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https://issues.apache.org/jira/browse/BEAM-9085?focusedWorklogId=419086&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-419086
]
ASF GitHub Bot logged work on BEAM-9085:
----------------------------------------
Author: ASF GitHub Bot
Created on: 09/Apr/20 02:33
Start Date: 09/Apr/20 02:33
Worklog Time Spent: 10m
Work Description: tvalentyn commented on pull request #11092: [BEAM-9085]
Fix performance regression in SyntheticSource
URL: https://github.com/apache/beam/pull/11092#discussion_r405921080
##########
File path: sdks/python/apache_beam/testing/synthetic_pipeline.py
##########
@@ -61,6 +65,35 @@
np = None
+class _Random(Random):
+ """A subclass of `random.Random` from the Python Standard Library that
+ provides a method returning random bytes of arbitrary length.
+ """
+
+ # `numpy.random.RandomState` does not provide `random()` method, we keep this
+ # for compatibility reasons.
+ random_sample = Random.random
+
+ def bytes(self, length):
+ """Returns random bytes.
+
+ Args:
+ length (int): Number of random bytes.
+ """
+ n = length // 8 + 1
+ # pylint: disable=map-builtin-not-iterating
+ return struct.pack(
+ '{}Q'.format(n),
+ *map(self.getrandbits, itertools.repeat(64, n)))[:length]
Review comment:
Since we don't need py2 compatibility anymore, consider using `to_bytes`.
Here's an equivalent for chunk_size=8. It seems to be somewhat slower than
current method (perhaps since I'm not using `map+repeat()`), but with larger
`chuck_size`, seems to be more efficient. Large chunk size may be less
efficient for short bytesequences.
```
chunk_size_bytes = 8 // TBD - larger chunks seem to improve performance.
chunk_size_bits = chunk_size_bytes * 8
num_chunks = length // chunk_size_bytes + 1
return
b''.join([self.getrandbits(chunk_size_bits).to_bytes(chunk_size_bytes,
sys.byteorder) for _ in range(num_chunks)])[:length]
```
If you decide to keep current implementation - please add a comment
explaining the mechanics for readers not familiar with this code (we generate
8-byte stings, and then fit them into a representation of C++'s long-long,
which also takes up 8 bytes).
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Issue Time Tracking
-------------------
Worklog Id: (was: 419086)
Time Spent: 8h (was: 7h 50m)
> 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: 8h
> 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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