[jira] [Created] (SPARK-18608) Spark ML algorithms that check RDD cache level for internal caching double-cache data

["Nick Pentreath (JIRA)" <ji...@apache.org>]

Nick Pentreath created SPARK-18608:
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             Summary: Spark ML algorithms that check RDD cache level for internal caching double-cache data
                 Key: SPARK-18608
                 URL: https://issues.apache.org/jira/browse/SPARK-18608
             Project: Spark
          Issue Type: Bug
          Components: ML
            Reporter: Nick Pentreath


Some algorithms in Spark ML (e.g. {{LogisticRegression}}, {{LinearRegression}}, and I believe now {{KMeans}}) handle persistence internally. They check whether the input dataset is cached, and if not they cache it for performance.

However, the check is done using {{dataset.rdd.getStorageLevel == NONE}}. This will actually always be true, since even if the dataset itself is cached, the RDD returned by {{dataset.rdd}} will not be cached.

Hence if the input dataset is cached, the data will end up being cached twice, which is wasteful.

To see this:

{code}
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
{code}

Before SPARK-16063, there was no way to check the storage level of the input {{DataSet}}, but now we can, so the checks should be migrated to use {{dataset.storageLevel}}.



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