MaxGekk commented on code in PR #42755:
URL: https://github.com/apache/spark/pull/42755#discussion_r1323175490
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/Mode.scala:
##########
@@ -35,24 +42,57 @@ import org.apache.spark.util.collection.OpenHashMap
0-10
> SELECT _FUNC_(col) FROM VALUES (0), (10), (10), (null), (null), (null)
AS tab(col);
10
+ > SELECT _FUNC_(col, false) FROM VALUES (-10), (0), (10) AS tab(col);
+ 0
+ > SELECT _FUNC_(col, true) FROM VALUES (-10), (0), (10) AS tab(col);
+ -10
""",
group = "agg_funcs",
since = "3.4.0")
// scalastyle:on line.size.limit
case class Mode(
child: Expression,
mutableAggBufferOffset: Int = 0,
- inputAggBufferOffset: Int = 0) extends TypedAggregateWithHashMapAsBuffer
- with ImplicitCastInputTypes with UnaryLike[Expression] {
+ inputAggBufferOffset: Int = 0,
+ deterministicResult: Expression = Literal.FalseLiteral)
+ extends TypedAggregateWithHashMapAsBuffer with ImplicitCastInputTypes
+ with BinaryLike[Expression] {
def this(child: Expression) = this(child, 0, 0)
+ def this(child: Expression, deterministicResult: Expression) = {
+ this(child, 0, 0, deterministicResult)
+ }
+
+ override def left: Expression = child
+
+ override def right: Expression = deterministicResult
+
// Returns null for empty inputs
override def nullable: Boolean = true
override def dataType: DataType = child.dataType
- override def inputTypes: Seq[AbstractDataType] = Seq(AnyDataType)
+ override def inputTypes: Seq[AbstractDataType] = Seq(AnyDataType,
BooleanType)
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ val defaultCheck = super.checkInputDataTypes()
+ if (defaultCheck.isFailure) {
+ return defaultCheck
+ }
+ if (!deterministicResult.foldable) {
+ DataTypeMismatch(
+ errorSubClass = "NON_FOLDABLE_INPUT",
+ messageParameters = Map(
+ "inputName" -> "deterministicResult",
Review Comment:
It is an id, so, should be quoted by `toSQLId`
##########
python/pyspark/sql/functions.py:
##########
@@ -1195,12 +1195,12 @@ def mode(col: "ColumnOrName") -> Column:
... ("dotNET", 2013, 48000), ("Java", 2013, 30000)],
... schema=("course", "year", "earnings"))
>>> df.groupby("course").agg(mode("year")).show()
- +------+----------+
- |course|mode(year)|
- +------+----------+
- | Java| 2012|
- |dotNET| 2012|
- +------+----------+
+ +------+-----------------+
+ |course|mode(year, false)|
Review Comment:
Shell we support new parameter in Python API too?
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