Hi Matthias and Cody, thanks for the answer. This is the code that is
raising the runtime exception:
try{
messages.foreachRDD( rdd =>{
val count = rdd.count()
if (count > 0){
//someMessages should be AmazonRating...
val someMessages = rdd.take(count.toInt)
println("<------>")
println("someMessages is " + someMessages)
someMessages.foreach(println)
println("<------>")
println("<---POSSIBLE SOLUTION--->")
messages
.map { case (_, jsonRating) =>
val jsValue = Json.parse(jsonRating)
AmazonRating.amazonRatingFormat.reads(jsValue) match {
case JsSuccess(rating, _) => rating
case JsError(_) => AmazonRating.empty
}
}
.filter(_ != AmazonRating.empty)
*//this line raises the runtime error, but if i comment it another
different runtime exception happens!*
.foreachRDD(_.foreachPartition(it =>
recommender.predictWithALS(it.toSeq)))
println("<---POSSIBLE SOLUTION--->")
}
}
)
}catch{
case e: IllegalArgumentException => {println("illegal arg.
exception")};
case e: IllegalStateException => {println("illegal state
exception")};
case e: ClassCastException => {println("ClassCastException")};
case e: Exception => {println(" Generic Exception")};
}finally{
println("Finished taking data from kafka topic...")
}
If i comment the line with the second foreachRDD, the next runtime
exception happens within a fresh start, i mean, the kafka producer push
data within the topic:
16/05/25 17:26:12 ERROR JobScheduler: Error running job streaming job
1464189972000 ms.0
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage
0.0 (TID 0, localhost): java.lang.ClassCastException:
org.apache.spark.util.SerializableConfiguration cannot be cast to [B
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:62)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
If i push another json data within the topic, the next exception happens:
16/05/25 17:27:16 INFO DAGScheduler: Job 1 finished: runJob at
KafkaRDD.scala:98, took 0,039689 s
<------>
someMessages is [Lscala.Tuple2;@712ca120
(null,{"userId":"someUserId","productId":"0981531679","rating":9.0})
<------>
<---POSSIBLE SOLUTION--->
16/05/25 17:27:16 INFO JobScheduler: Finished job streaming job
1464190036000 ms.0 from job set of time 1464190036000 ms
16/05/25 17:27:16 INFO JobScheduler: Total delay: 0,063 s for time
1464190036000 ms (execution: 0,055 s)
16/05/25 17:27:16 INFO KafkaRDD: Removing RDD 43 from persistence list
16/05/25 17:27:16 ERROR JobScheduler: Error running job streaming job
1464190036000 ms.0
java.lang.IllegalStateException: Adding new inputs, transformations, and
output operations after starting a context is not supported
at
org.apache.spark.streaming.dstream.DStream.validateAtInit(DStream.scala:222)
at org.apache.spark.streaming.dstream.DStream.<init>(DStream.scala:64)
at
org.apache.spark.streaming.dstream.MappedDStream.<init>(MappedDStream.scala:25)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$map$1.apply(DStream.scala:558)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$map$1.apply(DStream.scala:558)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:714)
at
org.apache.spark.streaming.StreamingContext.withScope(StreamingContext.scala:260)
at org.apache.spark.streaming.dstream.DStream.map(DStream.scala:557)
at
example.spark.AmazonKafkaConnector$$anonfun$main$1.apply(AmazonKafkaConnectorWithMongo.scala:125)
at
example.spark.AmazonKafkaConnector$$anonfun$main$1.apply(AmazonKafkaConnectorWithMongo.scala:114)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
at
org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
at scala.util.Try$.apply(Try.scala:161)
at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
at
org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:224)
at
org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
at
org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at
org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:223)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
If i decomment the second foreachRDD, this exception happens within a fresh
start of the spark streaming process after the kafka producer sends data to
the topic:
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage
0.0 (TID 0, localhost): java.lang.ClassCastException:
org.apache.spark.util.SerializableConfiguration cannot be cast to [B
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:62)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
With a second call of the kafka producer, rises this runtime exception:
<------>
someMessages is [Lscala.Tuple2;@581b83b
(null,{"userId":"someUserId","productId":"0981531679","rating":8.0})
<------>
<---POSSIBLE SOLUTION--->
16/05/25 17:31:30 INFO JobScheduler: Finished job streaming job
1464190290000 ms.0 from job set of time 1464190290000 ms
16/05/25 17:31:30 INFO JobScheduler: Total delay: 0,066 s for time
1464190290000 ms (execution: 0,059 s)
16/05/25 17:31:30 INFO KafkaRDD: Removing RDD 37 from persistence list
16/05/25 17:31:30 ERROR JobScheduler: Error running job streaming job
1464190290000 ms.0
java.lang.IllegalStateException: Adding new inputs, transformations, and
output operations after starting a context is not supported
at
org.apache.spark.streaming.dstream.DStream.validateAtInit(DStream.scala:222)
at org.apache.spark.streaming.dstream.DStream.<init>(DStream.scala:64)
at
org.apache.spark.streaming.dstream.MappedDStream.<init>(MappedDStream.scala:25)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$map$1.apply(DStream.scala:558)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$map$1.apply(DStream.scala:558)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:714)
at
org.apache.spark.streaming.StreamingContext.withScope(StreamingContext.scala:260)
at org.apache.spark.streaming.dstream.DStream.map(DStream.scala:557)
at
example.spark.AmazonKafkaConnector$$anonfun$main$1.apply(AmazonKafkaConnectorWithMongo.scala:125)
at
example.spark.AmazonKafkaConnector$$anonfun$main$1.apply(AmazonKafkaConnectorWithMongo.scala:114)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
at
org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
at scala.util.Try$.apply(Try.scala:161)
at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
at
org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:224)
at
org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
at
org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at
org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:223)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
In your opinion, what changes do i have to do in order to get this code up
and running correctly?
The idea is to run every rating message that i receive from kafka topic in
order to run recommender.predictWithALS method and save results within a
mongo instance. I was thinking that this kind of task should be
asynchronous, wasn't he? if i am right, how should i change the method to
do such that way?
Recommender.predictWithALS method:
def predictWithALS(ratings: Seq[AmazonRating]) = {
// train model
val myRatings = ratings.map(toSparkRating)
val myRatingRDD = sc.parallelize(myRatings)
val startAls = DateTime.now
val model = ALS.train((sparkRatings ++
myRatingRDD).repartition(NumPartitions), 10, 20, 0.01)
val myProducts = myRatings.map(_.product).toSet
val candidates = sc.parallelize((0 until
productDict.size).filterNot(myProducts.contains))
// get ratings of all products not in my history ordered by rating
(higher first) and only keep the first NumRecommendations
val myUserId = userDict.getIndex(MyUsername)
val recommendations = model.predict(candidates.map((myUserId,
_))).collect
val endAls = DateTime.now
val result =
recommendations.sortBy(-_.rating).take(NumRecommendations).map(toAmazonRating)
val alsTime = Seconds.secondsBetween(startAls, endAls).getSeconds
println(s"ALS Time: $alsTime seconds")
result
}
thank you very much
Alonso Isidoro Roman
[image: https://]about.me/alonso.isidoro.roman
<https://about.me/alonso.isidoro.roman?promo=email_sig&utm_source=email_sig&utm_medium=email_sig&utm_campaign=external_links>
2016-05-25 16:54 GMT+02:00 Matthias Niehoff <[email protected]
>:
> Hi,
>
> you register some output actions (in this case foreachRDD) after starting
> the streaming context. StreamingContext.start() has to be called after all!
> output actions.
>
> 2016-05-25 15:59 GMT+02:00 Alonso <[email protected]>:
>
>> Hi, i am receiving this exception when direct spark streaming process
>> tries to pull data from kafka topic:
>>
>> 16/05/25 11:30:30 INFO CheckpointWriter: Checkpoint for time
>> 1464168630000 ms saved to file
>> 'file:/Users/aironman/my-recommendation-spark-engine/checkpoint/checkpoint-1464168630000',
>> took 5928 bytes and 8 ms
>>
>> 16/05/25 11:30:30 INFO Executor: Finished task 0.0 in stage 2.0 (TID 2).
