kings129 opened a new pull request, #40755:
URL: https://github.com/apache/spark/pull/40755
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### What changes were proposed in this pull request?
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When doing an outer join with joinWith on DataFrames, unmatched rows return
Row objects with null fields instead of a single null value. This is not a
expected behavior, and it's a regression introduced in [this
commit](https://github.com/apache/spark/commit/cd92f25be5a221e0d4618925f7bc9dfd3bb8cb59).
This pull request aims to fix the regression, note this is not a full
rollback of the commit, do not add back "schema" variable.
```
case class ClassData(a: String, b: Int)
val left = Seq(ClassData("a", 1), ClassData("b", 2)).toDF
val right = Seq(ClassData("x", 2), ClassData("y", 3)).toDF
left.joinWith(right, left("b") === right("b"), "left_outer").collect
```
```
Wrong results (current behavior): Array(([a,1],[null,null]),
([b,2],[x,2]))
Correct results: Array(([a,1],null), ([b,2],[x,2]))
```
### Why are the changes needed?
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We need to address the regression mentioned above. It results in unexpected
behavior changes in the Dataframe joinWith API between versions 2.4.8 and
3.0.0+. This could potentially cause data correctness issues for users who
expect the old behavior when using Spark 3.0.0+.
### Does this PR introduce _any_ user-facing change?
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No
### How was this patch tested?
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Added unit test (copied from previous [closed pull
request](https://github.com/apache/spark/pull/35140, credit to Clément de Groc)
Run sql-core and sql-catalyst submodules locally with ./build/mvn clean
package -pl sql/core,sql/catalyst
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