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https://issues.apache.org/jira/browse/BEAM-9085?focusedWorklogId=388872&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-388872
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ASF GitHub Bot logged work on BEAM-9085:
----------------------------------------
Author: ASF GitHub Bot
Created on: 18/Feb/20 16:00
Start Date: 18/Feb/20 16:00
Worklog Time Spent: 10m
Work Description: kamilwu commented on pull request #10885: [BEAM-9085]
Fix performance regression in SyntheticSource
URL: https://github.com/apache/beam/pull/10885
Beam was using `numpy.random.RandomState` during generation of Synthetic
input, which, starting from NumPy 1.17, is considered as a legacy generator.
This caused the slowdown on Python 3.
This PR fixes the regression.
## Details
The following snippet was used for measuring performance:
```
from timeit import Timer
t = Timer("pardo_test.ParDoTest().run()",
'''
from apache_beam.testing.load_tests import pardo_test
import sys
sys.argv.append('--test-pipeline-options=--input_options=\\'{"num_records":
200000, "value_size": 9, "key_size": 1}\\'')
''')
print(min(t.repeat(number=1)))
```
### Results
Before:
**Python 2.7**
4.52470111847
**Python 3.7**
46.89912317299999
After:
**Python 2.7**
6.37386107445
**Python 3.7**
5.6981477510000005
### Why not numpy?
It turns out that `numpy.random.bytes()` is a bit slower on Python 3:
**Python 2.7**
`python -m timeit -s 'import numpy as np' 'for i in xrange(20000):
np.random.seed(i); np.random.bytes(90)'`
10 loops, best of 3: 57.5 msec per loop
**Python 3.7**
`python -m timeit -s 'import numpy as np' 'for i in range(20000):
np.random.seed(i); np.random.bytes(90)'`
1 loop, best of 5: 414 msec per loop
I've achieved better results after switching to `random` standard module.
Unfortunately, the performance on Python 2 got a bit worse.
------------------------
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Issue Time Tracking
-------------------
Worklog Id: (was: 388872)
Remaining Estimate: 0h
Time Spent: 10m
> Performance regression in np.random.RandomState() skews performance test
> results across Python 2/3 on Dataflow
> --------------------------------------------------------------------------------------------------------------
>
> Key: BEAM-9085
> URL: https://issues.apache.org/jira/browse/BEAM-9085
> Project: Beam
> Issue Type: Bug
> Components: testing
> Reporter: Kamil Wasilewski
> Assignee: Kamil Wasilewski
> Priority: Major
> Time Spent: 10m
> Remaining Estimate: 0h
>
> Tests show that the performance of core Beam operations in Python 3.x on
> Dataflow can be a few time slower than in Python 2.7. We should investigate
> what's the cause of the problem.
> Currently, we have one ParDo test that is run both in Py3 and Py2 [1]. A
> dashboard with runtime results can be found here [2].
> [1] sdks/python/apache_beam/testing/load_tests/pardo_test.py
> [2] https://apache-beam-testing.appspot.com/explore?dashboard=5678187241537536
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