axel dahl created SPARK-8685:
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Summary: 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: Bug
Components: PySpark, SQL
Affects Versions: 1.4.0
Environment: ubuntu 14.04
Reporter: axel dahl
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':'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()
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'carol', 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'carol', 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()
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