comphead commented on code in PR #22813:
URL: https://github.com/apache/datafusion/pull/22813#discussion_r3381823694
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datafusion/spark/src/function/math/round.rs:
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@@ -187,20 +190,43 @@ fn get_scale(args: &[ColumnarValue]) ->
Result<Option<i32>> {
/// round_float(125.0, -1) → 130.0
/// ```
fn round_float<T: num_traits::Float>(value: T, scale: i32) -> T {
- if scale >= 0 {
- let factor = T::from(10.0f64.powi(scale)).unwrap_or_else(T::infinity);
- if factor.is_infinite() {
- // Very large positive scale — value is already precise enough,
return as-is
- return value;
- }
- (value * factor).round() / factor
- } else {
- let factor = T::from(10.0f64.powi(-scale)).unwrap_or_else(T::infinity);
- if factor.is_infinite() {
- // Very large negative scale — any finite value rounds to 0
- return T::zero();
- }
- (value / factor).round() * factor
+ // Widen to f64 first. For f32 inputs this matches Spark's `f.toDouble`
+ // step (FloatType: `BigDecimal(f.toDouble).setScale(..).toFloat`), which
+ // exposes the binary-float error before rounding. For f64 it is a no-op.
+ let Some(d) = value.to_f64() else {
Review Comment:
appreciate if we can name vars more meaningfully than `d, bd`
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