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https://issues.apache.org/jira/browse/BEAM-8335?focusedWorklogId=386156&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-386156
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ASF GitHub Bot logged work on BEAM-8335:
----------------------------------------
Author: ASF GitHub Bot
Created on: 12/Feb/20 19:21
Start Date: 12/Feb/20 19:21
Worklog Time Spent: 10m
Work Description: lukecwik 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_r378460923
##########
File path: model/pipeline/src/main/proto/beam_runner_api.proto
##########
@@ -576,7 +581,13 @@ 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 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.
+ repeated string tags = 1;
Review comment:
The usage of `tag` and `tags` is a poor name choice. I would suggest
replacing them with `output_id` and `output_ids` and please update the comments
to refer to the [PTransform outputs local
names](https://github.com/apache/beam/blob/dd9f5d28cc2f55697283058963fd2d8607aa3cd6/model/pipeline/src/main/proto/beam_runner_api.proto#L165).
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Issue Time Tracking
-------------------
Worklog Id: (was: 386156)
Time Spent: 56h 40m (was: 56.5h)
> 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: 56h 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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