Github user zhengruifeng commented on a diff in the pull request:
https://github.com/apache/spark/pull/16971#discussion_r102167055
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala ---
@@ -214,20 +214,29 @@ class DataFrameStatSuite extends QueryTest with
SharedSQLContext {
val q1 = 0.5
val q2 = 0.8
val epsilon = 0.1
- val rows = spark.sparkContext.parallelize(Seq(Row(Double.NaN, 1.0),
Row(1.0, 1.0),
+ val rows = spark.sparkContext.parallelize(Seq(Row(Double.NaN, 1.0),
Row(1.0, -1.0),
Row(-1.0, Double.NaN), Row(Double.NaN, Double.NaN), Row(null, null),
Row(null, 1.0),
Row(-1.0, null), Row(Double.NaN, null)))
val schema = StructType(Seq(StructField("input1", DoubleType, nullable
= true),
StructField("input2", DoubleType, nullable = true)))
val dfNaN = spark.createDataFrame(rows, schema)
- val resNaN = dfNaN.stat.approxQuantile("input1", Array(q1, q2),
epsilon)
- assert(resNaN.count(_.isNaN) === 0)
- assert(resNaN.count(_ == null) === 0)
+ val resNaN1 = dfNaN.stat.approxQuantile("input1", Array(q1, q2),
epsilon)
+ assert(resNaN1.count(_.isNaN) === 0)
+ assert(resNaN1.count(_ == null) === 0)
- val resNaN2 = dfNaN.stat.approxQuantile(Array("input1", "input2"),
+ val resNaN2 = dfNaN.stat.approxQuantile("input2", Array(q1, q2),
epsilon)
+ assert(resNaN2.count(_.isNaN) === 0)
+ assert(resNaN2.count(_ == null) === 0)
+
+ val resNaNAll = dfNaN.stat.approxQuantile(Array("input1", "input2"),
Array(q1, q2), epsilon)
- assert(resNaN2.flatten.count(_.isNaN) === 0)
- assert(resNaN2.flatten.count(_ == null) === 0)
+ assert(resNaNAll.flatten.count(_.isNaN) === 0)
+ assert(resNaNAll.flatten.count(_ == null) === 0)
+
+ assert(resNaN1(0) === resNaNAll(0)(0))
+ assert(resNaN1(1) === resNaNAll(0)(1))
+ assert(resNaN2(0) === resNaNAll(1)(0))
+ assert(resNaN2(1) === resNaNAll(1)(1))
--- End diff --
Yes, I create a new column containing only NaN/null.
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