franciscojavierarceo · Mar 10, 2026 · Mar 9, 2026 · Mar 9, 2026 · Mar 9, 2026 · Mar 9, 2026
@@ -70,7 +70,7 @@ driver_fv = BatchFeatureView(
         Field(name="conv_rate", dtype=Float32),
     ],
     aggregations=[
-        Aggregation(column="conv_rate", function="sum", time_window=timedelta(days=1)),
+        Aggregation(column="conv_rate", function="sum", time_window=timedelta(days=1), name="total_conv_rate_1d"),
     ],
     source=source,
 )
@@ -145,6 +145,7 @@ See:
 - `source` is optional; if omitted (`None`), the feature view has no associated batch data source.
 - Schema fields must be consistent with `sink_source`, `batch_source.field_mapping` if field mappings exist.
 - Aggregation logic must reference columns present in the raw source or transformed inputs.
+- The output feature name for an aggregation defaults to `{function}_{column}` (e.g., `sum_conv_rate`). Use the `name` parameter to override it (e.g., `name="total_conv_rate_1d"`).
 
 ---
 

@@ -206,9 +206,9 @@ customer_features = StreamFeatureView(
         batch_source=file_source,  # For historical data
     ),
     aggregations=[
-        Aggregation(column="amount", function="sum", time_window=timedelta(hours=1)),
-        Aggregation(column="amount", function="avg", time_window=timedelta(hours=1)),
-        Aggregation(column="amount", function="std", time_window=timedelta(hours=1)),
+        Aggregation(column="amount", function="sum", time_window=timedelta(hours=1), name="sum_amount_1h"),
+        Aggregation(column="amount", function="avg", time_window=timedelta(hours=1), name="avg_amount_1h"),
+        Aggregation(column="amount", function="std", time_window=timedelta(hours=1), name="std_amount_1h"),
     ],
     timestamp_field="event_timestamp",
     online=True,
@@ -229,7 +229,12 @@ customer_features = StreamFeatureView(
 
 ### Key Parameters
 
-- `aggregations`: List of time-windowed aggregations to compute
+- `aggregations`: List of time-windowed aggregations to compute. Each `Aggregation` accepts:
+  - `column`: source column to aggregate
+  - `function`: aggregation function (`sum`, `avg`, `mean`, `min`, `max`, `count`, `std`)
+  - `time_window`: duration of the aggregation window
+  - `slide_interval`: hop/slide size (defaults to `time_window`)
+  - `name` *(optional)*: output feature name. Defaults to `{function}_{column}` (e.g., `sum_amount`). Set this to use a custom name (e.g., `name="sum_amount_1h"`).
 - `timestamp_field`: Column name for timestamps (required when aggregations are specified)
 - `enable_tiling`: Enable tiling optimization (default: `False`)
   - Set to `True` for **streaming scenarios**

@@ -12,4 +12,5 @@ message Aggregation {
     string function = 2;
     google.protobuf.Duration time_window = 3;
     google.protobuf.Duration slide_interval = 4;
+    string name = 5;
 }
@@ -21,19 +21,22 @@ class Aggregation:
         function: str  # Provided built in aggregations sum, max, min, count mean
         time_window: timedelta  # The time window for this aggregation.
         slide_interval: timedelta # The sliding window for these aggregations
+        name: str  # Optional override for the output feature name (defaults to {function}_{column})
     """
 
     column: str
     function: str
     time_window: Optional[timedelta]
     slide_interval: Optional[timedelta]
+    name: str
 
     def __init__(
         self,
         column: Optional[str] = "",
         function: Optional[str] = "",
         time_window: Optional[timedelta] = None,
         slide_interval: Optional[timedelta] = None,
+        name: Optional[str] = None,
     ):
         self.column = column or ""
         self.function = function or ""
@@ -42,6 +45,7 @@ def __init__(
             self.slide_interval = self.time_window
         else:
             self.slide_interval = slide_interval
+        self.name = name or ""
 
     def to_proto(self) -> AggregationProto:
         window_duration = None
@@ -59,6 +63,7 @@ def to_proto(self) -> AggregationProto:
             function=self.function,
             time_window=window_duration,
             slide_interval=slide_interval_duration,
+            name=self.name,
         )
 
     @classmethod
@@ -79,6 +84,7 @@ def from_proto(cls, agg_proto: AggregationProto):
             function=agg_proto.function,
             time_window=time_window,
             slide_interval=slide_interval,
+            name=agg_proto.name or None,
         )
         return aggregation
 
@@ -91,11 +97,26 @@ def __eq__(self, other):
             or self.function != other.function
             or self.time_window != other.time_window
             or self.slide_interval != other.slide_interval
+            or self.name != other.name
         ):
             return False
 
         return True
 
+    def resolved_name(self, time_window: Optional[timedelta] = None) -> str:
+        """Return the output feature name for this aggregation.
+
+        If ``name`` is set it is returned as-is.  Otherwise the name is
+        derived as ``{function}_{column}``, with ``_{seconds}s`` appended
+        when *time_window* is provided.
+        """
+        if self.name:
+            return self.name
+        base = f"{self.function}_{self.column}"
+        if time_window is not None and time_window.total_seconds() > 0:
+            return f"{base}_{int(time_window.total_seconds())}s"
+        return base
+
 
 def aggregation_specs_to_agg_ops(
     agg_specs: Iterable[Any],
@@ -106,7 +127,7 @@ def aggregation_specs_to_agg_ops(
     for agg in agg_specs:
         if getattr(agg, "time_window", None) is not None:
             raise ValueError(time_window_unsupported_error_message)
-        alias = f"{agg.function}_{agg.column}"
+        alias = getattr(agg, "name", None) or f"{agg.function}_{agg.column}"
         agg_ops[alias] = (agg.function, agg.column)
     return agg_ops
 

@@ -61,9 +61,7 @@ def apply_sawtooth_window_tiling(
     ir_metadata_dict = {}
 
     for agg in aggregations:
-        feature_name = (
-            f"{agg.function}_{agg.column}_{int(window_size.total_seconds())}s"
-        )
+        feature_name = agg.resolved_name(window_size)
         _, metadata = get_ir_metadata_for_aggregation(agg, feature_name)
         ir_metadata_dict[feature_name] = metadata
 
@@ -161,9 +159,7 @@ def apply_sawtooth_window_tiling(
 
     # Step 5: Compute final feature values from IRs (for algebraic aggs, just rename)
     for agg in aggregations:
-        feature_name = (
-            f"{agg.function}_{agg.column}_{int(window_size.total_seconds())}s"
-        )
+        feature_name = agg.resolved_name(window_size)
         metadata = ir_metadata_dict[feature_name]
 
         if metadata.type == "algebraic":

@@ -76,9 +76,7 @@ def convert_cumulative_to_windowed(
 
             # Subtract previous tile values from current tile for each aggregation
             for agg in aggregations:
-                feature_name = (
-                    f"{agg.function}_{agg.column}_{int(window_size.total_seconds())}s"
-                )
+                feature_name = agg.resolved_name(window_size)
                 _, metadata = get_ir_metadata_for_aggregation(agg, feature_name)
 
                 if metadata.type == "algebraic":

@@ -461,9 +461,7 @@ def _execute_standard_aggregation(self, dataset: Dataset) -> DAGValue:
         # Convert aggregations to Ray's groupby format
         agg_dict = {}
         for agg in self.aggregations:
-            feature_name = f"{agg.function}_{agg.column}"
-            if agg.time_window:
-                feature_name += f"_{int(agg.time_window.total_seconds())}s"
+            feature_name = agg.resolved_name(agg.time_window)
 
             if agg.function == "count":
                 agg_dict[feature_name] = (agg.column, "count")

@@ -311,7 +311,7 @@ def _execute_tiled_aggregation(self, input_df: DataFrame) -> DAGValue:
             expected_columns = entity_keys + [self.timestamp_col]
             for time_window, window_aggs in aggs_by_window.items():
                 for agg in window_aggs:
-                    feature_name = f"{agg.function}_{agg.column}_{int(time_window.total_seconds())}s"
+                    feature_name = agg.resolved_name(time_window)
                     if feature_name not in expected_columns:
                         expected_columns.append(feature_name)
 
@@ -372,11 +372,7 @@ def _execute_standard_aggregation(self, input_df: DataFrame) -> DAGValue:
         agg_exprs = []
         for agg in self.aggregations:
             func = getattr(F, agg.function)
-            expr = func(agg.column).alias(
-                f"{agg.function}_{agg.column}_{int(agg.time_window.total_seconds())}s"
-                if agg.time_window
-                else f"{agg.function}_{agg.column}"
-            )
+            expr = func(agg.column).alias(agg.resolved_name(agg.time_window))
             agg_exprs.append(expr)
 
         if any(agg.time_window for agg in self.aggregations):

@@ -22,8 +22,10 @@ class Aggregation(google.protobuf.message.Message):
     FUNCTION_FIELD_NUMBER: builtins.int
     TIME_WINDOW_FIELD_NUMBER: builtins.int
     SLIDE_INTERVAL_FIELD_NUMBER: builtins.int
+    NAME_FIELD_NUMBER: builtins.int
     column: builtins.str
     function: builtins.str
+    name: builtins.str
     @property
     def time_window(self) -> google.protobuf.duration_pb2.Duration: ...
     @property
@@ -35,8 +37,9 @@ class Aggregation(google.protobuf.message.Message):
         function: builtins.str = ...,
         time_window: google.protobuf.duration_pb2.Duration | None = ...,
         slide_interval: google.protobuf.duration_pb2.Duration | None = ...,
+        name: builtins.str = ...,
     ) -> None: ...
     def HasField(self, field_name: typing_extensions.Literal["slide_interval", b"slide_interval", "time_window", b"time_window"]) -> builtins.bool: ...
-    def ClearField(self, field_name: typing_extensions.Literal["column", b"column", "function", b"function", "slide_interval", b"slide_interval", "time_window", b"time_window"]) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["column", b"column", "function", b"function", "name", b"name", "slide_interval", b"slide_interval", "time_window", b"time_window"]) -> None: ...
 
 global___Aggregation = Aggregation
@@ -1,13 +1,16 @@
+from datetime import timedelta
+
 import pytest
 
-from feast.aggregation import aggregation_specs_to_agg_ops
+from feast.aggregation import Aggregation, aggregation_specs_to_agg_ops
 
 
 class DummyAggregation:
-    def __init__(self, *, function: str, column: str, time_window=None):
+    def __init__(self, *, function: str, column: str, time_window=None, name: str = ""):
         self.function = function
         self.column = column
         self.time_window = time_window
+        self.name = name
 
 
 def test_aggregation_specs_to_agg_ops_success():
@@ -42,3 +45,54 @@ def test_aggregation_specs_to_agg_ops_time_window_unsupported(error_message: str
             agg_specs,
             time_window_unsupported_error_message=error_message,
         )
+
+
+def test_aggregation_specs_to_agg_ops_custom_name():
+    agg_specs = [
+        DummyAggregation(
+            function="sum",
+            column="seconds_watched",
+            name="sum_seconds_watched_per_ad_1d",
+        ),
+    ]
+
+    agg_ops = aggregation_specs_to_agg_ops(
+        agg_specs,
+        time_window_unsupported_error_message="Time window aggregation is not supported.",
+    )
+
+    assert agg_ops == {
+        "sum_seconds_watched_per_ad_1d": ("sum", "seconds_watched"),
+    }
+
+
+def test_aggregation_specs_to_agg_ops_mixed_names():
+    agg_specs = [
+        DummyAggregation(function="sum", column="trips", name="total_trips"),
+        DummyAggregation(function="mean", column="fare"),
+    ]
+
+    agg_ops = aggregation_specs_to_agg_ops(
+        agg_specs,
+        time_window_unsupported_error_message="Time window aggregation is not supported.",
+    )
+
+    assert agg_ops == {
+        "total_trips": ("sum", "trips"),
+        "mean_fare": ("mean", "fare"),
+    }
+
+
+def test_aggregation_round_trip_with_name():
+    agg = Aggregation(
+        column="seconds_watched",
+        function="sum",
+        time_window=timedelta(days=1),
+        name="sum_seconds_watched_per_ad_1d",
+    )
+    proto = agg.to_proto()
+    assert proto.name == "sum_seconds_watched_per_ad_1d"
+
+    restored = Aggregation.from_proto(proto)
+    assert restored.name == "sum_seconds_watched_per_ad_1d"
+    assert restored == agg