Optimize group by for single partition topics

quixstreams/dataframe/dataframe.py

@@ -92,7 +92,7 @@ class StreamingDataFrame:
     What it Does:
 
     - Builds a data processing pipeline, declaratively (not executed immediately)
-        - Executes this pipeline on inputs at runtime (Kafka message values)
+    - Executes this pipeline on inputs at runtime (Kafka message values)
     - Provides functions/interface similar to Pandas Dataframes/Series
     - Enables stateful processing (and manages everything related to it)
 
@@ -135,15 +135,20 @@ def __init__(
         registry: DataFrameRegistry,
         processing_context: ProcessingContext,
         stream: Optional[Stream] = None,
+        stream_id: Optional[str] = None,
     ):
         if not topics:
             raise ValueError("At least one Topic must be passed")
 
-        self._stream: Stream = stream or Stream()
         # Implicitly deduplicate Topic objects into a tuple and sort them by name
         self._topics: tuple[Topic, ...] = tuple(
             sorted({t.name: t for t in topics}.values(), key=attrgetter("name"))
         )
+
+        self._stream: Stream = stream or Stream()
+        self._stream_id: str = stream_id or topic_manager.stream_id_from_topics(
+            self.topics
+        )
         self._topic_manager = topic_manager
         self._registry = registry
         self._processing_context = processing_context
@@ -174,7 +179,7 @@ def stream_id(self) -> str:
 
         By default, a topic name or a combination of topic names are used as `stream_id`.
         """
-        return self._topic_manager.stream_id_from_topics(self.topics)
+        return self._stream_id
 
     @property
     def topics(self) -> tuple[Topic, ...]:
@@ -591,6 +596,11 @@ def func(d: dict, state: State):
         # Generate a config for the new repartition topic based on the underlying topics
         repartition_config = self._topic_manager.derive_topic_config(self._topics)
 
+        # If the topic has only one partition, we don't need a repartition topic
+        # we can directly change the messages key as they all go to the same partition.
+        if repartition_config.num_partitions == 1:
+            return self._single_partition_groupby(operation, key)
+
         groupby_topic = self._topic_manager.repartition_topic(
             operation=operation,
             stream_id=self.stream_id,
@@ -606,6 +616,29 @@ def func(d: dict, state: State):
         self._registry.register_groupby(source_sdf=self, new_sdf=groupby_sdf)
         return groupby_sdf
 
+    def _single_partition_groupby(
+        self, operation: str, key: Union[str, Callable[[Any], Any]]
+    ) -> "StreamingDataFrame":
+        if isinstance(key, str):
+
+            def _callback(value, _, timestamp, headers):
+                return value, value[key], timestamp, headers
+        else:
+
+            def _callback(value, _, timestamp, headers):
+                return value, key(value), timestamp, headers
+
+        stream = self.stream.add_transform(_callback, expand=False)
+
+        groupby_sdf = self.__dataframe_clone__(
+            stream=stream, stream_id=f"{self.stream_id}--groupby--{operation}"
+        )
+        self._registry.register_groupby(
+            source_sdf=self, new_sdf=groupby_sdf, register_new_root=False
+        )
+
+        return groupby_sdf
+
     def contains(self, keys: Union[str, list[str]]) -> StreamingSeries:
         """
         Check if keys are present in the Row value.
@@ -1679,6 +1712,7 @@ def __dataframe_clone__(
         self,
         *topics: Topic,
         stream: Optional[Stream] = None,
+        stream_id: Optional[str] = None,
     ) -> "StreamingDataFrame":
         """
         Clone the StreamingDataFrame with a new `stream`, `topics`,
@@ -1692,6 +1726,7 @@ def __dataframe_clone__(
         clone = self.__class__(
             *(topics or self._topics),
             stream=stream,
+            stream_id=stream_id,
             processing_context=self._processing_context,
             topic_manager=self._topic_manager,
             registry=self._registry,

quixstreams/dataframe/registry.py

@@ -70,27 +70,41 @@ def register_root(
         self._registry[topic.name] = dataframe.stream
 
     def register_groupby(
-        self, source_sdf: "StreamingDataFrame", new_sdf: "StreamingDataFrame"
+        self,
+        source_sdf: "StreamingDataFrame",
+        new_sdf: "StreamingDataFrame",
+        register_new_root: bool = True,
     ):
         """
         Register a "groupby" SDF, which is one generated with `SDF.group_by()`.
         :param source_sdf: the SDF used by `sdf.group_by()`
         :param new_sdf: the SDF generated by `sdf.group_by()`.
+        :param register_new_root: whether to register the new SDF as a root SDF.
         """
         if source_sdf.stream_id in self._repartition_origins:
             raise GroupByNestingLimit(
                 "Subsequent (nested) `SDF.group_by()` operations are not allowed."
             )
-        try:
-            self.register_root(new_sdf)
-        except StreamingDataFrameDuplicate:
+
+        if new_sdf.stream_id in self._repartition_origins:
             raise GroupByDuplicate(
-                "A `SDF.group_by()` operation appears to be the same as another, "
+                "An `SDF.group_by()` operation appears to be the same as another, "
                 "either from using the same column or name parameter; "
                 "adjust by setting a unique name with `SDF.group_by(name=<NAME>)` "
             )
+
         self._repartition_origins.add(new_sdf.stream_id)
 
