java.lang.UnsupportedOperationExceptionfieldIndex on a Row without schema is undefined: Exception on...











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The following code is throwing an Exception Caused by: java.lang.UnsupportedOperationException: fieldIndex on a Row without schema is undefined. This is happening when a on a dataframe that has been returned after a groupByKey and flatMap invocation on a dataframe using ExpressionEncoder, groupedByKey and a flatMap is invoked.



Logical flow:
originalDf->groupByKey->flatMap->groupByKey->flatMap->show



   import org.apache.spark.sql.catalyst.encoders.RowEncoder
import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.sql.types.{ IntegerType, StructField, StructType}

import scala.collection.mutable.ListBuffer



object Test {

def main(args: Array[String]): Unit = {

val values = List(List("1", "One") ,List("1", "Two") ,List("2", "Three"),List("2","4")).map(x =>(x(0), x(1)))
val session = SparkSession.builder.config("spark.master", "local").getOrCreate
import session.implicits._
val dataFrame = values.toDF


dataFrame.show()
dataFrame.printSchema()

val newSchema = StructType(dataFrame.schema.fields
++ Array(
StructField("Count", IntegerType, false)
)
)

val expr = RowEncoder.apply(newSchema)

val tranform = dataFrame.groupByKey(row => row.getAs[String]("_1")).flatMapGroups((key, inputItr) => {
val inputSeq = inputItr.toSeq

val length = inputSeq.size
var listBuff = new ListBuffer[Row]()
var counter : Int= 0
for(i <- 0 until(length))
{
counter+=1

}

for(i <- 0 until length ) {
var x = inputSeq(i)
listBuff += Row.fromSeq(x.toSeq ++ Array[Int](counter))
}
listBuff.iterator
})(expr)

tranform.show

val newSchema1 = StructType(tranform.schema.fields
++ Array(
StructField("Count1", IntegerType, false)
)
)
val expr1 = RowEncoder.apply(newSchema1)
val tranform2 = tranform.groupByKey(row => row.getAs[String]("_1")).flatMapGroups((key, inputItr) => {
val inputSeq = inputItr.toSeq

val length = inputSeq.size
var listBuff = new ListBuffer[Row]()
var counter : Int= 0
for(i <- 0 until(length))
{
counter+=1

}

for(i <- 0 until length ) {
var x = inputSeq(i)
listBuff += Row.fromSeq(x.toSeq ++ Array[Int](counter))
}
listBuff.iterator
})(expr1)

tranform2.show
}
}


Following is the stacktrace



18/11/21 19:39:03 WARN TaskSetManager: Lost task 144.0 in stage 11.0 (TID 400, localhost, executor driver): java.lang.UnsupportedOperationException: fieldIndex on a Row without schema is undefined.
at org.apache.spark.sql.Row$class.fieldIndex(Row.scala:342)
at org.apache.spark.sql.catalyst.expressions.GenericRow.fieldIndex(rows.scala:166)
at org.apache.spark.sql.Row$class.getAs(Row.scala:333)
at org.apache.spark.sql.catalyst.expressions.GenericRow.getAs(rows.scala:166)
at com.quantuting.sparkutils.main.Test$$anonfun$4.apply(Test.scala:59)
at com.quantuting.sparkutils.main.Test$$anonfun$4.apply(Test.scala:59)
at org.apache.spark.sql.execution.AppendColumnsWithObjectExec$$anonfun$9$$anonfun$apply$3.apply(objects.scala:300)
at org.apache.spark.sql.execution.AppendColumnsWithObjectExec$$anonfun$9$$anonfun$apply$3.apply(objects.scala:298)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:149)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
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)


How to fix this code?










share|improve this question
























  • Please edit the question and include the traceback. Also consider adding a Minimal, Complete, and Verifiable example (you can check How to make good reproducible Apache Spark Dataframe examples) for an inspiration.
    – user6910411
    Nov 20 at 16:06






