2014 at 12:47 PM, Padmanabhan, Mahesh (contractor) <ma...@twc-contractor.com>> wrote:
Thanks TD, Amit.
I think I figured out where the problem is through the process of commenting out individual lines of code one at a time :(
Can either of you help me find the right solution? I tried creating the SparkContext outside the foreachRDD but that didn’t help.
I have an object (let’s say A) that is passed a SparkContext like this:
object A {
def func1(sc: SparkContext) {
//Do something with sc
}
In my main object that creates the StreamingContext, I call object A’s func1 method like this:
val ssc = new StreamingContext(spark, Seconds(batchTime))
ssc.checkpoint(checkPointDir)
val messageStream = KafkaConsumer.messageStream(ssc)
messageStream.foreachRDD(rdd => {
A.func1(ssc.sparkContext)
}
Seems like the call A.func1(ssc.sparkContext) above is the cause of the exception.
Thanks,
Mahesh
From: Tathagata Das <ta...@gmail.com>>
Date: Thursday, August 7, 2014 at 1:11 PM
To: amit <am...@gmail.com>>
Cc: "user@spark.incubator.apache.org<ma...@spark.incubator.apache.org>" <us...@spark.incubator.apache.org>>
Subject: Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
>From the extended info, I see that you have a function called createStreamingContext() in your code. Somehow that is getting referenced in in the foreach function. Is the whole foreachRDD code inside the createStreamingContext() function? Did you try marking the ssc field as transient?
Here is a significantly different approach. Put the whole function to apply on each item in an object.
object MyFunctions {
def processItem(enable: Boolean)(item: (Int, (Long, Long)) = {
val (key, (oc, dc)) = item
DebugLogger.log("Original event count = " + oc)
DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+" events")
if (enableOpStat) {
try {
val statBody = Array(("batchCount", oc.toString()),
("duplicateCount", (oc-dc).toString()))
OperationalStatProducer.produce(statBody)
} catch { case e: Exception => DebugLogger.report(e) }
}
}
}
}
And then use that
msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
val enable = enableOptStat
rdd.foreach(MyFunction.processItem(enable) _ )
}
On Thu, Aug 7, 2014 at 11:52 AM, amit <am...@gmail.com>> wrote:
There is one more configuration option called spark.closure.serializer that
can be used to specify serializer for closures.
Maybe in the the class you have Streaming Context as a field, so when spark
tries to serialize the whole class it uses the spark.closure.serializer to
serialize even the streaming context. Classes like StreamingContext may not
work if serialized and deserialized in a different JVM(?).
So I see two solutions one is to somehow avoid serializing StreamingContext,
other is to override the default serialization method to serialize only the
params required by streaming context and recreate it in the serialization
step from the params
--
View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-1-0-1-NotSerialized-exception-a-bit-of-a-head-scratcher-tp11666p11703.html
Sent from the Apache Spark User List mailing list archive at Nabble.com.
---------------------------------------------------------------------
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________________________________
This E-mail and any of its attachments may contain Time Warner Cable proprietary information, which is privileged, confidential, or subject to copyright belonging to Time Warner Cable. This E-mail is intended solely for the use of the individual or entity to which it is addressed. If you are not the intended recipient of this E-mail, you are hereby notified that any dissemination, distribution, copying, or action taken in relation to the contents of and attachments to this E-mail is strictly prohibited and may be unlawful. If you have received this E-mail in error, please notify the sender immediately and permanently delete the original and any copy of this E-mail and any printout.
Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Posted by Tathagata Das <ta...@gmail.com>.
Well I dont see the rdd in the foreachRDD being passed into the A.func1()
so I am not sure what is purpose of the function. Assuming that you do want
to pass on that RDD into that function, and also want to have access to the
sparkContext, you can only pass on the RDD and then access the associated
context as rdd.context. This will avoid referring of the ssc inside the
foreachRDD.
See if that helps.
TD
On Thu, Aug 7, 2014 at 12:47 PM, Padmanabhan, Mahesh (contractor) <
mahesh.padmanabhan@twc-contractor.com> wrote:
> Thanks TD, Amit.
>
> I think I figured out where the problem is through the process of
> commenting out individual lines of code one at a time :(
>
> Can either of you help me find the right solution? I tried creating the
> SparkContext outside the foreachRDD but that didn’t help.
>
> I have an object (let’s say A) that is passed a SparkContext like this:
>
> object A {
> def func1(sc: SparkContext) {
> //Do something with sc
> }
>
> In my main object that creates the StreamingContext, I call object A’s
> func1 method like this:
>
> val ssc = new StreamingContext(spark, Seconds(batchTime))
>
> ssc.checkpoint(checkPointDir)
>
> val messageStream = KafkaConsumer.messageStream(ssc)
>
> messageStream.foreachRDD(rdd => {
> A.func1(ssc.sparkContext)
> }
>
> Seems like the call A.func1(ssc.sparkContext) above is the cause of the
> exception.
