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https://issues.apache.org/jira/browse/BEAM-7760?focusedWorklogId=290907&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-290907
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ASF GitHub Bot logged work on BEAM-7760:
----------------------------------------
Author: ASF GitHub Bot
Created on: 08/Aug/19 00:55
Start Date: 08/Aug/19 00:55
Worklog Time Spent: 10m
Work Description: KevinGG commented on issue #9278: [BEAM-7760] Added
iBeam module
URL: https://github.com/apache/beam/pull/9278#issuecomment-519320986
Updated the PR with 2nd commit, also sent out an email briefing the
interactive Beam work for this PR and future plan.
We should expect many PRs in the near future re-writing the interactive Beam
(how cache used, how DOT renders, streaming has different interactive behavior
from batch) since we changed how the underlying magic caching works.
The other main work is PCollection Data visualization.
However, except the interactive_beam module, everything else should not be
concerned by either Beam users or developers. The change is within interactive
Beam scope and any magic implemented is implicit. We shall update the README
under interactive package as work evolves.
PTAL
R: @aaltay
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Issue Time Tracking
-------------------
Worklog Id: (was: 290907)
Time Spent: 2h 10m (was: 2h)
> Interactive Beam Caching PCollections bound to user defined vars in notebook
> ----------------------------------------------------------------------------
>
> Key: BEAM-7760
> URL: https://issues.apache.org/jira/browse/BEAM-7760
> Project: Beam
> Issue Type: New Feature
> Components: examples-python
> Reporter: Ning Kang
> Assignee: Ning Kang
> Priority: Major
> Time Spent: 2h 10m
> Remaining Estimate: 0h
>
> Cache only PCollections bound to user defined variables in a pipeline when
> running pipeline with interactive runner in jupyter notebooks.
> [Interactive
> Beam|[https://github.com/apache/beam/tree/master/sdks/python/apache_beam/runners/interactive]]
> has been caching and using caches of "leaf" PCollections for interactive
> execution in jupyter notebooks.
> The interactive execution is currently supported so that when appending new
> transforms to existing pipeline for a new run, executed part of the pipeline
> doesn't need to be re-executed.
> A PCollection is "leaf" when it is never used as input in any PTransform in
> the pipeline.
> The problem with building caches and pipeline to execute around "leaf" is
> that when a PCollection is consumed by a sink with no output, the pipeline to
> execute built will miss the subgraph generating and consuming that
> PCollection.
> An example, "ReadFromPubSub --> WirteToPubSub" will result in an empty
> pipeline.
> Caching around PCollections bound to user defined variables and replacing
> transforms with source and sink of caches could resolve the pipeline to
> execute properly under the interactive execution scenario. Also, cached
> PCollection now can trace back to user code and can be used for user data
> visualization if user wants to do it.
> E.g.,
> {code:java}
> // ...
> p = beam.Pipeline(interactive_runner.InteractiveRunner(),
> options=pipeline_options)
> messages = p | "Read" >> beam.io.ReadFromPubSub(subscription='...')
> messages | "Write" >> beam.io.WriteToPubSub(topic_path)
> result = p.run()
> // ...
> visualize(messages){code}
> The interactive runner automatically figures out that PCollection
> {code:java}
> messages{code}
> created by
> {code:java}
> p | "Read" >> beam.io.ReadFromPubSub(subscription='...'){code}
> should be cached and reused if the notebook user appends more transforms.
> And once the pipeline gets executed, the user could use any
> visualize(PCollection) module to visualize the data statically (batch) or
> dynamically (stream)
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