Ondemand Feature with STRING type input fails on apply

Expected Behavior

New on demand feature view created.

Current Behavior

Attempting to apply a feature view with STRING type field fails with an exception below.

/home/user/source_code/feast_exp/.venv/bin/python /home/user/source_code/feast_exp/src/od.py 
/home/user/source_code/feast_exp/.venv/lib/python3.11/site-packages/feast/feature_store.py:580: RuntimeWarning: On demand feature view is an experimental feature. This API is stable, but the functionality does not scale well for offline retrieval
  warnings.warn(
Traceback (most recent call last):
  File "/home/user/source_code/feast_exp/src/od.py", line 23, in <module>
    STORE.apply([r,transform])
  File "/home/user/source_code/feast_exp/.venv/lib/python3.11/site-packages/feast/feature_store.py", line 936, in apply
    self._make_inferences(
  File "/home/user/source_code/feast_exp/.venv/lib/python3.11/site-packages/feast/feature_store.py", line 643, in _make_inferences
    odfv.infer_features()
  File "/home/user/source_code/feast_exp/.venv/lib/python3.11/site-packages/feast/on_demand_feature_view.py", line 654, in infer_features
    inferred_features = self.feature_transformation.infer_features(
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/user/source_code/feast_exp/.venv/lib/python3.11/site-packages/feast/transformation/pandas_transformation.py", line 98, in infer_features
    python_type_to_feast_value_type(
  File "/home/user/source_code/feast_exp/.venv/lib/python3.11/site-packages/feast/type_map.py", line 214, in python_type_to_feast_value_type
    raise ValueError(
ValueError: Value with native type object cannot be converted into Feast value type

Process finished with exit code 1

Steps to reproduce

Attempt to apply the feature view below.

from feast import on_demand_feature_view
from feast import RequestSource
from feast import Field
from feast.types import String,Int32

from feature_store.configs.store import STORE

r=RequestSource(
    name="r",
    timestamp_field="event_ts",
    schema=[
        Field(name="input_1", dtype=String)
    ]
)


@on_demand_feature_view.on_demand_feature_view(name="a",schema=[],entities=[],sources=[r])
def transform(df):
    return df

if __name__ == "__main__":
    STORE.apply([r,transform])

Specifications

Version: 0.53.0
Platform: Linux
Subsystem: Ubuntu

Possible Solution

Running the apply on debug mode shows that the input column is of type object instead of string .
When the column is explicitly type cast to string with the line below in the transform method, the apply successfully passes.
df["input_1"]=df["input_1"].astype("string")

One solution would be to ensure that the input source attempts to type cast to defined type and raise an exception if it cannot be type cast.

did you try following this example:

https://docs.feast.dev/reference/beta-on-demand-feature-view#example-1-on-demand-transformation-on-read-using-pandas-mode

Also, you have an extra on_demand_feature_view after the decorator.

@on_demand_feature_view(
    name="a",
    schema=[],
    entities=[],
    sources=[r]
)
def transform(df):
    return df

did you try following this example:

https://docs.feast.dev/reference/beta-on-demand-feature-view#example-1-on-demand-transformation-on-read-using-pandas-mode

Also, you have an extra on_demand_feature_view after the decorator.

@on_demand_feature_view(
name="a",
schema=[],
entities=[],
sources=[r]
)
def transform(df):
return df

I modified the example from the example link.


from feast import Field, RequestSource, on_demand_feature_view
from feast.types import String
import pandas as pd

# Define a request data source for request-time features
input_request = RequestSource(
    name="vals_to_add",
    schema=[
        Field(name="val_to_add", dtype=String),
        Field(name="val_to_add_2", dtype=String),
    ],
)

# Use input data and feature view features to create new features in Pandas mode
@on_demand_feature_view.on_demand_feature_view(
    sources=[input_request],
    schema=[
        Field(name="conv_rate_plus_val1", dtype=String),
        Field(name="conv_rate_plus_val2", dtype=String),
    ],
    mode="pandas",
)
def transformed_conv_rate(features_df: pd.DataFrame) -> pd.DataFrame:
    df = pd.DataFrame()
    df["conv_rate_plus_val1"] = features_df["val_to_add"]
    df["conv_rate_plus_val2"] = features_df["val_to_add_2"]
    return df

if __name__ == "__main__":
    STORE.apply([input_request, transformed_conv_rate])

It still fails with the same error.

