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https://issues.apache.org/jira/browse/BEAM-1630?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16275295#comment-16275295
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ASF GitHub Bot commented on BEAM-1630:
--------------------------------------
chamikaramj commented on a change in pull request #4064: [BEAM-1630] Adds
support for processing Splittable DoFns using DirectRunner.
URL: https://github.com/apache/beam/pull/4064#discussion_r154449348
##########
File path: sdks/python/apache_beam/runners/direct/transform_evaluator.py
##########
@@ -826,3 +830,74 @@ def finish_bundle(self):
None, '', TimeDomain.WATERMARK, WatermarkManager.WATERMARK_POS_INF)
return TransformResult(self, [], [], None, {None: hold})
+
+
+class _ProcessElemenetsEvaluator(_TransformEvaluator):
+ """An evaluator for sdf_direct_runner.ProcessElements transform."""
+
+ DEFAULT_MAX_NUM_OUTPUTS = 100
+ DEFAULT_MAX_DURATION = 1
+
+ def __init__(self, evaluation_context, applied_ptransform,
+ input_committed_bundle, side_inputs, scoped_metrics_container):
+ super(_ProcessElemenetsEvaluator, self).__init__(
+ evaluation_context, applied_ptransform, input_committed_bundle,
+ side_inputs, scoped_metrics_container)
+
+ process_elements_transform = applied_ptransform.transform
+ assert isinstance(process_elements_transform, ProcessElements)
+
+ # Replacing the do_fn of the transform with a wrapper do_fn that performs
+ # SDF magic.
+ transform = applied_ptransform.transform
+ sdf = transform.sdf
+ self._process_fn = transform.new_process_fn(sdf)
+ transform.dofn = self._process_fn
+
+ assert isinstance(self._process_fn, ProcessFn)
+
+ self.step_context = self._execution_context.get_step_context()
+ # self.global_state = self.step_context.get_keyed_state(None)
+ self._process_fn.set_step_context(self.step_context)
+
+ process_element_invoker = (
+ SDFProcessElementInvoker(
+ max_num_outputs=self.DEFAULT_MAX_NUM_OUTPUTS,
+ max_duration=self.DEFAULT_MAX_DURATION))
+ self._process_fn.set_process_element_invoker(process_element_invoker)
+
+ self._par_do_evaluator = _ParDoEvaluator(
+ evaluation_context, applied_ptransform, input_committed_bundle,
+ side_inputs, scoped_metrics_container)
+ self.keyed_holds = {}
+
+ def start_bundle(self):
+ self._par_do_evaluator.start_bundle()
+
+ def process_element(self, element):
+ assert isinstance(element, WindowedValue)
+ assert len(element.windows) == 1
+ window = element.windows[0]
+ if isinstance(element.value, KeyedWorkItem):
+ encoded_k = element.value.encoded_key
+ else:
+ assert isinstance(element.value, tuple)
Review comment:
Done.
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> Add Splittable DoFn to Python SDK
> ---------------------------------
>
> Key: BEAM-1630
> URL: https://issues.apache.org/jira/browse/BEAM-1630
> Project: Beam
> Issue Type: Improvement
> Components: sdk-py-core
> Reporter: Chamikara Jayalath
> Assignee: Chamikara Jayalath
>
> Splittable DoFn [1] is currently being implemented for Java SDK [2]. We
> should add this to Python SDK as well.
> Following document proposes an API for this.
> https://docs.google.com/document/d/1h_zprJrOilivK2xfvl4L42vaX4DMYGfH1YDmi-s_ozM/edit?usp=sharing
> [1] https://s.apache.org/splittable-do-fn
> [2]
> https://lists.apache.org/thread.html/0ce61ac162460a149d5c93cdface37cc383f8030fe86ca09e5699b18@%3Cdev.beam.apache.org%3E
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