feat: Support aggregation in odfv

docs/reference/beta-on-demand-feature-view.md

@@ -35,10 +35,40 @@ When defining an ODFV, you can specify the transformation mode using the `mode`
 
 ### Singleton Transformations in Native Python Mode
 
-Native Python mode supports transformations on singleton dictionaries by setting `singleton=True`. This allows you to 
-write transformation functions that operate on a single row at a time, making the code more intuitive and aligning with 
+Native Python mode supports transformations on singleton dictionaries by setting `singleton=True`. This allows you to
+write transformation functions that operate on a single row at a time, making the code more intuitive and aligning with
 how data scientists typically think about data transformations.
 
+## Aggregations
+
+On Demand Feature Views support aggregations that compute aggregate statistics over groups of rows. When using aggregations, data is grouped by entity columns (e.g., `driver_id`) and aggregated before being passed to the transformation function.
+
+**Important**: Aggregations and transformations are mutually exclusive. When aggregations are specified, they replace the transformation function.
+
+### Usage
+
+```python
+from feast import Aggregation
+from datetime import timedelta
+
+@on_demand_feature_view(
+    sources=[driver_hourly_stats_view],
+    schema=[
+        Field(name="total_trips", dtype=Int64),
+        Field(name="avg_rating", dtype=Float64),
+    ],
+    aggregations=[
+        Aggregation(column="trips", function="sum"),
+        Aggregation(column="rating", function="mean"),
+    ],
+)
+def driver_aggregated_stats(inputs):
+    # No transformation function needed when using aggregations
+    pass
+```
+
+Aggregated columns are automatically named using the pattern `{function}_{column}` (e.g., `sum_trips`, `mean_rating`).
+
 ## Example
 See [https://github.com/feast-dev/on-demand-feature-views-demo](https://github.com/feast-dev/on-demand-feature-views-demo) for an example on how to use on demand feature views.
 

sdk/python/docs/source/feast.infra.compute_engines.local.backends.rst

@@ -7,39 +7,39 @@ Submodules
 feast.infra.compute\_engines.local.backends.base module
 -------------------------------------------------------
 
-.. automodule:: feast.infra.compute_engines.local.backends.base
+.. automodule:: feast.infra.compute_engines.backends.base
    :members:
    :undoc-members:
    :show-inheritance:
 
 feast.infra.compute\_engines.local.backends.factory module
 ----------------------------------------------------------
 
-.. automodule:: feast.infra.compute_engines.local.backends.factory
+.. automodule:: feast.infra.compute_engines.backends.factory
    :members:
    :undoc-members:
    :show-inheritance:
 
 feast.infra.compute\_engines.local.backends.pandas\_backend module
 ------------------------------------------------------------------
 
-.. automodule:: feast.infra.compute_engines.local.backends.pandas_backend
+.. automodule:: feast.infra.compute_engines.backends.pandas_backend
    :members:
    :undoc-members:
    :show-inheritance:
 
 feast.infra.compute\_engines.local.backends.polars\_backend module
 ------------------------------------------------------------------
 
-.. automodule:: feast.infra.compute_engines.local.backends.polars_backend
+.. automodule:: feast.infra.compute_engines.backends.polars_backend
    :members:
    :undoc-members:
    :show-inheritance:
 
 Module contents
 ---------------
 
-.. automodule:: feast.infra.compute_engines.local.backends
+.. automodule:: feast.infra.compute_engines.backends
    :members:
    :undoc-members:
    :show-inheritance:

sdk/python/docs/source/feast.infra.compute_engines.local.rst

@@ -7,7 +7,7 @@ Subpackages
 .. toctree::
    :maxdepth: 4
 
-   feast.infra.compute_engines.local.backends
+   feast.infra.compute_engines.backends
 
 Submodules
 ----------

sdk/python/feast/infra/compute_engines/local/backends/factory.py → sdk/python/feast/infra/compute_engines/backends/factory.py

