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  1. Spark
  2. SPARK-29818 Missing persist on RDD
  3. SPARK-29815

Missing persist in ml.tuning.CrossValidator.fit()

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Details

    • Sub-task
    • Status: Resolved
    • Major
    • Resolution: Duplicate
    • 2.4.3
    • None
    • ML
    • None

    Description

      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.

          // 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()

      This issue is reported by our tool CacheCheck, which is used to dynamically detecting persist()/unpersist() api misuses.

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        Issue Links

          duplicates

          Improvement - An improvement or enhancement to an existing feature or task. SPARK-29818 Missing persist on RDD

          • Major - Major loss of function.
          • Resolved
          links to

          Web Link GitHub Pull Request #26450

          Activity

            People

              Unassigned Unassigned
              spark_cachecheck IcySanwitch
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                Created:
                Updated:
                Resolved: