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: 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 at Nabble.com.