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https://issues.apache.org/jira/browse/SPARK-34910?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-34910:
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Assignee: (was: Apache Spark)
> JDBC - Add an option for different stride orders
> ------------------------------------------------
>
> Key: SPARK-34910
> URL: https://issues.apache.org/jira/browse/SPARK-34910
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.2.0
> Reporter: Jason Yarbrough
> Priority: Trivial
>
> Currently, the JDBCRelation columnPartition function orders the strides in
> ascending order, starting from the lower bound and working its way towards
> the upper bound.
> I'm proposing leaving that as the default, but adding an option (such as
> strideOrder) in JDBCOptions. Since it will default to the current behavior,
> this will keep people's current code working as expected. However, people who
> may have data skew closer to the upper bound might appreciate being able to
> have the strides in descending order, thus filling up the first partition
> with the last stride and so forth. Also, people with nondeterministic data
> skew or sporadic data density might be able to benefit from a random ordering
> of the strides.
> I have the code created to implement this, and it creates a pattern that can
> be used to add other algorithms that people may want to add (such as counting
> the rows and ranking each stride, and then ordering from most dense to
> least). The current two options I have coded is 'descending' and 'random.'
> The original idea was to create something closer to Spark's hash partitioner,
> but for JDBC and pushed down to the database engine for efficiency. However,
> that would require adding hashing algorithms for each dialect, and the
> performance from those algorithms may outweigh the benefit. The method I'm
> proposing in this ticket avoids those complexities while still giving some of
> the benefit (in the case of random ordering).
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