You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Gengliang Wang (Jira)" <ji...@apache.org> on 2020/11/06 05:24:00 UTC
[jira] [Updated] (SPARK-33369) Skip schema inference in
DataframeWriter.save() if table provider supports external metadata
[ https://issues.apache.org/jira/browse/SPARK-33369?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Gengliang Wang updated SPARK-33369:
-----------------------------------
Description:
For all the v2 data sources which are not FileDataSourceV2, Spark always infers the table schema/partitioning on DataframeWriter.save().
The inference of table schema/partitioning can be expensive. However, there is no such trait or flag for indicating a V2 source can use the input DataFrame's schema on DataframeWriter.save(). We can resolve the problem by adding a new expected behavior for the method TableProvider.supportsExternalMetadata():
When TableProvider.supportsExternalMetadata() is true, Spark will use the input Dataframe's schema in DataframeWriter.save() and skip schema/partitioning inference.
was:
For all the v2 data sources which are not FileDataSourceV2, Spark always infer the table schema/partitioning on DataframeWriter.save().
Currently, there is no such trait or flag for indicating a V2 source can use the input DataFrame's schema on DataframeWriter.save(). We can resolve the problem by adding a new expected behavior for the method TableProvider.supportsExternalMetadata():
when TableProvider.supportsExternalMetadata() is true, Spark will use the input Dataframe's schema in DataframeWriter.save() and skip schema/partitioning inference.
> Skip schema inference in DataframeWriter.save() if table provider supports external metadata
> --------------------------------------------------------------------------------------------
>
> Key: SPARK-33369
> URL: https://issues.apache.org/jira/browse/SPARK-33369
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Affects Versions: 3.1.0
> Reporter: Gengliang Wang
> Assignee: Gengliang Wang
> Priority: Major
>
> For all the v2 data sources which are not FileDataSourceV2, Spark always infers the table schema/partitioning on DataframeWriter.save().
> The inference of table schema/partitioning can be expensive. However, there is no such trait or flag for indicating a V2 source can use the input DataFrame's schema on DataframeWriter.save(). We can resolve the problem by adding a new expected behavior for the method TableProvider.supportsExternalMetadata():
> When TableProvider.supportsExternalMetadata() is true, Spark will use the input Dataframe's schema in DataframeWriter.save() and skip schema/partitioning inference.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org