+        if register_new_root:
+            try:
+                self.register_root(new_sdf)
+            except StreamingDataFrameDuplicate:
+                raise GroupByDuplicate(
+                    "An `SDF.group_by()` operation appears to be the same as another, "
+                    "either from using the same column or name parameter; "
+                    "adjust by setting a unique name with `SDF.group_by(name=<NAME>)` "
+                )
+
     def compose_all(
         self, sink: Optional[VoidExecutor] = None
     ) -> dict[str, VoidExecutor]:

tests/test_quixstreams/test_app.py

@@ -513,12 +513,14 @@ def test_state_dir_env(self):
         assert app.config.state_dir == Path("/path/to/other")
 
 
+@pytest.mark.parametrize("number_of_partitions", [1, 2])
 class TestAppGroupBy:
     def test_group_by(
         self,
         app_factory,
         internal_consumer_factory,
         executor,
+        number_of_partitions,
     ):
         """
         Test that StreamingDataFrame processes 6 messages from Kafka and groups them
@@ -540,8 +542,12 @@ def on_message_processed(*_):
         timestamp_ms = int(time.time() * 1000)
         user_id = "abc123"
         value_in = {"user": user_id}
-        expected_message_count = 1
-        total_messages = expected_message_count * 2  # groupby reproduces each message
+
+        if number_of_partitions == 1:
+            total_messages = 1  # groupby optimisation for 1 partition
+        else:
+            total_messages = 2  # groupby reproduces each message
+
         app = app_factory(
             auto_offset_reset="earliest",
             on_message_processed=on_message_processed,
@@ -551,6 +557,9 @@ def on_message_processed(*_):
             str(uuid.uuid4()),
             value_deserializer="json",
             value_serializer="json",
+            config=TopicConfig(
+                num_partitions=number_of_partitions, replication_factor=1
+            ),
         )
         app_topic_out = app.topic(
             str(uuid.uuid4()),
@@ -607,6 +616,7 @@ def test_group_by_with_window(
         internal_consumer_factory,
         executor,
         processing_guarantee,
+        number_of_partitions,
     ):
         """
         Test that StreamingDataFrame processes 6 messages from Kafka and groups them
@@ -631,8 +641,12 @@ def on_message_processed(*_):
         timestamp_ms = timestamp_ms - (timestamp_ms % window_duration_ms)
         user_id = "abc123"
         value_in = {"user": user_id}
-        expected_message_count = 1
-        total_messages = expected_message_count * 2  # groupby reproduces each message
+
+        if number_of_partitions == 1:
+            total_messages = 1  # groupby optimisation for 1 partition
+        else:
+            total_messages = 2  # groupby reproduces each message
+
         app = app_factory(
             auto_offset_reset="earliest",
             on_message_processed=on_message_processed,
@@ -643,6 +657,9 @@ def on_message_processed(*_):
             str(uuid.uuid4()),
             value_deserializer="json",
             value_serializer="json",
+            config=TopicConfig(
+                num_partitions=number_of_partitions, replication_factor=1
+            ),
         )
         app_topic_out = app.topic(
             str(uuid.uuid4()),
@@ -2380,11 +2397,9 @@ def on_message_processed(topic_, partition, offset):
             assert row.timestamp == timestamp_ms
             assert row.headers == headers
 
+    @pytest.mark.parametrize("number_of_partitions", [1, 2])
     def test_group_by(
-        self,
-        app_factory,
-        internal_consumer_factory,
-        executor,
+        self, app_factory, internal_consumer_factory, executor, number_of_partitions
     ):
         """
         Test that StreamingDataFrame processes 6 messages from Kafka and groups them
@@ -2411,11 +2426,17 @@ def on_message_processed(*_):
             str(uuid.uuid4()),
             value_deserializer="json",
             value_serializer="json",
+            config=TopicConfig(
+                num_partitions=number_of_partitions, replication_factor=1
+            ),
         )
         input_topic_b = app.topic(
             str(uuid.uuid4()),
             value_deserializer="json",
             value_serializer="json",
+            config=TopicConfig(
+                num_partitions=number_of_partitions, replication_factor=1
+            ),
         )
         input_topics = [input_topic_a, input_topic_b]
         output_topic_user = app.topic(
@@ -2433,8 +2454,14 @@ def on_message_processed(*_):
         user_id = "abc123"
         account_id = "def456"
         value_in = {"user": user_id, "account": account_id}
-        # expected_processed = 1 (input msg per SDF) * 3 (2 groupbys, each reprocesses input) * 2 SDFs
-        expected_processed = 6
+
+        if number_of_partitions == 1:
+            # expected_processed = 1 (input msg per SDF) * 1 (2 optimized groupbys that don't reprocesses input) * 2 SDFs
+            expected_processed = 2
+        else:
+            # expected_processed = 1 (input msg per SDF) * 3 (2 groupbys, each reprocesses input) * 2 SDFs
+            expected_processed = 6
+
         expected_output_topic_count = 2
 
         sdf_a = app.dataframe(topic=input_topic_a)