Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/16971#discussion_r102601793
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala ---
@@ -89,18 +89,17 @@ final class DataFrameStatFunctions private[sql](df:
DataFrame) {
* Note that values greater than 1 are accepted but give the same
result as 1.
* @return the approximate quantiles at the given probabilities of each
column
*
- * @note Rows containing any null or NaN values will be removed before
calculation. If
- * the dataframe is empty or all rows contain null or NaN, null is
returned.
+ * @note null and NaN values will be removed from the numerical column
before calculation. If
+ * the dataframe is empty, or all rows in some column contain null or
NaN, null is returned.
*
* @since 2.2.0
*/
def approxQuantile(
cols: Array[String],
probabilities: Array[Double],
relativeError: Double): Array[Array[Double]] = {
- // TODO: Update NaN/null handling to keep consistent with the
single-column version
try {
- StatFunctions.multipleApproxQuantiles(df.select(cols.map(col):
_*).na.drop(), cols,
+ StatFunctions.multipleApproxQuantiles(df.select(cols.map(col): _*),
cols,
probabilities, relativeError).map(_.toArray).toArray
} catch {
case e: NoSuchElementException => null
--- End diff --
In Spark SQL, all the other built-in functions will not throw an exception
if the input data set is empty. An empty inupt data set is pretty normal.
Returning either `null` or empty `Array` looks ok to me.
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