Use a method inside a UDF function Spark Scala - scala

I want to use a method located in another class inside a user-designed function but it's not working.
I have a method:
def traitementDataFrameEleve(sc:SparkSession, dfRedis:DataFrame, domainMail:String, dir:String):Boolean ={
def loginUDF = udf((sn: String, givenName:String) => {
LoginClass.GenerateloginPersone(sn,givenName,dfr)
})
dfEleve.withColumn("ENTPersonLogin",loginUDF(dfEleve("sn"),dfEleve("givenName")))
}
LoginClass is a class that contains the GenerateloginPersone method.
Output error :
org.apache.spark.SparkException: Failed to execute user defined function(anonfun$loginUDF$1$1: (string, string) => string)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:377)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:231)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:225)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:826)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:826)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
at java.lang.Thread.run(Unknown Source)
Caused by: java.lang.NullPointerException
at org.apache.spark.sql.Dataset.schema(Dataset.scala:410)
at org.apache.spark.sql.Dataset.printSchema(Dataset.scala:419)
at IntegrationDonneesENTLea_V1_AcBordeaux.LoginClass$.GenerateloginPersone(LoginClass.scala:16)
at IntegrationDonneesENTLea_V1_AcBordeaux.Eleve$$anonfun$loginUDF$1$1.apply(Eleve.scala:25)
at IntegrationDonneesENTLea_V1_AcBordeaux.Eleve$$anonfun$loginUDF$1$1.apply(Eleve.scala:23)
... 16 more
Thank you.

It is not allowed to access:
distributed data structures (like Dataset or RDD).
SparkConext / SparkSession
from Spark task (transformation, udf application). This is why you get a NPE.

Related

Job aborted due to stage failure on Magellan Spark

I am trying to make work the Magellan Package on Spark.
I am following the tutorial by harsha2010.
The repository is available at: https://github.com/harsha2010/magellan
I run this code on Spylon-kernel:
import magellan.Point
import magellan.Polygon
import spark.implicits._
case class PolygonRecord(polygon: Polygon)
val ring = Array(Point(1.0, 1.0), Point(1.0, -1.0),
Point(-1.0, -1.0), Point(-1.0, 1.0),
Point(1.0, 1.0))
val polygons = sc.parallelize(Seq(
PolygonRecord(Polygon(Array(0), ring))
)).toDF()
polygons.show()
I get the following error which is difficult to debug:
org.apache.spark.SparkException: Job aborted due to stage failure: Task 6 in stage 16.0 failed 1 times, most recent failure: Lost task 6.0 in stage 16.0 (TID 43, localhost, executor driver): java.lang.ClassCastException: PolygonRecord cannot be cast to PolygonRecord
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1517)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1505)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1504)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1504)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1732)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1687)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1676)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2029)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2050)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2069)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:336)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:2861)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2150)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2150)
at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2842)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2841)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2150)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2363)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:241)
at org.apache.spark.sql.Dataset.show(Dataset.scala:637)
at org.apache.spark.sql.Dataset.show(Dataset.scala:596)
at org.apache.spark.sql.Dataset.show(Dataset.scala:605)
... 31 elided
Caused by: java.lang.ClassCastException: PolygonRecord cannot be cast to PolygonRecord
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
Normally, the output should be:
+--------------------+
| polygon|
+--------------------+
|Polygon(5, Vector...|
+--------------------+
Could you please help me fix the issue?
Thanks to you all!

