zhengruifeng created SPARK-29756:
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Summary: CountVectorizer forget to unpersist intermediate rdd
Key: SPARK-29756
URL: https://issues.apache.org/jira/browse/SPARK-29756
Project: Spark
Issue Type: Improvement
Components: ML
Affects Versions: 3.0.0
Reporter: zhengruifeng
{code:java}
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_5edcfe4828c2scala> sc.getPersistentRDDs
res0: scala.collection.Map[Int,org.apache.spark.rdd.RDD[_]] = Map(9 ->
MapPartitionsRDD[9] at map at CountVectorizer.scala:223)
{code}
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