fn is_datafusion_spark_compatible()

in native/spark-expr/src/conversion_funcs/cast.rs [992:1072]

• 76 lines of code
• 3 McCabe index (conditional complexity)

fn is_datafusion_spark_compatible(
    from_type: &DataType,
    to_type: &DataType,
    allow_incompat: bool,
) -> bool {
    if from_type == to_type {
        return true;
    }
    match from_type {
        DataType::Null => {
            matches!(to_type, DataType::List(_))
        }
        DataType::Boolean => matches!(
            to_type,
            DataType::Int8
                | DataType::Int16
                | DataType::Int32
                | DataType::Int64
                | DataType::Float32
                | DataType::Float64
                | DataType::Utf8
        ),
        DataType::Int8 | DataType::Int16 | DataType::Int32 | DataType::Int64 => {
            // note that the cast from Int32/Int64 -> Decimal128 here is actually
            // not compatible with Spark (no overflow checks) but we have tests that
            // rely on this cast working so we have to leave it here for now
            matches!(
                to_type,
                DataType::Boolean
                    | DataType::Int8
                    | DataType::Int16
                    | DataType::Int32
                    | DataType::Int64
                    | DataType::Float32
                    | DataType::Float64
                    | DataType::Decimal128(_, _)
                    | DataType::Utf8
            )
        }
        DataType::Float32 | DataType::Float64 => matches!(
            to_type,
            DataType::Boolean
                | DataType::Int8
                | DataType::Int16
                | DataType::Int32
                | DataType::Int64
                | DataType::Float32
                | DataType::Float64
        ),
        DataType::Decimal128(_, _) | DataType::Decimal256(_, _) => matches!(
            to_type,
            DataType::Int8
                | DataType::Int16
                | DataType::Int32
                | DataType::Int64
                | DataType::Float32
                | DataType::Float64
                | DataType::Decimal128(_, _)
                | DataType::Decimal256(_, _)
                | DataType::Utf8 // note that there can be formatting differences
        ),
        DataType::Utf8 if allow_incompat => matches!(
            to_type,
            DataType::Binary | DataType::Float32 | DataType::Float64 | DataType::Decimal128(_, _)
        ),
        DataType::Utf8 => matches!(to_type, DataType::Binary),
        DataType::Date32 => matches!(to_type, DataType::Utf8),
        DataType::Timestamp(_, _) => {
            matches!(
                to_type,
                DataType::Int64 | DataType::Date32 | DataType::Utf8 | DataType::Timestamp(_, _)
            )
        }
        DataType::Binary => {
            // note that this is not completely Spark compatible because
            // DataFusion only supports binary data containing valid UTF-8 strings
            matches!(to_type, DataType::Utf8)
        }
        _ => false,
    }
}