Getting error while writing RDD data to avro file

Below is my RDD
val title = movies.map(f=>(f.toString().split("::")(0).replaceAll("\\[", "")trim(),f.toString().split("::")(1)))
//movieID,MovieName
println(title.toDebugString)
val view = ratings.map(f=>(f.toString().split("::")(1).trim(),1)).reduceByKey(_+_).sortBy(f=>f._2, false).take(10).toSeq
val viewRDD = sc.parallelize(view)
val join = title.join(viewRDD).map(f=>(f._2._1,f._2._2))
val dataRdd = join.map(row=>(row._1,row._2))
I am trying to save the dataRdd in avro format for which I am using saveAsNewAPIHadoopFile method.
Below is how I am saving the RDD:
dataRdd.saveAsNewAPIHadoopFile("E:\\ml-1m\\ml-1m\\movieAvro2",classOf[AvroKey[_]], classOf[AvroValue[_]], classOf[AvroKeyValueOutputFormat[_, _]], sc.hadoopConfiguration)
When I run the program I am getting below error:
java.lang.IllegalStateException: Writer schema for output key was not set. Use AvroJob.setOutputKeySchema().
at org.apache.avro.hadoop.io.AvroDatumConverterFactory.create(AvroDatumConverterFactory.java:94)
at org.apache.avro.mapreduce.AvroKeyValueOutputFormat.getRecordWriter(AvroKeyValueOutputFormat.java:55)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1$$anonfun$12.apply(PairRDDFunctions.scala:1119)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1$$anonfun$12.apply(PairRDDFunctions.scala:1102)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
at java.lang.Thread.run(Unknown Source)
18/10/18 11:27:46 WARN TaskSetManager: Lost task 0.0 in stage 5.0 (TID 5, localhost, executor driver): java.lang.IllegalStateException: Writer schema for output key was not set. Use AvroJob.setOutputKeySchema().
at org.apache.avro.hadoop.io.AvroDatumConverterFactory.create(AvroDatumConverterFactory.java:94)
at org.apache.avro.mapreduce.AvroKeyValueOutputFormat.getRecordWriter(AvroKeyValueOutputFormat.java:55)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1$$anonfun$12.apply(PairRDDFunctions.scala:1119)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1$$anonfun$12.apply(PairRDDFunctions.scala:1102)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
at java.lang.Thread.run(Unknown Source)

