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https://issues.apache.org/jira/browse/BEAM-8335?focusedWorklogId=402581&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-402581
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ASF GitHub Bot logged work on BEAM-8335:
----------------------------------------
Author: ASF GitHub Bot
Created on: 13/Mar/20 00:34
Start Date: 13/Mar/20 00:34
Worklog Time Spent: 10m
Work Description: KevinGG commented on pull request #11109: [BEAM-8335]
Final PR to merge the InteractiveBeam feature branch
URL: https://github.com/apache/beam/pull/11109#discussion_r391973438
##########
File path: sdks/python/apache_beam/runners/interactive/interactive_beam.py
##########
@@ -314,6 +327,26 @@ def show(*pcolls, **configs):
bcj.attempt_to_run_background_caching_job(
runner, user_pipeline, user_pipeline.options)
+ pcolls = set(pcolls)
+ computed_pcolls = set()
+ for pcoll in pcolls:
+ if pcoll in ie.current_env().computed_pcollections:
Review comment:
All PCollections in the pipeline that is being executed will be marked as
computed once a job (main job or background caching job) completes.
So it looks like this:
pcoll_a, pcoll_b belongs to pipeline
pipeline runs into job, returns a pipeline result (main job) or a
BackgroundCachingJob (backgroun caching job).
Say pcoll_a and pcoll_b are not depending on each other, and the user
invokes `show(pcoll_a)`.
A pipeline fragment that only contributes to pcoll_a will be executed.
Once pipeline result is in a successful state or
BackgroundCachingJob.is_done(), pcoll_a is marked as computed.
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Issue Time Tracking
-------------------
Worklog Id: (was: 402581)
Time Spent: 111h 40m (was: 111.5h)
> 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: 111h 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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