Michael, thank you for the workaround and for letting me know of the
upcoming enhancements, both of which sound appealing.
On Sun, Mar 22, 2015 at 1:25 PM, Michael Armbrust <mich...@databricks.com>
wrote:
> You can include * and a column alias in the same select clause
> var df1 = sqlContext.sql("select *, column_id AS table1_id from table1")
>
>
> I'm also hoping to resolve SPARK-6376
> <https://issues.apache.org/jira/browse/SPARK-6376> before Spark 1.3.1
> which will let you do something like:
> var df1 = sqlContext.sql("select * from table1").as("t1")
> var df2 = sqlContext.sql("select * from table2).as("t2")
> df1.join(df2, df1("column_id") === df2("column_id")).select("t1.column_id")
>
> Finally, there is SPARK-6380
> <https://issues.apache.org/jira/browse/SPARK-6380> that hopes to simplify
> this particular case.
>
> Michael
>
> On Sat, Mar 21, 2015 at 3:02 PM, Eric Friedman <eric.d.fried...@gmail.com>
> wrote:
>
>> I have a couple of data frames that I pulled from SparkSQL and the
>> primary key of one is a foreign key of the same name in the other. I'd
>> rather not have to specify each column in the SELECT statement just so that
>> I can rename this single column.
>>
>> When I try to join the data frames, I get an exception because it finds
>> the two columns of the same name to be ambiguous. Is there a way to
>> specify which side of the join comes from data frame A and which comes from
>> B?
>>
>> var df1 = sqlContext.sql("select * from table1")
>> var df2 = sqlContext.sql("select * from table2)
>>
>> df1.join(df2, df1("column_id") === df2("column_id"))
>>
>
>
