To whom may concern, This is Ning from Google. We are currently making efforts to leverage an interactive runner under python beam sdk.
There is already an interactive Beam (iBeam for short) runner with jupyter notebook in the repo <https://github.com/apache/beam/tree/master/sdks/python/apache_beam/runners/interactive> . Following the instructions on that page, one can set up an interactive environment to develop and execute Beam pipeline interactively. However, there are many issues with existing iBeam. One issue is that it uses a concept of leaf PCollection to cache and materialize intermediate PCollection. If the user wants to reuse/introspect a non-leaf PCollection, the interactive runner will run into errors. Our initial effort will be fixing the existing issues. And we also want to make iBeam easy to use. Since iBeam uses the same model Beam uses, there isn't really any difference for users between creating a pipeline with interactive runner and other runners. So we want to minimize the interfaces a user needs to learn while giving the user some capability to interact with the interactive environment. See this initial PR <https://github.com/apache/beam/pull/9278>, the interactive_beam module will provide mainly 4 interfaces: - For advanced users who define pipeline outside __main__, let them tell current interactive environment where they define their pipeline: watch() - This is very useful for tests where pipeline can be defined in test methods. - If the user simply creates pipeline in a Jupyter notebook or a plain Python script, they don't have to know/use this feature at all. - Let users create an interactive pipeline: create_pipeline() - invoking create_pipeline(), the user gets a Pipeline object that works as any other Pipeline object created from apache_beam.Pipeline() - However, the pipeline object p, when invoking p.run(), does some extra interactive magic. - We'll support interactive execution for DirectRunner at this moment. - Let users run the interactive pipeline as a normal pipeline: run_pipeline() - In an interactive environment, a user only needs to add and execute 1 line of code run_pipeline(pipeline) to execute any existing interactive pipeline object as normal pipeline in any selected platform. - We'll probably support Dataflow only. Other implementations can be added though. - Let users introspect any intermediate PCollection they have handler to: visualize() - If a user ever writes pcoll = p | "Some Transform" >> some_transform() ..., they can visualize(pcoll) once the pipeline p is executed. - p can be batch or streaming - The visualization will be some plot graph of data for the given PCollection as if it's materialized. If the PCollection is unbounded, the graph is dynamic. The PR will implement 1 and 2. We'll use https://issues.apache.org/jira/browse/BEAM-7923 as the top level JIRA and add blocking JIRAs as development goes. External Beam users will not worry about any of the underlying implementation details. Except the 4 interfaces above, they learn and write normal Beam code and can execute the pipeline immediately when they are done with prototyping. Ning.
