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https://issues.apache.org/jira/browse/SPARK-7230?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14520478#comment-14520478
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Patrick Wendell commented on SPARK-7230:
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Yeah the goal is absolutely to support higher level apps, this has always been 
a goal in every part of Spark. In fact from the beginning we've made sure R is 
first class in the way we think about things like our package ecosystem (spark 
packages). The issue with what is there now is that there is this huge RDD API 
exposed, way beyond the basic parallelization primitives, and that needs more 
vetting before we ship it in a public API.

> Make RDD API private in SparkR for Spark 1.4
> --------------------------------------------
>
>                 Key: SPARK-7230
>                 URL: https://issues.apache.org/jira/browse/SPARK-7230
>             Project: Spark
>          Issue Type: Sub-task
>          Components: SparkR
>    Affects Versions: 1.4.0
>            Reporter: Shivaram Venkataraman
>            Assignee: Shivaram Venkataraman
>            Priority: Critical
>
> This ticket proposes making the RDD API in SparkR private for the 1.4 
> release. The motivation for doing so are discussed in a larger design 
> document aimed at a more top-down design of the SparkR APIs. A first cut that 
> discusses motivation and proposed changes can be found at http://goo.gl/GLHKZI
> The main points in that document that relate to this ticket are:
> - The RDD API requires knowledge of the distributed system and is pretty low 
> level. This is not very suitable for a number of R users who are used to more 
> high-level packages that work out of the box.
> - The RDD implementation in SparkR is not fully robust right now: we are 
> missing features like spilling for aggregation, handling partitions which 
> don't fit in memory etc. There are further limitations like lack of hashCode 
> for non-native types etc. which might affect user experience.
> The only change we will make for now is to not export the RDD functions as 
> public methods in the SparkR package and I will create another ticket for 
> discussing more details public API for 1.5



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