This is https://issues.apache.org/jira/browse/SPARK-6231
Unfortunately this is pretty hard to fix as its hard for us to
differentiate these without aliases. However you can add an alias as
follows:
from pyspark.sql.functions import *
df.alias("a").join(df.alias("b"), col("a.col1") == col("b.col1"))
On Tue, Apr 21, 2015 at 8:10 AM, Karlson <[email protected]> wrote:
> Sorry, my code actually was
>
> df_one = df.select('col1', 'col2')
> df_two = df.select('col1', 'col3')
>
> But in Spark 1.4.0 this does not seem to make any difference anyway and
> the problem is the same with both versions.
>
>
>
> On 2015-04-21 17:04, ayan guha wrote:
>
>> your code should be
>>
>> df_one = df.select('col1', 'col2')
>> df_two = df.select('col1', 'col3')
>>
>> Your current code is generating a tupple, and of course df_1 and df_2 are
>> different, so join is yielding to cartesian.
>>
>> Best
>> Ayan
>>
>> On Wed, Apr 22, 2015 at 12:42 AM, Karlson <[email protected]> wrote:
>>
>> Hi,
>>>
>>> can anyone confirm (and if so elaborate on) the following problem?
>>>
>>> When I join two DataFrames that originate from the same source DataFrame,
>>> the resulting DF will explode to a huge number of rows. A quick example:
>>>
>>> I load a DataFrame with n rows from disk:
>>>
>>> df = sql_context.parquetFile('data.parquet')
>>>
>>> Then I create two DataFrames from that source.
>>>
>>> df_one = df.select(['col1', 'col2'])
>>> df_two = df.select(['col1', 'col3'])
>>>
>>> Finally I want to (inner) join them back together:
>>>
>>> df_joined = df_one.join(df_two, df_one['col1'] == df_two['col2'],
>>> 'inner')
>>>
>>> The key in col1 is unique. The resulting DataFrame should have n rows,
>>> however it does have n*n rows.
>>>
>>> That does not happen, when I load df_one and df_two from disk directly. I
>>> am on Spark 1.3.0, but this also happens on the current 1.4.0 snapshot.
>>>
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