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Outer JOIN column ambiguity #251

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linhr opened this issue Oct 17, 2024 · 1 comment
Open

Outer JOIN column ambiguity #251

linhr opened this issue Oct 17, 2024 · 1 comment
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@linhr
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linhr commented Oct 17, 2024

Spark handles projection differently for outer join outputs, based on how the join column is specified in .select(). Here is an example for the behavior that we should support.

>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")]).toDF("age", "name")
>>> df2 = spark.createDataFrame([Row(height=80, name="Tom"), Row(height=85, name="Bob")])

>>> df.join(df2, 'name', 'outer').sort(desc("name")).show()
 ----- ---- ------ 
| name| age|height|
 ----- ---- ------ 
|  Tom|NULL|    80|
|  Bob|   5|    85|
|Alice|   2|  NULL|
 ----- ---- ------ 

>>> df.join(df2, 'name', 'outer').select('name', 'height').sort(desc("name")).show()
 ----- ------ 
| name|height|
 ----- ------ 
|  Tom|    80|
|  Bob|    85|
|Alice|  NULL|
 ----- ------ 

>>> df.join(df2, 'name', 'outer').select(df.name, 'height').sort(desc("name")).show()
 ----- ------ 
| name|height|
 ----- ------ 
|  Bob|    85|
|Alice|  NULL|
| NULL|    80|
 ----- ------ 

>>> df.join(df2, 'name', 'outer').select(df2.name, 'height').sort(desc("name")).show()
 ---- ------ 
|name|height|
 ---- ------ 
| Tom|    80|
| Bob|    85|
|NULL|  NULL|
 ---- ------

The example is extended from DataFrame.join() doctest in Spark.
@linhr linhr mentioned this issue Oct 17, 2024
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@linhr linhr added the bug Something isn't working label Oct 17, 2024
@linhr
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linhr commented Oct 18, 2024

Here are more examples.

>>> df.join(df2, 'name', 'outer').sort(desc(df.name)).show()
 ----- ---- ------                                                              
| name| age|height|
 ----- ---- ------ 
|  Bob|   5|    85|
|Alice|   2|  NULL|
|  Tom|NULL|    80|
 ----- ---- ------ 

>>> df.join(df2, 'name', 'outer').sort(desc(df2.name)).show()
 ----- ---- ------ 
| name| age|height|
 ----- ---- ------ 
|  Tom|NULL|    80|
|  Bob|   5|    85|
|Alice|   2|  NULL|
 ----- ---- ------

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