BryanCutler commented on a change in pull request #24095: [SPARK-27163][PYTHON]
Cleanup and consolidate Pandas UDF functionality
URL: https://github.com/apache/spark/pull/24095#discussion_r266586337
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/python/FlatMapGroupsInPandasExec.scala
##########
@@ -145,7 +146,15 @@ case class FlatMapGroupsInPandasExec(
sessionLocalTimeZone,
pythonRunnerConf).compute(grouped, context.partitionId(), context)
-
columnarBatchIter.flatMap(_.rowIterator.asScala).map(UnsafeProjection.create(output,
output))
+ columnarBatchIter.flatMap { batch =>
+ // Grouped Map UDF returns a StructType column in ColumnarBatch,
select the children here
+ // TODO: ColumnVector getChild is protected, so use ArrowColumnVector
which is public
+ val structVector = batch.column(0).asInstanceOf[ArrowColumnVector]
+ val outputVectors =
output.indices.map(structVector.getChild(_).asInstanceOf[ColumnVector])
Review comment:
>Another concern is tho .. I think all of Arrow implementations (including
SparkR ones) dont modify the batch's outputs but use the batch as are.
Yeah, makes the Scala side a bit different but I think it is worth it to
make things in `worker.py` more consistent. With this cleanup, all of the
Pandas UDFs go through the same logic to be serialized.
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