[jira] [Updated] (SPARK-4148) PySpark's sample uses the same seed for all partitions

["Josh Rosen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-4148?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Josh Rosen updated SPARK-4148:
------------------------------
    Fix Version/s:     (was: 1.1.1)
                   1.1.2

> PySpark's sample uses the same seed for all partitions
> ------------------------------------------------------
>
>                 Key: SPARK-4148
>                 URL: https://issues.apache.org/jira/browse/SPARK-4148
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 1.0.2, 1.1.0
>            Reporter: Xiangrui Meng
>            Assignee: Xiangrui Meng
>             Fix For: 1.2.0, 1.1.2
>
>
> The current way of seed distribution makes the random sequences from partition i and i+1 offset by 1.
> {code}
> In [14]: import random
> In [15]: r1 = random.Random(10)
> In [16]: r1.randint(0, 1)
> Out[16]: 1
> In [17]: r1.random()
> Out[17]: 0.4288890546751146
> In [18]: r1.random()
> Out[18]: 0.5780913011344704
> In [19]: r2 = random.Random(10)
> In [20]: r2.randint(0, 1)
> Out[20]: 1
> In [21]: r2.randint(0, 1)
> Out[21]: 0
> In [22]: r2.random()
> Out[22]: 0.5780913011344704
> {code}
> So the second value from partition 1 is the same as the first value from partition 2.



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