Context
I'm using an ODFV with a PandasTransformation and a RequestSource input. Furthermore, I'm using Postgres offline and online store.
Expected Behavior
I would expect both the get_online_features_async method and the get_historical_features method to return the same set of features. However, when retrieving features with get_online_features_async, the input data from the RequestSource is not present in the output response. On the other hand, when retrieving features with get_historical_features, the input data from the RequestSource is present in the output response.
I'm not sure which behavior is to be expected. However, I think think that both approaches should return the same columns.
Current Behavior
When calling the get_online_features_async method I do not see the input from the RequestSource back in the output response, while I would expect it to be there.
One could also argue that it should not be in the output response. That would also be an option. However, I would assume that the online and offline feature retrieval would return the same output in terms of columns.
Steps to reproduce
docker-compose.yml
---
version: "3"
services:
offline_store:
image: postgres:16-alpine
container_name: offline_store
ports:
- "6543:5432"
environment:
- POSTGRES_DB=offline_store
- POSTGRES_USER=postgres
- POSTGRES_PASSWORD=postgres
volumes:
- ./postgres_init:/docker-entrypoint-initdb.d
online_store:
image: postgres:16-alpine
container_name: online_store
ports:
- "5432:5432"
environment:
- POSTGRES_DB=online_store
- POSTGRES_USER=postgres
- POSTGRES_PASSWORD=postgres
feature_store.yml
project: feast_tryout
provider: local
registry:
registry_type: sql
path: postgresql+psycopg2://postgres:postgres@0.0.0.0:5432/online_store
cache_ttl_seconds: 60
online_store:
type: postgres
host: 0.0.0.0
port: 5432
database: online_store
db_schema: online
user: postgres
password: postgres
offline_store:
type: postgres
host: 0.0.0.0
port: 6543
database: offline_store
db_schema: offline
user: postgres
password: postgres
entity_key_serialization_version: 2
Insert into offline store (postgres)
postgres_init/create-offline-store-database.sql
CREATE SCHEMA offline;
CREATE TABLE offline.features (
"ENTITY_ID" VARCHAR,
"EVENT_TIMESTAMP" TIMESTAMP,
"ENTITY_FLOAT" FLOAT,
);
INSERT INTO offline.features
SELECT *
FROM (
VALUES ('11111111', '2024-01-01 13:00:00' :: TIMESTAMP, 1.1),
('11111111', '2024-01-01 14:00:00' :: TIMESTAMP, 1.11),
('11111111', '2024-01-01 15:00:00' :: TIMESTAMP, 1.111),
('22222222', '2024-01-01 13:00:00' :: TIMESTAMP, 2.2),
('22222222', '2024-01-01 14:00:00' :: TIMESTAMP, 2.22),
('33333333', '2024-01-01 13:00:00' :: TIMESTAMP, 3.3),
('44444444', '2024-01-02 22:00:00' :: TIMESTAMP, 4.4)
)
bootstrap.py
from datetime import timedelta
from typing import Any
import pandas as pd
from feast import (
Entity,
FeatureService,
FeatureStore,
FeatureView,
Field,
RequestSource,
ValueType,
)
from feast.infra.offline_stores.contrib.postgres_offline_store.postgres_source import (
PostgreSQLSource as PostgresSource,
)
from feast.on_demand_feature_view import on_demand_feature_view
from feast.types import Float32, Float64
feature_store = FeatureStore()
features_entity = Entity(
name="entity_id",
join_keys=["ENTITY_ID"],
value_type=ValueType.STRING,
)
features_source = PostgresSource(
name="features",
timestamp_field="EVENT_TIMESTAMP",
table="offline.features",
)
features_feature_view = FeatureView(
name="features_feature_view",
entities=[features_entity],
ttl=timedelta(days=0),
schema=[Field(name="ENTITY_FLOAT", dtype=Float32)],
online=True,
source=features_source,
)
request_source = RequestSource(
name="request_feature",
schema=[Field(name="REQUEST_FLOAT", dtype=Float32)],
)
@on_demand_feature_view(
sources=[features_feature_view, request_source],
schema=[
Field(name="ENTITY_FLOAT_TRANSFORMED_PANDAS", dtype=Float64),
Field(name="ENTITY_FLOAT_PLUS_REQUEST_SOURCE", dtype=Float64),
],
mode="pandas",
)
def odfv_pandas(input: pd.DataFrame) -> pd.DataFrame:
output = pd.DataFrame()
output["ENTITY_FLOAT_TRANSFORMED_PANDAS"] = input["ENTITY_FLOAT"] * 2
output["ENTITY_FLOAT_PLUS_REQUEST_SOURCE"] = (
input["ENTITY_FLOAT"] * input["REQUEST_FLOAT"]
)
return output
