Although you understand the two are semantically equivalent, the
second case involves an arbitrary condition not a join on a column per
se. In the general case there is not even a shared column between the
two being joined, so all of both are included.
On Fri, Mar 25, 2016 at 9:19 PM, 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
> ----
> https://medium.com/@jaceklaskowski/
> Mastering Apache Spark http://bit.ly/mastering-apache-spark
> Follow me at https://twitter.com/jaceklaskowski
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