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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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Activity
franciscojavierarceo commentedon Oct 22, 2025
@ntkathole @jyejare FYI
astronautas commentedon Nov 11, 2025
Also seems ODFV materialize has a completely different compute engine flow, be careful there. Maybe legacy split or smth.
franciscojavierarceo commentedon Nov 11, 2025
yeah we need to think through how to align the compute engine with ODFVs. ODFVs basically use 2 compute engines: one batch one and the feature server. We have to work out how best to make that cleaner.
haoxu0 commentedon Nov 12, 2025
yep. So we need to refactor OnDemandFeatureView to:
haoxu0 commentedon Nov 12, 2025
another tech debt found: #5716, forcing to refactor the OnDemandFeatureView
feat: Make udf optional if agg defined (feast-dev#5689)
feat: Make udf optional if agg defined (#5689) (#6328)
chore(release): release 0.63.0