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Posted to issues@spark.apache.org by "Xiao Li (JIRA)" <ji...@apache.org> on 2019/04/12 22:03:00 UTC
[jira] [Updated] (SPARK-27453) DataFrameWriter.partitionBy is
Silently Dropped by DSV1
[ https://issues.apache.org/jira/browse/SPARK-27453?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiao Li updated SPARK-27453:
----------------------------
Target Version/s: 2.4.2, 3.0.0 (was: 2.4.2, 2.5.0)
> DataFrameWriter.partitionBy is Silently Dropped by DSV1
> -------------------------------------------------------
>
> Key: SPARK-27453
> URL: https://issues.apache.org/jira/browse/SPARK-27453
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.4.1, 1.5.2, 1.6.3, 2.0.2, 2.1.3, 2.2.3, 2.4.1
> Reporter: Michael Armbrust
> Priority: Critical
>
> This is a long standing quirk of the interaction between {{DataFrameWriter}} and {{CreatableRelationProvider}} (and the other forms of the DSV1 API). Users can specify columns in {{partitionBy}} and our internal data sources will use this information. Unfortunately, for external systems, this data is silently dropped with no feedback given to the user.
> In the long run, I think that DataSourceV2 is a better answer. However, I don't think we should wait for that API to stabilize before offering some kind of solution to developers of external data sources. I also do not think we should break binary compatibility of this API, but I do think that small surgical fix could alleviate the issue.
> I would propose that we could propagate partitioning information (when present) along with the other configuration options passed to the data source in the {{String, String}} map.
> I think its very unlikely that there are both data sources that validate extra options and users who are using (no-op) partitioning with them, but out of an abundance of caution we should protect the behavior change behind a {{legacy}} flag that can be turned off.
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