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https://issues.apache.org/jira/browse/SPARK-3250?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14115213#comment-14115213
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RJ Nowling commented on SPARK-3250:
-----------------------------------
Very clever! Once it's verified to sample correctly, it would make a nice
incremental improvement to the current sampling in Spark.
Can you try different data sizes? It shouldn't change the O(n) scaling but it
would be good to verify.
> 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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