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https://issues.apache.org/jira/browse/BEAM-7760?focusedWorklogId=299769&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-299769
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ASF GitHub Bot logged work on BEAM-7760:
----------------------------------------
Author: ASF GitHub Bot
Created on: 22/Aug/19 21:15
Start Date: 22/Aug/19 21:15
Worklog Time Spent: 10m
Work Description: KevinGG commented on pull request #9278: [BEAM-7760]
Added Interactive Beam module
URL: https://github.com/apache/beam/pull/9278
**Please** add a meaningful description for your change here
1. Added interactive_beam module that will serve sugar syntax and
shorthand functions to apply interactivity, create iBeam pipeline,
visualize PCollection data and execute iBeam pipeline as normal pipeline
with selected Beam runners without interactivity.
2. This commit implemented the implicitly managed Interactive Beam
environment to track definition of user pipelines. It exposed a watch()
interface for users to explicitly instruct Interactive Beam the
whereabout of their pipeline definition when it's not in __main__.
3. This commit implemented a shorthand function create_pipeline() to
create a pipeline that is backed by direct runner with interactivity
when running.
4. This commit also implemented a shorthand function run_pipeline() to
run a pipeline created with interactivity on a different runner and
pipeline options without interactivity. It's useful when interactivity
is not needed and a one-shot in production-like environment is desired.
5. This commit exposed a PCollection data exploration interface
visualize(). Implementation is yet to be added.
6. Added interactive_environment module for internal usage without
backward-compatibility. It holds the cache manager and watchable
metadata for current interactive environment/session/context. Interfaces
are provided to interact with the environment and its components.
7. Unit tests included.
------------------------
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Issue Time Tracking
-------------------
Worklog Id: (was: 299769)
Time Spent: 3h 10m (was: 3h)
> 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: 3h 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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