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https://issues.apache.org/jira/browse/SPARK-19256?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16345397#comment-16345397
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Fernando Pereira edited comment on SPARK-19256 at 2/2/18 8:50 AM:
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Thanks a lot for this great contribution to Spark.
I was just wondering, would it make sense to apply this to direct outputs (e.g.
write.parquet()), so that we could keep partitioning information - and again
avoid reshuffling data before a merge? I believe this is most what
saveAsTable() does by default in Spark, but to my mind it would improve the
DataFrame write API and make these performance benefits more accessible.
Update:
I've just noticed that it has been considered in
[https://github.com/apache/spark/pull/13452.
] [~cloud_fan] [ |https://github.com/apache/spark/pull/13452.]- Is there an
Issue to follow up on this feature? Eventually we could simply store a metadata
json file together with the data files.
was (Author: ferdonline):
Thanks a lot for this great contribution to Spark.
I was just wondering, would it make sense to apply this to direct outputs (e.g.
write.parquet()), so that we could keep partitioning information - and again
avoid reshuffling data before a merge? I believe this is most what
saveAsTable() does by default in Spark, but to my mind it would improve the
DataFrame write API and make these performance benefits more accessible.
> Hive bucketing support
> ----------------------
>
> Key: SPARK-19256
> URL: https://issues.apache.org/jira/browse/SPARK-19256
> Project: Spark
> Issue Type: Umbrella
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: Tejas Patil
> Priority: Minor
>
> JIRA to track design discussions and tasks related to Hive bucketing support
> in Spark.
> Proposal :
> https://docs.google.com/document/d/1a8IDh23RAkrkg9YYAeO51F4aGO8-xAlupKwdshve2fc/edit?usp=sharing
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