>
> Thanks,
> Mahesh
>
> From: Tathagata Das <ta...@gmail.com>
> Date: Thursday, August 7, 2014 at 1:11 PM
> To: amit <am...@gmail.com>
> Cc: "user@spark.incubator.apache.org" <us...@spark.incubator.apache.org>
>
> Subject: Re: Spark 1.0.1 NotSerialized exception (a bit of a head
> scratcher)
>
> From the extended info, I see that you have a function called
> createStreamingContext() in your code. Somehow that is getting referenced
> in in the foreach function. Is the whole foreachRDD code inside the
> createStreamingContext() function? Did you try marking the ssc field as
> transient?
>
> Here is a significantly different approach. Put the whole function to
> apply on each item in an object.
>
> object MyFunctions {
> def processItem(enable: Boolean)(item: (Int, (Long, Long)) = {
> val (key, (oc, dc)) = item
> DebugLogger.log("Original event count = " + oc)
> DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+"
> events")
> if (enableOpStat) {
> try {
> val statBody = Array(("batchCount", oc.toString()),
> ("duplicateCount", (oc-dc).toString()))
> OperationalStatProducer.produce(statBody)
> } catch { case e: Exception => DebugLogger.report(e) }
> }
> }
> }
> }
>
>
>
> And then use that
>
> msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
> val enable = enableOptStat
> rdd.foreach(MyFunction.processItem(enable) _ )
> }
>
>
>
> On Thu, Aug 7, 2014 at 11:52 AM, amit <am...@gmail.com> wrote:
>
>> There is one more configuration option called spark.closure.serializer
>> that
>> can be used to specify serializer for closures.
>>
>> Maybe in the the class you have Streaming Context as a field, so when
>> spark
>> tries to serialize the whole class it uses the spark.closure.serializer to
>> serialize even the streaming context. Classes like StreamingContext may
>> not
>> work if serialized and deserialized in a different JVM(?).
>>
>> So I see two solutions one is to somehow avoid serializing
>> StreamingContext,
>> other is to override the default serialization method to serialize only
>> the
>> params required by streaming context and recreate it in the serialization
>> step from the params
>>
>>
>>
>> --
>> View this message in context:
>> http://apache-spark-user-list.1001560.n3.nabble.com/Spark-1-0-1-NotSerialized-exception-a-bit-of-a-head-scratcher-tp11666p11703.html
>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>
>> ---------------------------------------------------------------------
>> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
>> For additional commands, e-mail: user-help@spark.apache.org
>>
>>
>
> ------------------------------
> This E-mail and any of its attachments may contain Time Warner Cable
> proprietary information, which is privileged, confidential, or subject to
> copyright belonging to Time Warner Cable. This E-mail is intended solely
> for the use of the individual or entity to which it is addressed. If you
> are not the intended recipient of this E-mail, you are hereby notified that
> any dissemination, distribution, copying, or action taken in relation to
> the contents of and attachments to this E-mail is strictly prohibited and
> may be unlawful. If you have received this E-mail in error, please notify
> the sender immediately and permanently delete the original and any copy of
> this E-mail and any printout.
>
Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Posted by Padmanabhan,
"Mahesh (contractor)" <ma...@twc-contractor.com>.
Thanks TD, Amit.
I think I figured out where the problem is through the process of commenting out individual lines of code one at a time :(
Can either of you help me find the right solution? I tried creating the SparkContext outside the foreachRDD but that didn’t help.
I have an object (let’s say A) that is passed a SparkContext like this:
object A {
def func1(sc: SparkContext) {
//Do something with sc
}
In my main object that creates the StreamingContext, I call object A’s func1 method like this:
val ssc = new StreamingContext(spark, Seconds(batchTime))
ssc.checkpoint(checkPointDir)
val messageStream = KafkaConsumer.messageStream(ssc)
messageStream.foreachRDD(rdd => {
A.func1(ssc.sparkContext)
}
Seems like the call A.func1(ssc.sparkContext) above is the cause of the exception.
Thanks,
Mahesh
From: Tathagata Das <ta...@gmail.com>>
Date: Thursday, August 7, 2014 at 1:11 PM
To: amit <am...@gmail.com>>
Cc: "user@spark.incubator.apache.org<ma...@spark.incubator.apache.org>" <us...@spark.incubator.apache.org>>
Subject: Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
>From the extended info, I see that you have a function called createStreamingContext() in your code. Somehow that is getting referenced in in the foreach function. Is the whole foreachRDD code inside the createStreamingContext() function? Did you try marking the ssc field as transient?
Here is a significantly different approach. Put the whole function to apply on each item in an object.
object MyFunctions {
def processItem(enable: Boolean)(item: (Int, (Long, Long)) = {
val (key, (oc, dc)) = item
DebugLogger.log("Original event count = " + oc)
DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+" events")
if (enableOpStat) {
try {
val statBody = Array(("batchCount", oc.toString()),
("duplicateCount", (oc-dc).toString()))
OperationalStatProducer.produce(statBody)
} catch { case e: Exception => DebugLogger.report(e) }
}
}
}
}
And then use that
msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
val enable = enableOptStat
rdd.foreach(MyFunction.processItem(enable) _ )
}
On Thu, Aug 7, 2014 at 11:52 AM, amit <am...@gmail.com>> wrote:
There is one more configuration option called spark.closure.serializer that
can be used to specify serializer for closures.
Maybe in the the class you have Streaming Context as a field, so when spark
tries to serialize the whole class it uses the spark.closure.serializer to
serialize even the streaming context. Classes like StreamingContext may not
work if serialized and deserialized in a different JVM(?).
