EnricoMi commented on code in PR #37407:
URL: https://github.com/apache/spark/pull/37407#discussion_r972844049
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala:
##########
@@ -869,26 +873,55 @@ class Analyzer(override val catalogManager:
CatalogManager)
def apply(plan: LogicalPlan): LogicalPlan =
plan.resolveOperatorsWithPruning(
_.containsPattern(UNPIVOT), ruleId) {
- // once children and ids are resolved, we can determine values, if non
were given
- case up: Unpivot if up.childrenResolved && up.ids.forall(_.resolved) &&
up.values.isEmpty =>
- up.copy(values = up.child.output.diff(up.ids))
+ // once children are resolved, we can determine values from ids and vice
versa
+ // if only either is given
+ case up: Unpivot if up.childrenResolved &&
+ up.ids.exists(_.forall(_.resolved)) && up.values.isEmpty =>
+ up.copy(values =
+ Some(
+ up.child.output.diff(up.ids.get.flatMap(_.references))
Review Comment:
I thought, the following would be annoying:
```
scala> val df = spark.range(5).select($"id", ($"id"*10).as("val"))
scala> df.show()
+---+---+
| id|val|
+---+---+
| 0| 0|
| 1| 10|
| 2| 20|
| 3| 30|
| 4| 40|
+---+---+
df.unpivot(Array($"id" * 2), "col", "val").show()
+--------+---+---+
|(id * 2)|col|val|
+--------+---+---+
| 0|id| 0|
| 0|val| 0|
| 2|id| 1|
| 2|val| 10|
| 4|id 2|
| 4|val| 20|
| 6|id 3|
| 6|val| 30|
| 8|id 4|
| 8|val| 40|
+--------+---+---+
```
Of course, that id manipulation can be done before `unpivot` to
"materialize" it as a reference:
```
df.withColumn("id", $"id" * 2).unpivot(Array($"id"), "col", "val").show()
+---+---+---+
| id|col|val|
+---+---+---+
| 0|val| 0|
| 2|val| 10|
| 4|val| 20|
| 6|val| 30|
| 8|val| 40|
+---+---+---+
```
Happy to remove that complexity.
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