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Date: Sat, 2 Dec 2017 00:28:13 +0000 (UTC)
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Subject: [jira] [Commented] (BEAM-1630) Add Splittable DoFn to Python SDK
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archived-at: Sat, 02 Dec 2017 00:28:25 -0000


    [ https://issues.apache.org/jira/browse/BEAM-1630?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16275295#comment-16275295 ] 

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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