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https://issues.apache.org/jira/browse/BEAM-8335?focusedWorklogId=352125&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-352125
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ASF GitHub Bot logged work on BEAM-8335:
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Author: ASF GitHub Bot
Created on: 02/Dec/19 19:25
Start Date: 02/Dec/19 19:25
Worklog Time Spent: 10m
Work Description: lukecwik commented on pull request #9720: [BEAM-8335]
Add initial modules for interactive streaming support
URL: https://github.com/apache/beam/pull/9720#discussion_r352781174
##########
File path: model/pipeline/src/main/proto/beam_runner_api.proto
##########
@@ -489,33 +490,66 @@ message TestStreamPayload {
// (Required) the coder for elements in the TestStream events
string coder_id = 1;
+ // (Optional) If specified, the TestStream will replay these events.
repeated Event events = 2;
+ // (Optional) If specified, the TestStream will use the InteractiveService
+ // protocol found in beam_interactive_api.proto to retrieve events.
+ ApiServiceDescriptor endpoint = 3;
+
message Event {
oneof event {
AdvanceWatermark watermark_event = 1;
AdvanceProcessingTime processing_time_event = 2;
AddElements element_event = 3;
}
+ // Advances the watermark to the specified timestamp.
message AdvanceWatermark {
+ // (Required) The watermark to advance to.
int64 new_watermark = 1;
+
+ // (Optional) The output watermark tag for a PCollection. If unspecified
Review comment:
I believe this should be required instead of having SDKs specially handle
this logic.
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Issue Time Tracking
-------------------
Worklog Id: (was: 352125)
Time Spent: 37h (was: 36h 50m)
> 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: 37h
> 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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