Yeah, you shouldn't have to rename the columns before joining them.
Do you see the same behavior on 1.3 vs 1.4?
Nick
2015년 6월 27일 (토) 오전 2:51, Axel Dahl <a...@whisperstream.com>님이 작성:
> still feels like a bug to have to create unique names before a join.
>
> On Fri, Jun 26, 2015 at 9:51 PM, ayan guha <guha.a...@gmail.com> wrote:
>
>> You can declare the schema with unique names before creation of df.
>> On 27 Jun 2015 13:01, "Axel Dahl" <a...@whisperstream.com> wrote:
>>
>>>
>>> I have the following code:
>>>
>>> from pyspark import SQLContext
>>>
>>> d1 = [{'name':'bob', 'country': 'usa', 'age': 1}, {'name':'alice',
>>> 'country': 'jpn', 'age': 2}, {'name':'carol', 'country': 'ire', 'age': 3}]
>>> d2 = [{'name':'bob', 'country': 'usa', 'colour':'red'}, {'name':'alice',
>>> 'country': 'ire', 'colour':'green'}]
>>>
>>> r1 = sc.parallelize(d1)
>>> r2 = sc.parallelize(d2)
>>>
>>> sqlContext = SQLContext(sc)
>>> df1 = sqlContext.createDataFrame(d1)
>>> df2 = sqlContext.createDataFrame(d2)
>>> df1.join(df2, df1.name == df2.name and df1.country == df2.country,
>>> 'left_outer').collect()
>>>
>>>
>>> When I run it I get the following, (notice in the first row, all join
>>> keys are take from the right-side and so are blanked out):
>>>
>>> [Row(age=2, country=None, name=None, colour=None, country=None,
>>> name=None),
>>> Row(age=1, country=u'usa', name=u'bob', colour=u'red', country=u'usa',
>>> name=u'bob'),
>>> Row(age=3, country=u'ire', name=u'alice', colour=u'green',
>>> country=u'ire', name=u'alice')]
>>>
>>> I would expect to get (though ideally without duplicate columns):
>>> [Row(age=2, country=u'ire', name=u'Alice', colour=None, country=None,
>>> name=None),
>>> Row(age=1, country=u'usa', name=u'bob', colour=u'red', country=u'usa',
>>> name=u'bob'),
>>> Row(age=3, country=u'ire', name=u'alice', colour=u'green',
>>> country=u'ire', name=u'alice')]
>>>
>>> The workaround for now is this rather clunky piece of code:
>>> df2 = sqlContext.createDataFrame(d2).withColumnRenamed('name',
>>> 'name2').withColumnRenamed('country', 'country2')
>>> df1.join(df2, df1.name == df2.name2 and df1.country == df2.country2,
>>> 'left_outer').collect()
>>>
>>> So to me it looks like a bug, but am I doing something wrong?
>>>
>>> Thanks,
>>>
>>> -Axel
>>>
>>>
>>>
>>>
>>>
>
