Hi,
I tried;
val df1 = Seq((1, 1), (2, 2), (3, 3)).toDF("a", "b")
val df2 = Seq((1, 1), (2, 2), (3, 3)).toDF("a", "b")
val df3 = df1.join(df2, "a")
val df4 = df3.join(df2, "b")
And I got; org.apache.spark.sql.AnalysisException: Reference 'b' is
ambiguous, could be: b#6, b#14.;
If same case, this message makes sense and this is clear.
Thought?
// maropu
On Wed, Apr 27, 2016 at 6:09 AM, Prasad Ravilla <pras...@slalom.com> wrote:
> Also, check the column names of df1 ( after joining df2 and df3 ).
>
> Prasad.
>
> From: Ted Yu
> Date: Monday, April 25, 2016 at 8:35 PM
> To: Divya Gehlot
> Cc: "user @spark"
> Subject: Re: Cant join same dataframe twice ?
>
> Can you show us the structure of df2 and df3 ?
>
> Thanks
>
> On Mon, Apr 25, 2016 at 8:23 PM, Divya Gehlot <divya.htco...@gmail.com>
> wrote:
>
>> Hi,
>> I am using Spark 1.5.2 .
>> I have a use case where I need to join the same dataframe twice on two
>> different columns.
>> I am getting error missing Columns
>>
>> For instance ,
>> val df1 = df2.join(df3,"Column1")
>> Below throwing error missing columns
>> val df 4 = df1.join(df3,"Column2")
>>
>> Is the bug or valid scenario ?
>>
>>
>>
>>
>> Thanks,
>> Divya
>>
>
>
--
---
Takeshi Yamamuro
