Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6042#discussion_r53743898
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
---
@@ -27,6 +30,312 @@ import org.apache.spark.unsafe.types.UTF8String
private[sql] object StatFunctions extends Logging {
+ import QuantileSummaries.Stats
+
+ /**
+ * Calculates the approximate quantile for the given column.
+ *
+ * If you need to compute multiple quantiles at once, you should use
[[multipleApproxQuantiles]]
+ *
+ * Note on the target error.
+ *
+ * The result of this algorithm has the following deterministic bound:
+ * if the DataFrame has N elements and if we request the quantile `phi`
up to error `epsi`,
+ * then the algorithm will return a sample `x` from the DataFrame so
that the *exact* rank
+ * of `x` close to (phi * N). More precisely:
+ *
+ * floor((phi - epsi) * N) <= rank(x) <= ceil((phi + epsi) * N)
+ *
+ * Note on the algorithm used.
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm
+ * (with some speed optimizations). The algorithm was first present in
the following article:
+ * "Space-efficient Online Computation of Quantile Summaries" by
Greenwald, Michael
+ * and Khanna, Sanjeev. (http://dl.acm.org/citation.cfm?id=375670)
+ *
+ * The performance optimizations are detailed in the comments of the
implementation.
+ *
+ * @param df the dataframe to estimate quantiles on
+ * @param col the name of the column
+ * @param quantile the target quantile of interest
+ * @param epsilon the target error. Should be >= 0.
+ * */
+ def approxQuantile(
--- End diff --
This is unnecessary. If we only have `approxQuantiles` in the public API,
we can change `quantile: Double` to `quantiles: Seq[Double]` here.
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