  • 1




    @user6910411: added the stacktrace. Will be difficult to put the reproducible code, as the flow is integrated in a framework over multiple libraries. But can answer whatever details would be required
    – Bay Max
    Nov 20 at 16:28










  • Can you post the case class definitions for the two datasets? Did you add the naturalRank field to the second?
    – sramalingam24
    Nov 20 at 18:37










  • Also you can just do row => row.ticker if the schema is specified correctly
    – sramalingam24
    Nov 20 at 19:23






  • 1




    @user6910411 I have edited the question and put a similar code that throws the given Exception and has the same semantic flow.
    – Bay Max
    Nov 21 at 14:18















up vote
1
down vote

favorite












The following code is throwing an Exception Caused by: java.lang.UnsupportedOperationException: fieldIndex on a Row without schema is undefined. This is happening when a on a dataframe that has been returned after a groupByKey and flatMap invocation on a dataframe using ExpressionEncoder, groupedByKey and a flatMap is invoked.



Logical flow:
originalDf->groupByKey->flatMap->groupByKey->flatMap->show



   import org.apache.spark.sql.catalyst.encoders.RowEncoder
import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.sql.types.{ IntegerType, StructField, StructType}

import scala.collection.mutable.ListBuffer



object Test {

def main(args: Array[String]): Unit = {

val values = List(List("1", "One") ,List("1", "Two") ,List("2", "Three"),List("2","4")).map(x =>(x(0), x(1)))
val session = SparkSession.builder.config("spark.master", "local").getOrCreate
import session.implicits._
val dataFrame = values.toDF


dataFrame.show()
dataFrame.printSchema()

val newSchema = StructType(dataFrame.schema.fields
++ Array(
StructField("Count", IntegerType, false)
)
)

val expr = RowEncoder.apply(newSchema)

val tranform = dataFrame.groupByKey(row => row.getAs[String]("_1")).flatMapGroups((key, inputItr) => {
val inputSeq = inputItr.toSeq

val length = inputSeq.size
var listBuff = new ListBuffer[Row]()
var counter : Int= 0
for(i <- 0 until(length))
{
counter+=1

}

for(i <- 0 until length ) {
var x = inputSeq(i)
listBuff += Row.fromSeq(x.toSeq ++ Array[Int](counter))
}
listBuff.iterator
})(expr)

tranform.show

val newSchema1 = StructType(tranform.schema.fields
++ Array(
StructField("Count1", IntegerType, false)
)
)
val expr1 = RowEncoder.apply(newSchema1)
val tranform2 = tranform.groupByKey(row => row.getAs[String]("_1")).flatMapGroups((key, inputItr) => {
val inputSeq = inputItr.toSeq

val length = inputSeq.size
var listBuff = new ListBuffer[Row]()
var counter : Int= 0
for(i <- 0 until(length))
{
counter+=1

}

for(i <- 0 until length ) {
var x = inputSeq(i)
listBuff += Row.fromSeq(x.toSeq ++ Array[Int](counter))
}
listBuff.iterator
})(expr1)

tranform2.show
}
}


Following is the stacktrace



18/11/21 19:39:03 WARN TaskSetManager: Lost task 144.0 in stage 11.0 (TID 400, localhost, executor driver): java.lang.UnsupportedOperationException: fieldIndex on a Row without schema is undefined.
at org.apache.spark.sql.Row$class.fieldIndex(Row.scala:342)
at org.apache.spark.sql.catalyst.expressions.GenericRow.fieldIndex(rows.scala:166)
at org.apache.spark.sql.Row$class.getAs(Row.scala:333)
at org.apache.spark.sql.catalyst.expressions.GenericRow.getAs(rows.scala:166)
at com.quantuting.sparkutils.main.Test$$anonfun$4.apply(Test.scala:59)
at com.quantuting.sparkutils.main.Test$$anonfun$4.apply(Test.scala:59)
at org.apache.spark.sql.execution.AppendColumnsWithObjectExec$$anonfun$9$$anonfun$apply$3.apply(objects.scala:300)
at org.apache.spark.sql.execution.AppendColumnsWithObjectExec$$anonfun$9$$anonfun$apply$3.apply(objects.scala:298)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:149)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
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)


