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Apache Spark commented on SPARK-42442: -------------------------------------- User 'gengliangwang' has created a pull request for this issue: 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 > > 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. -- This message was sent by Atlassian Jira (v8.20.10#820010) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org