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https://issues.apache.org/jira/browse/SPARK-6917?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14546160#comment-14546160
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Davies Liu edited comment on SPARK-6917 at 5/15/15 8:58 PM:
------------------------------------------------------------
[~yhuai] It's a bug in SQL or Parquet library:
{code}
scala> sqlContext.parquetFile("/Users/davies/Downloads/part-r-00001.parquet")
res1: org.apache.spark.sql.DataFrame = [long_col: decimal(18,0), str_col:
string, int_col: decimal(18,0), date_col: timestamp]
scala>
sqlContext.parquetFile("/Users/davies/Downloads/part-r-00001.parquet").first()
res2: org.apache.spark.sql.Row = [(),Hello!,(),0001-12-31 16:00:00.0]
scala>
sqlContext.parquetFile("/Users/davies/Downloads/part-r-00001.parquet").select("long_col").first()
res3: org.apache.spark.sql.Row = [10]
scala>
sqlContext.parquetFile("/Users/davies/Downloads/part-r-00001.parquet").select("long_col",
"date_col").first()
res4: org.apache.spark.sql.Row = [(),0001-12-31 16:00:00.0]
scala>
sqlContext.parquetFile("/Users/davies/Downloads/part-r-00001.parquet").select("date_col").first()
res5: org.apache.spark.sql.Row = [0001-12-31 16:00:00.0]
{code}
was (Author: davies):
[~yhuai] It's a bug in SQL or Parquet library:
[[code]]
scala> sqlContext.parquetFile("/Users/davies/Downloads/part-r-00001.parquet")
SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further
details.
res1: org.apache.spark.sql.DataFrame = [long_col: decimal(18,0), str_col:
string, int_col: decimal(18,0), date_col: timestamp]
scala>
sqlContext.parquetFile("/Users/davies/Downloads/part-r-00001.parquet").first()
res2: org.apache.spark.sql.Row = [(),Hello!,(),0001-12-31 16:00:00.0]
scala>
sqlContext.parquetFile("/Users/davies/Downloads/part-r-00001.parquet").select("long_col").first()
res3: org.apache.spark.sql.Row = [10]
scala>
sqlContext.parquetFile("/Users/davies/Downloads/part-r-00001.parquet").select("long_col",
"date_col").first()
res4: org.apache.spark.sql.Row = [(),0001-12-31 16:00:00.0]
scala>
sqlContext.parquetFile("/Users/davies/Downloads/part-r-00001.parquet").select("date_col").first()
res5: org.apache.spark.sql.Row = [0001-12-31 16:00:00.0]
[[code]]
> 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
> 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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