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Posted to issues@spark.apache.org by "Apache Spark (Jira)" <ji...@apache.org> on 2023/02/14 23:45:00 UTC

[jira] [Assigned] (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 ]

Apache Spark reassigned SPARK-42442:
------------------------------------

    Assignee: Apache Spark  (was: Gengliang Wang)

> 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: Apache Spark
>            Priority: Major
>
> 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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