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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 ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org 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) -- This message was sent by Atlassian JIRA (v7.6.14#76016)