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https://issues.apache.org/jira/browse/BEAM-8335?focusedWorklogId=399350&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-399350
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ASF GitHub Bot logged work on BEAM-8335:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 06/Mar/20 20:17
            Start Date: 06/Mar/20 20:17
    Worklog Time Spent: 10m 
      Work Description: robertwb commented on pull request #11005: [BEAM-8335] 
Modify the StreamingCache to subclass the CacheManager 
URL: https://github.com/apache/beam/pull/11005#discussion_r389122984
 
 

 ##########
 File path: sdks/python/apache_beam/runners/direct/transform_evaluator.py
 ##########
 @@ -421,8 +440,12 @@ def process_element(self, element):
       main_output = list(self._outputs)[0]
       bundle = self._evaluation_context.create_bundle(main_output)
       for tv in event.timestamped_values:
-        bundle.output(
-            GlobalWindows.windowed_value(tv.value, timestamp=tv.timestamp))
+        # Unreify the value into the correct window.
+        try:
+          bundle.output(WindowedValue(**tv.value))
 
 Review comment:
   Alternatively, it might be cleaner to just pass the test stream event proto 
here rather than decoding it and then interpreting it. (Maybe this'd be a 
larger change.)
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 399350)
    Time Spent: 99h 40m  (was: 99.5h)

> Add streaming support to Interactive Beam
> -----------------------------------------
>
>                 Key: BEAM-8335
>                 URL: https://issues.apache.org/jira/browse/BEAM-8335
>             Project: Beam
>          Issue Type: Improvement
>          Components: runner-py-interactive
>            Reporter: Sam Rohde
>            Assignee: Sam Rohde
>            Priority: Major
>          Time Spent: 99h 40m
>  Remaining Estimate: 0h
>
> This issue tracks the work items to introduce streaming support to the 
> Interactive Beam experience. This will allow users to:
>  * Write and run a streaming job in IPython
>  * Automatically cache records from unbounded sources
>  * Add a replay experience that replays all cached records to simulate the 
> original pipeline execution
>  * Add controls to play/pause/stop/step individual elements from the cached 
> records
>  * Add ability to inspect/visualize unbounded PCollections



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