So I see two solutions one is to somehow avoid serializing StreamingContext,
other is to override the default serialization method to serialize only the
params required by streaming context and recreate it in the serialization
step from the params
--
View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-1-0-1-NotSerialized-exception-a-bit-of-a-head-scratcher-tp11666p11703.html
Sent from the Apache Spark User List mailing list archive at Nabble.com.
---------------------------------------------------------------------
To unsubscribe, e-mail: user-unsubscribe@spark.apache.org<ma...@spark.apache.org>
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________________________________
This E-mail and any of its attachments may contain Time Warner Cable proprietary information, which is privileged, confidential, or subject to copyright belonging to Time Warner Cable. This E-mail is intended solely for the use of the individual or entity to which it is addressed. If you are not the intended recipient of this E-mail, you are hereby notified that any dissemination, distribution, copying, or action taken in relation to the contents of and attachments to this E-mail is strictly prohibited and may be unlawful. If you have received this E-mail in error, please notify the sender immediately and permanently delete the original and any copy of this E-mail and any printout.
Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Posted by Tathagata Das <ta...@gmail.com>.
>From the extended info, I see that you have a function called
createStreamingContext() in your code. Somehow that is getting referenced
in in the foreach function. Is the whole foreachRDD code inside the
createStreamingContext() function? Did you try marking the ssc field as
transient?
Here is a significantly different approach. Put the whole function to apply
on each item in an object.
object MyFunctions {
def processItem(enable: Boolean)(item: (Int, (Long, Long)) = {
val (key, (oc, dc)) = item
DebugLogger.log("Original event count = " + oc)
DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+"
events")
if (enableOpStat) {
try {
val statBody = Array(("batchCount", oc.toString()),
("duplicateCount", (oc-dc).toString()))
OperationalStatProducer.produce(statBody)
} catch { case e: Exception => DebugLogger.report(e) }
}
}
}
}
And then use that
msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
val enable = enableOptStat
rdd.foreach(MyFunction.processItem(enable) _ )
}
On Thu, Aug 7, 2014 at 11:52 AM, amit <am...@gmail.com> wrote:
> There is one more configuration option called spark.closure.serializer that
> can be used to specify serializer for closures.
>
> Maybe in the the class you have Streaming Context as a field, so when spark
> tries to serialize the whole class it uses the spark.closure.serializer to
> serialize even the streaming context. Classes like StreamingContext may not
> work if serialized and deserialized in a different JVM(?).
>
> So I see two solutions one is to somehow avoid serializing
> StreamingContext,
> other is to override the default serialization method to serialize only the
> params required by streaming context and recreate it in the serialization
> step from the params
>
>
>
> --
> View this message in context:
> http://apache-spark-user-list.1001560.n3.nabble.com/Spark-1-0-1-NotSerialized-exception-a-bit-of-a-head-scratcher-tp11666p11703.html
> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>
> ---------------------------------------------------------------------
> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
> For additional commands, e-mail: user-help@spark.apache.org
>
>
Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Posted by amit <am...@gmail.com>.
There is one more configuration option called spark.closure.serializer that
can be used to specify serializer for closures.
Maybe in the the class you have Streaming Context as a field, so when spark
tries to serialize the whole class it uses the spark.closure.serializer to
serialize even the streaming context. Classes like StreamingContext may not
work if serialized and deserialized in a different JVM(?).
So I see two solutions one is to somehow avoid serializing StreamingContext,
other is to override the default serialization method to serialize only the
params required by streaming context and recreate it in the serialization
step from the params
--
View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-1-0-1-NotSerialized-exception-a-bit-of-a-head-scratcher-tp11666p11703.html
Sent from the Apache Spark User List mailing list archive at Nabble.com.
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To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
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Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Posted by Padmanabhan,
"Mahesh (contractor)" <ma...@twc-contractor.com>.
As a follow up, I commented out that entire code and I am still getting the exception. It may be related to what you are suggesting so are there any best practices so that I can audit other parts of the code?
Thanks,
Mahesh
From: <Padmanabhan>, Mahesh Padmanabhan <ma...@twc-contractor.com>>
Date: Thursday, August 7, 2014 at 11:23 AM
To: Tathagata Das <ta...@gmail.com>>
Cc: "user@spark.apache.org<ma...@spark.apache.org>" <us...@spark.apache.org>>
Subject: Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Thanks TD but unfortunately that did not work.
From: Tathagata Das <ta...@gmail.com>>
Date: Thursday, August 7, 2014 at 10:55 AM
To: Mahesh Padmanabhan <ma...@twc-contractor.com>>
Cc: "user@spark.apache.org<ma...@spark.apache.org>" <us...@spark.apache.org>>
Subject: Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
It could be because of the variable "enableOpStat". Since its defined outside foreachRDD, referring to it inside the rdd.foreach is probably causing the whole streaming context being included in the closure. Scala funkiness. Try this, see if it works.
msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
val enable = enableOpStat
rdd.foreach(item => item match {
case (key, (oc, dc)) => {
DebugLogger.log("Original event count = " + oc)
DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+" events")
if (enable) {
try {
val statBody = Array(("batchCount", oc.toString()),
("duplicateCount", (oc-dc).toString()))
OperationalStatProducer.produce(statBody)
} catch { case e: Exception => DebugLogger.report(e) }
}
}
})
})
On Thu, Aug 7, 2014 at 9:03 AM, Padmanabhan, Mahesh (contractor) <ma...@twc-contractor.com>> wrote:
Hello all,
I am not sure what is going on – I am getting a NotSerializedException and initially I thought it was due to not registering one of my classes with Kryo but that doesn’t seem to be the case. I am essentially eliminating duplicates in a spark streaming application by using a “window” to eliminate duplicates from the current batch and sending de-dup stats.
