Github user zhengruifeng commented on a diff in the pull request:
https://github.com/apache/spark/pull/16776#discussion_r100003717
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala ---
@@ -63,44 +63,49 @@ 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
*
- * @note NaN values will be removed from the numerical column before
calculation
+ * @note null and NaN values will be removed from the numerical column
before calculation
*
* @since 2.0.0
*/
def approxQuantile(
col: String,
probabilities: Array[Double],
relativeError: Double): Array[Double] = {
- StatFunctions.multipleApproxQuantiles(df.select(col).na.drop(),
- Seq(col), probabilities, relativeError).head.toArray
+ val res = approxQuantile(Array(col), probabilities, relativeError)
+ if (res != null) {
+ res.head
+ } else {
+ null
+ }
}
/**
* Calculates the approximate quantiles of numerical columns of a
DataFrame.
- * @see [[DataFrameStatsFunctions.approxQuantile(col:Str*
approxQuantile]] for
- * detailed description.
+ * @see `DataFrameStatsFunctions.approxQuantile` for detailed
description.
*
- * Note that rows containing any null or NaN values values will be
removed before
- * calculation.
* @param cols the names of the numerical columns
* @param probabilities a list of quantile probabilities
* Each number must belong to [0, 1].
* For example 0 is the minimum, 0.5 is the median, 1 is the maximum.
- * @param relativeError The relative target precision to achieve (>= 0).
+ * @param relativeError The relative target precision to achieve
(greater or equal to 0).
* If set to zero, the exact quantiles are computed, which could be
very expensive.
* 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 NaN values will be removed before
calculation
+ * @note Rows containing any null or NaN values will be removed before
calculation
*
* @since 2.2.0
*/
def approxQuantile(
cols: Array[String],
probabilities: Array[Double],
relativeError: Double): Array[Array[Double]] = {
- StatFunctions.multipleApproxQuantiles(df.select(cols.map(col):
_*).na.drop(), cols,
- probabilities, relativeError).map(_.toArray).toArray
+ try {
+ StatFunctions.multipleApproxQuantiles(df.select(cols.map(col):
_*).na.drop(), cols,
--- End diff --
@gatorsmile Good catch! Agree that this will cause confusing results.
I think there are two way to make them consistent:
1, The behavior of na-droping was included in SPARK-17219 to enhanced NaN
value handling, and the single-column version of `approxQuantile` is only used
in `QuantileDiscretizer`. So we can make the na-droping happen in
`QuantileDiscretizer`, and remove the na-drop in `approxQuantile`.
2, modify the impl `StatFunctions.multipleApproxQuantiles` to deal with
null and NaN, and remove the na-drop in `approxQuantile`.
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