@@ -3,8 +3,8 @@
 import pandas as pd
 import pyarrow
 
-from feast.infra.compute_engines.local.backends.base import DataFrameBackend
-from feast.infra.compute_engines.local.backends.pandas_backend import PandasBackend
+from feast.infra.compute_engines.backends.base import DataFrameBackend
+from feast.infra.compute_engines.backends.pandas_backend import PandasBackend
 
 
 class BackendFactory:
@@ -46,7 +46,7 @@ def _is_polars(entity_df) -> bool:
 
     @staticmethod
     def _get_polars_backend():
-        from feast.infra.compute_engines.local.backends.polars_backend import (
+        from feast.infra.compute_engines.backends.polars_backend import (
             PolarsBackend,
         )
 

sdk/python/feast/infra/compute_engines/local/backends/pandas_backend.py → sdk/python/feast/infra/compute_engines/backends/pandas_backend.py

@@ -3,7 +3,7 @@
 import pandas as pd
 import pyarrow as pa
 
-from feast.infra.compute_engines.local.backends.base import DataFrameBackend
+from feast.infra.compute_engines.backends.base import DataFrameBackend
 
 
 class PandasBackend(DataFrameBackend):

sdk/python/feast/infra/compute_engines/local/backends/polars_backend.py → sdk/python/feast/infra/compute_engines/backends/polars_backend.py

@@ -3,7 +3,7 @@
 import polars as pl
 import pyarrow as pa
 
-from feast.infra.compute_engines.local.backends.base import DataFrameBackend
+from feast.infra.compute_engines.backends.base import DataFrameBackend
 
 
 class PolarsBackend(DataFrameBackend):

sdk/python/feast/infra/compute_engines/local/compute.py

@@ -12,10 +12,10 @@
     MaterializationTask,
 )
 from feast.infra.common.retrieval_task import HistoricalRetrievalTask
+from feast.infra.compute_engines.backends.base import DataFrameBackend
+from feast.infra.compute_engines.backends.factory import BackendFactory
 from feast.infra.compute_engines.base import ComputeEngine
 from feast.infra.compute_engines.dag.context import ExecutionContext
-from feast.infra.compute_engines.local.backends.base import DataFrameBackend
-from feast.infra.compute_engines.local.backends.factory import BackendFactory
 from feast.infra.compute_engines.local.feature_builder import LocalFeatureBuilder
 from feast.infra.compute_engines.local.job import (
     LocalMaterializationJob,

sdk/python/feast/infra/compute_engines/local/feature_builder.py

@@ -2,8 +2,8 @@
 
 from feast.infra.common.materialization_job import MaterializationTask
 from feast.infra.common.retrieval_task import HistoricalRetrievalTask
+from feast.infra.compute_engines.backends.base import DataFrameBackend
 from feast.infra.compute_engines.feature_builder import FeatureBuilder
-from feast.infra.compute_engines.local.backends.base import DataFrameBackend
 from feast.infra.compute_engines.local.nodes import (
     LocalAggregationNode,
     LocalDedupNode,

sdk/python/feast/infra/compute_engines/local/nodes.py

@@ -5,11 +5,11 @@
 
 from feast import BatchFeatureView, StreamFeatureView
 from feast.data_source import DataSource
+from feast.infra.compute_engines.backends.base import DataFrameBackend
 from feast.infra.compute_engines.dag.context import ColumnInfo, ExecutionContext
 from feast.infra.compute_engines.dag.model import DAGFormat
 from feast.infra.compute_engines.dag.node import DAGNode
 from feast.infra.compute_engines.local.arrow_table_value import ArrowTableValue
-from feast.infra.compute_engines.local.backends.base import DataFrameBackend
 from feast.infra.compute_engines.local.local_node import LocalNode
 from feast.infra.compute_engines.utils import (
     create_offline_store_retrieval_job,

sdk/python/feast/utils.py

@@ -33,6 +33,7 @@
     RequestDataNotFoundInEntityRowsException,
 )
 from feast.field import Field
+from feast.infra.compute_engines.backends.pandas_backend import PandasBackend
 from feast.infra.key_encoding_utils import deserialize_entity_key
 from feast.protos.feast.serving.ServingService_pb2 import (
     FieldStatus,
@@ -561,6 +562,76 @@ def construct_response_feature_vector(
     )
 
