Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/11356#discussion_r54047942
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala ---
@@ -71,6 +71,41 @@ final class DataFrameStatFunctions private[sql](df:
DataFrame) {
}
/**
+ * Calculates the approximate quantiles of a numerical column of a
DataFrame.
+ * Provided for the Python API.
+ *
+ * The result of this algorithm has the following deterministic bound:
+ * If the DataFrame has N elements and if we request the quantile at
probability `p` up to error
+ * `err`, then the algorithm will return a sample `x` from the DataFrame
so that the *exact* rank
+ * of `x` is close to (p * N).
+ * More precisely,
+ *
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N).
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm
(with some speed
+ * optimizations).
+ * The algorithm was first present in
[[http://dx.doi.org/10.1145/375663.375670 Space-efficient
+ * Online Computation of Quantile Summaries]] by Greenwald and Khanna.
+ *
+ * @param col the name of the numerical column
+ * @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).
+ * 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
+ *
+ * @since 2.0.0
+ */
+ private[spark] def approxQuantile(
+ col: String,
+ probabilities: List[Double],
+ relativeError: Double): Array[Double] = {
--- End diff --
Yeah, again I was trying to follow other code there...but probably
shouldn't. Fixed now.
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