Rdd verify Date format and remove row if date format is incorrect Scala Spark

Sample rddDate: [2016-08-01,"pm",5,"ri"]
There are some rows with incorrect format of a date in this RDD, so I can't count rows in RDD. That throws IndexOutOfBound exception.
Used Date format is java.sql.Date
The expected Date Format is for every row in RDD: "yyyy-mm-dd"
2016-08-01
To verify date format in RDD, below code is implemented,
val rddVerified: RDD[(Date, String, Long, String)] = rddDate.map{
a => {
val fmt = DateTimeFormat forPattern "yyyy-mm-dd"
val input = a._1.toString
try {
val output = fmt parseDateTime input
} catch {
case e: Exception => {
val v1 = new java.util.Date("2016-08-01")
val v2 = new Date(a1.getTime)
val ed:(Date,String, Int, String) = (v2, "p1",2,"r1")
Some(ed) // This gives compile time error
}
} finally {
Some(a._1, a._2,a._3,a._4)
}
}
}
I am not able to handle the exception in catch section. I want to either remove that row from the RDD or correct the format of date in that row.
I want to get returned RDD in this format:
RDD[(Date, String, Long, String)]
Thanks.
UPDATE
Exception when counting Dataframe:
COUNT : :
[error] o.a.s.e.Executor - Exception in task 0.0 in stage 7.0 (TID 7)
java.lang.IndexOutOfBoundsException: 1
at scala.collection.LinearSeqOptimized$class.apply(LinearSeqOptimized.scala:65)
at scala.collection.immutable.List.apply(List.scala:84)
at controllers.Spark$$anonfun$5.apply(Spark.scala:78)
at controllers.Spark$$anonfun$5.apply(Spark.scala:78)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:464)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithoutKey$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
[warn] o.a.s.s.TaskSetManager - Lost task 0.0 in stage 7.0 (TID 7, localhost, executor driver): java.lang.IndexOutOfBoundsException: 1
at scala.collection.LinearSeqOptimized$class.apply(LinearSeqOptimized.scala:65)
at scala.collection.immutable.List.apply(List.scala:84)
at controllers.Spark$$anonfun$5.apply(Spark.scala:78)
at controllers.Spark$$anonfun$5.apply(Spark.scala:78)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:464)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithoutKey$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)
[error] o.a.s.s.TaskSetManager - Task 0 in stage 7.0 failed 1 times; aborting job
[warn] o.a.s.s.BlockManager - Putting block rdd_1_1 failed due to an exception
[warn] o.a.s.s.BlockManager - Block rdd_1_1 could not be removed as it was not found on disk or in memory
[warn] o.a.s.s.BlockManager - Putting block rdd_1_2 failed due to an exception
[warn] o.a.s.s.BlockManager - Block rdd_1_2 could not be removed as it was not found on disk or in memory
[warn] o.a.s.s.TaskSetManager - Lost task 1.0 in stage 7.0 (TID 8, localhost, executor driver): TaskKilled (unknown reason)
[warn] o.a.s.s.TaskSetManager - Lost task 2.0 in stage 7.0 (TID 9, localhost, executor driver): TaskKilled (unknown reason)
[error] application -
stage failure: Task 0 in stage 7.0 failed 1 times, most recent failure: Lost task 0.0 in stage 7.0 (TID 7, localhost, executor driver): java.lang.IndexOutOfBoundsException: 1
at scala.collection.LinearSeqOptimized$class.apply(LinearSeqOptimized.scala:65)
at scala.collection.immutable.List.apply(List.scala:84)
at controllers.Spark$$anonfun$5.apply(Spark.scala:78)
at controllers.Spark$$anonfun$5.apply(Spark.scala:78)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:464)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithoutKey$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:]]
at play.api.http.HttpErrorHandlerExceptions$.throwableToUsefulException(HttpErrorHandler.scala:293)
at play.api.http.DefaultHttpErrorHandler.onServerError(HttpErrorHandler.scala:220)
at play.api.GlobalSettings$class.onError(GlobalSettings.scala:160)
at play.api.DefaultGlobal$.onError(GlobalSettings.scala:188)
at play.api.http.GlobalSettingsHttpErrorHandler.onServerError(HttpErrorHandler.scala:100)
at play.core.server.netty.PlayRequestHandler$$anonfun$2$$anonfun$apply$1.applyOrElse(PlayRequestHandler.scala:100)
at play.core.server.netty.PlayRequestHandler$$anonfun$2$$anonfun$apply$1.applyOrElse(PlayRequestHandler.scala:99)
at scala.concurrent.Future$$anonfun$recoverWith$1.apply(Future.scala:346)
at scala.concurrent.Future$$anonfun$recoverWith$1.apply(Future.scala:345)
at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:36)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 7.0 failed 1 times, most recent failure: Lost task 0.0 in stage 7.0 (TID 7, localhost, executor driver): java.lang.IndexOutOfBoundsException: 1
at scala.collection.LinearSeqOptimized$class.apply(LinearSeqOptimized.scala:65)
at scala.collection.immutable.List.apply(List.scala:84)
at controllers.Spark$$anonfun$5.apply(Spark.scala:78)
at controllers.Spark$$anonfun$5.apply(Spark.scala:78)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:464)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithoutKey$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
Caused by: java.lang.IndexOutOfBoundsException: 1
at scala.collection.LinearSeqOptimized$class.apply(LinearSeqOptimized.scala:65)
at scala.collection.immutable.List.apply(List.scala:84)
at controllers.Spark$$anonfun$5.apply(Spark.scala:78)
at controllers.Spark$$anonfun$5.apply(Spark.scala:78)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:464)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithoutKey$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
I would recommend using Dataset. It is not only faster and simpler, but also future friendly
import org.apache.spark.sql.functions.to_date
val spark: SparkSession = ???
import spark.implicits._
rddDate.toDF.withColumn("_1", to_date($"_1"))
.na.drop(Seq("_1))
.as[(java.sql.Date, String, Long, String)]
Edit
But the problem is some else in your code.
Caused by: java.lang.IndexOutOfBoundsException: 1
Suggest that you make some mistake, probably in the parsing logic. You have to step back to the place where you call apply add exception handling there.

