cloud-fan commented on code in PR #57119:
URL: https://github.com/apache/spark/pull/57119#discussion_r3572353359
##########
sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala:
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@@ -605,6 +605,50 @@ class DataFrameStatSuite extends SharedSparkSession {
val df = spark.range(1).selectExpr("CAST(id as DECIMAL) as
x").selectExpr("percentile(x, 0.5)")
checkAnswer(df, Row(BigDecimal(0)) :: Nil)
}
+
+ test("SPARK-57849: DataFrame approxQuantile should support time type
columns") {
+ val df = sql(
+ "SELECT * FROM VALUES (CAST('06:00:00' AS TIME(9))), (CAST('06:00:00' AS
TIME(9))), " +
+ "(CAST('08:00:00' AS TIME(9))), (CAST('08:00:00' AS TIME(9))), " +
+ "(CAST('08:00:00' AS TIME(9))), (CAST('10:00:00' AS TIME(9)));"
+ )
+
+ val res = df.stat.approxQuantile("col1", Array(0.1, 0.5, 0.9), 0.01)
+ assert(res.length === 3)
+ assert(res.count(_.isNaN) === 0)
+ assert(res(1) === 28800.0)
+ }
+
+ test("SPARK-57849: DataFrame approxQuantile on TIME preserves sub-second
precision") {
+ val df = sql(
+ "SELECT * FROM VALUES (CAST('00:00:00.000000001' AS TIME(9))), " +
+ "(CAST('23:59:59.999999999' AS TIME(9)));"
+ )
+
+ val Array(min, max) = df.stat.approxQuantile("col1", Array(0.0, 1.0), 0.0)
+ assert(min === 1e-9 +- 1e-12)
+ assert(max === 86399.999999999 +- 1e-6)
+ }
+
+ test("SPARK-57849: summary() computes typed TIME percentiles") {
Review Comment:
The PR description states `df.describe()` accepts TIME columns, but no test
exercises `describe()` on a TIME column. It's a thin delegation to
`summary("count","mean","stddev","min","max")` over stats the new summary tests
already cover, so the mechanism is tested — this is optional. A one-line
`describe()` assertion would pin down the user-facing claim directly.
Non-blocking.
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