Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/6400#discussion_r31100442
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala ---
@@ -31,26 +31,19 @@ import org.apache.spark.sql.{Row, SQLContext}
*/
@DeveloperApi
object RDDConversions {
- def productToRowRdd[A <: Product](data: RDD[A], schema: StructType):
RDD[Row] = {
+ def productToRowRdd[A <: Product](data: RDD[A], outputTypes:
Seq[DataType]): RDD[Row] = {
data.mapPartitions { iterator =>
- if (iterator.isEmpty) {
- Iterator.empty
- } else {
- val bufferedIterator = iterator.buffered
- val mutableRow = new
SpecificMutableRow(schema.fields.map(_.dataType))
- val schemaFields = schema.fields.toArray
- val converters = schemaFields.map {
- f => CatalystTypeConverters.createToCatalystConverter(f.dataType)
- }
- bufferedIterator.map { r =>
- var i = 0
- while (i < mutableRow.length) {
- mutableRow(i) = converters(i)(r.productElement(i))
- i += 1
- }
-
- mutableRow
+ val numColumns = outputTypes.length
+ val mutableRow = new GenericMutableRow(numColumns)
+ val converters =
outputTypes.map(CatalystTypeConverters.createToCatalystConverter)
+ iterator.map { r =>
--- End diff --
This is actually the return value of the `mapPartitions` call, which must
be a Scala iterator. Note that we do use a while loop to iterate over the
columns.
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