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https://issues.apache.org/jira/browse/SPARK-11757?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Petri Kärkäs updated SPARK-11757:
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Description:
Reading in dataframes from Parquet format in s3, and executing a join between
them fails when evoked by column name. Works correctly if a join condition is
used instead:
{code:none}
sqlContext = SQLContext(sc)
a = sqlContext.read.parquet('s3://path-to-data-a/')
b = sqlContext.read.parquet('s3://path-to-data-b/')
# result 0 rows
c = a.join(b, on='id', how='left_outer')
c.count()
# correct output
d = a.join(b, a['id']==b['id'], how='left_outer')
d.count()
{code}
was:
Reading in dataframes from Parquet format in s3, and executing a join between
them fails when evoked by column name. Works correctly if a join condition is
used instead:
sqlContext = SQLContext(sc)
a = sqlContext.read.parquet('s3://path-to-data-a/')
b = sqlContext.read.parquet('s3://path-to-data-b/')
# results 0 rows
c = a.join(b, on='id', how='left_outer')
c.count()
# correct result
d = a.join(b, a['id']==b['id'], how='left_outer')
d.count()
> Incorrect join output for joining two dataframes loaded from Parquet format
> ---------------------------------------------------------------------------
>
> Key: SPARK-11757
> URL: https://issues.apache.org/jira/browse/SPARK-11757
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 1.5.0
> Environment: Python 2.7, Spark 1.5.0, Amazon linux ami
> https://aws.amazon.com/amazon-linux-ami/2015.03-release-notes/
> Reporter: Petri Kärkäs
> Labels: dataframe, emr, join, pyspark
>
> Reading in dataframes from Parquet format in s3, and executing a join between
> them fails when evoked by column name. Works correctly if a join condition is
> used instead:
> {code:none}
> sqlContext = SQLContext(sc)
> a = sqlContext.read.parquet('s3://path-to-data-a/')
> b = sqlContext.read.parquet('s3://path-to-data-b/')
> # result 0 rows
> c = a.join(b, on='id', how='left_outer')
> c.count()
> # correct output
> d = a.join(b, a['id']==b['id'], how='left_outer')
> d.count()
> {code}
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