Jefffrey commented on code in PR #24409:
URL: https://github.com/apache/datafusion/pull/24409#discussion_r3944517400
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datafusion/spark/src/function/math/modulus.rs:
##########
@@ -101,23 +105,89 @@ pub fn spark_mod(
Ok(ColumnarValue::Array(result))
}
+/// Spark derives the decimal result type of `pmod` with
`Pmod.resultDecimalType`,
+/// which follows the `Remainder` rule:
+///
+/// ```text
+/// scale = max(s1, s2)
+/// precision = min(p1 - s1, p2 - s2) + scale
+/// ```
+///
+/// The rule is applied to the input argument types.
+fn pmod_decimal_result_type(p1: u8, s1: i8, p2: u8, s2: i8) -> DataType {
+ let scale = s1.max(s2);
+ let whole_digits = (i32::from(p1) - i32::from(s1)).min(i32::from(p2) -
i32::from(s2));
+ let precision =
+ (whole_digits + i32::from(scale)).clamp(1,
i32::from(DECIMAL128_MAX_PRECISION));
+ DataType::Decimal128(precision as u8, scale)
+}
+
/// Spark-compatible `pmod` function
/// In ANSI mode, division by zero throws an error.
/// In legacy mode, division by zero returns NULL (Spark behavior).
pub fn spark_pmod(
args: &[ColumnarValue],
enable_ansi_mode: bool,
+ result_type: &DataType,
) -> Result<ColumnarValue> {
assert_eq_or_internal_err!(args.len(), 2, "pmod expects exactly two
arguments");
let args = ColumnarValue::values_to_arrays(args)?;
- let left = &args[0];
- let right = &args[1];
+
+ // A null argument is passed through uncoerced by `Coercible` (#19458), so
+ // it still carries `DataType::Null` here. Every operation below needs a
+ // concrete numeric type, and the answer is null regardless.
Review Comment:
```suggestion
// Need to handle nulls separately as they are pass through by the
signature
```
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