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https://issues.apache.org/jira/browse/SPARK-22614?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Adrian Ionescu updated SPARK-22614:
-----------------------------------
Description:
Right now, the Dataset API only offers two possibilities for explicitly
repartitioning a dataset:
- round robin partitioning, via {{def repartition(numPartitions: Int)}}
- hash partitioning, via {{def repartition(numPartitions: Int, partitionExprs:
Column*)}}
It would be useful to also expose range partitioning, which can, for example,
improve compression when writing data out to disk, or potentially enable new
use cases.
was:
Right now, the Dataset API only offers two possibilities for explicitly
repartitioning a dataset:
- round robin partitioning, via {{def repartition(numPartitions: Int): Dataset}}
- hash partitioning, via {{def repartition(numPartitions: Int, partitionExprs:
Column*)}}
It would be useful to also expose range partitioning, which can, for example,
improve compression when writing data out to disk, or potentially enable new
use cases.
> Expose range partitioning shuffle
> ---------------------------------
>
> Key: SPARK-22614
> URL: https://issues.apache.org/jira/browse/SPARK-22614
> Project: Spark
> Issue Type: Improvement
> Components: Shuffle, SQL
> Affects Versions: 2.3.0
> Reporter: Adrian Ionescu
>
> Right now, the Dataset API only offers two possibilities for explicitly
> repartitioning a dataset:
> - round robin partitioning, via {{def repartition(numPartitions: Int)}}
> - hash partitioning, via {{def repartition(numPartitions: Int,
> partitionExprs: Column*)}}
> It would be useful to also expose range partitioning, which can, for example,
> improve compression when writing data out to disk, or potentially enable new
> use cases.
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