>> 1041 bytes result sent to driver
>> 16/05/25 11:30:30 INFO TaskSetManager: Finished task 0.0 in stage 2.0 (TID
>> 2) in 4 ms on localhost (1/1)
>> 16/05/25 11:30:30 INFO TaskSchedulerImpl: Removed TaskSet 2.0, whose tasks
>> have all completed, from pool
>> 16/05/25 11:30:30 INFO DAGScheduler: ResultStage 2 (runJob at
>> KafkaRDD.scala:98) finished in 0,004 s
>> 16/05/25 11:30:30 INFO DAGScheduler: Job 2 finished: runJob at
>> KafkaRDD.scala:98, took 0,008740 s
>> <------>
>> someMessages is [Lscala.Tuple2;@2641d687
>> (null,{"userId":"someUserId","productId":"0981531679","rating":6.0})
>> <------>
>> <---POSSIBLE SOLUTION--->
>> 16/05/25 11:30:30 INFO JobScheduler: Finished job streaming job
>> 1464168630000 ms.0 from job set of time 1464168630000 ms
>> 16/05/25 11:30:30 INFO KafkaRDD: Removing RDD 105 from persistence list
>> 16/05/25 11:30:30 INFO JobScheduler: Total delay: 0,020 s for time
>> 1464168630000 ms (execution: 0,012 s)
>> 16/05/25 11:30:30 ERROR JobScheduler: Error running job streaming job
>> 1464168630000 ms.0*java.lang.IllegalStateException: Adding new inputs,
>> transformations, and output operations after starting a context is not
>> supported
>> at*
>> org.apache.spark.streaming.dstream.DStream.validateAtInit(DStream.scala:222)
>> at org.apache.spark.streaming.dstream.DStream.<init>(DStream.scala:64)
>> at
>> org.apache.spark.streaming.dstream.MappedDStream.<init>(MappedDStream.scala:25)
>> at
>> org.apache.spark.streaming.dstream.DStream$$anonfun$map$1.apply(DStream.scala:558)
>> at
>> org.apache.spark.streaming.dstream.DStream$$anonfun$map$1.apply(DStream.scala:558)
>> at
>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
>> at
>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
>> at org.apache.spark.SparkContext.withScope(SparkContext.scala:714)
>> at
>> org.apache.spark.streaming.StreamingContext.withScope(StreamingContext.scala:260)
>> at org.apache.spark.streaming.dstream.DStream.map(DStream.scala:557)
>> at
>> example.spark.AmazonKafkaConnector$$anonfun$main$1.apply(AmazonKafkaConnectorWithMongo.scala:125)
>> at
>> example.spark.AmazonKafkaConnector$$anonfun$main$1.apply(AmazonKafkaConnectorWithMongo.scala:114)
>> at
>> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
>> at
>> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
>> at
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
>> at
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
>> at
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
>> at
>> org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
>> at
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
>> at
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
>> at
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
>> at scala.util.Try$.apply(Try.scala:161)
>> at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
>> at
>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:224)
>> at
>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
>> at
>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
>> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
>> at
>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:223)
>> at
>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>> at
>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>> at java.lang.Thread.run(Thread.java:745)
>> 16/05/25 11:30:30 INFO BlockManager: Removing RDD 105
>>
>>
>> This is the code that rises the exception in the spark streaming process:
>>
>> try{
>> messages.foreachRDD( rdd =>{
>> val count = rdd.count()
>> if (count > 0){
>> //someMessages should be AmazonRating...