 
+def _get_aggregate_operations(agg_specs) -> dict:
+    """
+    Convert Aggregation specs to agg_ops format for PandasBackend.
+
+    Reused from LocalFeatureBuilder logic.
+    TODO: This logic is duplicated from feast.infra.compute_engines.local.feature_builder.LocalFeatureBuilder._get_aggregate_operations().
+    Consider refactoring to a shared utility module in the future.
+    """
+    agg_ops = {}
+    for agg in agg_specs:
+        if agg.time_window is not None:
+            raise ValueError(
+                "Time window aggregation is not supported in online serving."
+            )
+        alias = f"{agg.function}_{agg.column}"
+        agg_ops[alias] = (agg.function, agg.column)
+    return agg_ops
+
+
+def _apply_aggregations_to_response(
+    response_data: Union[pyarrow.Table, Dict[str, List[Any]]],
+    aggregations,
+    group_keys: Optional[List[str]],
+    mode: str,
+) -> Union[pyarrow.Table, Dict[str, List[Any]]]:
+    """
+    Apply aggregations using PandasBackend.
+
+    Args:
+        response_data: Either a pyarrow.Table or dict of lists containing the data
+        aggregations: List of Aggregation objects to apply
+        group_keys: List of column names to group by (optional)
+        mode: Transformation mode ("python", "pandas", or "substrait")
+
+    Returns:
+        Aggregated data in the same format as input
+
+    TODO: Consider refactoring to support backends other than pandas in the future.
+    """
+    if not aggregations:
+        return response_data
+
+    backend = PandasBackend()
+
+    # Convert to pandas DataFrame
+    if isinstance(response_data, dict):
+        df = pd.DataFrame(response_data)
+    else:  # pyarrow.Table
+        df = backend.from_arrow(response_data)
+
+    if df.empty:
+        return response_data
+
+    # Convert aggregations to agg_ops format
+    agg_ops = _get_aggregate_operations(aggregations)
+
+    # Apply aggregations using PandasBackend
+    if group_keys:
+        result_df = backend.groupby_agg(df, group_keys, agg_ops)
+    else:
+        # No grouping - aggregate over entire dataset
+        result_df = backend.groupby_agg(df, [], agg_ops)
+
+    # Convert back to original format
+    if mode == "python":
+        return {col: result_df[col].tolist() for col in result_df.columns}
+    else:  # pandas or substrait
+        return backend.to_arrow(result_df)
+
+
 def _augment_response_with_on_demand_transforms(
     online_features_response: GetOnlineFeaturesResponse,
     feature_refs: List[str],
@@ -605,7 +676,31 @@ def _augment_response_with_on_demand_transforms(
     for odfv_name, _feature_refs in odfv_feature_refs.items():
         odfv = requested_odfv_map[odfv_name]
         if not odfv.write_to_online_store:
-            if odfv.mode == "python":
+            # Apply aggregations if configured.
+            if odfv.aggregations:
+                if odfv.mode == "python":
+                    if initial_response_dict is None:
+                        initial_response_dict = initial_response.to_dict()
+                    initial_response_dict = _apply_aggregations_to_response(
+                        initial_response_dict,
+                        odfv.aggregations,
+                        odfv.entities,
+                        odfv.mode,
+                    )
+                elif odfv.mode in {"pandas", "substrait"}:
+                    if initial_response_arrow is None:
+                        initial_response_arrow = initial_response.to_arrow()
+                    initial_response_arrow = _apply_aggregations_to_response(
+                        initial_response_arrow,
+                        odfv.aggregations,
+                        odfv.entities,
+                        odfv.mode,
+                    )
+
+            # Apply transformation. Note: aggregations and transformation configs are mutually exclusive
+            # TODO: Fix to make it work for having both aggregation and transformation
+            #  ticket: https://github.com/feast-dev/feast/issues/5689
+            elif odfv.mode == "python":
                 if initial_response_dict is None:
                     initial_response_dict = initial_response.to_dict()
                 transformed_features_dict: Dict[str, List[Any]] = odfv.transform_dict(

sdk/python/tests/unit/infra/compute_engines/local/test_nodes.py

@@ -4,9 +4,9 @@
 import pandas as pd
 import pyarrow as pa
 
+from feast.infra.compute_engines.backends.pandas_backend import PandasBackend
 from feast.infra.compute_engines.dag.context import ColumnInfo, ExecutionContext
 from feast.infra.compute_engines.local.arrow_table_value import ArrowTableValue
-from feast.infra.compute_engines.local.backends.pandas_backend import PandasBackend
 from feast.infra.compute_engines.local.nodes import (
     LocalAggregationNode,
     LocalDedupNode,

sdk/python/tests/unit/test_on_demand_feature_view_aggregation.py

@@ -0,0 +1,89 @@
+# Copyright 2025 The Feast Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tests for OnDemandFeatureView aggregations in online serving."""
+
+import pyarrow as pa
+
+from feast.aggregation import Aggregation
+from feast.utils import _apply_aggregations_to_response
+
+
+def test_aggregation_python_mode():
+    """Test aggregations in Python mode (dict format)."""
+    data = {
+        "driver_id": [1, 1, 2, 2],
+        "trips": [10, 20, 15, 25],
+    }
+    aggs = [Aggregation(column="trips", function="sum")]
+
+    result = _apply_aggregations_to_response(data, aggs, ["driver_id"], "python")
+
+    assert result == {"driver_id": [1, 2], "sum_trips": [30, 40]}
+
+
+def test_aggregation_pandas_mode():
+    """Test aggregations in Pandas mode (Arrow table format)."""
+    table = pa.table(
+        {
+            "driver_id": [1, 1, 2, 2],
+            "trips": [10, 20, 15, 25],
+        }
+    )
+    aggs = [Aggregation(column="trips", function="sum")]
+
+    result = _apply_aggregations_to_response(table, aggs, ["driver_id"], "pandas")
+
+    assert isinstance(result, pa.Table)
+    result_df = result.to_pandas()
+    assert list(result_df["driver_id"]) == [1, 2]
+    assert list(result_df["sum_trips"]) == [30, 40]
+
+
+def test_multiple_aggregations():
+    """Test multiple aggregation functions."""
+    data = {
+        "driver_id": [1, 1, 2, 2],
+        "trips": [10, 20, 15, 25],
+        "revenue": [100.0, 200.0, 150.0, 250.0],
+    }
+    aggs = [
+        Aggregation(column="trips", function="sum"),
+        Aggregation(column="revenue", function="mean"),
+    ]
+
+    result = _apply_aggregations_to_response(data, aggs, ["driver_id"], "python")
+
+    assert result["driver_id"] == [1, 2]
+    assert result["sum_trips"] == [30, 40]
+    assert result["mean_revenue"] == [150.0, 200.0]
+
+
+def test_no_aggregations_returns_original():
+    """Test that no aggregations returns original data."""
+    data = {"driver_id": [1, 2], "trips": [10, 20]}
+
+    result = _apply_aggregations_to_response(data, [], ["driver_id"], "python")
+
+    assert result == data
+
+
+def test_empty_data_returns_empty():
+    """Test that empty data returns empty result."""
+    data = {"driver_id": [], "trips": []}
+    aggs = [Aggregation(column="trips", function="sum")]
+
+    result = _apply_aggregations_to_response(data, aggs, ["driver_id"], "python")
+
+    assert result == data