How to fix this code?










share|improve this question
























  • Please edit the question and include the traceback. Also consider adding a Minimal, Complete, and Verifiable example (you can check How to make good reproducible Apache Spark Dataframe examples) for an inspiration.
    – user6910411
    Nov 20 at 16:06






  • 1




    @user6910411: added the stacktrace. Will be difficult to put the reproducible code, as the flow is integrated in a framework over multiple libraries. But can answer whatever details would be required
    – Bay Max
    Nov 20 at 16:28










  • Can you post the case class definitions for the two datasets? Did you add the naturalRank field to the second?
    – sramalingam24
    Nov 20 at 18:37










  • Also you can just do row => row.ticker if the schema is specified correctly
    – sramalingam24
    Nov 20 at 19:23






  • 1




    @user6910411 I have edited the question and put a similar code that throws the given Exception and has the same semantic flow.
    – Bay Max
    Nov 21 at 14:18













up vote
1
down vote

favorite









up vote
1
down vote

favorite











The following code is throwing an Exception Caused by: java.lang.UnsupportedOperationException: fieldIndex on a Row without schema is undefined. This is happening when a on a dataframe that has been returned after a groupByKey and flatMap invocation on a dataframe using ExpressionEncoder, groupedByKey and a flatMap is invoked.



Logical flow:
originalDf->groupByKey->flatMap->groupByKey->flatMap->show



   import org.apache.spark.sql.catalyst.encoders.RowEncoder
import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.sql.types.{ IntegerType, StructField, StructType}

import scala.collection.mutable.ListBuffer



object Test {

def main(args: Array[String]): Unit = {

val values = List(List("1", "One") ,List("1", "Two") ,List("2", "Three"),List("2","4")).map(x =>(x(0), x(1)))
val session = SparkSession.builder.config("spark.master", "local").getOrCreate
import session.implicits._
val dataFrame = values.toDF


dataFrame.show()
dataFrame.printSchema()

val newSchema = StructType(dataFrame.schema.fields
++ Array(
StructField("Count", IntegerType, false)
)
)

val expr = RowEncoder.apply(newSchema)

val tranform = dataFrame.groupByKey(row => row.getAs[String]("_1")).flatMapGroups((key, inputItr) => {
val inputSeq = inputItr.toSeq

val length = inputSeq.size
var listBuff = new ListBuffer[Row]()
var counter : Int= 0
for(i <- 0 until(length))
{
counter+=1

}

for(i <- 0 until length ) {
var x = inputSeq(i)
listBuff += Row.fromSeq(x.toSeq ++ Array[Int](counter))
}
listBuff.iterator
})(expr)

tranform.show

val newSchema1 = StructType(tranform.schema.fields
++ Array(
StructField("Count1", IntegerType, false)
)
)
val expr1 = RowEncoder.apply(newSchema1)
val tranform2 = tranform.groupByKey(row => row.getAs[String]("_1")).flatMapGroups((key, inputItr) => {
val inputSeq = inputItr.toSeq

val length = inputSeq.size
var listBuff = new ListBuffer[Row]()
var counter : Int= 0
for(i <- 0 until(length))
{
counter+=1

