Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/10911#discussion_r50878077
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala ---
@@ -309,4 +311,84 @@ final class DataFrameStatFunctions private[sql](df:
DataFrame) {
def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed:
Long): DataFrame = {
sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]],
seed)
}
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName name of the column over which the sketch is built
+ * @param depth depth of the sketch
+ * @param width width of the sketch
+ * @param seed random seed
+ * @return a [[CountMinSketch]] over column `colName`
+ * @since 2.0.0
+ */
+ def countMinSketch(colName: String, depth: Int, width: Int, seed: Int):
CountMinSketch = {
+ countMinSketch(Column(colName), depth, width, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName name of the column over which the sketch is built
+ * @param eps relative error of the sketch
+ * @param confidence confidence of the sketch
+ * @param seed random seed
+ * @return a [[CountMinSketch]] over column `colName`
+ * @since 2.0.0
+ */
+ def countMinSketch(
+ colName: String, eps: Double, confidence: Double, seed: Int):
CountMinSketch = {
+ countMinSketch(Column(colName), eps, confidence, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col the column over which the sketch is built
+ * @param depth depth of the sketch
+ * @param width width of the sketch
+ * @param seed random seed
+ * @return a [[CountMinSketch]] over column `colName`
+ * @since 2.0.0
+ */
+ def countMinSketch(col: Column, depth: Int, width: Int, seed: Int):
CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(depth, width, seed))
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col the column over which the sketch is built
+ * @param eps relative error of the sketch
+ * @param confidence confidence of the sketch
+ * @param seed random seed
+ * @return a [[CountMinSketch]] over column `colName`
+ * @since 2.0.0
+ */
+ def countMinSketch(col: Column, eps: Double, confidence: Double, seed:
Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(eps, confidence, seed))
+ }
+
+ private def countMinSketch(col: Column, zero: CountMinSketch):
CountMinSketch = {
+ val singleCol = df.select(col)
+ val colType = singleCol.schema.head.dataType
+ val supportedTypes: Set[DataType] = Set(ByteType, ShortType,
IntegerType, LongType, StringType)
+
+ require(
+ supportedTypes.contains(colType),
--- End diff --
Actually after thinking about it - let's avoid doing that and list the
explicit types. It is plausible in the future we introduce an int96 or int128
data type, and I bet we won't remember this is one place we need to update it.
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