The strange thing is that my application is not affected by this error at all.
I tried registering with Kryo like this (though it was more out of desperation):
class MyRegistrator extends KryoRegistrator {
override def registerClasses(kryo: Kryo) {
kryo.register(classOf[Long])
kryo.register(classOf[Tuple2[Int, Tuple2[Long, Long]]])
}
}
Immediately before the exception, I have a piece of code that seems to be executed (at rdd.foreach) though I am not sure if that is the culprit:
Here is the code:
msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
rdd.foreach(item => item match {
case (key, (oc, dc)) => {
DebugLogger.log("Original event count = " + oc)
DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+" events")
if (enableOpStat) {
try {
val statBody = Array(("batchCount", oc.toString()),
("duplicateCount", (oc-dc).toString()))
OperationalStatProducer.produce(statBody)
} catch { case e: Exception => DebugLogger.report(e) }
}
}
})
})
Here is the exception:
ERROR akka.actor.OneForOneStrategy - org.apache.spark.streaming.StreamingContext
java.io.NotSerializableException: org.apache.spark.streaming.StreamingContext
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)
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)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
at java.io.ObjectOutputStream.writeArray(ObjectOutputStream.java:1377)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1173)
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.defaultWriteObject(ObjectOutputStream.java:440)
at org.apache.spark.streaming.DStreamGraph.writeObject(DStreamGraph.scala:168)
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 java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:988)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1495)
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)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
at org.apache.spark.streaming.CheckpointWriter.write(Checkpoint.scala:185)
at org.apache.spark.streaming.scheduler.JobGenerator.doCheckpoint(JobGenerator.scala:259)
at org.apache.spark.streaming.scheduler.JobGenerator.org<http://org.apache.spark.streaming.scheduler.JobGenerator.org>$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:167)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$start$1$$anon$1$$anonfun$receive$1.applyOrElse(JobGenerator.scala:76)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
at akka.actor.ActorCell.invoke(ActorCell.scala:456)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
at akka.dispatch.Mailbox.run(Mailbox.scala:219)
at akka.dispatch.ForkJoinExecutorConfigurator$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)
________________________________
This E-mail and any of its attachments may contain Time Warner Cable proprietary information, which is privileged, confidential, or subject to copyright belonging to Time Warner Cable. This E-mail is intended solely for the use of the individual or entity to which it is addressed. If you are not the intended recipient of this E-mail, you are hereby notified that any dissemination, distribution, copying, or action taken in relation to the contents of and attachments to this E-mail is strictly prohibited and may be unlawful. If you have received this E-mail in error, please notify the sender immediately and permanently delete the original and any copy of this E-mail and any printout.
Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Posted by Padmanabhan,
"Mahesh (contractor)" <ma...@twc-contractor.com>.
Does this help? I can’t figure out anything new from this extra information.
Thanks,
Mahesh
2014-08-07 12:27:00,170 [spark-akka.actor.default-dispatcher-4] ERROR akka.actor.OneForOneStrategy - org.apache.spark.streaming.StreamingContext
- field (class "com.twc.needle.ep.EventPersister$$anonfun$createStreamingContext$1", name: "ssc$1", type: "class org.apache.spark.streaming.StreamingContext")
- object (class "com.twc.needle.ep.EventPersister$$anonfun$createStreamingContext$1", <function1>)
- field (class "org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1", name: "foreachFunc$1", type: "interface scala.Function1")
- object (class "org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1", <function2>)
- field (class "org.apache.spark.streaming.dstream.ForEachDStream", name: "org$apache$spark$streaming$dstream$ForEachDStream$$foreachFunc", type: "interface scala.Function2")
- object (class "org.apache.spark.streaming.dstream.ForEachDStream", org.apache.spark.streaming.dstream.ForEachDStream@7d6dcf80)
- element of array (index: 1)
- array (class "[Ljava.lang.Object;", size: 16)
- field (class "scala.collection.mutable.ArrayBuffer", name: "array", type: "class [Ljava.lang.Object;")
