Hisoka-X opened a new pull request, #40865:
URL: https://github.com/apache/spark/pull/40865
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### What changes were proposed in this pull request?
Example query:
```sql
create or replace temp view t0 (a, b)
as values
(1, 1.0),
(2, 2.0);
create or replace temp view t1 (c, d)
as values
(2, 3.0);
spark.sql("select *, (select (count(1)) is null from t1 where t0.a = t1.c)
from t0").collect()
res6: Array[org.apache.spark.sql.Row] = Array([1,1.0,null], [2,2.0,false])
```
In this subquery, count(1) always evaluates to a non-null integer value, so
count(1) is null is always false. The correct evaluation of the subquery is
always false.
We incorrectly evaluate it to null for empty groups. The reason is that
NullPropagation rewrites `Aggregate [c] [isnull(count(1))]` to `Aggregate [c]
[false]`, this rewrite would be correct normally, but in the context of a
scalar subquery it breaks our count bug handling in
RewriteCorrelatedScalarSubquery.constructLeftJoins . By the time we get there,
the query appears to not have the count bug - it looks the same as if the
original query had a subquery with `select any_value(false) from r...`, and
that case is not subject to the count bug.
Postgres comparison show correct always-false result:
http://sqlfiddle.com/#!17/67822/5
Solution:
In scalar subquery must return only one column, and this return column is
the result of aggregation. We can judge whether the column returned by subquery
is foldable. If it is, it means that the result of this value has no
substantial relationship with the data, and the presence or absence of data
will not affect this column. So in this case, this column can be extracted from
the JOIN to ensure that this value can be obtained regardless of whether the
data JOIN is successful or not.
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### Why are the changes needed?
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### Does this PR introduce _any_ user-facing change?
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