@on_demand_feature_view(
sources=[features_feature_view, request_source],
schema=[Field(name="ENTITY_FLOAT_TRANSFORMED_PYTHON", dtype=Float64)],
mode="python",
)
def odfv_python(input: dict[str, Any]) -> dict[str, Any]:
output = {}
output["ENTITY_FLOAT_TRANSFORMED_PYTHON"] = [
value * 2 for value in input["ENTITY_FLOAT"]
]
output["ENTITY_FLOAT_PLUS_REQUEST_SOURCE_PYTHON"] = [
e + r for e, r in zip(input["ENTITY_FLOAT"], input["REQUEST_FLOAT"])
]
return output
features_feature_service_pandas = FeatureService(
name="features_feature_service_pandas",
features=[features_feature_view, odfv_pandas],
)
features_feature_service_python = FeatureService(
name="features_feature_service_python",
features=[features_feature_view, odfv_python],
)
feature_store.apply(
[
features_entity,
features_source,
features_feature_view,
odfv_pandas,
odfv_python,
features_feature_service_pandas,
features_feature_service_python,
]
)
materialize.py
from datetime import datetime
from feast import FeatureStore
feature_store = FeatureStore()
feature_store.materialize(
start_date=datetime(1900, 1, 1),
end_date=datetime(9999, 1, 1),
feature_views=["features_feature_view"],
)
inference.py
"""Inference example."""
import pandas as pd
from feast import FeatureStore
feature_store = FeatureStore()
feature_service_pandas = feature_store.get_feature_service(
name="features_feature_service_pandas"
)
feature_service_python = feature_store.get_feature_service(
name="features_feature_service_python"
)
entity_rows = [
{"ENTITY_ID": "11111111", "REQUEST_FLOAT": 1.0},
{"ENTITY_ID": "22222222", "REQUEST_FLOAT": 1.0},
]
entity_df = pd.DataFrame(entity_rows)
entity_df["event_timestamp"] = pd.to_datetime("now", utc=True)
print("offline with pandas")
offline_features = feature_store.get_historical_features(
entity_df=entity_df,
features=feature_service_pandas,
).to_df()
print(list(offline_features.to_dict().keys()))
print("online with pandas")
online_features = feature_store.get_online_features(
entity_rows=entity_rows,
features=feature_service_pandas,
).to_dict()
print(list(online_features.keys()))
print("online with python")
online_features = feature_store.get_online_features(
entity_rows=entity_rows,
features=feature_service_python,
).to_dict()
print(list(online_features.keys()))
## OUTPUT:
# offline with pandas
# ['ENTITY_ID', 'REQUEST_FLOAT', 'event_timestamp', 'ENTITY_FLOAT', 'ENTITY_FLOAT_TRANSFORMED_PANDAS', 'ENTITY_FLOAT_PLUS_REQUEST_SOURCE']
# online with pandas
# ['ENTITY_ID', 'ENTITY_FLOAT', 'ENTITY_FLOAT_TRANSFORMED_PANDAS', 'ENTITY_FLOAT_PLUS_REQUEST_SOURCE']
# online with python
# ['ENTITY_ID', 'ENTITY_FLOAT', 'ENTITY_FLOAT_TRANSFORMED_PYTHON']
# not possible to do offline transformation with python mode.
Specifications
Version: 0.36.0
Platform: macOS - M1
Subsystem: Sonoma 14.1.1
Possible Solution
Context
I'm using an ODFV with a PandasTransformation and a RequestSource input. Furthermore, I'm using Postgres offline and online store.
Expected Behavior
I would expect both the get_online_features_async method and the get_historical_features method to return the same set of features. However, when retrieving features with get_online_features_async, the input data from the RequestSource is not present in the output response. On the other hand, when retrieving features with get_historical_features, the input data from the RequestSource is present in the output response.
I'm not sure which behavior is to be expected. However, I think think that both approaches should return the same columns.
Current Behavior
When calling the get_online_features_async method I do not see the input from the RequestSource back in the output response, while I would expect it to be there.
One could also argue that it should not be in the output response. That would also be an option. However, I would assume that the online and offline feature retrieval would return the same output in terms of columns.
Steps to reproduce
docker-compose.yml
feature_store.yml
Insert into offline store (postgres)
postgres_init/create-offline-store-database.sql
bootstrap.py
materialize.py
inference.py
Specifications
Version: 0.36.0
Platform: macOS - M1
Subsystem: Sonoma 14.1.1
Possible Solution