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
