[GitHub] [beam] robertwb commented on a change in pull request #11148: [BEAM-8335] Adds a streaming wordcount integration test

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robertwb commented on a change in pull request #11148: [BEAM-8335] Adds a streaming wordcount integration test
URL: https://github.com/apache/beam/pull/11148#discussion_r397489367
 
 

 ##########
 File path: sdks/python/apache_beam/runners/interactive/interactive_runner_test.py
 ##########
 @@ -147,6 +150,97 @@ def process(self, element):
     ]
     self.assertEqual(actual_reified, expected_reified)
 
+  def test_streaming_wordcount(self):
+    class WordExtractingDoFn(beam.DoFn):
+      def process(self, element):
+        text_line = element.strip()
+        words = text_line.split()
+        return words
+
+    # Add the TestStream so that it can be cached.
+    ib.options.capturable_sources.add(TestStream)
+    ib.options.capture_duration = timedelta(seconds=1)
+
+    p = beam.Pipeline(
+        runner=interactive_runner.InteractiveRunner(),
+        options=StandardOptions(streaming=True))
+
+    data = (
+        p
+        | TestStream()
+            .advance_watermark_to(0)
+            .advance_processing_time(1)
+            .add_elements(['to', 'be', 'or', 'not', 'to', 'be'])
+            .advance_watermark_to(20)
+            .advance_processing_time(1)
+            .add_elements(['to', 'be', 'or', 'not', 'to', 'be'])
+            .advance_watermark_to(40)
+            .advance_processing_time(1)
+            .add_elements(['to', 'be', 'or', 'not', 'to', 'be'])
+        | beam.WindowInto(beam.window.FixedWindows(10))) # yapf: disable
+
+    counts = (
+        data
+        | 'split' >> beam.ParDo(WordExtractingDoFn())
+        | 'pair_with_one' >> beam.Map(lambda x: (x, 1))
+        | 'group' >> beam.GroupByKey()
+        | 'count' >> beam.Map(lambda wordones: (wordones[0], sum(wordones[1]))))
+
+    # Watch the local scope for Interactive Beam so that referenced PCollections
+    # will be cached.
+    ib.watch(locals())
+
+    # This is normally done in the interactive_utils when a transform is
+    # applied but needs an IPython environment. So we manually run this here.
+    ie.current_env().track_user_pipelines()
+
+    # This tests that the data was correctly cached.
+    pane_info = PaneInfo(True, True, PaneInfoTiming.UNKNOWN, 0, 0)
+    expected_data_df = pd.DataFrame(
+        [('to', 0, [beam.window.IntervalWindow(0, 10)], pane_info),
 
 Review comment:
   I agree that making things explicit can make it easier to understand. What I was asking was whether adding six items three times each increased the coverage of adding (say) three items (one duplicate), advancing the watermark, then adding one or two more. 

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