[jira] [Updated] (SPARK-15867) TABLESAMPLE BUCKET semantics don't match Hive's

2016-11-01 Thread Reynold Xin (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-15867?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Reynold Xin updated SPARK-15867:

Target Version/s: 2.2.0  (was: 2.1.0)

> 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: 1.6.0, 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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[jira] [Updated] (SPARK-15867) TABLESAMPLE BUCKET semantics don't match Hive's

2016-06-10 Thread Andrew Or (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-15867?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Andrew Or updated SPARK-15867:
--
Affects Version/s: 1.6.0

> 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: 1.6.0, 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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