Gengliang Wang created SPARK-42442:
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Summary: 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
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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