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https://issues.apache.org/jira/browse/BEAM-8335?focusedWorklogId=389626&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-389626
 ]

ASF GitHub Bot logged work on BEAM-8335:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 19/Feb/20 21:12
            Start Date: 19/Feb/20 21:12
    Worklog Time Spent: 10m 
      Work Description: aaltay commented on pull request #10899: [BEAM-8335] 
Background Caching job
URL: https://github.com/apache/beam/pull/10899#discussion_r381546032
 
 

 ##########
 File path: 
sdks/python/apache_beam/runners/interactive/interactive_environment.py
 ##########
 @@ -130,6 +139,17 @@ def __init__(self, cache_manager=None):
           'You have limited Interactive Beam features since your '
           'ipython kernel is not connected any notebook frontend.')
 
+  @property
+  def options(self):
+    """A reference to the global interactive options.
+
+    Provided to avoid import loop or excessive dynamic import. All internal
+    Interactive Beam modules should access interactive_beam.options through
+    this property.
+    """
+    from apache_beam.runners.interactive.interactive_beam import options
+    return options
 
 Review comment:
   Why is the options defined globally in a different file, but setter is here?
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 389626)
    Time Spent: 62h 20m  (was: 62h 10m)

> 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: 62h 20m
>  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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