EnricoMi commented on code in PR #36150:
URL: https://github.com/apache/spark/pull/36150#discussion_r882411766
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sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala:
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@@ -2012,6 +2012,152 @@ class Dataset[T] private[sql](
@scala.annotation.varargs
def agg(expr: Column, exprs: Column*): DataFrame = groupBy().agg(expr, exprs
: _*)
+ /**
+ * (Scala-specific)
+ * Unpivot a DataFrame from wide format to long format, optionally
+ * leaving identifier variables set.
+ *
+ * This function is useful to massage a DataFrame into a format where some
+ * columns are identifier variables (`ids`), while all other columns,
+ * considered measured variables (`values`), are "unpivoted" to the rows,
+ * leaving just two non-identifier columns, 'variable' and 'value'.
+ *
+ * {{{
+ * val df = Seq((1, 11, 12L), (2, 21, 22L)).toDF("id", "int", "long")
+ * df.show()
+ * // output:
+ * // +---+---+----+
+ * // | id|int|long|
+ * // +---+---+----+
+ * // | 1| 11| 12|
+ * // | 2| 21| 22|
+ * // +---+---+----+
+ *
+ * df.melt(Seq("id")).show()
+ * // output:
+ * // +---+--------+-----+
+ * // | id|variable|value|
Review Comment:
I could remove the default values for `variableColumnName` and
`valueColumnName` from `Dataset.melt` as they are generally not useful (may be
changed by users anyway). And in SQL they have to be given as well. This
removes one overloaded `melt` from `Dataset`.
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