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https://issues.apache.org/jira/browse/SPARK-14343?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cheng Lian resolved SPARK-14343.
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Resolution: Fixed
Fix Version/s: 2.0.0
Issue resolved by pull request 13431
[https://github.com/apache/spark/pull/13431]
> Dataframe operations on a partitioned dataset (using partition discovery)
> return invalid results
> ------------------------------------------------------------------------------------------------
>
> Key: SPARK-14343
> URL: https://issues.apache.org/jira/browse/SPARK-14343
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.1, 2.0.0
> Environment: Mac OS X 10.11.4 / Ubuntu 16.04 LTS
> Reporter: Jurriaan Pruis
> Assignee: Cheng Lian
> Priority: Critical
> Fix For: 2.0.0
>
>
> When reading a dataset using {{sqlContext.read.text()}} queries on the
> partitioned column return invalid results.
> h2. How to reproduce:
> h3. Generate datasets
> {code:title=repro.sh}
> #!/bin/sh
> mkdir -p dataset/year=2014
> mkdir -p dataset/year=2015
> echo "data from 2014" > dataset/year=2014/part01.txt
> echo "data from 2015" > dataset/year=2015/part01.txt
> {code}
> {code:title=repro2.sh}
> #!/bin/sh
> mkdir -p dataset2/month=june
> mkdir -p dataset2/month=july
> echo "data from june" > dataset2/month=june/part01.txt
> echo "data from july" > dataset2/month=july/part01.txt
> {code}
> h3. using first dataset
> {code:none}
> >>> df = sqlContext.read.text('dataset')
> ...
> >>> df
> DataFrame[value: string, year: int]
> >>> df.show()
> +--------------+----+
> | value|year|
> +--------------+----+
> |data from 2014|2014|
> |data from 2015|2015|
> +--------------+----+
> >>> df.select('year').show()
> +----+
> |year|
> +----+
> | 14|
> | 14|
> +----+
> {code}
> This is clearly wrong. Seems like it returns the length of the value column?
> h3. using second dataset
> With another dataset it looks like this:
> {code:none}
> >>> df = sqlContext.read.text('dataset2')
> >>> df
> DataFrame[value: string, month: string]
> >>> df.show()
> +--------------+-----+
> | value|month|
> +--------------+-----+
> |data from june| june|
> |data from july| july|
> +--------------+-----+
> >>> df.select('month').show()
> +--------------+
> | month|
> +--------------+
> |data from june|
> |data from july|
> +--------------+
> {code}
> Here it returns the value of the value column instead of the month partition.
> h3. Workaround
> When I convert the dataframe to an RDD and back to a DataFrame I get the
> following result (which is the expected behaviour):
> {code:none}
> >>> df.rdd.toDF().select('month').show()
> +-----+
> |month|
> +-----+
> | june|
> | july|
> +-----+
> {code}
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