zhengruifeng commented on issue #26398: [SPARK-29756][ML] CountVectorizer forget to unpersist intermediate rdd URL: https://github.com/apache/spark/pull/26398#issuecomment-549751299 ```scala scala> val df = spark.createDataFrame(Seq( | (0, Array("a", "b", "c")), | (1, Array("a", "b", "b", "c", "a")) | )).toDF("id", "words") df: org.apache.spark.sql.DataFrame = [id: int, words: array<string>] scala> import org.apache.spark.ml.feature._ import org.apache.spark.ml.feature._ scala> val cvModel: CountVectorizerModel = new CountVectorizer().setInputCol("words").setOutputCol("features").setVocabSize(3).setMinDF(2).fit(df) cvModel: org.apache.spark.ml.feature.CountVectorizerModel = cntVec_5edcfe4828c2 scala> sc.getPersistentRDDs res0: scala.collection.Map[Int,org.apache.spark.rdd.RDD[_]] = Map(9 -> MapPartitionsRDD[9] at map at CountVectorizer.scala:223) ```
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