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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