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https://issues.apache.org/jira/browse/SPARK-18608?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley updated SPARK-18608:
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Target Version/s: 2.2.1, 2.3.0
> 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
> Assignee: zhengruifeng
>
> 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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