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[SPARK-37829][SQL][3.3] Dataframe.joinWith outer-join should return a null value for unmatched row - #40858
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[SPARK-37829][SQL][3.3] Dataframe.joinWith outer-join should return a null value for unmatched row#40858kings129 wants to merge 1 commit into
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… value for unmatched row ### What changes were proposed in this pull request? 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](apache@cd92f25). 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? 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? No ### How was this patch tested? Added unit test (use the same test in previous [closed pull request](apache#35140), credit to Clément de Groc) Run sql-core and sql-catalyst submodules locally with ./build/mvn clean package -pl sql/core,sql/catalyst Closes apache#40755 from kings129/encoder_bug_fix. Authored-by: --global <xuqiang129@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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+1, LGTM. Merged to branch-3.3.
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… null value for unmatched row ### What changes were proposed in this pull request? This is a pull request to port the fix from the master branch to version 3.3. [PR](#40755) 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](cd92f25). 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? 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? No ### How was this patch tested? Added unit test (use the same test in previous [closed pull request](#35140), credit to Clément de Groc) Run sql-core and sql-catalyst submodules locally with ./build/mvn clean package -pl sql/core,sql/catalyst Closes #40755 from kings129/encoder_bug_fix. Authored-by: --global <xuqiang129gmail.com> Closes #40858 from kings129/fix_encoder_branch_33. Authored-by: --global <xuqiang129@gmail.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
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What changes were proposed in this pull request?
This is a pull request to port the fix from the master branch to version 3.3. PR
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. This pull request aims to fix the regression, note this is not a full rollback of the commit, do not add back "schema" variable.
Why are the changes needed?
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?
No
How was this patch tested?
Added unit test (use the same test in previous closed pull request, credit to Clément de Groc) Run sql-core and sql-catalyst submodules locally with ./build/mvn clean package -pl sql/core,sql/catalyst
Closes #40755 from kings129/encoder_bug_fix.
Authored-by: --global xuqiang129@gmail.com