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https://issues.apache.org/jira/browse/BEAM-8335?focusedWorklogId=393714&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-393714
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ASF GitHub Bot logged work on BEAM-8335:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 26/Feb/20 19:11
            Start Date: 26/Feb/20 19:11
    Worklog Time Spent: 10m 
      Work Description: lukecwik commented on pull request #10892: [BEAM-8335] 
Make TestStream to/from runner_api include the output_tags property.
URL: https://github.com/apache/beam/pull/10892#discussion_r384699002
 
 

 ##########
 File path: sdks/python/apache_beam/testing/test_stream.py
 ##########
 @@ -133,15 +140,21 @@ def __eq__(self, other):
     return self.new_watermark == other.new_watermark and self.tag == other.tag
 
   def __hash__(self):
-    return hash(self.new_watermark)
+    return hash(str(self.new_watermark) + str(self.tag))
 
   def __lt__(self, other):
     return self.new_watermark < other.new_watermark
 
   def to_runner_api(self, unused_element_coder):
+    tag = 'None' if self.tag is None else self.tag
+
+    # Assert that no prevision is lost.
+    assert 1000 * (
+        self.new_watermark.micros // 1000) == self.new_watermark.micros
 
 Review comment:
   nit: This would be clearer by checking `%` has no remainder
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 393714)
    Time Spent: 74h 40m  (was: 74.5h)

> 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: 74h 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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