add test

Signed-off-by: HaoXuAI <sduxuhao@gmail.com>
feast-dev · franciscojavierarceo · Sep 14, 2025 · Sep 13, 2025 · Sep 13, 2025 · Sep 13, 2025
commit 866b81e9c32eb781c3c0da2e2db642d926969b68
@@ -0,0 +1,163 @@
+"""Tests for RetrievalJob FeastDataFrame integration."""
+
+from unittest.mock import Mock
+
+import pandas as pd
+import pyarrow as pa
+
+from feast.dataframe import DataFrameEngine, FeastDataFrame
+from feast.infra.offline_stores.offline_store import RetrievalJob
+
+
+class MockRetrievalJob(RetrievalJob):
+    """Mock RetrievalJob for testing."""
+
+    def __init__(
+        self, arrow_table: pa.Table, features: list = None, odfvs: list = None
+    ):
+        self.arrow_table = arrow_table
+        self.features = features or []
+        self.odfvs = odfvs or []
+
+    def _to_arrow_internal(self, timeout=None):
+        return self.arrow_table
+
+    @property
+    def full_feature_names(self):
+        return False
+
+    @property
+    def on_demand_feature_views(self):
+        return self.odfvs
+
+
+class TestRetrievalJobFeastDataFrame:
+    """Test RetrievalJob FeastDataFrame integration."""
+
+    def test_to_feast_df_basic(self):
+        """Test basic to_feast_df functionality."""
+        # Create test data
+        test_data = pa.table(
+            {
+                "feature1": [1, 2, 3],
+                "feature2": ["a", "b", "c"],
+                "timestamp": pd.to_datetime(["2023-01-01", "2023-01-02", "2023-01-03"]),
+            }
+        )
+
+        # Create mock retrieval job
+        job = MockRetrievalJob(test_data, features=["feature1", "feature2"])
+
+        # Test to_feast_df
+        feast_df = job.to_feast_df()
+
+        # Assertions
+        assert isinstance(feast_df, FeastDataFrame)
+        assert feast_df.engine == DataFrameEngine.ARROW
+        assert isinstance(feast_df.data, pa.Table)
+        assert feast_df.data.num_rows == 3
+        assert feast_df.data.num_columns == 3
+
+    def test_to_feast_df_metadata(self):
+        """Test to_feast_df metadata population."""
+        # Create test data
+        test_data = pa.table({"feature1": [1, 2, 3], "feature2": [4.0, 5.0, 6.0]})
+
+        # Create mock on-demand feature views
+        mock_odfv1 = Mock()
+        mock_odfv1.name = "odfv1"
+        mock_odfv2 = Mock()
+        mock_odfv2.name = "odfv2"
+
+        # Create mock retrieval job with features and ODFVs
+        job = MockRetrievalJob(
+            test_data, features=["feature1", "feature2"], odfvs=[mock_odfv1, mock_odfv2]
+        )
+
+        # Test to_feast_df
+        feast_df = job.to_feast_df()
+
+        # Check metadata
+        assert "features" in feast_df.metadata
+        assert "on_demand_feature_views" in feast_df.metadata
+        assert feast_df.metadata["features"] == ["feature1", "feature2"]
+        assert feast_df.metadata["on_demand_feature_views"] == ["odfv1", "odfv2"]
+
+    def test_to_feast_df_with_timeout(self):
+        """Test to_feast_df with timeout parameter."""
+        test_data = pa.table({"feature1": [1, 2, 3]})
+        job = MockRetrievalJob(test_data)
+
+        # Test with timeout - should not raise any errors
+        feast_df = job.to_feast_df(timeout=30)
+
+        assert isinstance(feast_df, FeastDataFrame)
+        assert feast_df.engine == DataFrameEngine.ARROW
+
+    def test_to_feast_df_empty_metadata(self):
+        """Test to_feast_df with empty features and ODFVs."""
+        test_data = pa.table({"feature1": [1, 2, 3]})
+        job = MockRetrievalJob(test_data)  # No features or ODFVs provided
+
+        feast_df = job.to_feast_df()
+
+        # Should handle missing features gracefully
+        assert feast_df.metadata["features"] == []
+        assert feast_df.metadata["on_demand_feature_views"] == []
+
+    def test_to_feast_df_preserves_arrow_data(self):
+        """Test that to_feast_df preserves the original Arrow data."""
+        # Create test data with specific types
+        test_data = pa.table(
+            {
+                "int_feature": pa.array([1, 2, 3], type=pa.int64()),
+                "float_feature": pa.array([1.1, 2.2, 3.3], type=pa.float64()),
+                "string_feature": pa.array(["a", "b", "c"], type=pa.string()),
+                "bool_feature": pa.array([True, False, True], type=pa.bool_()),
+            }
+        )
+
+        job = MockRetrievalJob(test_data)
+        feast_df = job.to_feast_df()
+
+        # Check that the Arrow data is exactly the same
+        assert feast_df.data.equals(test_data)
+        assert feast_df.data.schema == test_data.schema
+
+        # Check column names and types are preserved
+        assert feast_df.data.column_names == test_data.column_names
+        for i, column in enumerate(test_data.schema):
+            assert feast_df.data.schema.field(i).type == column.type
+
+    def test_to_df_still_works(self):
+        """Test that the original to_df method still works unchanged."""
+        test_data = pa.table({"feature1": [1, 2, 3], "feature2": ["a", "b", "c"]})
+
+        job = MockRetrievalJob(test_data)
+
+        # Test to_df returns pandas DataFrame
+        df = job.to_df()
+
+        assert isinstance(df, pd.DataFrame)
+        assert len(df) == 3
+        assert list(df.columns) == ["feature1", "feature2"]
+        assert df["feature1"].tolist() == [1, 2, 3]
+        assert df["feature2"].tolist() == ["a", "b", "c"]
+
+    def test_both_methods_return_same_data(self):
+        """Test that to_df and to_feast_df return equivalent data."""
+        test_data = pa.table(
+            {"feature1": [1, 2, 3, 4], "feature2": [10.5, 20.5, 30.5, 40.5]}
+        )
+
+        job = MockRetrievalJob(test_data)
+
+        # Get data from both methods
+        df = job.to_df()
+        feast_df = job.to_feast_df()
+
+        # Convert FeastDataFrame to pandas for comparison
+        feast_as_pandas = feast_df.data.to_pandas().reset_index(drop=True)
+
+        # Should be equivalent
+        pd.testing.assert_frame_equal(df, feast_as_pandas)