bersprockets opened a new pull request, #47876:
URL: https://github.com/apache/spark/pull/47876
### What changes were proposed in this pull request?
Before this PR, `RewriteDistinctAggregates` could potentially replace
literals in the aggregate expressions with output attributes from the `Expand`
operator. This can occur when a group-by expression is a literal that happens
by chance to match a literal used in an aggregate expression. E.g.:
```
create or replace temp view v1(a, b, c) as values
(1, 1.001d, 2), (2, 3.001d, 4), (2, 3.001, 4);
cache table v1;
select
round(sum(b), 6) as sum1,
count(distinct a) as count1,
count(distinct c) as count2
from (
select
6 as gb,
*
from v1
)
group by a, gb;
```
In the optimized plan, you can see that the literal 6 in the `round`
function invocation has been patched with an output attribute (6#163) from the
`Expand` operator:
```
== Optimized Logical Plan ==
'Aggregate [a#123, 6#163],
[round(first(sum(__auto_generated_subquery_name.b)#167, true) FILTER (WHERE
(gid#162 = 0)), 6#163) AS sum1#114, count(__auto_generated_subquery_name.a#164)
FILTER (WHERE (gid#162 = 1)) AS count1#115L,
count(__auto_generated_subquery_name.c#165) FILTER (WHERE (gid#162 = 2)) AS
count2#116L]
+- Aggregate [a#123, 6#163, __auto_generated_subquery_name.a#164,
__auto_generated_subquery_name.c#165, gid#162], [a#123, 6#163,
__auto_generated_subquery_name.a#164, __auto_generated_subquery_name.c#165,
gid#162, sum(__auto_generated_subquery_name.b#166) AS
sum(__auto_generated_subquery_name.b)#167]
+- Expand [[a#123, 6, null, null, 0, b#124], [a#123, 6, a#123, null, 1,
null], [a#123, 6, null, c#125, 2, null]], [a#123, 6#163,
__auto_generated_subquery_name.a#164, __auto_generated_subquery_name.c#165,
gid#162, __auto_generated_subquery_name.b#166]
+- InMemoryRelation [a#123, b#124, c#125], StorageLevel(disk, memory,
deserialized, 1 replicas)
+- LocalTableScan [a#6, b#7, c#8]
```
This is because the literal 6 was used in the group-by expressions (referred
to as gb in the query, and renamed 6#163 in the `Expand` operator's output
attributes).
After this PR, foldable expressions in the aggregate expressions are kept
as-is.
### Why are the changes needed?
Some expressions require a foldable argument. In the above example, the
`round` function requires a foldable expression as the scale argument. Because
the scale argument is patched with an attribute,
`RoundBase#checkInputDataTypes` returns an error, which leaves the `Aggregate`
operator unresolved:
```
[INTERNAL_ERROR] Invalid call to dataType on unresolved object SQLSTATE:
XX000
org.apache.spark.sql.catalyst.analysis.UnresolvedException: [INTERNAL_ERROR]
Invalid call to dataType on unresolved object SQLSTATE: XX000
at
org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute.dataType(unresolved.scala:255)
at
org.apache.spark.sql.catalyst.types.DataTypeUtils$.$anonfun$fromAttributes$1(DataTypeUtils.scala:241)
at scala.collection.immutable.List.map(List.scala:247)
at scala.collection.immutable.List.map(List.scala:79)
at
org.apache.spark.sql.catalyst.types.DataTypeUtils$.fromAttributes(DataTypeUtils.scala:241)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.schema$lzycompute(QueryPlan.scala:428)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.schema(QueryPlan.scala:428)
at
org.apache.spark.sql.execution.SparkPlan.executeCollectPublic(SparkPlan.scala:474)
...
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New tests.
### Was this patch authored or co-authored using generative AI tooling?
No.
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