TheNeuralBit commented on code in PR #17384:
URL: https://github.com/apache/beam/pull/17384#discussion_r863290541
##########
sdks/python/apache_beam/runners/worker/operations.py:
##########
@@ -223,6 +235,120 @@ def current_element_progress(self):
return self.consumer.current_element_progress()
+class GeneralPurposeConsumerSet(ConsumerSet):
+ """ConsumerSet implementation that handles all combinations of possible
edges.
+ """
+ def __init__(self,
+ counter_factory,
+ step_name, # type: str
+ output_index,
+ coder,
+ producer_type_hints,
+ consumers, # type: List[Operation]
+ producer_batch_converter):
+ super().__init__(
+ counter_factory,
+ step_name,
+ output_index,
+ consumers,
+ coder,
+ producer_type_hints)
+
+ self.producer_batch_converter = producer_batch_converter
+
+ # Partition consumers into three groups:
+ # - consumers that will be passed elements
+ # - consumers that will be passed batches (where their input batch type
+ # matches the output of the producer)
+ # - consumers that will be passed converted batches
+ self.element_consumers: List[Operation] = []
+ self.passthrough_batch_consumers: List[Operation] = []
+ other_batch_consumers: DefaultDict[
+ BatchConverter, List[Operation]] = collections.defaultdict(lambda: [])
+
+ for consumer in consumers:
+ if not consumer.get_batching_preference().supports_batches:
+ self.element_consumers.append(consumer)
+ elif (consumer.get_input_batch_converter() ==
+ self.producer_batch_converter):
+ self.passthrough_batch_consumers.append(consumer)
+ else:
+ # Batch consumer with a mismatched batch type
+ if consumer.get_batching_preference().supports_elements:
+ # Pass it elements if we can
+ self.element_consumers.append(consumer)
+ else:
+ # As a last resort, explode and rebatch
+ consumer_batch_converter = consumer.get_input_batch_converter()
+ # This consumer supports batches, it must have a batch converter
+ assert consumer_batch_converter is not None
+ other_batch_consumers[consumer_batch_converter].append(consumer)
+
+ self.other_batch_consumers: Dict[BatchConverter, List[Operation]] = dict(
+ other_batch_consumers)
+
+ self.has_batch_consumers = (
+ self.passthrough_batch_consumers or self.other_batch_consumers)
+ self._batched_elements: List[Any] = []
+
+ def receive(self, windowed_value):
+ # type: (WindowedValue) -> None
+ self.update_counters_start(windowed_value)
+
+ for consumer in self.element_consumers:
+ cython.cast(Operation, consumer).process(windowed_value)
+
+ # TODO: Do this branching when contstructing ConsumerSet
+ if self.has_batch_consumers:
+ self._batched_elements.append(windowed_value)
+
+ self.update_counters_finish()
+
+ def receive_batch(self, windowed_batch):
+ #self.update_counters_start(windowed_value)
+ if self.element_consumers:
+ for wv in windowed_batch.as_windowed_values(
+ self.producer_batch_converter.explode_batch):
+ for consumer in self.element_consumers:
+ cython.cast(Operation, consumer).process(wv)
+
+ for consumer in self.passthrough_batch_consumers:
+ cython.cast(Operation, consumer).process_batch(windowed_batch)
+
+ for (consumer_batch_converter,
+ consumers) in self.other_batch_consumers.items():
+ # Explode and rebatch into the new batch type (ouch!)
Review Comment:
> No, it will be logged once per instance (which could be a lot, hundreds of
times in streaming).
Is there any way to reduce the number of times it's logged?
> Ideally we could just name the two operations involved.
My concern about it not being actionable was just that it might be difficult
to translate from the Operation names back to the code that produced them.
After looking at it closer it does look like just Operation has a
user-interpretable `__str__` implementation.
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