Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/22365#discussion_r219034294
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala ---
@@ -370,29 +370,76 @@ final class DataFrameStatFunctions private[sql](df:
DataFrame) {
* @since 1.5.0
*/
def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long):
DataFrame = {
+ sampleBy(Column(col), fractions, seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction
given on each stratum.
+ * @param col column that defines strata
+ * @param fractions sampling fraction for each stratum. If a stratum is
not specified, we treat
+ * its fraction as zero.
+ * @param seed random seed
+ * @tparam T stratum type
+ * @return a new `DataFrame` that represents the stratified sample
+ *
+ * @since 1.5.0
+ */
+ def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed:
Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]],
seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction
given on each stratum.
+ * @param col column that defines strata
+ * @param fractions sampling fraction for each stratum. If a stratum is
not specified, we treat
+ * its fraction as zero.
+ * @param seed random seed
+ * @tparam T stratum type
+ * @return a new `DataFrame` that represents the stratified sample
+ *
+ * The stratified sample can be performed over multiple columns:
+ * {{{
+ * import org.apache.spark.sql.Row
+ * import org.apache.spark.sql.functions.struct
+ *
+ * val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10),
("Nico", 8), ("Bob", 17),
+ * ("Alice", 10))).toDF("name", "age")
+ * val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0)
+ * df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show()
+ * +-----+---+
+ * | name|age|
+ * +-----+---+
+ * | Nico| 8|
+ * |Alice| 10|
+ * +-----+---+
+ * }}}
+ *
+ * @since 3.0.0
--- End diff --
the next release is 2.5.0
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