Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/15513#discussion_r84597837
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala
---
@@ -49,21 +49,29 @@ import org.apache.spark.sql.types._
* DEFAULT_PERCENTILE_ACCURACY.
*/
@ExpressionDescription(
- usage =
- """
- _FUNC_(col, percentage [, accuracy]) - Returns the approximate
percentile value of numeric
+ usage = """
+ _FUNC_(col, percentage [, accuracy]) - 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 (default: 10000) is a positive
integer 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.
-
- _FUNC_(col, array(percentage1 [, percentage2]...) [, accuracy]) -
Returns the approximate
- percentile array of column `col` at the given percentage array. Each
value of the
- percentage array must be between 0.0 and 1.0. The `accuracy`
parameter (default: 10000) is
- a positive integer 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.
- """)
+ When percentage is an array, each value of the percentage array must
be between 0.0 and 1.0.
+ In this case, returns the approximate percentile array of column
`col` at the given
+ percentage array.
+ """,
+ extended = """
+ Arguments:
+ col - a numeric expression.
+ percentage - a numeric literal or an array literal of numeric type
that defines the
+ percentile between 0.0 and 1.0. For example, 0.5 means
50-percentile.
+ accuracy - a numeric literal that defines approximation accuracy.
--- End diff --
What is the difference between `a numeric expression` and `a numeric
literal`?
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