Reuse GrpcStreamBroadcasters for aggregated data streams (frequenz-floss#190)

Also remove unneecessary `async` declarations.

Author: shsms

RELEASE_NOTES.md

@@ -6,12 +6,13 @@
 
 ## Upgrading
 
-<!-- Here goes notes on how to upgrade from previous versions, including deprecations and what they should be replaced with -->
+- The `receive_aggregated_data` method now returns a `Receiver`, which provides more flexibility that the previous `AsyncIterator`.
+- The `receive_microgrid_sensors_data`, `receive_single_sensor_data` and `receive_aggregated_data` methods are no-longer `async`.
 
 ## New Features
 
 <!-- Here goes the main new features and examples or instructions on how to use them -->
 
 ## Bug Fixes
 
-* Take out 'async' from  'receive_microgrid_sensors_data' and 'receive_single_sensor_data'.
+<!-- Here goes notable bug fixes that are worth a special mention or explanation -->

src/frequenz/client/reporting/_client.py

@@ -3,7 +3,7 @@
 
 """Client for requests to the Reporting API."""
 
-from collections.abc import AsyncIterable, AsyncIterator
+from collections.abc import AsyncIterable
 from datetime import datetime, timedelta
 from typing import cast
 
@@ -117,6 +117,17 @@ def __init__(
                 PBReceiveMicrogridSensorsDataStreamResponse, SensorsDataBatch
             ],
         ] = {}
+        self._aggregated_data_streams: dict[
+            tuple[
+                int,  # microgrid_id
+                str,  # metric name
+                str,  # aggregation_formula
+                float | None,  # start_time timestamp
+                float | None,  # end_time timestamp
+                int | None,  # resampling period in seconds
+            ],
+            GrpcStreamBroadcaster[PBAggregatedStreamResponse, MetricSample],
+        ] = {}
 
         self._metadata = (("key", key),) if key else ()
 
@@ -219,7 +230,7 @@ def _receive_microgrid_components_data_batch(
         include_states: bool = False,
         include_bounds: bool = False,
     ) -> Receiver[ComponentsDataBatch]:
-        """Return a GrpcStreamBroadcaster for microgrid component data."""
+        """Return a Receiver for the microgrid component data stream."""
         stream_key = (
             tuple((mid, tuple(cids)) for mid, cids in microgrid_components),
             tuple(metric.name for metric in metrics),
@@ -294,17 +305,16 @@ def transform_response(
             ) -> ComponentsDataBatch:
                 return ComponentsDataBatch(response)
 
-            async def stream_method() -> (
+            def stream_method() -> (
                 AsyncIterable[PBReceiveMicrogridComponentsDataStreamResponse]
             ):
                 call_iterator = self.stub.ReceiveMicrogridComponentsDataStream(
                     request, metadata=self._metadata
                 )
-                async for response in cast(
+                return cast(
                     AsyncIterable[PBReceiveMicrogridComponentsDataStreamResponse],
                     call_iterator,
-                ):
-                    yield response
+                )
 
             self._components_data_streams[stream_key] = GrpcStreamBroadcaster(
                 stream_name="microgrid-components-data-stream",
@@ -339,7 +349,7 @@ def receive_single_sensor_data(
             include_states: Whether to include the state data.
 
         Returns:
-                A receiver of `MetricSample`s.
+            A receiver of `MetricSample`s.
         """
         receiver = self._receive_microgrid_sensors_data_batch(
             microgrid_sensors=[(microgrid_id, [sensor_id])],
@@ -409,7 +419,7 @@ def _receive_microgrid_sensors_data_batch(
             include_states: Whether to include the state data.
 
         Returns:
-            A GrpcStreamBroadcaster that can be used to receive sensor data batches.
+            A receiver of `SensorsDataBatch`s.
         """
         stream_key = (
             tuple((mid, tuple(sids)) for mid, sids in microgrid_sensors),
@@ -480,17 +490,16 @@ def transform_response(
             ) -> SensorsDataBatch:
                 return SensorsDataBatch(response)
 
-            async def stream_method() -> (
+            def stream_method() -> (
                 AsyncIterable[PBReceiveMicrogridSensorsDataStreamResponse]
             ):
                 call_iterator = self.stub.ReceiveMicrogridSensorsDataStream(
                     request, metadata=self._metadata
                 )
-                async for response in cast(
+                return cast(
                     AsyncIterable[PBReceiveMicrogridSensorsDataStreamResponse],
                     call_iterator,
-                ):
-                    yield response
+                )
 
             self._sensors_data_streams[stream_key] = GrpcStreamBroadcaster(
                 stream_name="microgrid-sensors-data-stream",
@@ -500,7 +509,7 @@ async def stream_method() -> (
 
         return self._sensors_data_streams[stream_key].new_receiver()
 
-    async def receive_aggregated_data(
+    def receive_aggregated_data(
         self,
         *,
         microgrid_id: int,
@@ -509,7 +518,7 @@ async def receive_aggregated_data(
         start_time: datetime | None,
         end_time: datetime | None,
         resampling_period: timedelta,
-    ) -> AsyncIterator[MetricSample]:
+    ) -> Receiver[MetricSample]:
         """Iterate over aggregated data for a single metric using GrpcStreamBroadcaster.
 
