Correct Takeshi
Even I am facing the same issue .
How to avoid the ambiguity ?
On 27 April 2016 at 11:54, Takeshi Yamamuro <linguin....@gmail.com> wrote:
> 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
>
