hvanhovell commented on code in PR #40145:
URL: https://github.com/apache/spark/pull/40145#discussion_r1116493326
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -234,4 +239,132 @@ class RelationalGroupedDataset protected[sql] (
def sum(colNames: String*): DataFrame = {
toDF(colNames.map(colName => functions.sum(colName)))
}
+
+ /**
+ * Pivots a column of the current `DataFrame` and performs the specified
aggregation. There are
+ * two versions of pivot function: one that requires the caller to specify
the list of distinct
+ * values to pivot on, and one that does not. The latter is more concise but
less efficient,
+ * because Spark needs to first compute the list of distinct values
internally.
+ *
+ * {{{
+ * // Compute the sum of earnings for each year by course with each course
as a separate column
+ * df.groupBy("year").pivot("course", Seq("dotNET",
"Java")).sum("earnings")
+ *
+ * // Or without specifying column values (less efficient)
+ * df.groupBy("year").pivot("course").sum("earnings")
+ * }}}
+ *
+ * From Spark 3.0.0, values can be literal columns, for instance, struct.
For pivoting by
+ * multiple columns, use the `struct` function to combine the columns and
values:
+ *
+ * {{{
+ * df.groupBy("year")
+ * .pivot("trainingCourse", Seq(struct(lit("java"), lit("Experts"))))
+ * .agg(sum($"earnings"))
+ * }}}
+ *
+ * @see
+ * `org.apache.spark.sql.Dataset.unpivot` for the reverse operation,
except for the
+ * aggregation.
+ *
+ * @param pivotColumn
+ * Name of the column to pivot.
+ * @param values
+ * List of values that will be translated to columns in the output
DataFrame.
+ * @since 3.4.0
+ */
+ def pivot(pivotColumn: String, values: Seq[Any]): RelationalGroupedDataset =
{
+ pivot(Column(pivotColumn), values)
+ }
+
+ /**
+ * (Java-specific) Pivots a column of the current `DataFrame` and performs
the specified
+ * aggregation.
+ *
+ * There are two versions of pivot function: one that requires the caller to
specify the list of
+ * distinct values to pivot on, and one that does not. The latter is more
concise but less
+ * efficient, because Spark needs to first compute the list of distinct
values internally.
+ *
+ * {{{
+ * // Compute the sum of earnings for each year by course with each course
as a separate column
+ * df.groupBy("year").pivot("course", Arrays.<Object>asList("dotNET",
"Java")).sum("earnings");
+ *
+ * // Or without specifying column values (less efficient)
+ * df.groupBy("year").pivot("course").sum("earnings");
+ * }}}
+ *
+ * @see
+ * `org.apache.spark.sql.Dataset.unpivot` for the reverse operation,
except for the
+ * aggregation.
+ *
+ * @param pivotColumn
+ * Name of the column to pivot.
+ * @param values
+ * List of values that will be translated to columns in the output
DataFrame.
+ * @since 3.4.0
+ */
+ def pivot(pivotColumn: String, values: java.util.List[Any]):
RelationalGroupedDataset = {
+ pivot(Column(pivotColumn), values)
+ }
+
+ /**
+ * Pivots a column of the current `DataFrame` and performs the specified
aggregation. This is an
+ * overloaded version of the `pivot` method with `pivotColumn` of the
`String` type.
+ *
+ * {{{
+ * // Compute the sum of earnings for each year by course with each course
as a separate column
+ * df.groupBy($"year").pivot($"course", Seq("dotNET",
"Java")).sum($"earnings")
+ * }}}
+ *
+ * @see
+ * `org.apache.spark.sql.Dataset.unpivot` for the reverse operation,
except for the
+ * aggregation.
+ *
+ * @param pivotColumn
+ * the column to pivot.
+ * @param values
+ * List of values that will be translated to columns in the output
DataFrame.
+ * @since 3.4.0
+ */
+ def pivot(pivotColumn: Column, values: Seq[Any]): RelationalGroupedDataset =
{
+ groupType match {
+ case proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY =>
+ val valueExprs = values.map(_ match {
+ case c: Column if c.expr.hasLiteral => c.expr.getLiteral
+ case v => functions.lit(v).expr.getLiteral
Review Comment:
I would throw an exception.
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