Kimahriman commented on pull request #32972:
URL: https://github.com/apache/spark/pull/32972#issuecomment-864554473
Yeah I'm saying it only properly handles one level of a nested struct, not
recursively like it should. Got your code running to show an example:
```
>>> df1 = spark.createDataFrame([Row(a=Row(aa=Row(aaa=1)))])
>>> df2 = spark.createDataFrame([Row(a=Row(aa=Row(aab=1)))])
>>> df1
DataFrame[a: struct<aa:struct<aaa:bigint>>]
>>> df2
DataFrame[a: struct<aa:struct<aab:bigint>>]
>>> df1.unionByName(df2)
DataFrame[a: struct<aa:struct<aaa:bigint,aab:bigint>>]
>>> df1.unionByName(df2).explain()
== Physical Plan ==
Union
:- *(1) Project [if (isnull(a#21)) null else named_struct(aa, if
(isnull(a#21.aa)) null else named_struct(aaa, a#21.aa.aaa, aab, null)) AS a#37]
: +- *(1) Scan ExistingRDD[a#21]
+- *(2) Project [if (isnull(a#23)) null else named_struct(aa, if
(isnull(a#23.aa)) null else named_struct(aaa, null, aab, a#23.aa.aab)) AS a#34]
+- *(2) Scan ExistingRDD[a#23]
```
The inner struct gets merged adding missing columns even though
allowMissingCol is false
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