Hi Jacek ,
The difference is being mentioned in Spark doc itself
Note that if you perform a self-join using this function without aliasing
the input
* [[DataFrame]]s, you will NOT be able to reference any columns after the
join, since
* there is no way to disambiguate which side of the join you would like to
reference.
*
On 26 March 2016 at 04:19, Jacek Laskowski <ja...@japila.pl> wrote:
> Hi,
>
> I've read the note about both columns included when DataFrames are
> joined, but don't think it differentiated between versions of join. Is
> this a feature or a bug that the following session shows one _1 column
> with Seq("_1") and two columns for ===?
>
> {code}
> scala> left.join(right, Seq("_1")).show
> +---+---+---+
> | _1| _2| _2|
> +---+---+---+
> | 1| a| a|
> | 2| b| b|
> +---+---+---+
>
>
> scala> left.join(right, left("_1") === right("_1")).show
> +---+---+---+---+
> | _1| _2| _1| _2|
> +---+---+---+---+
> | 1| a| 1| a|
> | 2| b| 2| b|
> +---+---+---+---+
> {code}
>
> Pozdrawiam,
> Jacek Laskowski
> ----
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