- object (class "scala.collection.mutable.ArrayBuffer", ArrayBuffer(org.apache.spark.streaming.dstream.ForEachDStream@7129162d, org.apache.spark.streaming.dstream.ForEachDStream@7d6dcf80))
- field (class "org.apache.spark.streaming.DStreamGraph", name: "outputStreams", type: "class scala.collection.mutable.ArrayBuffer")
- custom writeObject data (class "org.apache.spark.streaming.DStreamGraph")
- object (class "org.apache.spark.streaming.DStreamGraph", org.apache.spark.streaming.DStreamGraph@23e0520a)
- field (class "org.apache.spark.streaming.Checkpoint", name: "graph", type: "class org.apache.spark.streaming.DStreamGraph")
- root object (class "org.apache.spark.streaming.Checkpoint", org.apache.spark.streaming.Checkpoint@73a68f0)
java.io.NotSerializableException: org.apache.spark.streaming.StreamingContext
- field (class "com.twc.needle.ep.EventPersister$$anonfun$createStreamingContext$1", name: "ssc$1", type: "class org.apache.spark.streaming.StreamingContext")
- object (class "com.twc.needle.ep.EventPersister$$anonfun$createStreamingContext$1", <function1>)
- field (class "org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1", name: "foreachFunc$1", type: "interface scala.Function1")
- object (class "org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1", <function2>)
- field (class "org.apache.spark.streaming.dstream.ForEachDStream", name: "org$apache$spark$streaming$dstream$ForEachDStream$$foreachFunc", type: "interface scala.Function2")
- object (class "org.apache.spark.streaming.dstream.ForEachDStream", org.apache.spark.streaming.dstream.ForEachDStream@7d6dcf80)
- element of array (index: 1)
- array (class "[Ljava.lang.Object;", size: 16)
- field (class "scala.collection.mutable.ArrayBuffer", name: "array", type: "class [Ljava.lang.Object;")
- object (class "scala.collection.mutable.ArrayBuffer", ArrayBuffer(org.apache.spark.streaming.dstream.ForEachDStream@7129162d, org.apache.spark.streaming.dstream.ForEachDStream@7d6dcf80))
- field (class "org.apache.spark.streaming.DStreamGraph", name: "outputStreams", type: "class scala.collection.mutable.ArrayBuffer")
- custom writeObject data (class "org.apache.spark.streaming.DStreamGraph")
- object (class "org.apache.spark.streaming.DStreamGraph", org.apache.spark.streaming.DStreamGraph@23e0520a)
- field (class "org.apache.spark.streaming.Checkpoint", name: "graph", type: "class org.apache.spark.streaming.DStreamGraph")
- root object (class "org.apache.spark.streaming.Checkpoint", org.apache.spark.streaming.Checkpoint@73a68f0)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1180)
From: Tathagata Das <ta...@gmail.com>>
Date: Thursday, August 7, 2014 at 11:31 AM
To: Mahesh Padmanabhan <ma...@twc-contractor.com>>
Cc: "user@spark.apache.org<ma...@spark.apache.org>" <us...@spark.apache.org>>
Subject: Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Can you enable the java flag -Dsun.io.serialization.extendedDebugInfo=true for driver in your driver startup-script? That should give an indication of the sequence of object references that lead to the StremaingContext being included in the closure.
TD
On Thu, Aug 7, 2014 at 10:23 AM, Padmanabhan, Mahesh (contractor) <ma...@twc-contractor.com>> wrote:
Thanks TD but unfortunately that did not work.
From: Tathagata Das <ta...@gmail.com>>
Date: Thursday, August 7, 2014 at 10:55 AM
To: Mahesh Padmanabhan <ma...@twc-contractor.com>>
Cc: "user@spark.apache.org<ma...@spark.apache.org>" <us...@spark.apache.org>>
Subject: Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
It could be because of the variable "enableOpStat". Since its defined outside foreachRDD, referring to it inside the rdd.foreach is probably causing the whole streaming context being included in the closure. Scala funkiness. Try this, see if it works.
msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
val enable = enableOpStat
rdd.foreach(item => item match {
case (key, (oc, dc)) => {
DebugLogger.log("Original event count = " + oc)
DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+" events")
if (enable) {
try {
val statBody = Array(("batchCount", oc.toString()),
("duplicateCount", (oc-dc).toString()))
OperationalStatProducer.produce(statBody)
} catch { case e: Exception => DebugLogger.report(e) }
}
}
})
})
On Thu, Aug 7, 2014 at 9:03 AM, Padmanabhan, Mahesh (contractor) <ma...@twc-contractor.com>> wrote:
Hello all,
I am not sure what is going on – I am getting a NotSerializedException and initially I thought it was due to not registering one of my classes with Kryo but that doesn’t seem to be the case. I am essentially eliminating duplicates in a spark streaming application by using a “window” to eliminate duplicates from the current batch and sending de-dup stats.
The strange thing is that my application is not affected by this error at all.
