gaogaotiantian opened a new pull request, #53667:
URL: https://github.com/apache/spark/pull/53667
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### What changes were proposed in this pull request?
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A new mode controlled by a `SQLConf` -
`"spark.sql.session.enforceTimeZoneMatch"` is introduced to enforce timezone
check when converting timestamps.
Under this mode, only timezone aware `datetime()` can be converted from/to
`TimestampType()` and only naive `datetime()` can be converted from/to
`TimestampNTZType()`.
To make this work in UDF workers where `SQLConf` does not exist, a new class
variable is introduced in `DatetimeType` as the fallback config. We set this
class variable when we instantiate a worker to control the behavior.
The current implementation is a PoC. Once the direction is approved, I'll
fill the gaps.
TODO:
- [ ] Other Python runners besides vanilla UDF
- [ ] Better exception error class/message
- [ ] Tests
- [ ] Documentation
### Why are the changes needed?
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We have too many timezone related issues now. It's not even possible to
define how timestamps should work in spark. Python has rules about naive
timestamps which use the local machine timezone, which makes UDF workers super
unpredictable. Spark also has a session local timezone config which makes the
situation even more complicated.
The only way to make it explanable and consistent is to never mix
timezone-aware and timezone-naive timestamps. If the user just want a timestamp
without a timezone, they need to use `TimestampNTZType()`, period.
### Does this PR introduce _any_ user-facing change?
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This PR is backward compatible. It introduces a new config to change the
behavior.
### How was this patch tested?
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For now, locally tested the an error would be raised. Tests should be
written in the future.
### Was this patch authored or co-authored using generative AI tooling?
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No
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