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https://issues.apache.org/jira/browse/SPARK-12975?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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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` into `bucketBy` is useless. It is
just wasting extra CPU when reading or writing bucket tables. Thus, like Hive,
we can issue an exception and let users do the change.
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 the bucketing 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` into `bucketBy` is useless. It
> is just wasting extra CPU when reading or writing bucket tables. Thus, like
> Hive, we can issue an exception and let users do the change.
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