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[SPARK-31735][SQL] Include date/timestamp in the summary report #28554

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[SPARK-31735][SQL] Include date/timestamp in the summary report #28554

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[SPARK-31735][CORE] Include date/timestamp in the summary report
Fokko May 16, 2020
51c3df1
Add a test
Fokko Jun 3, 2020
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5 changes: 4 additions & 1 deletion sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -264,7 +264,10 @@ object StatFunctions extends Logging {
}

val selectedCols = ds.logicalPlan.output
.filter(a => a.dataType.isInstanceOf[NumericType] || a.dataType.isInstanceOf[StringType])
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@dongjoon-hyun dongjoon-hyun May 16, 2020 •
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Can we keep this whitelist style instead of allowing all, @Fokko ? You can add the missing type here.

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Sure, I've just updated the list.

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Thank you, @Fokko . Could you update the PR title and description accordingly?

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I've updated both the PR and commit, please let me know if this works for you

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@dongjoon-hyun dongjoon-hyun May 17, 2020 •
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Shall we remove Even something less common such as arrays are sortable: and its example in the PR description?

.filter(a => a.dataType.isInstanceOf[NumericType]
|| a.dataType.isInstanceOf[StringType]
|| a.dataType.isInstanceOf[DateType]
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Yes, let's write a UT. Does it work BTW? Looks at least mean and date type won't work here.

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I'm working on getting the test suite running on my machine, so I need some time. I don't think that the mean will be the issue, this is just the element in the middle of the sorted collection, however, the stddev will be tricky. For the StringType this is just null.

|| a.dataType.isInstanceOf[TimestampType])

val aggExprs = statisticFns.flatMap { func =>
selectedCols.map(c => Column(Cast(func(c), StringType)).as(c.name))
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32 changes: 16 additions & 16 deletions sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
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Expand Up @@ -832,30 +832,30 @@ class DataFrameSuite extends QueryTest
}

private lazy val person2: DataFrame = Seq(
("Bob", 16, 176),
("Alice", 32, 164),
("David", 60, 192),
("Amy", 24, 180)).toDF("name", "age", "height")
("Bob", 16, 176, new Date(2020, 1, 1)),
("Alice", 32, 164, new Date(2020, 1, 5)),
("David", 60, 192, new Date(2020, 1, 19)),
("Amy", 24, 180, new Date(2020, 1, 25))).toDF("name", "age", "height", "birthday")

test("describe") {
val describeResult = Seq(
Row("count", "4", "4", "4"),
Row("mean", null, "33.0", "178.0"),
Row("stddev", null, "19.148542155126762", "11.547005383792516"),
Row("min", "Alice", "16", "164"),
Row("max", "David", "60", "192"))
Row("count", "4", "4", "4", "4"),
Row("mean", null, "33.0", "178.0", "2020-1-25"),
Row("stddev", null, "19.148542155126762", "11.547005383792516", null),
Row("min", "Alice", "16", "164", "2020-1-1"),
Row("max", "David", "60", "192", "2020-1-25"))

val emptyDescribeResult = Seq(
Row("count", "0", "0", "0"),
Row("mean", null, null, null),
Row("stddev", null, null, null),
Row("min", null, null, null),
Row("max", null, null, null))
Row("count", "0", "0", "0", "0"),
Row("mean", null, null, null, null),
Row("stddev", null, null, null, null),
Row("min", null, null, null, null),
Row("max", null, null, null, null))

def getSchemaAsSeq(df: DataFrame): Seq[String] = df.schema.map(_.name)

val describeAllCols = person2.describe()
assert(getSchemaAsSeq(describeAllCols) === Seq("summary", "name", "age", "height"))
assert(getSchemaAsSeq(describeAllCols) === Seq("summary", "name", "age", "height", "birthday"))
checkAnswer(describeAllCols, describeResult)
// All aggregate value should have been cast to string
describeAllCols.collect().foreach { row =>
Expand All @@ -875,7 +875,7 @@ class DataFrameSuite extends QueryTest
checkAnswer(describeNoCol, describeResult.map { case Row(s, _, _, _) => Row(s)} )

val emptyDescription = person2.limit(0).describe()
assert(getSchemaAsSeq(emptyDescription) === Seq("summary", "name", "age", "height"))
assert(getSchemaAsSeq(emptyDescription) === Seq("summary", "name", "age", "height", "birthday"))
checkAnswer(emptyDescription, emptyDescribeResult)
}

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