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https://issues.apache.org/jira/browse/BEAM-4858?focusedWorklogId=147995&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-147995
]
ASF GitHub Bot logged work on BEAM-4858:
----------------------------------------
Author: ASF GitHub Bot
Created on: 26/Sep/18 07:14
Start Date: 26/Sep/18 07:14
Worklog Time Spent: 10m
Work Description: tvalentyn commented on a change in pull request #6375:
[BEAM-4858] Clean up division in batch size estimator.
URL: https://github.com/apache/beam/pull/6375#discussion_r220439390
##########
File path: sdks/python/apache_beam/transforms/util.py
##########
@@ -316,17 +357,22 @@ def next_batch_size(self):
last_batch_size = self._data[-1][0]
cap = min(last_batch_size * self._MAX_GROWTH_FACTOR, self._max_batch_size)
+ target = self._max_batch_size
+
if self._target_batch_duration_secs:
# Solution to a + b*x = self._target_batch_duration_secs.
- cap = min(cap, (self._target_batch_duration_secs - a) / b)
+ target = min(target, (self._target_batch_duration_secs - a) / b)
if self._target_batch_overhead:
# Solution to a / (a + b*x) = self._target_batch_overhead.
Review comment:
What is the interpretation of target_batch_overhead? Trying to understand
where this equation comes from.
Also, it would be helpful to explain constructor parameters in the
docstring. I think the meaning of `target_batch_overhead` and `variance` are
not obvious without diving into the code and a description may be helpful for
getting a high-level understanding how to tune this if necessary.
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Issue Time Tracking
-------------------
Worklog Id: (was: 147995)
Time Spent: 1h 40m (was: 1.5h)
> 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
> Time Spent: 1h 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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