Github user MLnick commented on a diff in the pull request: https://github.com/apache/spark/pull/16020#discussion_r89740051 --- Diff: mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala --- @@ -255,10 +256,19 @@ class BisectingKMeans @Since("2.0.0") ( @Since("2.0.0") override def fit(dataset: Dataset[_]): BisectingKMeansModel = { + val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE --- End diff -- By the way, I've been meaning to log a ticket for this issue, but have been tied up. This will actually never work. `dataset.rdd` will always have storage level `NONE`. To see this: ``` scala> import org.apache.spark.storage.StorageLevel import org.apache.spark.storage.StorageLevel scala> val df = spark.range(10).toDF("num") df: org.apache.spark.sql.DataFrame = [num: bigint] scala> df.storageLevel == StorageLevel.NONE res0: Boolean = true scala> df.persist res1: df.type = [num: bigint] scala> df.storageLevel == StorageLevel.MEMORY_AND_DISK res2: Boolean = true scala> df.rdd.getStorageLevel == StorageLevel.MEMORY_AND_DISK res3: Boolean = false scala> df.rdd.getStorageLevel == StorageLevel.NONE res4: Boolean = true ``` So in fact all the algorithms that are checking for storage level using `dataset.rdd` are actually double-caching the data if the input DataFrame is actually cached, because the RDD will not appear to be cached. So we should migrate all the checks to use `dataset.storageLevel` which was added in https://github.com/apache/spark/pull/13780
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