Postgresql UUID[] to Cassandra: Conversion Error

It gives me java.lang.ClassCastException: [Ljava.util.UUID; cannot be cast to [Ljava.lang.String;
My job reads data from a PostgreSQL table that contains columns of user_ids uuid[] type, so that I'm getting the error above when I'm trying to save data on Cassandra.
However, the creation of this same table on Cassandra works fine! user_ids list<text>.
I can't change the type on the source table, because I'm reading data from a legacy system.
I've been looking at point printed on log, on class org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils.scala
case StringType =>
(array: Object) =>
array.asInstanceOf[Array[java.lang.String]]
.map(UTF8String.fromString)```
Stacktrace
Caused by: java.lang.ClassCastException: [Ljava.util.UUID; cannot be cast to [Ljava.lang.String;
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$14.apply(JdbcUtils.scala:443)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$14.apply(JdbcUtils.scala:442)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$13$$anonfun$18.apply(JdbcUtils.scala:472)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$13$$anonfun$18.apply(JdbcUtils.scala:472)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$nullSafeConvert(JdbcUtils.scala:482)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$13.apply(JdbcUtils.scala:470)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$13.apply(JdbcUtils.scala:469)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:330)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:312)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1.hasNext(InMemoryRelation.scala:133)
at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:215)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1038)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1029)
at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:969)
at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1029)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:760)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:285)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
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:748)
Please look at datatype support in CQL here.
You should have created list<uuid> instead of list<text> in your table schema. The Java driver can't handle this conversion automatically.
If you want to use text instead, please cast it to String in your application before sending it to driver.
the value you have stored user_id in database is of type UUID ,the same type in java is of type java.util.UUID .
so instead of using java.lang.String,you should use java.util.UUID array or list and before storing in cassandra uuid_obj.toString() to store in Cassandra.

Program running on Spark 2.1.1 but not on 2.0.2

I have a very simple clustering program I developed on IntelliJ Idea with Spark 2.1.1. However when I launch the .jar with spark 2.0.2 on my cluster it gives the following error :
17/09/25 14:23:11 ERROR Executor: Exception in task 2.0 in stage 3.0 (TID 7)
org.apache.spark.SparkException: Failed to execute user defined function($anonfun$2: (vector) => vector)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1.next(InMemoryRelation.scala:106)
at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1.next(InMemoryRelation.scala:98)
at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:214)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:935)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:926)
at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:866)
at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:926)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:670)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:330)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:281)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:89)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
at java.lang.Thread.run(Unknown Source)
Caused by: java.lang.IllegalArgumentException: Do not support vector type class org.apache.spark.mllib.linalg.SparseVector
at org.apache.spark.mllib.feature.StandardScalerModel.transform(StandardScaler.scala:160)
at org.apache.spark.ml.feature.StandardScalerModel$$anonfun$2.apply(StandardScaler.scala:167)
at org.apache.spark.ml.feature.StandardScalerModel$$anonfun$2.apply(StandardScaler.scala:167)
... 37 more
Here is my code :
def main(args:Array[String]): Unit = {
val spark = SparkSession.builder.config("spark.eventLog.enabled", "true").config("spark.eventLog.dir", "").appName("S1").getOrCreate()
val df = spark.read.format("csv").option("header", true).csv("petitexport.csv")
var dff = df.drop("numeroCarte")
dff.cache()
for(field <- dff.schema.fields)
{
dff = dff.withColumn(field.name, dff(field.name).cast(DoubleType))
}
val featureCols = Array("NB de trx","NB de trx RD","Somme RD","Somme refus","NB Pays visite","NB trx nocturnes")
val assembler = new VectorAssembler().setInputCols(featureCols).setOutputCol("features")
val dff2 = assembler.transform(dff)
val scaler = new StandardScaler().setWithStd(true).setWithMean(true).setInputCol("features").setOutputCol("scaledFeatures")
val scalerModel = scaler.fit(dff2)
val scaledData2 = scalerModel.transform(dff2)
scaledData2.cache
val kmeans = new KMeans().setK(5).setMaxIter(10).setTol(0.001).setSeed(200).setFeaturesCol("scaledFeatures")
val model = kmeans.fit(scaledData2)
val predictions = model.transform(scaledData2)
predictions.show
Is it possible to fix this to make it work on Spark 2.0.2 ? I understand it is about SparseVector but I don't really see a solution.