HyukjinKwon commented on code in PR #36150:
URL: https://github.com/apache/spark/pull/36150#discussion_r881076458
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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|
+ * // +---+--------+-----+
+ * // | 1| int| 11|
+ * // | 1| long| 12|
+ * // | 2| int| 21|
+ * // | 2| long| 22|
+ * // +---+--------+-----+
+ *
+ * df.melt(Seq("id")).printSchema
+ * //root
+ * // |-- id: integer (nullable = false)
+ * // |-- variable: string (nullable = false)
+ * // |-- value: long (nullable = true)
+ * }}}
+ *
+ * When no id columns are given, the unpivoted DataFrame consists of only the
+ * `variable` and `value` columns. When no value columns are given, all
non-identifier
+ * columns are considered value columns.
+ *
+ * All value columns must be of the same data type. If they are not the same
data type,
+ * all value columns are cast to the nearest common data type. For instance,
+ * types `IntegerType` and `LongType` are compatible and cast to `LongType`,
+ * while `IntegerType` and `StringType` are not compatible and `melt` fails.
+ *
+ * The type of the `value` column is the nearest common data type of the
value columns.
+ *
+ * @param ids names of the id columns
+ * @param values names of the value columns
+ * @param dropNulls rows with null values are dropped from the returned
DataFrame
+ * @param variableColumnName name of the variable column, default `variable`
+ * @param valueColumnName name of the value column, default `value`
+ *
+ * @group untypedrel
+ * @since 3.4.0
+ */
+ def melt(ids: Seq[String],
Review Comment:
Let's avoid using default arguments for Java compat. See also
https://github.com/databricks/scala-style-guide#java-default-param-values
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