Dong Wang created SPARK-29815:
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Summary: Missing persist in ml.tuning.CrossValidator.fit()
Key: SPARK-29815
URL: https://issues.apache.org/jira/browse/SPARK-29815
Project: Spark
Issue Type: Improvement
Components: ML
Affects Versions: 2.4.3
Reporter: Dong Wang
dataset.toDF.rdd in ml.tuning.CrossValidator.fit(dataset: Dataset[_]) will
generate two rdds: training and validation. Some actions will be operated on
these two rdds, but dataset.toDF.rdd is not persisted, which will cause
recomputation.
{code:scala}
// Compute metrics for each model over each split
val splits = MLUtils.kFold(dataset.toDF.rdd, $(numFolds), $(seed)) //
dataset.toDF.rdd should be persisted
val metrics = splits.zipWithIndex.map { case ((training, validation),
splitIndex) =>
val trainingDataset = sparkSession.createDataFrame(training,
schema).cache()
val validationDataset = sparkSession.createDataFrame(validation,
schema).cache()
{scala}
This issue is reported by our tool CacheCheck, which is used to dynamically
detecting persist()/unpersist() api misuses.
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