[jira] [Resolved] (SPARK-16697) redundant RDD computation in LDAOptimizer

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-16697?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-16697.
-------------------------------
       Resolution: Fixed
    Fix Version/s: 2.1.0

Issue resolved by pull request 14335
[https://github.com/apache/spark/pull/14335]

> redundant RDD computation in LDAOptimizer
> -----------------------------------------
>
>                 Key: SPARK-16697
>                 URL: https://issues.apache.org/jira/browse/SPARK-16697
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, MLlib
>    Affects Versions: 2.0.1
>            Reporter: Weichen Xu
>             Fix For: 2.1.0
>
>   Original Estimate: 24h
>  Remaining Estimate: 24h
>
> In mllib.clustering.LDAOptimizer
> the submitMiniBatch method,
> the stats: RDD do not persist but the following code will use it twice.
> so it cause redundant computation on it.
> and there is another problem,
> the expElogbetaBc broadcast variable is unpersist too early,
> and the next statement 
> `
> val gammat: BDM[Double] = breeze.linalg.DenseMatrix.vertcat(val gammat: BDM[Double] = breeze.linalg.DenseMatrix.vertcat(
>        stats.map(_._2).flatMap(list => list).collect().map(_.toDenseMatrix): _*)
> `
> will re-compute the stats RDD, it will use expElogbetaBc broadcast variable again,
> so the  expElogbetaBc broadcast variable will be broadcast again.



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