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https://issues.apache.org/jira/browse/BEAM-8335?focusedWorklogId=389229&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-389229
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ASF GitHub Bot logged work on BEAM-8335:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 19/Feb/20 00:43
            Start Date: 19/Feb/20 00:43
    Worklog Time Spent: 10m 
      Work Description: KevinGG commented on pull request #10899: [BEAM-8335] 
Background Caching job
URL: https://github.com/apache/beam/pull/10899
 
 
   1. Exposed source data capture (implemented by background caching job) 
control APIs in 
       interactive_beam module.
   2. Abstract background caching job into a standalone class where
      pipeline jobs and limit checkers are self-contained.
   3. Added test_stream_service related control and tracking inside
      background_caching_job and interactive_environment.
   4. TODO items: 1) integrate streaming_cache once it implements
      cache_manager; 2) add implementation of `capture_size` control, now
      the capture size limit checker is simply a thread ends whenever the
      capture elapse limit checker terminates the infinitely running background
      caching job; 3) wire test_stream_service's creation when the dependencies 
are ready.
   
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 389229)
    Time Spent: 59h 40m  (was: 59.5h)

> 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: 59h 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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