viirya commented on a change in pull request #28745:
URL: https://github.com/apache/spark/pull/28745#discussion_r436380321
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/python/PandasGroupUtils.scala
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@@ -59,65 +59,65 @@ private[python] object PandasGroupUtils {
*/
def groupAndProject(
input: Iterator[InternalRow],
- groupingAttributes: Seq[Attribute],
+ groupingExprs: Seq[NamedExpression],
inputSchema: Seq[Attribute],
- dedupSchema: Seq[Attribute]): Iterator[(InternalRow,
Iterator[InternalRow])] = {
- val groupedIter = GroupedIterator(input, groupingAttributes, inputSchema)
+ dedupSchema: Seq[NamedExpression]): Iterator[(InternalRow,
Iterator[InternalRow])] = {
+ val groupedIter = GroupedIterator(input, groupingExprs, inputSchema)
val dedupProj = UnsafeProjection.create(dedupSchema, inputSchema)
groupedIter.map {
case (k, groupedRowIter) => (k, groupedRowIter.map(dedupProj))
}
}
/**
- * Returns a the deduplicated attributes of the spark plan and the arg
offsets of the
+ * Returns a the deduplicated named expressions of the spark plan and the
arg offsets of the
* keys and values.
*
- * The deduplicated attributes are needed because the spark plan may contain
an attribute
- * twice; once in the key and once in the value. For any such attribute we
need to
+ * The deduplicated expressions are needed because the spark plan may
contain an expression
+ * twice; once in the key and once in the value. For any such expression we
need to
* deduplicate.
*
- * The arg offsets are used to distinguish grouping grouping attributes and
data attributes
+ * The arg offsets are used to distinguish grouping expressions and data
expressions
* as following:
*
* argOffsets[0] is the length of the argOffsets array
*
- * argOffsets[1] is the length of grouping attribute
- * argOffsets[2 .. argOffsets[0]+2] is the arg offsets for grouping
attributes
+ * argOffsets[1] is the length of grouping expression
+ * argOffsets[2 .. argOffsets[0]+2] is the arg offsets for grouping
expressions
*
- * argOffsets[argOffsets[0]+2 .. ] is the arg offsets for data attributes
+ * argOffsets[argOffsets[0]+2 .. ] is the arg offsets for data expressions
*/
def resolveArgOffsets(
- child: SparkPlan, groupingAttributes: Seq[Attribute]): (Seq[Attribute],
Array[Int]) = {
+ dataExprs: Seq[NamedExpression], groupingExprs: Seq[NamedExpression])
+ : (Seq[NamedExpression], Array[Int]) = {
- val dataAttributes = child.output.drop(groupingAttributes.length)
- val groupingIndicesInData = groupingAttributes.map { attribute =>
- dataAttributes.indexWhere(attribute.semanticEquals)
+ val groupingIndicesInData = groupingExprs.map { expression =>
+ dataExprs.indexWhere(expression.semanticEquals)
}
Review comment:
I feel this looks not precisely correct at all cases. Seems `dataExprs`
are inputs to Python UDFs. Is it possible that `groupingExprs` are not just
child's outputs but expressions like `column + 1`?
In `RelationalGroupedDataset`, we added one projection previously to put
these grouping expressions with original child's outputs. Now we don't have it.
So can we always find semantically equal expr in `dataExprs` for a grouping
expression? `dataExprs` are input expressions in left/right plan for
`FlatMapCoGroupsInPandasExec`, so I guess we cannot find `column + 1` in it.
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