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fix: Support historical feature retrieval with start_date/end_date in RemoteOfflineStore by aniketpalu · Pull Request #5703 · feast-dev/feast · GitHub
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fix: Support historical feature retrieval with start_date/end_date in RemoteOfflineStore #5703

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Merged
ntkathole merged 6 commits into from
Nov 3, 2025
Merged

fix: Support historical feature retrieval with start_date/end_date in RemoteOfflineStore #5703

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b8a8ab6
Added start_date & end_date range for historical data retrieval from …
aniketpalu Oct 21, 2025
d6bb773
Minor linting & reformatting changes
aniketpalu Oct 21, 2025
0e17fdf
Fixed minor issue of empty df with only key as column being trated as…
aniketpalu Oct 28, 2025
434ab75
Merge branch 'master' into RHOAIENG-32904
aniketpalu Nov 1, 2025
0d52d17
Removed unnecessary logic to create mock df
aniketpalu Nov 3, 2025
4bf7de7
Minor linting & formatting changes
aniketpalu Nov 3, 2025
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Fixed minor issue of empty df with only key as column being trated as… 
… entity_df in offline server

Signed-off-by: Aniket Paluskar <apaluska@redhat.com>
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@aniketpalu
aniketpalu committed Oct 28, 2025
commit 0e17fdf42f06451da6a235b7cdf10f22ee499b31
11 changes: 10 additions & 1 deletion sdk/python/feast/infra/offline_stores/remote.py
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Expand Up @@ -443,7 +443,16 @@ def get_table_column_names_and_types_from_data_source(
return zip(table.column("name").to_pylist(), table.column("type").to_pylist())


def _create_retrieval_metadata(feature_refs: List[str], entity_df: pd.DataFrame):
def _create_retrieval_metadata(
feature_refs: List[str], entity_df: Optional[pd.DataFrame] = None
):
if entity_df is None:
return RetrievalMetadata(
features=feature_refs,
keys=[], # No entity keys when no entity_df provided
min_event_timestamp=None,
max_event_timestamp=None,
)
entity_schema = _get_entity_schema(
entity_df=entity_df,
)
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15 changes: 15 additions & 0 deletions sdk/python/feast/offline_server.py
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Expand Up @@ -8,6 +8,7 @@
from typing import Any, Dict, List, Optional, cast

import click
import pandas as pd
import pyarrow as pa
import pyarrow.flight as fl
from google.protobuf.json_format import Parse
Expand Down Expand Up @@ -431,6 +432,20 @@ def get_historical_features(self, command: dict, key: Optional[str] = None):
# Extract parameters from the internal flights dictionary
entity_df_value = self.flights[key]
entity_df = pa.Table.to_pandas(entity_df_value)
# Check if this is a mock/empty table (contains only 'key' column)
if len(entity_df.columns) == 1 and "key" in entity_df.columns:
entity_df = None

# If no entity_df provided, create minimal one with timestamps
if entity_df is None and "start_date" in command and "end_date" in command:
# Create minimal entity_df with event_timestamp when using start_date/end_date mode
start_date = utils.make_tzaware(
datetime.fromisoformat(command["start_date"])
)
end_date = utils.make_tzaware(datetime.fromisoformat(command["end_date"]))
entity_df = pd.DataFrame(

@jyejare jyejare Nov 2, 2025

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We should not need to create dummy entity_df at all. We should handle empty(entity_df=None) entity_df for and in offline_store.get_historical_features which we are returning in this function.

@aniketpalu aniketpalu Nov 3, 2025

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You are right, its not needed to create mock dataframe with timestamps, just had to make sure that empty_df is being passed.

{"event_timestamp": pd.to_datetime([start_date, end_date])}
)

feature_view_names = command["feature_view_names"]
name_aliases = command["name_aliases"]
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