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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-18356) Issue + Resolution: Kmeans Spark Performances (ML package)
Date Mon, 21 Nov 2016 14:19:58 GMT

     [ https://issues.apache.org/jira/browse/SPARK-18356?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-18356:
------------------------------------

    Assignee: Apache Spark

> Issue + Resolution: Kmeans Spark Performances (ML package)
> ----------------------------------------------------------
>
>                 Key: SPARK-18356
>                 URL: https://issues.apache.org/jira/browse/SPARK-18356
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 2.0.0, 2.0.1
>            Reporter: zakaria hili
>            Assignee: Apache Spark
>            Priority: Minor
>              Labels: easyfix
>
> Hello,
> I'm newbie in spark, but I think that I found a small problem that can affect spark Kmeans
performances.
> Before starting to explain the problem, I want to explain the warning that I faced.
> I tried to use Spark Kmeans with Dataframes to cluster my data
> df_Part = assembler.transform(df_Part)    
> df_Part.cache()
> while (k<=max_cluster) and (wssse > seuilStop):
>                     kmeans = KMeans().setK(k)
>                     model = kmeans.fit(df_Part)
>                     wssse = model.computeCost(df_Part)
>                     k=k+1
> but when I run the code I receive the warning :
> WARN KMeans: The input data is not directly cached, which may hurt performance if its
parent RDDs are also uncached.
> I searched in spark source code to find the source of this problem, then I realized there
is two classes responsible for this warning: 
> (mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala )
> (mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala )
>  
> When my  dataframe is cached, the fit method transform my dataframe into an internally
rdd which is not cached.
> Dataframe -> rdd -> run Training Kmeans Algo(rdd)
> -> The first class (ml package) responsible for converting the dataframe into rdd
then call Kmeans Algorithm
> ->The second class (mllib package) implements Kmeans Algorithm, and here spark verify
if the rdd is cached, if not a warning will be generated.  
> So, the solution of this problem is to cache the rdd before running Kmeans Algorithm.
> https://github.com/ZakariaHili/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
> All what we need is to add two lines:
> Cache rdd just after dataframe transformation, then uncached it after training algorithm.
> I hope that I was clear.
> If you think that I was wrong, please let me know.
> Sincerely,
> Zakaria HILI



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