Github user aray commented on a diff in the pull request:
https://github.com/apache/spark/pull/9929#discussion_r45806267
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala ---
@@ -282,74 +282,96 @@ class GroupedData protected[sql](
}
/**
- * (Scala-specific) Pivots a column of the current [[DataFrame]] and
preform the specified
- * aggregation.
- * {{{
- * // Compute the sum of earnings for each year by course with each
course as a separate column
- * df.groupBy($"year").pivot($"course", "dotNET",
"Java").agg(sum($"earnings"))
- * // Or without specifying column values
- * df.groupBy($"year").pivot($"course").agg(sum($"earnings"))
- * }}}
- * @param pivotColumn Column to pivot
- * @param values Optional list of values of pivotColumn that will be
translated to columns in the
- * output data frame. If values are not provided the
method with do an immediate
- * call to .distinct() on the pivot column.
- * @since 1.6.0
- */
- @scala.annotation.varargs
- def pivot(pivotColumn: Column, values: Column*): GroupedData = groupType
match {
- case _: GroupedData.PivotType =>
- throw new UnsupportedOperationException("repeated pivots are not
supported")
- case GroupedData.GroupByType =>
- val pivotValues = if (values.nonEmpty) {
- values.map {
- case Column(literal: Literal) => literal
- case other =>
- throw new UnsupportedOperationException(
- s"The values of a pivot must be literals, found $other")
- }
- } else {
- // This is to prevent unintended OOM errors when the number of
distinct values is large
- val maxValues =
df.sqlContext.conf.getConf(SQLConf.DATAFRAME_PIVOT_MAX_VALUES)
- // Get the distinct values of the column and sort them so its
consistent
- val values = df.select(pivotColumn)
- .distinct()
- .sort(pivotColumn)
- .map(_.get(0))
- .take(maxValues + 1)
- .map(Literal(_)).toSeq
- if (values.length > maxValues) {
- throw new RuntimeException(
- s"The pivot column $pivotColumn has more than $maxValues
distinct values, " +
- "this could indicate an error. " +
- "If this was intended, set \"" +
SQLConf.DATAFRAME_PIVOT_MAX_VALUES.key + "\" " +
- s"to at least the number of distinct values of the pivot
column.")
- }
- values
- }
- new GroupedData(df, groupingExprs,
GroupedData.PivotType(pivotColumn.expr, pivotValues))
- case _ =>
- throw new UnsupportedOperationException("pivot is only supported
after a groupBy")
+ * Pivots a column of the current [[DataFrame]] and preform 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")
+ * }}}
+ *
+ * @param pivotColumn Name of the column to pivot.
+ * @since 1.6.0
+ */
+ def pivot(pivotColumn: String): GroupedData = {
+ // This is to prevent unintended OOM errors when the number of
distinct values is large
+ val maxValues =
df.sqlContext.conf.getConf(SQLConf.DATAFRAME_PIVOT_MAX_VALUES)
+ // Get the distinct values of the column and sort them so its
consistent
+ val values = df.select(pivotColumn)
+ .distinct()
+ .sort(pivotColumn)
--- End diff --
The sort is there to ensure that the output columns are in a consistent
logical order.
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