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Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2023/02/15 11:40:00 UTC
[jira] [Resolved] (SPARK-42442) Use spark.sql.timestampType for data source inference
[ https://issues.apache.org/jira/browse/SPARK-42442?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-42442.
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Fix Version/s: 3.5.0
Resolution: Fixed
Issue resolved by pull request 40022
[https://github.com/apache/spark/pull/40022]
> Use spark.sql.timestampType for data source inference
> -----------------------------------------------------
>
> Key: SPARK-42442
> URL: https://issues.apache.org/jira/browse/SPARK-42442
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Affects Versions: 3.4.0
> Reporter: Gengliang Wang
> Assignee: Gengliang Wang
> Priority: Major
> Fix For: 3.5.0
>
>
> With the configuration `spark.sql.timestampType`, TIMESTAMP in Spark is a user-specified alias associated with one of the TIMESTAMP_LTZ and TIMESTAMP_NTZ variations. This is quite complicated to Spark users.
> There is another option `spark.sql.sources.timestampNTZTypeInference.enabled` for schema inference. I would like to introduce it in [https://github.com/apache/spark/pull/40005] but having two flags seems too much. After thoughts, I decide to merge `spark.sql.sources.timestampNTZTypeInference.enabled` into `spark.sql.timestampType` and let `spark.sql.timestampType` control the schema inference behavior.
> We can have followups to add data source options "inferTimestampNTZType" for CSV/JSON/partiton column like JDBC data source did.
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