kumarUjjawal commented on code in PR #24943:
URL: https://github.com/apache/datafusion/pull/24943#discussion_r3934677228
##########
datafusion/spark/src/function/math/factorial.rs:
##########
@@ -43,7 +47,14 @@ impl Default for SparkFactorial {
impl SparkFactorial {
pub fn new() -> Self {
Self {
- signature: Signature::exact(vec![Int32], Volatility::Immutable),
+ signature: Signature::coercible(
+ vec![Coercion::new_implicit(
Review Comment:
Could we use `Coercion::new_implicit_native(logical_int32(),
vec![TypeSignatureClass::Integer])` here? That constructor was added for
native-target coercions so the desired `Int32` type and `NativeType::Int32`
cannot accidentally diverge. It would also remove the `NativeType` import.
##########
datafusion/sqllogictest/test_files/spark/math/factorial.slt:
##########
@@ -62,5 +62,35 @@ NULL
NULL
NULL
-query error Error during planning: Failed to coerce arguments to satisfy a
call to 'factorial' function
-SELECT factorial(5::BIGINT);
+# Spark declares factorial(INT) with ImplicitCastInputTypes, so every integer
width is
+# accepted; an untyped literal (Int64 in DataFusion) must work too. Values
from Spark 4.2.0.
Review Comment:
Could we clarify the test provenance here? This row includes `UInt64`, but
Spark has no unsigned integer type, so that expected value cannot come from
Spark 4.2.0. I suggest identifying the signed cases as Spark-verified and the
unsigned case as DataFusion-specific coverage, or removing the unsigned case.
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