vbarua commented on code in PR #13803:
URL: https://github.com/apache/datafusion/pull/13803#discussion_r1889342502
##########
datafusion/substrait/src/logical_plan/consumer.rs:
##########
@@ -1503,370 +1858,416 @@ pub async fn from_substrait_func_args(
/// Convert Substrait AggregateFunction to DataFusion Expr
pub async fn from_substrait_agg_func(
- state: &dyn SubstraitPlanningState,
+ consumer: &impl SubstraitConsumer,
f: &AggregateFunction,
input_schema: &DFSchema,
- extensions: &Extensions,
filter: Option<Box<Expr>>,
order_by: Option<Vec<SortExpr>>,
distinct: bool,
) -> Result<Arc<Expr>> {
- let args =
- from_substrait_func_args(state, &f.arguments, input_schema,
extensions).await?;
-
- let Some(function_name) = extensions.functions.get(&f.function_reference)
else {
+ let Some(fn_signature) = consumer
+ .get_extensions()
Review Comment:
I was trying to figure out the right abstraction not just for resolving the
function via the extension, but also mapping it into DataFusion. However I
found the existing logic a little unwieldy to reason about. Specifically scalar
functions first try and lookup the function by name, then look it up as an
operator and then try and treat is a BuiltinExpr.
I want to think about function mapping in isolation, because I suspect it
will be easier to figure out the right abstraction if we simplify the lookup
logic first.
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