[jira] [Assigned] (SPARK-32676) Fix double caching in KMeans/BiKMeans

["Apache Spark (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-32676?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-32676:
------------------------------------

    Assignee: Apache Spark

> Fix double caching in KMeans/BiKMeans
> -------------------------------------
>
>                 Key: SPARK-32676
>                 URL: https://issues.apache.org/jira/browse/SPARK-32676
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 3.0.0, 3.1.0
>            Reporter: zhengruifeng
>            Assignee: Apache Spark
>            Priority: Major
>
> In the .mllib side, if the storageLevel of input {{data}} is always ignored and cached twice:
> {code:java}
> @Since("0.8.0")
> def run(data: RDD[Vector]): KMeansModel = {
>   val instances = data.map(point => (point, 1.0))
>   runWithWeight(instances, None)
> }
>  {code}
> {code:java}
> private[spark] def runWithWeight(
>     data: RDD[(Vector, Double)],
>     instr: Option[Instrumentation]): KMeansModel = {
>   // Compute squared norms and cache them.
>   val norms = data.map { case (v, _) =>
>     Vectors.norm(v, 2.0)
>   }
>   val zippedData = data.zip(norms).map { case ((v, w), norm) =>
>     new VectorWithNorm(v, norm, w)
>   }
>   if (data.getStorageLevel == StorageLevel.NONE) {
>     zippedData.persist(StorageLevel.MEMORY_AND_DISK)
>   }
>   val model = runAlgorithmWithWeight(zippedData, instr)
>   zippedData.unpersist()
>   model
> } {code}



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