}

for(i <- 0 until length ) {
var x = inputSeq(i)
listBuff += Row.fromSeq(x.toSeq ++ Array[Int](counter))
}
listBuff.iterator
})(expr1)

tranform2.show
}
}


Following is the stacktrace



18/11/21 19:39:03 WARN TaskSetManager: Lost task 144.0 in stage 11.0 (TID 400, localhost, executor driver): java.lang.UnsupportedOperationException: fieldIndex on a Row without schema is undefined.
at org.apache.spark.sql.Row$class.fieldIndex(Row.scala:342)
at org.apache.spark.sql.catalyst.expressions.GenericRow.fieldIndex(rows.scala:166)
at org.apache.spark.sql.Row$class.getAs(Row.scala:333)
at org.apache.spark.sql.catalyst.expressions.GenericRow.getAs(rows.scala:166)
at com.quantuting.sparkutils.main.Test$$anonfun$4.apply(Test.scala:59)
at com.quantuting.sparkutils.main.Test$$anonfun$4.apply(Test.scala:59)
at org.apache.spark.sql.execution.AppendColumnsWithObjectExec$$anonfun$9$$anonfun$apply$3.apply(objects.scala:300)
at org.apache.spark.sql.execution.AppendColumnsWithObjectExec$$anonfun$9$$anonfun$apply$3.apply(objects.scala:298)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:149)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
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)


How to fix this code?










share|improve this question















The following code is throwing an Exception Caused by: java.lang.UnsupportedOperationException: fieldIndex on a Row without schema is undefined. This is happening when a on a dataframe that has been returned after a groupByKey and flatMap invocation on a dataframe using ExpressionEncoder, groupedByKey and a flatMap is invoked.



Logical flow:
originalDf->groupByKey->flatMap->groupByKey->flatMap->show



   import org.apache.spark.sql.catalyst.encoders.RowEncoder
import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.sql.types.{ IntegerType, StructField, StructType}

import scala.collection.mutable.ListBuffer



object Test {

def main(args: Array[String]): Unit = {

val values = List(List("1", "One") ,List("1", "Two") ,List("2", "Three"),List("2","4")).map(x =>(x(0), x(1)))
val session = SparkSession.builder.config("spark.master", "local").getOrCreate
import session.implicits._
val dataFrame = values.toDF


dataFrame.show()
dataFrame.printSchema()

val newSchema = StructType(dataFrame.schema.fields
++ Array(
StructField("Count", IntegerType, false)
)
)

val expr = RowEncoder.apply(newSchema)

val tranform = dataFrame.groupByKey(row => row.getAs[String]("_1")).flatMapGroups((key, inputItr) => {
val inputSeq = inputItr.toSeq

val length = inputSeq.size
var listBuff = new ListBuffer[Row]()
var counter : Int= 0
for(i <- 0 until(length))
{
counter+=1

}

for(i <- 0 until length ) {
var x = inputSeq(i)
listBuff += Row.fromSeq(x.toSeq ++ Array[Int](counter))
}
listBuff.iterator
})(expr)

tranform.show

val newSchema1 = StructType(tranform.schema.fields
++ Array(
StructField("Count1", IntegerType, false)
)
)
val expr1 = RowEncoder.apply(newSchema1)
val tranform2 = tranform.groupByKey(row => row.getAs[String]("_1")).flatMapGroups((key, inputItr) => {
val inputSeq = inputItr.toSeq

val length = inputSeq.size
var listBuff = new ListBuffer[Row]()
var counter : Int= 0
for(i <- 0 until(length))
{
counter+=1

}

for(i <- 0 until length ) {
var x = inputSeq(i)
listBuff += Row.fromSeq(x.toSeq ++ Array[Int](counter))
}
listBuff.iterator
})(expr1)

tranform2.show
}
}


Following is the stacktrace



18/11/21 19:39:03 WARN TaskSetManager: Lost task 144.0 in stage 11.0 (TID 400, localhost, executor driver): java.lang.UnsupportedOperationException: fieldIndex on a Row without schema is undefined.
at org.apache.spark.sql.Row$class.fieldIndex(Row.scala:342)
at org.apache.spark.sql.catalyst.expressions.GenericRow.fieldIndex(rows.scala:166)
at org.apache.spark.sql.Row$class.getAs(Row.scala:333)
at org.apache.spark.sql.catalyst.expressions.GenericRow.getAs(rows.scala:166)
at com.quantuting.sparkutils.main.Test$$anonfun$4.apply(Test.scala:59)
at com.quantuting.sparkutils.main.Test$$anonfun$4.apply(Test.scala:59)
at org.apache.spark.sql.execution.AppendColumnsWithObjectExec$$anonfun$9$$anonfun$apply$3.apply(objects.scala:300)
at org.apache.spark.sql.execution.AppendColumnsWithObjectExec$$anonfun$9$$anonfun$apply$3.apply(objects.scala:298)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:149)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
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)


