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[SPARK-25048][SQL] Pivoting by multiple columns in Scala/Java #22316

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[SPARK-25048][SQL] Pivoting by multiple columns in Scala/Java #22316

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0580725
Support columns as values
MaxGekk Sep 2, 2018
1221db3
Added a test for the case when values are not specified
MaxGekk Sep 2, 2018
a097b29
Added a test for Java
MaxGekk Sep 2, 2018
ef8e22a
Minor changes in an example
MaxGekk Sep 2, 2018
673ef00
Improving comments
MaxGekk Sep 3, 2018
8ccf845
Targeting Spark 3.0.0
MaxGekk Sep 8, 2018
382640b
Re-targeting to 2.5.0
MaxGekk Sep 21, 2018
49b47fb
Merge remote-tracking branch 'origin/master' into pivoting-by-multipl…
MaxGekk Sep 24, 2018
4daeb92
Merge remote-tracking branch 'origin/master' into pivoting-by-multipl…
MaxGekk Sep 28, 2018
d645d06
Replacing lit() by Literal.apply and Column case
MaxGekk Sep 28, 2018
43972ef
Addressing a review comment
MaxGekk Sep 29, 2018
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13 changes: 11 additions & 2 deletions sql/core/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,7 @@ import org.apache.spark.sql.catalyst.expressions.aggregate._
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.util.toPrettySQL
import org.apache.spark.sql.execution.aggregate.TypedAggregateExpression
import org.apache.spark.sql.functions.lit
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.{NumericType, StructType}

Expand Down Expand Up @@ -406,6 +407,14 @@ class RelationalGroupedDataset protected[sql](
* df.groupBy($"year").pivot($"course", Seq("dotNET", "Java")).sum($"earnings")
* }}}
*
* For pivoting by multiple columns, use the `struct` function to combine the columns and values:
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Since the documentation states it's an overloaded version of the `pivot` method with `pivotColumn` of the `String` type., shall we move this contents to that method?

Also, I would document this, for instance,

From Spark 2.4.0, values can be literal columns, for instance, struct. For pivoting by multiple columns, use the struct function to combine the columns and values.

*
* {{{
* df.groupBy($"year")
* .pivot(struct($"course", $"training"), Seq(struct(lit("java"), lit("Experts"))))
* .agg(sum($"earnings"))
* }}}
*
* @param pivotColumn the column to pivot.
* @param values List of values that will be translated to columns in the output DataFrame.
* @since 2.4.0
Expand All @@ -416,7 +425,7 @@ class RelationalGroupedDataset protected[sql](
new RelationalGroupedDataset(
df,
groupingExprs,
RelationalGroupedDataset.PivotType(pivotColumn.expr, values.map(Literal.apply)))
RelationalGroupedDataset.PivotType(pivotColumn.expr, values.map(lit(_).expr)))
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What do you think about map(lit).map(_.expr) instead?

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Don't see any advantages of this. It is longer and slower.

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@MaxGekk, just for doubly doubly sure, shell we Try(...).getOrElse(lit(...).expr)? Looks at least there's one case of a potential behaviour change about scale and precision.

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Looks at least there's one case of a potential behaviour change about scale and precision.

Could you explain, please. Why do you expect some behavior change?

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now we eventually call Literal.create instead of Literal.apply. I'm not sure if there is a behavior change though.

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from a quick look, seems Literal.create is more powerful and should not have regressions.

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That's true in general but specifically is decimal precision more correct?

case _: RelationalGroupedDataset.PivotType =>
throw new UnsupportedOperationException("repeated pivots are not supported")
case _ =>
Expand Down Expand Up @@ -561,5 +570,5 @@ private[sql] object RelationalGroupedDataset {
/**
* To indicate it's the PIVOT
*/
private[sql] case class PivotType(pivotCol: Expression, values: Seq[Literal]) extends GroupType
private[sql] case class PivotType(pivotCol: Expression, values: Seq[Expression]) extends GroupType
}
16 changes: 16 additions & 0 deletions sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
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Expand Up @@ -306,6 +306,22 @@ public void pivot() {
Assert.assertEquals(30000.0, actual.get(1).getDouble(2), 0.01);
}

