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https://issues.apache.org/jira/browse/BEAM-13080?focusedWorklogId=670995&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-670995
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ASF GitHub Bot logged work on BEAM-13080:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 27/Oct/21 19:56
Start Date: 27/Oct/21 19:56
Worklog Time Spent: 10m
Work Description: InigoSJ commented on a change in pull request #15753:
URL: https://github.com/apache/beam/pull/15753#discussion_r737798869
##########
File path: sdks/python/apache_beam/transforms/util.py
##########
@@ -723,12 +723,33 @@ class Reshuffle(PTransform):
Reshuffle is experimental. No backwards compatibility guarantees.
"""
+ def __init__(self, num_buckets=None):
+ """
+ :param num_buckets: If set, specifies the maximum random keys that would be
+ generated.
+ """
+ self.num_buckets = num_buckets
+
+ valid_buckets = isinstance(num_buckets, int) and num_buckets > 0
+ if not (num_buckets is None or valid_buckets):
+ raise ValueError(
+ 'If `num_buckets` is set, it has to be an '
+ 'integer greater than 0, got %s' % num_buckets)
+
def expand(self, pcoll):
# type: (pvalue.PValue) -> pvalue.PCollection
+ if self.num_buckets:
+ keyed = pcoll | 'AddRandomKeys' >> Map(
+ lambda t: (random.randint(0, self.num_buckets), t)).with_input_types(
Review comment:
That looks way better!
I made the change using `1 << 32`, since [`getrandbits`]
(https://docs.python.org/3/library/random.html#random.getrandbits) returns a
_positive_ integer of that many bits and `1 << 32 - 2**32 = 0`
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Issue Time Tracking
-------------------
Worklog Id: (was: 670995)
Time Spent: 2h (was: 1h 50m)
> 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: 2h
> 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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