How to fix this code?







scala apache-spark






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 21 at 14:24

























asked Nov 20 at 16:03









Bay Max

1158




1158












  • Please edit the question and include the traceback. Also consider adding a Minimal, Complete, and Verifiable example (you can check How to make good reproducible Apache Spark Dataframe examples) for an inspiration.
    – user6910411
    Nov 20 at 16:06






  • 1




    @user6910411: added the stacktrace. Will be difficult to put the reproducible code, as the flow is integrated in a framework over multiple libraries. But can answer whatever details would be required
    – Bay Max
    Nov 20 at 16:28










  • Can you post the case class definitions for the two datasets? Did you add the naturalRank field to the second?
    – sramalingam24
    Nov 20 at 18:37










  • Also you can just do row => row.ticker if the schema is specified correctly
    – sramalingam24
    Nov 20 at 19:23






  • 1




    @user6910411 I have edited the question and put a similar code that throws the given Exception and has the same semantic flow.
    – Bay Max
    Nov 21 at 14:18


















  • Please edit the question and include the traceback. Also consider adding a Minimal, Complete, and Verifiable example (you can check How to make good reproducible Apache Spark Dataframe examples) for an inspiration.
    – user6910411
    Nov 20 at 16:06






  • 1




    @user6910411: added the stacktrace. Will be difficult to put the reproducible code, as the flow is integrated in a framework over multiple libraries. But can answer whatever details would be required
    – Bay Max
    Nov 20 at 16:28










  • Can you post the case class definitions for the two datasets? Did you add the naturalRank field to the second?
    – sramalingam24
    Nov 20 at 18:37










  • Also you can just do row => row.ticker if the schema is specified correctly
    – sramalingam24
    Nov 20 at 19:23






  • 1




    @user6910411 I have edited the question and put a similar code that throws the given Exception and has the same semantic flow.
    – Bay Max
    Nov 21 at 14:18
















Please edit the question and include the traceback. Also consider adding a Minimal, Complete, and Verifiable example (you can check How to make good reproducible Apache Spark Dataframe examples) for an inspiration.
– user6910411
Nov 20 at 16:06




Please edit the question and include the traceback. Also consider adding a Minimal, Complete, and Verifiable example (you can check How to make good reproducible Apache Spark Dataframe examples) for an inspiration.
– user6910411
Nov 20 at 16:06




1




1




@user6910411: added the stacktrace. Will be difficult to put the reproducible code, as the flow is integrated in a framework over multiple libraries. But can answer whatever details would be required
– Bay Max
Nov 20 at 16:28




@user6910411: added the stacktrace. Will be difficult to put the reproducible code, as the flow is integrated in a framework over multiple libraries. But can answer whatever details would be required
– Bay Max
Nov 20 at 16:28












Can you post the case class definitions for the two datasets? Did you add the naturalRank field to the second?
– sramalingam24
Nov 20 at 18:37




Can you post the case class definitions for the two datasets? Did you add the naturalRank field to the second?
– sramalingam24
Nov 20 at 18:37












Also you can just do row => row.ticker if the schema is specified correctly
– sramalingam24
Nov 20 at 19:23




Also you can just do row => row.ticker if the schema is specified correctly
– sramalingam24
Nov 20 at 19:23




1




1




@user6910411 I have edited the question and put a similar code that throws the given Exception and has the same semantic flow.
– Bay Max
Nov 21 at 14:18




@user6910411 I have edited the question and put a similar code that throws the given Exception and has the same semantic flow.
– Bay Max
Nov 21 at 14:18

















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