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Alexey Grishchenko commented on SPARK-10246: -------------------------------------------- Cannot reproduce, all the options with multiple conditions work on master branch: {code} >>> df.join(df4, ['name', 'age']).collect() [Row(age=5, name=u'Bob', height=None)] >>> df.join(df4, (df.name == df4.name) & (df.age == df4.age)).collect() [Row(age=5, name=u'Bob', age=5, height=None, name=u'Bob')] >>> cond = [df.name == df4.name, df.age == df4.age] >>> df.join(df4, cond).collect() Row(age=5, name=u'Bob', age=5, height=None, name=u'Bob')] {code} > Join in PySpark using a list of column names > -------------------------------------------- > > Key: SPARK-10246 > URL: https://issues.apache.org/jira/browse/SPARK-10246 > Project: Spark > Issue Type: Bug > Components: PySpark, SQL > Reporter: Michal Monselise > > Currently, there are two supported methods to perform a join: join condition > and one column name. > The documentation specifies that the join function can accept a list of > conditions or a list of column names but neither are currently supported. > This is discussed in issue SPARK-7197 as well. > Functionality should match the documentation which currently contains an > example in /spark/python/pyspark/sql/dataframe.py line 560: > >>> df.join(df4, ['name', 'age']).select(df.name, df.age).collect() > [Row(name=u'Bob', age=5)] > """ -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org