Github user hvanhovell commented on a diff in the pull request:
https://github.com/apache/spark/pull/9566#discussion_r44514711
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
---
@@ -545,19 +576,21 @@ abstract class AggregationQuerySuite extends
QueryTest with SQLTestUtils with Te
| count(distinct value2),
| sum(distinct value2),
| count(distinct value1, value2),
+ | longProductSum(distinct value1, value2),
--- End diff --
Yes and No.
The input we care about only consists of these tuples: ```[value1=null,
value2=null], [value1=null, value2=1], [value1=1, value2=null], and [value1=1,
value2=1]```
However in the current implementation a distinct aggregate will see more
input than those. It will also see records from other groups. However, the
values in these records are nulled out. The assumption here is that an
AggregateFunction is not changed by an all NULL update. The only case I can
think of that would be problematic is a ```FIRST(DISTINCT ...)```; which
shouldn't be used like that anyway.
We could solve this by wrapping AggregateFunctions with an operator which
will only update if the group id is correct.
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