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https://issues.apache.org/jira/browse/BEAM-8335?focusedWorklogId=344719&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-344719
]
ASF GitHub Bot logged work on BEAM-8335:
----------------------------------------
Author: ASF GitHub Bot
Created on: 16/Nov/19 00:26
Start Date: 16/Nov/19 00:26
Worklog Time Spent: 10m
Work Description: pabloem commented on pull request #9953: DO NOT MERGE
[BEAM-8335] Adds support for multi-output TestStream
URL: https://github.com/apache/beam/pull/9953#discussion_r347055451
##########
File path: sdks/python/apache_beam/testing/test_stream.py
##########
@@ -267,3 +303,141 @@ def from_runner_api_parameter(payload, context):
return TestStream(
coder=coder,
events=[Event.from_runner_api(e, coder) for e in payload.events])
+
+
+class _WatermarkController(PTransform):
+ """A runner-overridable PTransform Primitive to control the watermark.
+
+ Expected implementation behavior:
+ - If the instance recieves a WatermarkEvent, it sets its output watermark to
+ the specified value then drops the event.
+ - If the instance receives an ElementEvent, it emits all specified elements
+ to the Global Window with the event time set to the element's timestamp.
+ """
+ def get_windowing(self, _):
+ return core.Windowing(window.GlobalWindows())
+
+ def expand(self, pcoll):
+ return pvalue.PCollection.from_(pcoll)
Review comment:
Ah this is implemented by the runner. FWIW, it seems that this will be
possible to do natively on the SDK via SDF
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Issue Time Tracking
-------------------
Worklog Id: (was: 344719)
Time Spent: 30.5h (was: 30h 20m)
> 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: 30.5h
> 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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