I tried registering with Kryo like this (though it was more out of desperation):
class MyRegistrator extends KryoRegistrator {
override def registerClasses(kryo: Kryo) {
kryo.register(classOf[Long])
kryo.register(classOf[Tuple2[Int, Tuple2[Long, Long]]])
}
}
Immediately before the exception, I have a piece of code that seems to be executed (at rdd.foreach) though I am not sure if that is the culprit:
Here is the code:
msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
rdd.foreach(item => item match {
case (key, (oc, dc)) => {
DebugLogger.log("Original event count = " + oc)
DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+" events")
if (enableOpStat) {
try {
val statBody = Array(("batchCount", oc.toString()),
("duplicateCount", (oc-dc).toString()))
OperationalStatProducer.produce(statBody)
} catch { case e: Exception => DebugLogger.report(e) }
}
}
})
})
Here is the exception:
ERROR akka.actor.OneForOneStrategy - org.apache.spark.streaming.StreamingContext
java.io.NotSerializableException: org.apache.spark.streaming.StreamingContext
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)
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)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
at java.io.ObjectOutputStream.writeArray(ObjectOutputStream.java:1377)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1173)
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.defaultWriteObject(ObjectOutputStream.java:440)
at org.apache.spark.streaming.DStreamGraph.writeObject(DStreamGraph.scala:168)
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 java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:988)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1495)
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)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
at org.apache.spark.streaming.CheckpointWriter.write(Checkpoint.scala:185)
at org.apache.spark.streaming.scheduler.JobGenerator.doCheckpoint(JobGenerator.scala:259)
at org.apache.spark.streaming.scheduler.JobGenerator.org<http://org.apache.spark.streaming.scheduler.JobGenerator.org>$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:167)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$start$1$$anon$1$$anonfun$receive$1.applyOrElse(JobGenerator.scala:76)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
at akka.actor.ActorCell.invoke(ActorCell.scala:456)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
at akka.dispatch.Mailbox.run(Mailbox.scala:219)
at akka.dispatch.ForkJoinExecutorConfigurator$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)
________________________________
This E-mail and any of its attachments may contain Time Warner Cable proprietary information, which is privileged, confidential, or subject to copyright belonging to Time Warner Cable. This E-mail is intended solely for the use of the individual or entity to which it is addressed. If you are not the intended recipient of this E-mail, you are hereby notified that any dissemination, distribution, copying, or action taken in relation to the contents of and attachments to this E-mail is strictly prohibited and may be unlawful. If you have received this E-mail in error, please notify the sender immediately and permanently delete the original and any copy of this E-mail and any printout.
Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Posted by Tathagata Das <ta...@gmail.com>.
Can you enable the java flag -Dsun.io.serialization.extendedDebugInfo=true
for driver in your driver startup-script? That should give an indication of
the sequence of object references that lead to the StremaingContext being
included in the closure.
TD
On Thu, Aug 7, 2014 at 10:23 AM, Padmanabhan, Mahesh (contractor) <
mahesh.padmanabhan@twc-contractor.com> wrote:
> Thanks TD but unfortunately that did not work.
>
> From: Tathagata Das <ta...@gmail.com>
> Date: Thursday, August 7, 2014 at 10:55 AM
> To: Mahesh Padmanabhan <ma...@twc-contractor.com>
> Cc: "user@spark.apache.org" <us...@spark.apache.org>
> Subject: Re: Spark 1.0.1 NotSerialized exception (a bit of a head
> scratcher)
>
> It could be because of the variable "enableOpStat". Since its defined
> outside foreachRDD, referring to it inside the rdd.foreach is probably
> causing the whole streaming context being included in the closure. Scala
> funkiness. Try this, see if it works.
>
> msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
> *val enable = enableOpStat*
>
> rdd.foreach(item => item match {
> case (key, (oc, dc)) => {
> DebugLogger.log("Original event count = " + oc)
> DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+"
> events")
> if (*enable*) {
> try {
> val statBody = Array(("batchCount", oc.toString()),
> ("duplicateCount", (oc-dc).toString()))
> OperationalStatProducer.produce(statBody)
> } catch { case e: Exception => DebugLogger.report(e) }
> }
> }
> })
> })
>
>
>
> On Thu, Aug 7, 2014 at 9:03 AM, Padmanabhan, Mahesh (contractor) <
> mahesh.padmanabhan@twc-contractor.com> wrote:
>
>> Hello all,
>>
>> I am not sure what is going on – I am getting a NotSerializedException
>> and initially I thought it was due to not registering one of my classes
>> with Kryo but that doesn’t seem to be the case. I am essentially
>> eliminating duplicates in a spark streaming application by using a “window”
>> to eliminate duplicates from the current batch and sending de-dup stats.
>>
>> The strange thing is that my application is not affected by this error at
>> all.
>>
>> I tried registering with Kryo like this (though it was more out of
>> desperation):
>>
>> class MyRegistrator extends KryoRegistrator {
>> override def registerClasses(kryo: Kryo) {
>> kryo.register(classOf[Long])
>> kryo.register(classOf[Tuple2[Int, Tuple2[Long, Long]]])
>> }
>> }
>>
>> Immediately before the exception, I have a piece of code that seems to be
>> executed (at rdd.foreach) though I am not sure if that is the culprit:
>>
>> Here is the code:
>>
>> msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
>> rdd.foreach(item => item match {
>> case (key, (oc, dc)) => {
>> DebugLogger.log("Original event count = " + oc)
>> DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+"
>> events")
>> if (enableOpStat) {
>> try {
>> val statBody = Array(("batchCount", oc.toString()),
>> ("duplicateCount", (oc-dc).toString()))
>> OperationalStatProducer.produce(statBody)
>> } catch { case e: Exception => DebugLogger.report(e) }
>> }
>> }
>> })
>> })
>>
>> Here is the exception:
>>
>> ERROR akka.actor.OneForOneStrategy -
>> org.apache.spark.streaming.StreamingContext
>> java.io.NotSerializableException:
>> org.apache.spark.streaming.StreamingContext
>> 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)
>> 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)
>> at
>> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
>> at
>> java.io.ObjectOutputStream.writeArray(ObjectOutputStream.java:1377)
>> at
>> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1173)
>> 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.defaultWriteObject(ObjectOutputStream.java:440)
>> at
>> org.apache.spark.streaming.DStreamGraph.writeObject(DStreamGraph.scala:168)
>> 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
>> java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:988)
>> at
>> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1495)
>> 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)
>> at
>> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
>> at
>> java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
>> at
>> org.apache.spark.streaming.CheckpointWriter.write(Checkpoint.scala:185)
>> at
>> org.apache.spark.streaming.scheduler.JobGenerator.doCheckpoint(JobGenerator.scala:259)
>> at org.apache.spark.streaming.scheduler.JobGenerator.org
>> $apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:167)
>> at
>> org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$start$1$$anon$1$$anonfun$receive$1.applyOrElse(JobGenerator.scala:76)
>> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
>> at akka.actor.ActorCell.invoke(ActorCell.scala:456)
>> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
>> at akka.dispatch.Mailbox.run(Mailbox.scala:219)
>> at
>> akka.dispatch.ForkJoinExecutorConfigurator$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)
>>
>> ------------------------------
>> This E-mail and any of its attachments may contain Time Warner Cable
>> proprietary information, which is privileged, confidential, or subject to
>> copyright belonging to Time Warner Cable. This E-mail is intended solely
>> for the use of the individual or entity to which it is addressed. If you
>> are not the intended recipient of this E-mail, you are hereby notified that
>> any dissemination, distribution, copying, or action taken in relation to
>> the contents of and attachments to this E-mail is strictly prohibited and
>> may be unlawful. If you have received this E-mail in error, please notify
>> the sender immediately and permanently delete the original and any copy of
>> this E-mail and any printout.
>>
>
>
Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Posted by Padmanabhan,
"Mahesh (contractor)" <ma...@twc-contractor.com>.
Thanks TD but unfortunately that did not work.
From: Tathagata Das <ta...@gmail.com>>
Date: Thursday, August 7, 2014 at 10:55 AM
To: Mahesh Padmanabhan <ma...@twc-contractor.com>>
Cc: "user@spark.apache.org<ma...@spark.apache.org>" <us...@spark.apache.org>>
Subject: Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
It could be because of the variable "enableOpStat". Since its defined outside foreachRDD, referring to it inside the rdd.foreach is probably causing the whole streaming context being included in the closure. Scala funkiness. Try this, see if it works.
msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
val enable = enableOpStat
rdd.foreach(item => item match {
case (key, (oc, dc)) => {
DebugLogger.log("Original event count = " + oc)
DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+" events")
if (enable) {
try {
val statBody = Array(("batchCount", oc.toString()),
("duplicateCount", (oc-dc).toString()))
OperationalStatProducer.produce(statBody)
} catch { case e: Exception => DebugLogger.report(e) }
}
}
})
})
On Thu, Aug 7, 2014 at 9:03 AM, Padmanabhan, Mahesh (contractor) <ma...@twc-contractor.com>> wrote:
Hello all,
I am not sure what is going on – I am getting a NotSerializedException and initially I thought it was due to not registering one of my classes with Kryo but that doesn’t seem to be the case. I am essentially eliminating duplicates in a spark streaming application by using a “window” to eliminate duplicates from the current batch and sending de-dup stats.
The strange thing is that my application is not affected by this error at all.
I tried registering with Kryo like this (though it was more out of desperation):
class MyRegistrator extends KryoRegistrator {
override def registerClasses(kryo: Kryo) {
kryo.register(classOf[Long])
kryo.register(classOf[Tuple2[Int, Tuple2[Long, Long]]])
}
}
Immediately before the exception, I have a piece of code that seems to be executed (at rdd.foreach) though I am not sure if that is the culprit:
Here is the code:
msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
rdd.foreach(item => item match {
case (key, (oc, dc)) => {
DebugLogger.log("Original event count = " + oc)
DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+" events")
if (enableOpStat) {
try {
val statBody = Array(("batchCount", oc.toString()),
("duplicateCount", (oc-dc).toString()))
OperationalStatProducer.produce(statBody)
} catch { case e: Exception => DebugLogger.report(e) }
}
}
})
})
Here is the exception:
ERROR akka.actor.OneForOneStrategy - org.apache.spark.streaming.StreamingContext
java.io.NotSerializableException: org.apache.spark.streaming.StreamingContext
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)
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)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
at java.io.ObjectOutputStream.writeArray(ObjectOutputStream.java:1377)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1173)
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.defaultWriteObject(ObjectOutputStream.java:440)
at org.apache.spark.streaming.DStreamGraph.writeObject(DStreamGraph.scala:168)
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 java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:988)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1495)
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)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
at org.apache.spark.streaming.CheckpointWriter.write(Checkpoint.scala:185)
at org.apache.spark.streaming.scheduler.JobGenerator.doCheckpoint(JobGenerator.scala:259)
at org.apache.spark.streaming.scheduler.JobGenerator.org<http://org.apache.spark.streaming.scheduler.JobGenerator.org>$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:167)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$start$1$$anon$1$$anonfun$receive$1.applyOrElse(JobGenerator.scala:76)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
at akka.actor.ActorCell.invoke(ActorCell.scala:456)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
at akka.dispatch.Mailbox.run(Mailbox.scala:219)
at akka.dispatch.ForkJoinExecutorConfigurator$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)
________________________________
This E-mail and any of its attachments may contain Time Warner Cable proprietary information, which is privileged, confidential, or subject to copyright belonging to Time Warner Cable. This E-mail is intended solely for the use of the individual or entity to which it is addressed. If you are not the intended recipient of this E-mail, you are hereby notified that any dissemination, distribution, copying, or action taken in relation to the contents of and attachments to this E-mail is strictly prohibited and may be unlawful. If you have received this E-mail in error, please notify the sender immediately and permanently delete the original and any copy of this E-mail and any printout.
