Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/12421#discussion_r60854163
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
---
@@ -1321,7 +1321,9 @@ object DecimalAggregates extends Rule[LogicalPlan] {
/** Maximum number of decimal digits representable precisely in a Double
*/
private val MAX_DOUBLE_DIGITS = 15
- def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions
{
+ def apply(plan: LogicalPlan): LogicalPlan = plan
transformExpressionsDown {
+ case we @ WindowExpression(AggregateExpression(_, _, _, _), _) => we
--- End diff --
Oh, thank you for coming back, @hvanhovell . I tried that last week that,
but it faced the following error in WindowExec.scala. (last week, it was
Window.scala)
```
Unsupported window function:
cast(((avg(UnscaledValue(a#14)),mode=Complete,isDistinct=false) / 10.0) as
decimal(6,5))
java.lang.RuntimeException: Unsupported window function:
cast(((avg(UnscaledValue(a#14)),mode=Complete,isDistinct=false) / 10.0) as
decimal(6,5))
at scala.sys.package$.error(package.scala:27)
at
org.apache.spark.sql.execution.WindowExec$$anonfun$windowFrameExpressionFactoryPairs$1$$anonfun$apply$2.apply(WindowExec.scala:183)
```
After fixing that, there occured errors at compiling generated code
(codeGen result) due to the mismatched type between input schema (Decimal to
Long and vice versa). I did try the input schema or BoundedReferences, too.
Actually, this observation is the same one I mentioned 9 days ago. I tried
other approaches in other ways. But nothing was a clean solution for this. So,
I decided to ask your help.
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