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https://issues.apache.org/jira/browse/SPARK-42442?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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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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