@Test
public void pivotColumnValues() {
Dataset<Row> df = spark.table("courseSales");
List<Row> actual = df.groupBy("year")
.pivot(col("course"), Arrays.asList(lit("dotNET"), lit("Java")))
.agg(sum("earnings")).orderBy("year").collectAsList();

Assert.assertEquals(2012, actual.get(0).getInt(0));
Assert.assertEquals(15000.0, actual.get(0).getDouble(1), 0.01);
Assert.assertEquals(20000.0, actual.get(0).getDouble(2), 0.01);

Assert.assertEquals(2013, actual.get(1).getInt(0));
Assert.assertEquals(48000.0, actual.get(1).getDouble(1), 0.01);
Assert.assertEquals(30000.0, actual.get(1).getDouble(2), 0.01);
}

private String getResource(String resource) {
try {
// The following "getResource" has different behaviors in SBT and Maven.
Expand Down
23 changes: 23 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/DataFramePivotSuite.scala
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Expand Up @@ -308,4 +308,27 @@ class DataFramePivotSuite extends QueryTest with SharedSQLContext {

assert(exception.getMessage.contains("aggregate functions are not allowed"))
}

test("pivoting column list with values") {
val expected = Row(2012, 10000.0, null) :: Row(2013, 48000.0, 30000.0) :: Nil
val df = trainingSales
.groupBy($"sales.year")
.pivot(struct(lower($"sales.course"), $"training"), Seq(
struct(lit("dotnet"), lit("Experts")),
struct(lit("java"), lit("Dummies")))
).agg(sum($"sales.earnings"))

checkAnswer(df, expected)
}

test("pivoting column list") {
val exception = intercept[RuntimeException] {
trainingSales
.groupBy($"sales.year")
.pivot(struct(lower($"sales.course"), $"training"))
.agg(sum($"sales.earnings"))
.collect()
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@maropu maropu Sep 3, 2018 •
edited
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Don't need this .collect() to cactch the RuntimeException? btw, IMHO AnalysisException is better than RuntimeException in this case? Can't we?

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My changes don't throw the exception. It is thrown in the collect() :

spark/sql/core/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala

Line 385 in 41c2227

.collect()

@maropu Do you propose to catch RuntimeException and replace it by AnalysisException?

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I tried in your branch;

scala> df.show
+--------+--------------------+
|training|               sales|
+--------+--------------------+
| Experts|[dotNET, 2012, 10...|
| Experts|[JAVA, 2012, 2000...|
| Dummies|[dotNet, 2012, 50...|
| Experts|[dotNET, 2013, 48...|
| Dummies|[Java, 2013, 3000...|
+--------+--------------------+

scala> df.groupBy($"sales.year").pivot(struct(lower($"sales.course"), $"training")).agg(sum($"sales.earnings"))
java.lang.RuntimeException: Unsupported literal type class org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema [dotnet,Dummies]
  at org.apache.spark.sql.catalyst.expressions.Literal$.apply(literals.scala:78)
  at org.apache.spark.sql.catalyst.expressions.Literal$$anonfun$create$2.apply(literals.scala:164)
  at org.apache.spark.sql.catalyst.expressions.Literal$$anonfun$create$2.apply(literals.scala:164)
  at scala.util.Try.getOrElse(Try.scala:79)
  at org.apache.spark.sql.catalyst.expressions.Literal$.create(literals.scala:163)
  at org.apache.spark.sql.functions$.typedLit(functions.scala:127)

I miss something?

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I miss something?

No, you don't. The exception for sure is thrown inside of lit because collect() returns a complex value which cannot be "wrapped" by lit. This is exactly checked in the test which I added to show existing behavior.

btw, IMHO AnalysisException is better than RuntimeException in this case?

@maropu Could you explain, please, why do you think AnalysisException is better for the error occurs in run-time?

Just in case, in the PR, I don't aim to change behavior of existing method: def pivot(pivotColumn: Column): RelationalGroupedDataset. I believe it should be discussed separately regarding to needs for changing user visible behavior. The PR aims to improve def pivot(pivotColumn: Column, values: Seq[Any]): RelationalGroupedDataset to allow users to specify struct literals in particular. Please, see the description.

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I think invalid queries basically throw `AnalysisException. But, yea, indeed, we'd better to keep the current behaivour. Thanks!

}
assert(exception.getMessage.contains("Unsupported literal type"))
}
}