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

["zakaria hili (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-18608?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15761233#comment-15761233 ] 

zakaria hili commented on SPARK-18608:
--------------------------------------

Hi, 
Can I join this discussion?
Could you please explain to me why do you believe that the generated rdd is cached ?
As you can see in https://github.com/apache/spark/blob/master/python/pyspark/sql/dataframe.py 

we generate a new rdd, so it's normal that this rdd is not cached

    def rdd(self):
        """Returns the content as an :class:`pyspark.RDD` of :class:`Row`.
        """
        if self._lazy_rdd is None:
            jrdd = self._jdf.javaToPython()
            self._lazy_rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
        return self._lazy_rdd

> 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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