GitHub user gatorsmile opened a pull request:
https://github.com/apache/spark/pull/9899
[SPARK-11914] [SQL] Support coalesce and repartition in Dataset APIs
This PR is to provide two common `coalesce` and `repartition` in Dataset
APIs.
After reading the comments of SPARK-9999, I am unclear about the plan for
supporting re-partitioning in Dataset APIs. Currently, both RDD APIs and
Dataframe APIs provide users such a flexibility to control the number of
partitions.
In most traditional RDBMS, they expose the number of partitions, the
partitioning columns, the table partitioning methods to DBAs for performance
tuning and storage optimization. Normally, the changes could largely affect the
query performance. Since the actual performance depends on the workload types,
I do not think we can always find the best partitioning strategy for all the
scenarios.
I am wondering if Dataset APIs are planning to hide these APIs from users?
Feel free to reject my PR if it does not match your plan.
Thank you for your answers. @marmbrus @rxin @cloud-fan
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/gatorsmile/spark coalesce
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/9899.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #9899
----
commit 41d3adecfa1dbbab8165587d38f99036e443b465
Author: gatorsmile <[email protected]>
Date: 2015-11-23T03:12:42Z
Support coalesce and repartition in Dataset APIs
commit db4accceb3fff4c4018662da3d12df40015550f8
Author: gatorsmile <[email protected]>
Date: 2015-11-23T03:14:42Z
style clean
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