         For now this only supports a single metric and aggregation formula.
@@ -521,63 +530,75 @@ async def receive_aggregated_data(
             end_time: end datetime, if None starts streaming indefinitely from start_time
             resampling_period: The period for resampling the data.
 
-        Yields:
-            An iterator over the aggregated metric samples.
+        Returns:
+            A receiver of `MetricSample`s.
 
         Raises:
             ValueError: If the resampling_period is not provided.
         """
-        if not resampling_period:
-            raise ValueError("resampling_period must be provided")
-
-        aggregation_config = PBAggregationConfig(
-            microgrid_id=microgrid_id,
-            metric=metric.to_proto(),
-            aggregation_formula=aggregation_formula,
+        stream_key = (
+            microgrid_id,
+            metric.name,
+            aggregation_formula,
+            start_time.timestamp() if start_time else None,
+            end_time.timestamp() if end_time else None,
+            round(resampling_period.total_seconds()) if resampling_period else None,
         )
+        if (
+            stream_key not in self._aggregated_data_streams
+            or not self._aggregated_data_streams[stream_key].is_running
+        ):
 
-        def dt2ts(dt: datetime) -> PBTimestamp:
-            ts = PBTimestamp()
-            ts.FromDatetime(dt)
-            return ts
+            if not resampling_period:
+                raise ValueError("resampling_period must be provided")
 
-        time_filter = PBTimeFilter(
-            start=dt2ts(start_time) if start_time else None,
-            end=dt2ts(end_time) if end_time else None,
-        )
+            aggregation_config = PBAggregationConfig(
+                microgrid_id=microgrid_id,
+                metric=metric.to_proto(),
+                aggregation_formula=aggregation_formula,
+            )
 
-        stream_filter = PBAggregatedStreamRequest.AggregationStreamFilter(
-            time_filter=time_filter,
-            resampling_options=PBResamplingOptions(
-                resolution=round(resampling_period.total_seconds())
-            ),
-        )
+            def dt2ts(dt: datetime) -> PBTimestamp:
+                ts = PBTimestamp()
+                ts.FromDatetime(dt)
+                return ts
 
-        request = PBAggregatedStreamRequest(
-            aggregation_configs=[aggregation_config],
-            filter=stream_filter,
-        )
+            time_filter = PBTimeFilter(
+                start=dt2ts(start_time) if start_time else None,
+                end=dt2ts(end_time) if end_time else None,
+            )
 
-        def transform_response(response: PBAggregatedStreamResponse) -> MetricSample:
-            return AggregatedMetric(response).sample()
+            stream_filter = PBAggregatedStreamRequest.AggregationStreamFilter(
+                time_filter=time_filter,
+                resampling_options=PBResamplingOptions(
+                    resolution=round(resampling_period.total_seconds())
+                ),
+            )
 
-        async def stream_method() -> AsyncIterable[PBAggregatedStreamResponse]:
-            call_iterator = self.stub.ReceiveAggregatedMicrogridComponentsDataStream(
-                request, metadata=self._metadata
+            request = PBAggregatedStreamRequest(
+                aggregation_configs=[aggregation_config],
+                filter=stream_filter,
             )
 
-            async for response in cast(
-                AsyncIterable[PBAggregatedStreamResponse], call_iterator
-            ):
-                yield response
+            def transform_response(
+                response: PBAggregatedStreamResponse,
+            ) -> MetricSample:
+                return AggregatedMetric(response).sample()
+
+            def stream_method() -> AsyncIterable[PBAggregatedStreamResponse]:
+                call_iterator = (
+                    self.stub.ReceiveAggregatedMicrogridComponentsDataStream(
+                        request, metadata=self._metadata
+                    )
+                )
 
-        broadcaster = GrpcStreamBroadcaster(
-            stream_name="aggregated-microgrid-data-stream",
-            stream_method=stream_method,
-            transform=transform_response,
-            retry_strategy=None,
-        )
+                return cast(AsyncIterable[PBAggregatedStreamResponse], call_iterator)
+
+            self._aggregated_data_streams[stream_key] = GrpcStreamBroadcaster(
+                stream_name="aggregated-microgrid-data-stream",
+                stream_method=stream_method,
+                transform=transform_response,
+                retry_strategy=None,
+            )
 
-        receiver = broadcaster.new_receiver()
-        async for data in receiver:
-            yield data
+        return self._aggregated_data_streams[stream_key].new_receiver()