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From "Xiao Li (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-12975) Eliminate Bucketing Columns that are part of Partitioning Columns
Date Mon, 25 Jan 2016 02:03:39 GMT

     [ https://issues.apache.org/jira/browse/SPARK-12975?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Xiao Li updated SPARK-12975:
----------------------------
    Description: 
When users are using partitionBy and bucketBy at the same time, some bucketing columns might
be part of partitioning columns. For example, 
{code}
        df.write
          .format(source)
          .partitionBy("i")
          .bucketBy(8, "i", "k")
          .sortBy("k")
          .saveAsTable("bucketed_table")
{code}

However, in the above case, adding column `i` is useless. It is just wasting extra CPU when
reading or writing bucket tables. Thus, we can automatically remove these overlapping columns
from the bucketing columns. 

  was:
When users are using partitionBy and bucketBy at the same time, some bucketing columns might
be part of partitioning columns. For example, 
{code}
        df.write
          .format(source)
          .partitionBy("i")
          .bucketBy(8, "i", "k")
          .sortBy("k")
          .saveAsTable("bucketed_table")
{code}

However, in the above case, adding column `i` is useless. It is just wasting extra CPU when
reading or writing bucket tables. Thus, we can automatically remove these overlapping columns
from bucketing columns. 


> Eliminate Bucketing Columns that are part of Partitioning Columns
> -----------------------------------------------------------------
>
>                 Key: SPARK-12975
>                 URL: https://issues.apache.org/jira/browse/SPARK-12975
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 2.0.0
>            Reporter: Xiao Li
>
> When users are using partitionBy and bucketBy at the same time, some bucketing columns
might be part of partitioning columns. For example, 
> {code}
>         df.write
>           .format(source)
>           .partitionBy("i")
>           .bucketBy(8, "i", "k")
>           .sortBy("k")
>           .saveAsTable("bucketed_table")
> {code}
> However, in the above case, adding column `i` is useless. It is just wasting extra CPU
when reading or writing bucket tables. Thus, we can automatically remove these overlapping
columns from the bucketing columns. 



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