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https://issues.apache.org/jira/browse/BEAM-10824?focusedWorklogId=480469&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-480469
]
ASF GitHub Bot logged work on BEAM-10824:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 08/Sep/20 22:12
Start Date: 08/Sep/20 22:12
Worklog Time Spent: 10m
Work Description: tvalentyn commented on a change in pull request #12756:
URL: https://github.com/apache/beam/pull/12756#discussion_r485223986
##########
File path: sdks/python/apache_beam/transforms/stats_test.py
##########
@@ -41,13 +40,15 @@
from apache_beam.transforms.display import DisplayData
from apache_beam.transforms.display_test import DisplayDataItemMatcher
from apache_beam.transforms.stats import ApproximateQuantilesCombineFn
+from apache_beam.transforms.stats import ApproximateUniqueCombineFn
class ApproximateUniqueTest(unittest.TestCase):
- """Unit tests for ApproximateUnique.Globally and ApproximateUnique.PerKey.
- Hash() with Python3 is nondeterministic, so Approximation algorithm generates
- different result each time and sometimes error rate is out of range, so add
- retries for all tests who actually running approximation algorithm."""
+ """Unit tests for ApproximateUnique.Globally, ApproximateUnique.PerKey,
+ and ApproximateUniqueCombineFn.
Review comment:
Can we parameterize the test to exercise both md5/mmh3 codepaths. We
could force md5 by a patch for test purposes if mmh3 is installed.
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Issue Time Tracking
-------------------
Worklog Id: (was: 480469)
Remaining Estimate: 12h (was: 12h 10m)
Time Spent: 12h (was: 11h 50m)
> Hash in stats.ApproximateUniqueCombineFn NON-deterministic
> ----------------------------------------------------------
>
> Key: BEAM-10824
> URL: https://issues.apache.org/jira/browse/BEAM-10824
> Project: Beam
> Issue Type: Bug
> Components: sdk-py-core
> Reporter: Monica Song
> Priority: P1
> Labels: hash
> Original Estimate: 24h
> Time Spent: 12h
> Remaining Estimate: 12h
>
> The python hash() function is non-deterministic. As a result, different
> workers will map identical values to different hashes. This leads to
> overestimation of the number of unique values (by several magnitudes, in my
> experience x1000) in a distributed processing model.
> [https://github.com/apache/beam/blob/master/sdks/python/apache_beam/transforms/stats.py#L218]
>
>
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