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https://issues.apache.org/jira/browse/BEAM-8335?focusedWorklogId=385323&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-385323
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ASF GitHub Bot logged work on BEAM-8335:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 11/Feb/20 18:11
            Start Date: 11/Feb/20 18:11
    Worklog Time Spent: 10m 
      Work Description: rohdesamuel commented on pull request #10826: 
[BEAM-8335] Modify the TestStreamPayload to accept an argument of output_tags 
and…
URL: https://github.com/apache/beam/pull/10826#discussion_r377808214
 
 

 ##########
 File path: model/pipeline/src/main/proto/beam_runner_api.proto
 ##########
 @@ -576,7 +581,12 @@ service TestStreamService {
   // A TestStream will request for events using this RPC.
   rpc Events(EventsRequest) returns (stream TestStreamPayload.Event) {}
 }
-message EventsRequest {}
+
+message EventsRequest {
+  // The set of keys to read from. The keys are the specific PCollections to
+  // read.
+  repeated string keys = 1;
 
 Review comment:
   Changed to comment to: The set of tags to read from. These tags are a subset 
of the TestStreamPayload's output_tags. This allows Interactive Beam to cache 
many PCollections from a pipeline then replay a subset of them.
   
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 385323)
    Time Spent: 56h 10m  (was: 56h)

> 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: 56h 10m
>  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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