[GitHub] [spark] HyukjinKwon commented on a diff in pull request #36150: [SPARK-38864][SQL] Add melt / unpivot to Dataset

[GitBox <gi...@apache.org>]

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:
##########
@@ -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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