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https://issues.apache.org/jira/browse/SPARK-15867?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15324444#comment-15324444
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Herman van Hovell commented on SPARK-15867:
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This would change the behavior in comparison with 1.6, see:
https://github.com/apache/spark/blob/branch-1.6/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala#L1282-L1286
I don't think changing is much of a big deal though.
> TABLESAMPLE BUCKET semantics don't match Hive's
> -----------------------------------------------
>
> Key: SPARK-15867
> URL: https://issues.apache.org/jira/browse/SPARK-15867
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0
> Reporter: Andrew Or
>
> {code}
> SELECT * FROM boxes TABLESAMPLE (BUCKET 3 OUT OF 16)
> {code}
> In Hive, this would select the 3rd bucket out of every 16 buckets there are
> in the table. E.g. if the table was clustered by 32 buckets then this would
> sample the 3rd and the 19th bucket. (See
> https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Sampling)
> In Spark, however, we simply sample 3/16 of the number of input rows.
> Either we don't support it in Spark or do it in a way that's consistent with
> Hive.
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