zero323 commented on a change in pull request #27278:
[SPARK-30569][SQL][PYSPARK][SPARKR] Add percentile_approx DSL functions.
URL: https://github.com/apache/spark/pull/27278#discussion_r368969058
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/functions.scala
##########
@@ -652,6 +652,122 @@ object functions {
*/
def min(columnName: String): Column = min(Column(columnName))
+ /**
+ * Aggregate function: Returns and array of the approximate percentile values
+ * of numeric column col at the given percentages.
+ *
+ * Each value of the percentage array must be between 0.0 and 1.0.
+ *
+ * The accuracy parameter is a positive numeric literal
+ * which controls approximation accuracy at the cost of memory.
+ * Higher value of accuracy yields better accuracy, 1.0/accuracy
+ * is the relative error of the approximation.
+ *
+ * @group agg_funcs
+ * @since 3.0.0
+ */
+ def percentile_approx(e: Column, percentage: Array[Double], accuracy: Long):
Column = {
+ withAggregateFunction {
+ new ApproximatePercentile(
+ e.expr, typedLit(percentage).expr, lit(accuracy).expr
+ )
+ }
+ }
+
+ /**
+ * Aggregate function: Returns and array of the approximate percentile values
+ * of numeric column col at the given percentages.
+ *
+ * Each value of the percentage array must be between 0.0 and 1.0.
+ *
+ * The accuracy parameter is a positive numeric literal
+ * which controls approximation accuracy at the cost of memory.
+ * Higher value of accuracy yields better accuracy, 1.0/accuracy
+ * is the relative error of the approximation.
+ *
+ * @group agg_funcs
+ * @since 3.0.0
+ */
+ def percentile_approx(columnName: String, percentage: Array[Double],
accuracy: Long): Column = {
+ percentile_approx(Column(columnName), percentage, accuracy)
+ }
+
+ /**
+ * Aggregate function: Returns and array of the approximate percentile values
+ * of numeric column col at the given percentages.
+ *
+ * Each value of the percentage array must be between 0.0 and 1.0.
+ *
+ * The accuracy parameter is a positive numeric literal
+ * which controls approximation accuracy at the cost of memory.
+ * Higher value of accuracy yields better accuracy, 1.0/accuracy
+ * is the relative error of the approximation.
+ *
+ * @group agg_funcs
+ * @since 3.0.0
+ */
+ def percentile_approx(e: Column, percentage: Seq[Double], accuracy: Long):
Column = {
+ percentile_approx(e, percentage.toArray, accuracy)
+ }
+
+ /**
+ * Aggregate function: Returns and array of the approximate percentile values
+ * of numeric column col at the given percentages.
+ *
+ * Each value of the percentage array must be between 0.0 and 1.0.
+ *
+ * The accuracy parameter is a positive numeric literal
+ * which controls approximation accuracy at the cost of memory.
+ * Higher value of accuracy yields better accuracy, 1.0/accuracy
+ * is the relative error of the approximation.
+ *
+ * @group agg_funcs
+ * @since 3.0.0
+ */
+ def percentile_approx(columnName: String, percentage: Seq[Double], accuracy:
Long): Column = {
+ percentile_approx(Column(columnName), percentage.toArray, accuracy)
+ }
+
+ /**
+ * Aggregate function: Returns the approximate percentile value of numeric
+ * column col at the given percentage.
+ *
+ * The value of percentage must be between 0.0 and 1.0.\
+ *
+ * The accuracy parameter is a positive numeric literal
+ * which controls approximation accuracy at the cost of memory.
+ * Higher value of accuracy yields better accuracy, 1.0/accuracy
+ * is the relative error of the approximation.
+ *
+ * @group agg_funcs
+ * @since 3.0.0
+ */
+ def percentile_approx(e: Column, percentage: Double, accuracy: Long): Column
= {
+ withAggregateFunction {
+ new ApproximatePercentile(
+ e.expr, lit(percentage).expr, lit(accuracy).expr
+ )
+ }
+ }
+
+ /**
+ * Aggregate function: Returns the approximate percentile value of numeric
+ * column col at the given percentage.
+ *
+ * The value of percentage must be between 0.0 and 1.0.\
+ *
+ * The accuracy parameter is a positive numeric literal
+ * which controls approximation accuracy at the cost of memory.
+ * Higher value of accuracy yields better accuracy, 1.0/accuracy
+ * is the relative error of the approximation.
+ *
+ * @group agg_funcs
+ * @since 3.0.0
+ */
+ def percentile_approx(columnName: String, percentage: Double, accuracy:
Long): Column = {
Review comment:
To be honest I am not very enthusiastic about it and I am not even convinced
that it is consistent with the rest of `functions`.
The closest equivalents we have are
- `approx_count_distinct` with `rsd`
- `last` with `ignoreNulls`
and both use external types, not columns. Not to mention this is still
counter-intuitive and painful to use though:
> we don't need to duplicate docs with less maintenance.
is a fair point.
- I can easily remove `Seq` variants, that's for sure and cut number of
signatures by two, leaving us with four.
- If having not `Column` variant on JVM is fine, we can drop `(String, _, _)
=> Column` variant so that brings us to two variants.
- It is also not hard to build `Column` objects transparently for Python and
R users to support `(Column, Column, Column) => Column`. But I am still
concerned about confusing semantics.
If two variants are still to much, we could always have `(Column, Any,
Double) => Column` ‒ `o.a.sql.functions` is already quite full of `Any`s. Or if
we're fine with making Java users miserable, we could `(Column, Either[Double,
Array[Double], Double) => Column`, but this will require additional supporting
code for R and Python.
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