Make OnDemandFeatureView work for having both aggregations and transformations #5689
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With PR #5666, OnDemandFeatureView supports aggregations. But it is exclusive with transformation.
the reason is the infer_schema will break if having both defined in feature views.
This can be solved by reworking the infer_schema.
def infer_features(self) -> None:
random_input = self._construct_random_input(singleton=self.singleton)
inferred_features = self.feature_transformation.infer_features(
random_input=random_input, singleton=self.singleton
)
Schema inference doesn't run the aggregation step - it only:
- Looks at the source feature view's schema
- Creates fake random data matching those columns
- Passes it directly to the UDF
But at runtime, the UDF receives aggregated columns, not the original source columns.
So the UDF would need to be written for aggregated input:
def my_odfv(inputs):
return {"trips_doubled": inputs["sum_trips"] * 2} # expects sum_trips
But schema inference passes it:
{"trips": [10], "rating": [4.5]}
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