In some test benchmarks, using regular python dicts for inputs for executing the transformations is much faster (up to ~10x) than pandas for the online flow. This tends to be the more latency sensitive flow (offline flows seem to be ~40% slower if using vectorized operations).
Something that looks like:
@on_demand_feature_view(
sources=[driver_hourly_stats_view, val_to_add_request],
schema=[
Field(name="conv_rate_plus_val1", dtype=Float64),
Field(name="conv_rate_plus_val2", dtype=Float64),
],
mode="python"
)
def transformed_conv_rate(driver_hourly_stats: Dict[str, Any], vals_to_add: Dict[str, Any]) -> Dict[str, Any]:
features = {}
features['conv_rate_plus_val1'] = (driver_hourly_stats['conv_rate'] + vals_to_add['val_to_add'])
features['conv_rate_plus_val2'] = (driver_hourly_stats['conv_rate'] + vals_to_add['val_to_add_2'])
return featuresmight be similar to what we want
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In some test benchmarks, using regular python dicts for inputs for executing the transformations is much faster (up to ~10x) than pandas for the online flow. This tends to be the more latency sensitive flow (offline flows seem to be ~40% slower if using vectorized operations).
Something that looks like:
might be similar to what we want