diegoQuinas commented on code in PR #21710:
URL: https://github.com/apache/datafusion/pull/21710#discussion_r3198010830
##########
datafusion/spark/src/function/math/ceil.rs:
##########
@@ -153,7 +287,41 @@ fn spark_ceil_array(input: &Arc<dyn arrow::array::Array>)
-> Result<ColumnarValu
.as_primitive::<Float64Type>()
.unary::<_, Int64Type>(|x| x.ceil() as i64),
) as _,
+ // 2-arg integer: widen to Int64 and apply scale-aware ceiling.
+ DataType::Int8 if has_scale => impl_integer_array_ceil!(input,
Int8Type, scale),
+ DataType::Int16 if has_scale => impl_integer_array_ceil!(input,
Int16Type, scale),
+ DataType::Int32 if has_scale => impl_integer_array_ceil!(input,
Int32Type, scale),
+ DataType::Int64 if has_scale => impl_integer_array_ceil!(input,
Int64Type, scale),
+ DataType::UInt8 if has_scale => impl_integer_array_ceil!(input,
UInt8Type, scale),
+ DataType::UInt16 if has_scale => {
+ impl_integer_array_ceil!(input, UInt16Type, scale)
+ }
+ DataType::UInt32 if has_scale => {
+ impl_integer_array_ceil!(input, UInt32Type, scale)
+ }
+ DataType::UInt64 if has_scale => {
+ let array = input.as_primitive::<UInt64Type>();
Review Comment:
Okay, I fixed it
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