[GitHub] flink pull request: [FLINK-3802] Add Very Fast Reservoir Sampling

[gaoyike <gi...@git.apache.org>]

GitHub user gaoyike opened a pull request:

    https://github.com/apache/flink/pull/1924

    [FLINK-3802] Add Very Fast Reservoir Sampling

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    A in memory implementation of Very Fast Reservoir Sampling, the algorithm works well then the size of streaming data is much larger than size of reservoir.
    
      The algorithm runs in random sampling with P(R/j) where in R is the size of sampling and j is the current index of streaming data.
      The algorithm consists of two part:
      	(1) Before the size of streaming data reaches threshold, it uses regular reservoir sampling
      	(2) After the size of streaming data reaches threshold, it uses geometric distribution to generate the approximation gap
      		to skip data, and size of gap is determined by  geometric distribution with probability p = R/j
    
       Thanks to Erik Erlandson who is the author of this algorithm and help me with implementation.
    
    Reference: http://erikerlandson.github.io/blog/2015/11/20/very-fast-reservoir-sampling/

You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/gaoyike/flink master

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/flink/pull/1924.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #1924
    
----
commit 81e0622b20d8bc969dec1555bd55d4230d9b38de
Author: 晨光 何 <ga...@gmail.com>
Date:   2016-04-21T21:42:26Z

     A in memory implementation of Very Fast Reservoir Sampling. The algorithm works well then the size of streaming data is much larger than size of reservoir.
      The algorithm runs in random sampling with P(R/j) where in R is the size of sampling and j is the current index of streaming data.
      The algorithm consists of two part:
      	(1) Before the size of streaming data reaches threshold, it uses regular reservoir sampling
      	(2) After the size of streaming data reaches threshold, it uses geometric distribution to generate the approximation gap
      		to skip data, and size of gap is determined by  geometric distribution with probability p = R/j
    
       Thanks to Erik Erlandson who is the author of this algorithm and help me with implementation.

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