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perf: Optimize factorial scalar path #19949

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perf: Optimize factorial scalar path
kumarUjjawal Jan 23, 2026
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revert the comment
kumarUjjawal Jan 23, 2026
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94 changes: 43 additions & 51 deletions datafusion/functions/src/math/factorial.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -22,8 +22,9 @@ use std::sync::Arc;
use arrow::datatypes::DataType::Int64;
use arrow::datatypes::{DataType, Int64Type};

use crate::utils::make_scalar_function;
use datafusion_common::{Result, exec_err};
use datafusion_common::{
Result, ScalarValue, exec_err, internal_err, utils::take_function_args,
};
use datafusion_expr::{
ColumnarValue, Documentation, ScalarFunctionArgs, ScalarUDFImpl, Signature,
Volatility,
Expand Down Expand Up @@ -81,7 +82,39 @@ impl ScalarUDFImpl for FactorialFunc {
}

fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> {
make_scalar_function(factorial, vec![])(&args.args)
let [arg] = take_function_args(self.name(), args.args)?;

match arg {
ColumnarValue::Scalar(scalar) => {
if scalar.is_null() {
return Ok(ColumnarValue::Scalar(ScalarValue::Int64(None)));
}

match scalar {
ScalarValue::Int64(Some(v)) => {
let result = compute_factorial(v)?;
Ok(ColumnarValue::Scalar(ScalarValue::Int64(Some(result))))
}
_ => {
internal_err!(
"Unexpected data type {:?} for function factorial",
scalar.data_type()
)
}
}
}
ColumnarValue::Array(array) => match array.data_type() {
Int64 => {
let result: Int64Array = array
.as_primitive::<Int64Type>()
.try_unary(compute_factorial)?;
Ok(ColumnarValue::Array(Arc::new(result) as ArrayRef))
}
other => {
internal_err!("Unexpected data type {other:?} for function factorial")
}
},
}
}

fn documentation(&self) -> Option<&Documentation> {
Expand Down Expand Up @@ -113,53 +146,12 @@ const FACTORIALS: [i64; 21] = [
2432902008176640000,
]; // if return type changes, this constant needs to be updated accordingly
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@Jefffrey Jefffrey Jan 23, 2026

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Would be good to keep this comment


/// Factorial SQL function
fn factorial(args: &[ArrayRef]) -> Result<ArrayRef> {
match args[0].data_type() {
Int64 => {
let result: Int64Array =
args[0].as_primitive::<Int64Type>().try_unary(|a| {
if a < 0 {
Ok(1)
} else if a < FACTORIALS.len() as i64 {
Ok(FACTORIALS[a as usize])
} else {
exec_err!("Overflow happened on FACTORIAL({a})")
}
})?;
Ok(Arc::new(result) as ArrayRef)
}
other => exec_err!("Unsupported data type {other:?} for function factorial."),
}
}

#[cfg(test)]
mod test {
use super::*;
use datafusion_common::cast::as_int64_array;

#[test]
fn test_factorial_i64() {
let args: Vec<ArrayRef> = vec![
Arc::new(Int64Array::from(vec![0, 1, 2, 4, 20, -1])), // input
];

let result = factorial(&args).expect("failed to initialize function factorial");
let ints =
as_int64_array(&result).expect("failed to initialize function factorial");

let expected = Int64Array::from(vec![1, 1, 2, 24, 2432902008176640000, 1]);

assert_eq!(ints, &expected);
}

#[test]
fn test_overflow() {
let args: Vec<ArrayRef> = vec![
Arc::new(Int64Array::from(vec![21])), // input
];

let result = factorial(&args);
assert!(result.is_err());
fn compute_factorial(n: i64) -> Result<i64> {
if n < 0 {
Ok(1)
} else if n < FACTORIALS.len() as i64 {
Ok(FACTORIALS[n as usize])
} else {
exec_err!("Overflow happened on FACTORIAL({n})")
}
}