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Posted to issues@spark.apache.org by "Xiao Li (JIRA)" <ji...@apache.org> on 2016/01/25 20:18:39 UTC
[jira] [Updated] (SPARK-12975) Throwing Exception when Bucketing
Columns are part of Partitioning Columns
[ https://issues.apache.org/jira/browse/SPARK-12975?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiao Li updated SPARK-12975:
----------------------------
Summary: Throwing Exception when Bucketing Columns are part of Partitioning Columns (was: Eliminate Bucketing Columns that are part of Partitioning Columns)
> Throwing Exception when Bucketing Columns 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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