>> val someMessages = rdd.take(count.toInt)
>> println("<------>")
>> println("someMessages is " + someMessages)
>> someMessages.foreach(println)
>> println("<------>")
>> println("<---POSSIBLE SOLUTION--->")
>> messages
>> .map { case (_, jsonRating) =>
>> val jsValue = Json.parse(jsonRating)
>> AmazonRating.amazonRatingFormat.reads(jsValue) match {
>> case JsSuccess(rating, _) => rating
>> case JsError(_) => AmazonRating.empty
>> }
>> }
>> .filter(_ != AmazonRating.empty)
>> *//I think that this line provokes the runtime exception...*
>> * .foreachRDD(_.foreachPartition(it =>
>> recommender.predictWithALS(it.toSeq)))*
>>
>> println("<---POSSIBLE SOLUTION--->")
>>
>> }
>> }
>> )
>> }catch{
>> case e: IllegalArgumentException => {println("illegal arg.
>> exception")};
>> case e: IllegalStateException => {println("illegal state
>> exception")};
>> case e: ClassCastException => {println("ClassCastException")};
>> case e: Exception => {println(" Generic Exception")};
>> }finally{
>>
>> println("Finished taking data from kafka topic...")
>> }
>>
>> Recommender object:
>>
>> *def predictWithALS(ratings: Seq[AmazonRating])* = {
>> // train model
>> val myRatings = ratings.map(toSparkRating)
>> val myRatingRDD = sc.parallelize(myRatings)
>>
>> val startAls = DateTime.now
>> val model = ALS.train((sparkRatings ++
>> myRatingRDD).repartition(NumPartitions), 10, 20, 0.01)
>>
>> val myProducts = myRatings.map(_.product).toSet
>> val candidates = sc.parallelize((0 until
>> productDict.size).filterNot(myProducts.contains))
>>
>> // get ratings of all products not in my history ordered by rating
>> (higher first) and only keep the first NumRecommendations
>> val myUserId = userDict.getIndex(MyUsername)
>> val recommendations = model.predict(candidates.map((myUserId,
>> _))).collect
>> val endAls = DateTime.now
>> val result =
>> recommendations.sortBy(-_.rating).take(NumRecommendations).map(toAmazonRating)
>> val alsTime = Seconds.secondsBetween(startAls, endAls).getSeconds
>>
>> println(s"ALS Time: $alsTime seconds")
>> result
>> }
>> }
>>
>> And this is the kafka producer that push the json data within the topic:
>>
>> object AmazonProducerExample {
>> def main(args: Array[String]): Unit = {
>>
>> val productId = args(0).toString
>> val userId = args(1).toString
>> val rating = args(2).toDouble
>> val topicName = "amazonRatingsTopic"
>>
>> val producer = Producer[String](topicName)
>>
>> //0981531679 is Scala Puzzlers...
>> //AmazonProductAndRating
>> AmazonPageParser.parse(productId,userId,rating).onSuccess { case
>> amazonRating =>
>> //Is this the correct way? the best performance? possibly not, what
>> about using avro or parquet?
>> producer.send(Json.toJson(amazonRating).toString)
>> //producer.send(amazonRating)
>> println("amazon product with rating sent to kafka cluster..." +
>> amazonRating.toString)
>> System.exit(0)
>> }
>>
>> }
>> }
>>
>>
>> I have written a stack overflow post
>> <http://stackoverflow.com/questions/37303202/about-an-error-accessing-a-field-inside-tuple2>,
>> with more details, please help, i am stuck with this issue and i don't know
>> how to continue.
>>
>> Regards
>>
>> Alonso Isidoro Roman
>> [image: https://]about.me/alonso.isidoro.roman
>>
>> <https://about.me/alonso.isidoro.roman?promo=email_sig&utm_source=email_sig&utm_medium=email_sig&utm_campaign=external_links>
>>
>> ------------------------------
>> View this message in context: about an exception when receiving data
>> from kafka topic using Direct mode of Spark Streaming
>> <http://apache-spark-user-list.1001560.n3.nabble.com/about-an-exception-when-receiving-data-from-kafka-topic-using-Direct-mode-of-Spark-Streaming-tp27022.html>
>> Sent from the Apache Spark User List mailing list archive
>> <http://apache-spark-user-list.1001560.n3.nabble.com/> at Nabble.com.
>>
>
>
>
> --
> Matthias Niehoff | IT-Consultant | Agile Software Factory | Consulting
> codecentric AG | Zeppelinstr 2 | 76185 Karlsruhe | Deutschland
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