Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/16776#discussion_r100138206
--- 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 --
Great catch! I vote for modifying multipleApproxQuantiles to handle null
and NaN values. As far as reverting, I'm OK either way as long as we get the
fix into 2.2. I'd actually recommend going ahead and merging this PR and
creating a follow-up Critical Bug targeted at 2.2.
@MLnick I think dropping NAs from the cols passed as args still will not
work. Say the user passes cols "a" and "b" as args, but some rows have (a =
NaN, b = 1.0). Then those rows will be ignored.
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