sadikovi opened a new pull request #34596: URL: https://github.com/apache/spark/pull/34596
### What changes were proposed in this pull request? This PR adds support for TimestampNTZ type in the CSV data source. Most of the functionality has already been added, this patch verifies that writes + reads work for TimestampNTZ type and adds schema inference depending on the timestamp value format written. The following applies: - If there is a mixture of `TIMESTAMP_NTZ` and `TIMESTAMP_LTZ` values, use `TIMESTAMP_LTZ`. - If there are only `TIMESTAMP_NTZ` values, resolve using the the default timestamp type configured with `spark.sql.timestampType`. ### Why are the changes needed? The current CSV source could write values as TimestampNTZ into a file but could not preserve this type when reading the file back, this PR fixes the issue. ### Does this PR introduce _any_ user-facing change? <!-- Note that it means *any* user-facing change including all aspects such as the documentation fix. If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If possible, please also clarify if this is a user-facing change compared to the released Spark versions or within the unreleased branches such as master. If no, write 'No'. --> ### How was this patch tested? I extended `CSVSuite` with a few unit tests to verify that write-read roundtrip works for `TIMESTAMP_NTZ` and `TIMESTAMP_LTZ` values. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