Re: Spark 1.0.1 NotSerialized exception (a bit of a head scratcher)
Posted by Tathagata Das <ta...@gmail.com>.
It could be because of the variable "enableOpStat". Since its defined
outside foreachRDD, referring to it inside the rdd.foreach is probably
causing the whole streaming context being included in the closure. Scala
funkiness. Try this, see if it works.
msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) => {
*val enable = enableOpStat*
rdd.foreach(item => item match {
case (key, (oc, dc)) => {
DebugLogger.log("Original event count = " + oc)
DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+"
events")
if (*enable*) {
try {
val statBody = Array(("batchCount", oc.toString()),
("duplicateCount", (oc-dc).toString()))
OperationalStatProducer.produce(statBody)
} catch { case e: Exception => DebugLogger.report(e) }
}
}
})
})
On Thu, Aug 7, 2014 at 9:03 AM, Padmanabhan, Mahesh (contractor) <
mahesh.padmanabhan@twc-contractor.com> wrote:
> Hello all,
>
> I am not sure what is going on – I am getting a NotSerializedException
> and initially I thought it was due to not registering one of my classes
> with Kryo but that doesn’t seem to be the case. I am essentially
> eliminating duplicates in a spark streaming application by using a “window”
> to eliminate duplicates from the current batch and sending de-dup stats.
>
> The strange thing is that my application is not affected by this error
> at all.
>
> I tried registering with Kryo like this (though it was more out of
> desperation):
>
> class MyRegistrator extends KryoRegistrator {
> override def registerClasses(kryo: Kryo) {
> kryo.register(classOf[Long])
> kryo.register(classOf[Tuple2[Int, Tuple2[Long, Long]]])
> }
> }
>
> Immediately before the exception, I have a piece of code that seems to
> be executed (at rdd.foreach) though I am not sure if that is the culprit:
>
> Here is the code:
>
> msgCount.join(ddCount).foreachRDD((rdd: RDD[(Int, (Long, Long))]) =>
> {
> rdd.foreach(item => item match {
> case (key, (oc, dc)) => {
> DebugLogger.log("Original event count = " + oc)
> DebugLogger.log("Found "+(oc-dc)+" duplicate(s) in "+oc+"
> events")
> if (enableOpStat) {
> try {
> val statBody = Array(("batchCount", oc.toString()),
> ("duplicateCount", (oc-dc).toString()))
> OperationalStatProducer.produce(statBody)
> } catch { case e: Exception => DebugLogger.report(e) }
> }
> }
> })
> })
>
> Here is the exception:
>
> ERROR akka.actor.OneForOneStrategy -
> org.apache.spark.streaming.StreamingContext
> java.io.NotSerializableException:
> org.apache.spark.streaming.StreamingContext
> 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)
> 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)
> at
> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
> at
> java.io.ObjectOutputStream.writeArray(ObjectOutputStream.java:1377)
> at
> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1173)
> 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.defaultWriteObject(ObjectOutputStream.java:440)
> at
> org.apache.spark.streaming.DStreamGraph.writeObject(DStreamGraph.scala:168)
> 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
> java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:988)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1495)
> 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)
> at
> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
> at
> java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
> at
> org.apache.spark.streaming.CheckpointWriter.write(Checkpoint.scala:185)
> at
> org.apache.spark.streaming.scheduler.JobGenerator.doCheckpoint(JobGenerator.scala:259)
> at org.apache.spark.streaming.scheduler.JobGenerator.org
> $apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:167)
> at
> org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$start$1$$anon$1$$anonfun$receive$1.applyOrElse(JobGenerator.scala:76)
> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
> at akka.actor.ActorCell.invoke(ActorCell.scala:456)
> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
> at akka.dispatch.Mailbox.run(Mailbox.scala:219)
> at
> akka.dispatch.ForkJoinExecutorConfigurator$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)
>
> ------------------------------
> This E-mail and any of its attachments may contain Time Warner Cable
> proprietary information, which is privileged, confidential, or subject to
> copyright belonging to Time Warner Cable. This E-mail is intended solely
> for the use of the individual or entity to which it is addressed. If you
> are not the intended recipient of this E-mail, you are hereby notified that
> any dissemination, distribution, copying, or action taken in relation to
> the contents of and attachments to this E-mail is strictly prohibited and
> may be unlawful. If you have received this E-mail in error, please notify
> the sender immediately and permanently delete the original and any copy of
> this E-mail and any printout.
>