[jira] [Closed] (SPARK-8685) dataframe left joins are not working as expected in pyspark

["Reynold Xin (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-8685?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Reynold Xin closed SPARK-8685.
------------------------------
       Resolution: Duplicate
         Assignee: Davies Liu
    Fix Version/s: 1.5.0
                   1.4.1

This is already fixed. 

> dataframe left joins are not working as expected in pyspark
> -----------------------------------------------------------
>
>                 Key: SPARK-8685
>                 URL: https://issues.apache.org/jira/browse/SPARK-8685
>             Project: Spark
>          Issue Type: Sub-task
>          Components: PySpark, SQL
>    Affects Versions: 1.4.0
>         Environment: ubuntu 14.04
>            Reporter: axel dahl
>            Assignee: Davies Liu
>            Priority: Critical
>             Fix For: 1.4.1, 1.5.0
>
>
> I have the following code:
> {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':'carol', '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()
> {code}
> 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):
> {code}
> [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'carol', colour=u'green', country=u'ire', name=u'alice')]
> {code}
> I would expect to get (though ideally without duplicate columns):
> {code}
> [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'carol', colour=u'green', country=u'ire', name=u'alice')]
> {code}
> The workaround for now is this rather clunky piece of code:
> {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()
> {code}



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