Alexey Strokach created BEAM-7825:
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Summary: Python's DirectRunner emits multiple panes per window and
does not discard late data
Key: BEAM-7825
URL: https://issues.apache.org/jira/browse/BEAM-7825
Project: Beam
Issue Type: Bug
Components: sdk-py-core
Affects Versions: 2.13.0
Environment: OS: Debian rodete.
Beam versions: 2.15.0.dev.
Python versions: Python 2.7, Python 3.7
Reporter: Alexey Strokach
The documentation for Beam's Windowing and Triggers functionality [states
that|https://beam.apache.org/documentation/programming-guide/#triggers] _"if
you use Beam’s default windowing configuration and default trigger, Beam
outputs the aggregated result when it estimates all data has arrived, and
discards all subsequent data for that window"_. However, it seems that the
current behavior of Python's DirectRunner is inconsistent with both of those
points. In my experience, DirectRunner will process every data point that it
reads from the input stream, irrespective of whether or not the timestamp of
that data point is older than the timestamps of the windows that have already
been processed. Furthermore, it regularly generates multiple "panes" for the
same window, apparently disregarding the notion of a watermark?
An integration test demonstrating the inconsistencies between DirectRunner and
Dataflow is provided in the linked PR.
Until the limitations of DirectRunner are addressed, maybe they should be
listed on the [DirectRunner documentation
page](https://beam.apache.org/documentation/runners/direct/)?
As far as I understand (I have not seen this explicitly documented anywhere),
in the case of Cloud Dataflow, the pipeline will first process all elements
that have accumulated in a PubSub subscription before the start of the
pipeline, and will then process all new elements which have a timestamp within
a certain narrow range of the current time (UTC). Would this be the behavior
that DirectRunner should be trying to emulate? While Dataflow does emit a
single pane per window (by default) and discards late data, it may be too eager
in what data it calls "late"?
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