Added test cases for datetime range based feature retrieval in Ray

Signed-off-by: Aniket Paluskar <[email protected]>

Author: aniketpalu

sdk/python/feast/infra/offline_stores/contrib/ray_offline_store/tests/test_ray_integration.py

@@ -1,3 +1,5 @@
+from datetime import timedelta
+
 import pandas as pd
 import pytest
 
@@ -144,3 +146,126 @@ def test_ray_offline_store_persist(environment, universal_data_sources):
     import os
 
     assert os.path.exists(saved_path)
+
+
+@pytest.mark.integration
+@pytest.mark.universal_offline_stores
+def test_ray_offline_store_non_entity_mode_basic(environment, universal_data_sources):
+    """Test historical features retrieval without entity_df (non-entity mode).
+
+    This tests the basic functionality where entity_df=None and start_date/end_date
+    are provided to retrieve all features within the time range.
+    """
+    store = environment.feature_store
+
+    (entities, datasets, data_sources) = universal_data_sources
+    feature_views = construct_universal_feature_views(data_sources)
+
+    store.apply(
+        [
+            driver(),
+            feature_views.driver,
+        ]
+    )
+
+    # Use the environment's start and end dates for the query
+    start_date = environment.start_date
+    end_date = environment.end_date
+
+    # Non-entity mode: entity_df=None with start_date and end_date
+    result_df = store.get_historical_features(
+        entity_df=None,
+        features=[
+            "driver_stats:conv_rate",
+            "driver_stats:acc_rate",
+            "driver_stats:avg_daily_trips",
+        ],
+        full_feature_names=False,
+        start_date=start_date,
+        end_date=end_date,
+    ).to_df()
+
+    # Verify data was retrieved
+    assert len(result_df) > 0, "Non-entity mode should return data"
+    assert "conv_rate" in result_df.columns
+    assert "acc_rate" in result_df.columns
+    assert "avg_daily_trips" in result_df.columns
+    assert "event_timestamp" in result_df.columns
+    assert "driver_id" in result_df.columns
+
+    # Verify timestamps are within the requested range
+    result_df["event_timestamp"] = pd.to_datetime(
+        result_df["event_timestamp"], utc=True
+    )
+    assert (result_df["event_timestamp"] >= start_date).all()
+    assert (result_df["event_timestamp"] <= end_date).all()
+
+
+@pytest.mark.integration
+@pytest.mark.universal_offline_stores
+def test_ray_offline_store_non_entity_mode_preserves_multiple_timestamps(
+    environment, universal_data_sources
+):
+    """Test that non-entity mode preserves multiple transactions per entity ID.
+
+    This is a regression test for the fix that ensures distinct (entity_key, event_timestamp)
+    combinations are preserved, not just distinct entity keys. This is critical for
+    proper point-in-time joins when an entity has multiple transactions.
+    """
+    store = environment.feature_store
+
+    (entities, datasets, data_sources) = universal_data_sources
+    feature_views = construct_universal_feature_views(data_sources)
+
+    store.apply(
+        [
+            driver(),
+            feature_views.driver,
+        ]
+    )
+
+    now = _utc_now()
+    ts1 = pd.Timestamp(now - timedelta(hours=2)).round("ms")
+    ts2 = pd.Timestamp(now - timedelta(hours=1)).round("ms")
+    ts3 = pd.Timestamp(now).round("ms")
+
+    # Write data with multiple timestamps for the same entity (driver_id=9001)
+    df_to_write = pd.DataFrame.from_dict(
+        {
+            "event_timestamp": [ts1, ts2, ts3],
+            "driver_id": [9001, 9001, 9001],  # Same entity, different timestamps
+            "conv_rate": [0.1, 0.2, 0.3],
+            "acc_rate": [0.9, 0.8, 0.7],
+            "avg_daily_trips": [10, 20, 30],
+            "created": [ts1, ts2, ts3],
+        },
+    )
+
+    store.write_to_offline_store(
+        feature_views.driver.name, df_to_write, allow_registry_cache=False
+    )
+
+    # Query without entity_df - should get all 3 rows for driver_id=9001
+    result_df = store.get_historical_features(
+        entity_df=None,
+        features=[
+            "driver_stats:conv_rate",
+            "driver_stats:acc_rate",
+        ],
+        full_feature_names=False,
+        start_date=ts1 - timedelta(minutes=1),
+        end_date=ts3 + timedelta(minutes=1),
+    ).to_df()
+
+    # Filter to just our test entity
+    result_df = result_df[result_df["driver_id"] == 9001]
+
+    # Verify we got all 3 rows with different timestamps (not just 1 row)
+    assert len(result_df) == 3, (
+        f"Expected 3 rows for driver_id=9001 (one per timestamp), got {len(result_df)}"
+    )
+
+    # Verify the feature values are correct for each timestamp
+    result_df = result_df.sort_values("event_timestamp").reset_index(drop=True)
+    assert list(result_df["conv_rate"]) == [0.1, 0.2, 0.3]
+    assert list(result_df["acc_rate"]) == [0.9, 0.8, 0.7]