perf: Optimize trunc scalar performance (#19788)

## Which issue does this PR close?

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- Part of apache/datafusion-comet#2986.

## Rationale for this change

The current `trunc` implementation always converts scalar inputs to
arrays via `make_scalar_function`, which introduces unnecessary overhead
when processing single values.

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in the issue then this section is not needed.
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your changes and offer better suggestions for fixes.
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## What changes are included in this PR?

- Add scalar fast path for `trunc` function to process Float32/Float64
scalar inputs directly
- Handle optional precision argument for scalar inputs
- Add scalar benchmarks to measure performance

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## Are these changes tested?

Yes all sqllogictest pass

## Benchmark Results

| Type | Before | After | Speedup |
|------|--------|-------|---------|
| f64 scalar | 256 ns | 55 ns | **4.6x** |
| f32 scalar | 247 ns | 56 ns | **4.4x** |

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## Are there any user-facing changes?

No

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---------

Co-authored-by: Jeffrey Vo <jeffrey.vo.australia@gmail.com>

Author: kumarUjjawal

datafusion/functions/benches/trunc.rs

@@ -32,12 +32,13 @@ use std::sync::Arc;
 
 fn criterion_benchmark(c: &mut Criterion) {
     let trunc = trunc();
+    let config_options = Arc::new(ConfigOptions::default());
+
     for size in [1024, 4096, 8192] {
         let f32_array = Arc::new(create_primitive_array::<Float32Type>(size, 0.2));
         let f32_args = vec![ColumnarValue::Array(f32_array)];
         let arg_fields = vec![Field::new("a", DataType::Float32, false).into()];
         let return_field = Field::new("f", DataType::Float32, true).into();
-        let config_options = Arc::new(ConfigOptions::default());
 
         c.bench_function(&format!("trunc f32 array: {size}"), |b| {
             b.iter(|| {
@@ -74,6 +75,51 @@ fn criterion_benchmark(c: &mut Criterion) {
             })
         });
     }
+
+    // Scalar benchmarks - to measure optimized performance
+    let scalar_f64_args = vec![ColumnarValue::Scalar(
+        datafusion_common::ScalarValue::Float64(Some(std::f64::consts::PI)),
+    )];
+    let scalar_arg_fields = vec![Field::new("a", DataType::Float64, false).into()];
+    let scalar_return_field = Field::new("f", DataType::Float64, false).into();
+
+    c.bench_function("trunc f64 scalar", |b| {
+        b.iter(|| {
+            black_box(
+                trunc
+                    .invoke_with_args(ScalarFunctionArgs {
+                        args: scalar_f64_args.clone(),
+                        arg_fields: scalar_arg_fields.clone(),
+                        number_rows: 1,
+                        return_field: Arc::clone(&scalar_return_field),
+                        config_options: Arc::clone(&config_options),
+                    })
+                    .unwrap(),
+            )
+        })
+    });
+
+    let scalar_f32_args = vec![ColumnarValue::Scalar(
+        datafusion_common::ScalarValue::Float32(Some(std::f32::consts::PI)),
+    )];
+    let scalar_f32_arg_fields = vec![Field::new("a", DataType::Float32, false).into()];
+    let scalar_f32_return_field = Field::new("f", DataType::Float32, false).into();
+
+    c.bench_function("trunc f32 scalar", |b| {
+        b.iter(|| {
+            black_box(
+                trunc
+                    .invoke_with_args(ScalarFunctionArgs {
+                        args: scalar_f32_args.clone(),
+                        arg_fields: scalar_f32_arg_fields.clone(),
+                        number_rows: 1,
+                        return_field: Arc::clone(&scalar_f32_return_field),
+                        config_options: Arc::clone(&config_options),
+                    })
+                    .unwrap(),
+            )
+        })
+    });
 }
 
 criterion_group!(benches, criterion_benchmark);

datafusion/functions/src/math/trunc.rs

@@ -24,7 +24,7 @@ use arrow::array::{ArrayRef, AsArray, PrimitiveArray};
 use arrow::datatypes::DataType::{Float32, Float64};
 use arrow::datatypes::{DataType, Float32Type, Float64Type, Int64Type};
 use datafusion_common::ScalarValue::Int64;
-use datafusion_common::{Result, exec_err};
+use datafusion_common::{Result, ScalarValue, exec_err};
 use datafusion_expr::TypeSignature::Exact;
 use datafusion_expr::sort_properties::{ExprProperties, SortProperties};
 use datafusion_expr::{
@@ -110,7 +110,50 @@ impl ScalarUDFImpl for TruncFunc {
     }
 
     fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> {
-        make_scalar_function(trunc, vec![])(&args.args)
+        // Extract precision from second argument (default 0)
+        let precision = match args.args.get(1) {
+            Some(ColumnarValue::Scalar(Int64(Some(p)))) => Some(*p),
+            Some(ColumnarValue::Scalar(Int64(None))) => None, // null precision
+            Some(ColumnarValue::Array(_)) => {
+                // Precision is an array - use array path
+                return make_scalar_function(trunc, vec![])(&args.args);
+            }
+            None => Some(0), // default precision
+            Some(cv) => {
+                return exec_err!(
+                    "trunc function requires precision to be Int64, got {:?}",
+                    cv.data_type()
+                );
+            }
+        };
+
+        // Scalar fast path using tuple matching for (value, precision)
+        match (&args.args[0], precision) {
+            // Null cases
+            (ColumnarValue::Scalar(sv), _) if sv.is_null() => {
+                ColumnarValue::Scalar(ScalarValue::Null).cast_to(args.return_type(), None)
+            }
+            (_, None) => {
+                ColumnarValue::Scalar(ScalarValue::Null).cast_to(args.return_type(), None)
+            }
+            // Scalar cases
+            (ColumnarValue::Scalar(ScalarValue::Float64(Some(v))), Some(p)) => Ok(
+                ColumnarValue::Scalar(ScalarValue::Float64(Some(if p == 0 {
+                    v.trunc()
+                } else {
+                    compute_truncate64(*v, p)
+                }))),
+            ),
+            (ColumnarValue::Scalar(ScalarValue::Float32(Some(v))), Some(p)) => Ok(
+                ColumnarValue::Scalar(ScalarValue::Float32(Some(if p == 0 {
+                    v.trunc()
+                } else {
+                    compute_truncate32(*v, p)
+                }))),
+            ),
+            // Array path for everything else
+            _ => make_scalar_function(trunc, vec![])(&args.args),
+        }
     }
 
     fn output_ordering(&self, input: &[ExprProperties]) -> Result<SortProperties> {