spark-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Nick Pentreath (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-18608) Spark ML algorithms that check RDD cache level for internal caching double-cache data
Date Tue, 21 Feb 2017 06:55:44 GMT

    [ https://issues.apache.org/jira/browse/SPARK-18608?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15875465#comment-15875465
] 

Nick Pentreath commented on SPARK-18608:
----------------------------------------

[~podongfeng] [~yuhaoyan] I'm not aware of anyone working on this now, either of you want
to take it?

> 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}}.



--
This message was sent by Atlassian JIRA
(v6.3.15#6346)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org


Mime
View raw message