[
https://issues.apache.org/jira/browse/SPARK-26436?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Manish updated SPARK-26436:
---------------------------
Description:
There seems to be a bug on groupByKey api for cases when it (groupByKey) is
applied on a DataSet resulting from a former groupByKey and flatMapGroups
invocation.
In such cases groupByKey throws the following exception:
java.lang.UnsupportedException: fieldIndex on a Row without schema is undefined.
Although the dataframe has a valid schema and a groupBy("key") or
repartition($"key") api calls on the same Dataframe and key succeed.
Following is the code that reproduces the scenario:
{code}
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
}
}
Test.main(null){code}
was:
There seems to be a bug on groupByKey api for cases when it (groupByKey) is
applied on a DataSet resulting from a former groupByKey and flatMapGroups
invocation.
In such cases groupByKey throws the following exception:
java.lang.UnsupportedException: fieldIndex on a Row without schema is undefined.
Although the dataframe has a valid schema and a groupBy("key") or
repartition($"key") api calls on the same Dataframe and key succeed.
Following is the code that reproduces the scenario:
{code:java}
{{ 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 } }}}
{code}
> Dataframe as a result of GroupByKey and flatMapGroups operation throws
> java.lang.UnsupportedException when groupByKey is applied on it.
> ---------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-26436
> URL: https://issues.apache.org/jira/browse/SPARK-26436
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.4.0
> Reporter: Manish
> Priority: Major
>
> There seems to be a bug on groupByKey api for cases when it (groupByKey) is
> applied on a DataSet resulting from a former groupByKey and flatMapGroups
> invocation.
> In such cases groupByKey throws the following exception:
> java.lang.UnsupportedException: fieldIndex on a Row without schema is
> undefined.
>
> Although the dataframe has a valid schema and a groupBy("key") or
> repartition($"key") api calls on the same Dataframe and key succeed.
>
> Following is the code that reproduces the scenario:
>
> {code}
> 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
> }
> }
> Test.main(null){code}
>
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