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https://issues.apache.org/jira/browse/SPARK-3250?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14130809#comment-14130809
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Erik Erlandson commented on SPARK-3250:
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I developed prototype iterator classes for "fast gap sampling" with and without
replacement. The code, testing rig and test results can be seen here:
https://gist.github.com/erikerlandson/05db1f15c8d623448ff6
I also wrote up some discussion of the algorithms here:
Faster Random Samples With Gap Sampling
http://erikerlandson.github.io/blog/2014/09/11/faster-random-samples-with-gap-sampling/
> More Efficient Sampling
> -----------------------
>
> Key: SPARK-3250
> URL: https://issues.apache.org/jira/browse/SPARK-3250
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Reporter: RJ Nowling
>
> Sampling, as currently implemented in Spark, is an O\(n\) operation. A
> number of stochastic algorithms achieve speed ups by exploiting O\(k\)
> sampling, where k is the number of data points to sample. Examples of such
> algorithms include KMeans MiniBatch (SPARK-2308) and Stochastic Gradient
> Descent with mini batching.
> More efficient sampling may be achievable by packing partitions with an
> ArrayBuffer or other data structure supporting random access. Since many of
> these stochastic algorithms perform repeated rounds of sampling, it may be
> feasible to perform a transformation to change the backing data structure
> followed by multiple rounds of sampling.
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