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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-21057) Do not use a PascalDistribution in countApprox
Date Mon, 12 Jun 2017 10:00:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-21057?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-21057:
------------------------------------

    Assignee:     (was: Apache Spark)

> Do not use a PascalDistribution in countApprox
> ----------------------------------------------
>
>                 Key: SPARK-21057
>                 URL: https://issues.apache.org/jira/browse/SPARK-21057
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 2.1.1
>            Reporter: Lovasoa
>
> I was reading the source of Spark, and found this:
> https://github.com/apache/spark/blob/v2.1.1/core/src/main/scala/org/apache/spark/partial/CountEvaluator.scala#L50-L72
> This is the function that estimates the probability distribution of the total count of
elements in an RDD given the count of only some partitions.
> This function does a strange thing: when the number of elements counted so far is less
than 10 000, it models the total count with a negative binomial (Pascal) law, else, it models
it with a Poisson law.
> Modeling our number of uncounted elements with a negative binomial law is like saying
that we ran over elements, counting only some, and stopping after having counted a given number
of elements.
> But this does not model what really happened.  Our counting was limited in time, not
in number of counted elements, and we can't count only some of the elements in a partition.
> I propose to use the Poisson distribution in every case, as it can be justified under
the hypothesis that the number of elements in each partition is independent and follows a
Poisson law.



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