Github user gatorsmile commented on the pull request:
https://github.com/apache/spark/pull/9548#issuecomment-155183305
I can't fix the problem without a major code change. The current design of
dataFrame has a fundamental problem. When using column references, we might hit
various strange issues if the dataFrame has the columns with the same name and
expression id. Note that this might occur even if we do not have self joins.
For example, in the following code,
```scala
val df1 = Seq((1, 3), (2, 1)).toDF("keyCol1", "keyCol2")
val df2 = Seq((1, 4, 0), (2, 1, 0)).toDF("keyCol1", "keyCol3",
"keyColToDrop")
val df3 = df1.join(df2, df1("keyCol1") === df2("keyCol1"))
val col = df3("keyColToDrop")
val df = df2.drop(col)
df.printSchema()
```
Above, we can use a column reference of df3 to drop the column in df2. That
does not make sense, right?
In each column reference, we have to know the original data source.
@marmbrus @rxin @liancheng
Should I propose a solution to fix this problem? Does the new Dataset APIs
resolve this issue?
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