Github user dongjoon-hyun commented on a diff in the pull request:

    https://github.com/apache/spark/pull/14033#discussion_r69596328
  
    --- Diff: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
 ---
    @@ -94,6 +94,63 @@ case class UserDefinedGenerator(
     }
     
     /**
    + * Separate v1, ..., vk into n rows. Each row will have k/n columns. n 
must be constant.
    + * {{{
    + *   SELECT stack(2, 1, 2, 3)) ->
    + *   1      2
    + *   3      NULL
    + * }}}
    + */
    +@ExpressionDescription(
    +  usage = "_FUNC_(n, v1, ..., vk) - Separate v1, ..., vk into n rows.",
    +  extended = "> SELECT _FUNC_(2, 1, 2, 3);\n  [1,2]\n  [3,null]")
    +case class Stack(children: Seq[Expression])
    +    extends Expression with Generator with CodegenFallback {
    +
    +  private lazy val numRows = children.head.eval().asInstanceOf[Int]
    +  private lazy val numFields = ((children.length - 1) + numRows - 1) / 
numRows
    +
    +  override def checkInputDataTypes(): TypeCheckResult = {
    +    if (children.length <= 1) {
    +      TypeCheckResult.TypeCheckFailure(s"$prettyName requires at least 2 
arguments.")
    +    } else if (children.head.dataType != IntegerType || 
!children.head.foldable || numRows < 1) {
    +      TypeCheckResult.TypeCheckFailure("The number of rows must be a 
positive constant integer.")
    +    } else {
    +      for (i <- 1 until children.length) {
    +        val j = (i - 1) % numFields
    +        if (children(i).dataType != elementSchema.fields(j).dataType) {
    +          return TypeCheckResult.TypeCheckFailure(
    +            s"Argument ${j + 1} (${elementSchema.fields(j).dataType}) != " 
+
    +              s"Argument $i (${children(i).dataType})")
    +        }
    +      }
    +      TypeCheckResult.TypeCheckSuccess
    +    }
    +  }
    +
    +  override def elementSchema: StructType = {
    +    var schema = new StructType()
    +    val types = children.tail.take(numFields).map(_.dataType)
    +    for (i <- 0 until numFields) {
    +      schema = schema.add(s"col$i", types(i))
    +    }
    +    schema
    +  }
    +
    +  override def eval(input: InternalRow): TraversableOnce[InternalRow] = {
    +    val values = children.tail.map(_.eval(input))
    +    for (row <- 0 until numRows) yield {
    +      val fields = new Array[Any](numFields)
    +      for (col <- 0 until numFields) {
    +        val index = (row % numRows) * numFields + col
    --- End diff --
    
    Absolutely, it's unnecessary.


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