Also, the imported on_demand_feature_view in feast python sdk is the module and not the decorator. The example seems to be incorrect with the use of the decorator..

I updated the documentation on that README, that was an error.

@OptimeeringBigya can you try:

@on_demand_feature_view.on_demand_feature_view(
    sources=[input_request],
    schema=[
        Field(name="conv_rate_plus_val1", dtype=String),
        Field(name="conv_rate_plus_val2", dtype=String),
    ],
    mode="pandas",
)
def transformed_conv_rate(features_df: pd.DataFrame) -> pd.DataFrame:
    df = pd.DataFrame()
    df["conv_rate_plus_val1"] = features_df["val_to_add"].astype(str)
    df["conv_rate_plus_val2"] = features_df["val_to_add_2"].astype(str)
    return df

@franciscojavierarceo

@OptimeeringBigya can you try:

@on_demand_feature_view.on_demand_feature_view(
sources=[input_request],
schema=[
Field(name="conv_rate_plus_val1", dtype=String),
Field(name="conv_rate_plus_val2", dtype=String),
],
mode="pandas",
)
def transformed_conv_rate(features_df: pd.DataFrame) -> pd.DataFrame:
df = pd.DataFrame()
df["conv_rate_plus_val1"] = features_df["val_to_add"].astype(str)
df["conv_rate_plus_val2"] = features_df["val_to_add_2"].astype(str)
return df

@franciscojavierarceo This is still failing. Replacing .astype(str) with .astype("string") fixes the issue.

#5641 (comment) while this is a hack around the issue. This does not fix the source of the issue.

Example:

# Define a request data source for request-time features
input_request = RequestSource(
    name="vals_to_add",
    schema=[
        Field(name="val_to_add", dtype=String),
    ],
)

# Use input data and feature view features to create new features in Pandas mode
@on_demand_feature_view.on_demand_feature_view(
    sources=[input_request],
    schema=[
        Field(name="hour", dtype=Int32),
        Field(name="mins", dtype=Int32),
    ],
    mode="pandas",
)
def transformed_conv_rate(features_df: pd.DataFrame) -> pd.DataFrame:
    """
    This extracts hour and min from the ISO format input string
    """
    features_df["hour"]=3 # assume computed ints
    features_df["mins"]=4 # assume computed ints
    return features_df

if __name__ == "__main__":
    STORE.apply([input_request, transformed_conv_rate])

Applying this still causes an issue, as the input column vals_to_add is of type obj and not string (even though it is defined as string in the input source).

Type converting the input column using .astype(str) feels hackish. I do not think a transformation function should be modifying the input columns and should only be generating the output columns.

Possibly a duplicate of #2229

Navigation Menu

Ondemand Feature with STRING type input fails on apply #5641

Expected Behavior

Current Behavior

Steps to reproduce

Specifications

Possible Solution

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

Ondemand Feature with STRING type input fails on apply #5641

Description

Expected Behavior

Current Behavior

Steps to reproduce

Specifications

Possible Solution

Activity

franciscojavierarceo commented on Sep 29, 2025

BigyaPradhan commented on Sep 29, 2025

franciscojavierarceo commented on Sep 29, 2025

franciscojavierarceo commented on Sep 29, 2025

BigyaPradhan commented on Sep 29, 2025

BigyaPradhan commented on Oct 6, 2025

BigyaPradhan commented on Oct 9, 2025

Metadata

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

Issue actions