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https://issues.apache.org/jira/browse/BEAM-7825?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Alexey Strokach updated BEAM-7825:
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Description:
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. As the {{StreamingWordGroupIT.test_discard_late_data}} test shows,
DirectRunner appears to 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, as the {{StreamingWordGroupIT.test_single_output_per_window}} test
shows, DirectRunner generates multiple "panes" for the same window, apparently
disregarding the notion of a watermark?
The Dataflow runner passes both of those end-to-end tests.
was:
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. As the {{StreamingWordGroupIT.test_discard_late_data}} test shows,
DirectRunner appears to 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, as the {{StreamingWordGroupIT.test_single_output_per_window}} test
shows, DirectRunner generates multiple "panes" for the same window, apparently
disregarding the notion of a watermark?
The Dataflow runner passes both of those end-to-end tests.
Until the limitations of DirectRunner are addressed, maybe they should be
listed on the [DirectRunner documentation
page](https://beam.apache.org/documentation/runners/direct/)?
> 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
> Priority: Major
> Time Spent: 3.5h
> Remaining Estimate: 0h
>
> 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. As the {{StreamingWordGroupIT.test_discard_late_data}} test shows,
> DirectRunner appears to 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, as the {{StreamingWordGroupIT.test_single_output_per_window}}
> test shows, DirectRunner generates multiple "panes" for the same window,
> apparently disregarding the notion of a watermark?
> The Dataflow runner passes both of those end-to-end tests.
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