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https://issues.apache.org/jira/browse/SPARK-6917?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Reynold Xin resolved SPARK-6917.
--------------------------------
Resolution: Fixed
Fix Version/s: 1.4.0
> Broken data returned to PySpark dataframe if any large numbers used in Scala
> land
> ---------------------------------------------------------------------------------
>
> Key: SPARK-6917
> URL: https://issues.apache.org/jira/browse/SPARK-6917
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 1.3.0
> Environment: Spark 1.3, Python 2.7.6, Scala 2.10
> Reporter: Harry Brundage
> Assignee: Davies Liu
> Priority: Critical
> Fix For: 1.4.0
>
> Attachments: part-r-00001.parquet
>
>
> When trying to access data stored in a Parquet file with an INT96 column
> (read: TimestampType() encoded for Impala), if the INT96 column is included
> in the fetched data, other, smaller numeric types come back broken.
> {code}
> In [1]:
> sql.parquetFile("/Users/hornairs/Downloads/part-r-00001.parquet").select('int_col',
> 'long_col').first()
> Out[1]: Row(int_col=Decimal('1'), long_col=Decimal('10'))
> In [2]:
> sql.parquetFile("/Users/hornairs/Downloads/part-r-00001.parquet").first()
> Out[2]: Row(long_col={u'__class__': u'scala.runtime.BoxedUnit'},
> str_col=u'Hello!', int_col={u'__class__': u'scala.runtime.BoxedUnit'},
> date_col=datetime.datetime(1, 12, 31, 19, 0, tzinfo=<DstTzInfo
> 'America/Toronto' EDT-1 day, 19:00:00 DST>))
> {code}
> Note the {{\{u'__class__': u'scala.runtime.BoxedUnit'}}} values being
> returned for the {{int_col}} and {{long_col}} columns in the second loop
> above. This only happens if I select the {{date_col}} which is stored as
> {{INT96}}.
> I don't know much about Scala boxing, but I assume that somehow by including
> numeric columns that are bigger than a machine word I trigger some different,
> slower execution path somewhere that boxes stuff and causes this problem.
> If anyone could give me any pointers on where to get started fixing this I'd
> be happy to dive in!
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