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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