[
https://issues.apache.org/jira/browse/BEAM-13080?focusedWorklogId=677937&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-677937
]
ASF GitHub Bot logged work on BEAM-13080:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 05/Nov/21 22:49
Start Date: 05/Nov/21 22:49
Worklog Time Spent: 10m
Work Description: InigoSJ commented on pull request #15912:
URL: https://github.com/apache/beam/pull/15912#issuecomment-962264660
> Looking at #15753, the question to answer:
>
> Is `random.getrandbits(32)` equivalent to `random.randint(0, (1 << 32) -
1)`?
I have ran a quick test and the distribution is the same, hitting the same
low and high values:
```
def test_randoms(bits, M, epsilon = 0.05):
bit_d = collections.defaultdict(lambda: 0)
normal_d = collections.defaultdict(lambda: 0)
for _ in range(M):
bit_d[random.getrandbits(bits)] += 1
normal_d[random.randint(0, (1 << bits) - 1)] += 1
for key in bit_d:
div = bit_d[key] / normal_d[key]
if not 1 - epsilon < div < 1 + epsilon:
print(bit_d[key], normal_d[key], key, div)
return False
return True
test_randoms(8, 10 * 10**6)
```
Returns True
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Issue Time Tracking
-------------------
Worklog Id: (was: 677937)
Time Spent: 4.5h (was: 4h 20m)
> Specify numBuckets in Python Reshuffle
> --------------------------------------
>
> Key: BEAM-13080
> URL: https://issues.apache.org/jira/browse/BEAM-13080
> Project: Beam
> Issue Type: New Feature
> Components: sdk-py-core
> Reporter: Inigo San Jose Visiers
> Priority: P2
> Time Spent: 4.5h
> Remaining Estimate: 0h
>
> While Java has `withNumBuckets` to set the amount of generated keys, Python
> doesn't.
> This is particularly need when your input data is not so high and you need to
> take advantage for DoFn methods `start / finish bundle`.
> https://beam.apache.org/releases/javadoc/2.33.0/org/apache/beam/sdk/transforms/Reshuffle.ViaRandomKey.html#withNumBuckets-java.lang.Integer-
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