Copilot commented on code in PR #3265:
URL: https://github.com/apache/datafusion-comet/pull/3265#discussion_r2724324610
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native/spark-expr/src/datetime_funcs/extract_date_part.rs:
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@@ -75,14 +76,28 @@ macro_rules! extract_date_part {
match args {
[ColumnarValue::Array(array)] => {
- let array = array_with_timezone(
- array,
- self.timezone.clone(),
- Some(&DataType::Timestamp(
- Microsecond,
- Some(self.timezone.clone().into()),
- )),
- )?;
+ let array = match array.data_type() {
+ // TimestampNTZ → DO NOT apply timezone conversion
+ DataType::Timestamp(_, None) => array.clone(),
+
+ // Timestamp with timezone → convert from UTC to
session timezone
+ DataType::Timestamp(_, Some(_)) =>
array_with_timezone(
+ array,
Review Comment:
`invoke_with_args` now pattern-matches directly on `array.data_type()` and
rejects `DataType::Dictionary(_, _)` with an execution error. This is a
regression for dictionary-encoded timestamp columns (the function’s
`return_type` explicitly supports dictionary input, and `array_with_timezone`
also has dictionary handling). Add explicit handling for
`DataType::Dictionary(_, value_type)` where `value_type` is `Timestamp(_,
None)` (no conversion) or `Timestamp(_, Some(_))` (apply
`array_with_timezone`), instead of falling into the error branch.
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