[jira] [Resolved] (BEAM-4858) Clean up _BatchSizeEstimator in element-batching transform.

["Robert Bradshaw (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/BEAM-4858?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Robert Bradshaw resolved BEAM-4858.
-----------------------------------
       Resolution: Fixed
    Fix Version/s: 2.8.0

> Clean up _BatchSizeEstimator in element-batching transform.
> -----------------------------------------------------------
>
>                 Key: BEAM-4858
>                 URL: https://issues.apache.org/jira/browse/BEAM-4858
>             Project: Beam
>          Issue Type: Bug
>          Components: sdk-py-core
>            Reporter: Valentyn Tymofieiev
>            Assignee: Robert Bradshaw
>            Priority: Minor
>             Fix For: 2.8.0
>
>          Time Spent: 5h 40m
>  Remaining Estimate: 0h
>
> Beam Python 3 conversion [exposed|https://github.com/apache/beam/pull/5729] non-trivial performance-sensitive logic in element-batching transform. Let's take a look at [util.py#L271|https://github.com/apache/beam/blob/e98ff7c96afa2f72b3a98426dc1e9a47224da5c8/sdks/python/apache_beam/transforms/util.py#L271]. 
> Due to Python 2 language semantics, the result of {{x2 / x1}} will depend on the type of the keys - whether they are integers or floats. 
> The keys of key-value pairs contained in {{self._data}} are added as integers [here|https://github.com/apache/beam/blob/d2ac08da2dccce8930432fae1ec7c30953880b69/sdks/python/apache_beam/transforms/util.py#L260], however, when we 'thin' the collected entries [here|https://github.com/apache/beam/blob/d2ac08da2dccce8930432fae1ec7c30953880b69/sdks/python/apache_beam/transforms/util.py#L279], the keys will become floats. Surprisingly, using either integer or float division consistently [in the comparator|https://github.com/apache/beam/blob/e98ff7c96afa2f72b3a98426dc1e9a47224da5c8/sdks/python/apache_beam/transforms/util.py#L271]  negatively affects the performance of a custom pipeline I was using to benchmark these changes. The performance impact likely comes from changes in the logic that depends on  how division is evaluated, not from the performance of division operation itself.
> In terms of Python 3 conversion the best course of action that avoids regression seems to be to preserve the existing Python 2 behavior using {{old_div}} from {{past.utils.division}}, in the medium term we should clean up the logic. We may want to add a targeted microbenchmark to evaluate performance of this code, and maybe cythonize the code, since it seems to be performance-sensitive.



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