apache_beam.runners.interactive.interactive_beam module

Module of Interactive Beam features that can be used in notebook.

The purpose of the module is to reduce the learning curve of Interactive Beam users, provide a single place for importing and add sugar syntax for all Interactive Beam components. It gives users capability to interact with existing environment/session/context for Interactive Beam and visualize PCollections as bounded dataset. In the meantime, it hides the interactivity implementation from users so that users can focus on developing Beam pipeline without worrying about how hidden states in the interactive session are managed.

Note: If you want backward-compatibility, only invoke interfaces provided by this module in your notebook or application code.

apache_beam.runners.interactive.interactive_beam.watch(watchable)

Monitors a watchable.

This allows Interactive Beam to implicitly pass on the information about the location of your pipeline definition.

Current implementation mainly watches for PCollection variables defined in user code. A watchable can be a dictionary of variable metadata such as locals(), a str name of a module, a module object or an instance of a class. The variable can come from any scope even local variables in a method of a class defined in a module.

Below are all valid:

watch(__main__)  # if import __main__ is already invoked
watch('__main__')  # does not require invoking import __main__ beforehand
watch(self)  # inside a class
watch(SomeInstance())  # an instance of a class
watch(locals())  # inside a function, watching local variables within

If you write a Beam pipeline in the __main__ module directly, since the __main__ module is always watched, you don’t have to instruct Interactive Beam. If your Beam pipeline is defined in some module other than __main__, such as inside a class function or a unit test, you can watch() the scope.

For example:

class Foo(object)
  def run_pipeline(self):
    p = beam.Pipeline()
    init_pcoll = p |  'Init Create' >> beam.Create(range(10))
    watch(locals())
    p.run()
    return init_pcoll
init_pcoll = Foo().run_pipeline()

Interactive Beam caches init_pcoll for the first run.

Then you can use:

visualize(init_pcoll)

To visualize data from init_pcoll once the pipeline is executed.

apache_beam.runners.interactive.interactive_beam.visualize(pcoll)

Visualizes a PCollection.