mbutrovich commented on code in PR #2099:
URL: https://github.com/apache/datafusion-comet/pull/2099#discussion_r2268042922
##########
native/spark-expr/src/static_invoke/char_varchar_utils/read_side_padding.rs:
##########
@@ -71,44 +101,101 @@ fn spark_read_side_padding2(
}
}
+enum RPadArgument {
+ ConstLength(i32),
+ ColArray(ArrayRef),
+}
+
fn spark_read_side_padding_internal<T: OffsetSizeTrait>(
array: &ArrayRef,
- length: i32,
truncate: bool,
+ rpad_argument: RPadArgument,
) -> Result<ColumnarValue, DataFusionError> {
let string_array = as_generic_string_array::<T>(array)?;
- let length = 0.max(length) as usize;
- let space_string = " ".repeat(length);
+ match rpad_argument {
+ RPadArgument::ColArray(array_int) => {
+ let int_pad_array = array_int.as_primitive::<Int32Type>();
+ let mut str_pad_value_map = HashMap::new();
Review Comment:
I might be missing something obvious: why do we need a `HashMap`? It doubles
the memory usage of this operator. If we iterate through the arrays to build
the `HashMap`, why can't we iterate through the arrays with the builder below
(i.e. `zip` through them or something). This approach would also have the
benefit of preserving input order (though I'm not sure that's required).
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