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https://issues.apache.org/jira/browse/BEAM-7926?focusedWorklogId=332920&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-332920
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ASF GitHub Bot logged work on BEAM-7926:
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Author: ASF GitHub Bot
Created on: 23/Oct/19 21:50
Start Date: 23/Oct/19 21:50
Worklog Time Spent: 10m
Work Description: KevinGG commented on issue #9741: [BEAM-7926] Visualize
PCollection
URL: https://github.com/apache/beam/pull/9741#issuecomment-545650837
I've added two tox env and corresponding Py36 and Py37 test suite. Removed
[interactive] from test required packages. Added it to py2:docs env.
I'll add [interactive] for other specific envs defined in tox.ini if any of
the gradle task needs it.
I've also added checks and warnings in
`interactive_environment.InteractiveEnvironment` to check prerequisites for
interactive beam (didn't put them in the global scope because global scoped
warning level logging will fail gradle tasks in pre-commit):
1. If Python is above 3.5.3;
2. If [interactive] dependencies are fully available;
3. If current runtime is within an interactive environment, i.e., within an
ipython kernel.
For the `time.sleep()` and `pcoll_visualization` NOOP strategy when
dependencies are not fully installed, please let me know your preference,
@aaltay. Personally I'm fine with either approaches, and I'll make change if
needed.
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Issue Time Tracking
-------------------
Worklog Id: (was: 332920)
Time Spent: 13h 50m (was: 13h 40m)
> Visualize PCollection with Interactive Beam
> -------------------------------------------
>
> Key: BEAM-7926
> URL: https://issues.apache.org/jira/browse/BEAM-7926
> Project: Beam
> Issue Type: New Feature
> Components: runner-py-interactive
> Reporter: Ning Kang
> Assignee: Ning Kang
> Priority: Major
> Time Spent: 13h 50m
> Remaining Estimate: 0h
>
> Support auto plotting / charting of materialized data of a given PCollection
> with Interactive Beam.
> Say an Interactive Beam pipeline defined as
> p = create_pipeline()
> pcoll = p | 'Transform' >> transform()
> The use can call a single function and get auto-magical charting of the data
> as materialized pcoll.
> e.g., visualize(pcoll)
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