Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/14580
@hvanhovell The output schema is different. They are not equivalent. Thus,
the existing way to handle `using/natural joins` is wrong. We need to introduce
new join types.
Let me show you the examples.
```Scala
Seq((1, "val_1"), (2, "val_2")).toDF("key",
"value").createOrReplaceTempView("A")
Seq((2, "val_2"), (3, "val_3")).toDF("key",
"value").createOrReplaceTempView("B")
```
```Scala
sql("select * from a full join b using(key)").show(true)
+---+-----+-----+
|key|value|value|
+---+-----+-----+
| 3| null|val_3|
| 1|val_1| null|
| 2|val_2|val_2|
+---+-----+-----+
```
```Scala
sql("select * from a full join b on a.key = b.key").show(true)
+----+-----+----+-----+
| key|value| key|value|
+----+-----+----+-----+
|null| null| 3|val_3|
| 1|val_1|null| null|
| 2|val_2| 2|val_2|
+----+-----+----+-----+
```
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