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https://issues.apache.org/jira/browse/SPARK-16519?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15417541#comment-15417541
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Felix Cheung edited comment on SPARK-16519 at 8/11/16 4:40 PM:
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since we are undecided on what to export for RDD, should we just go ahead and
rename in these internal methods so their names won't match the generics we are
exporting? That should eliminate the warnings.
If that makes sense, I could start this shortly.
was (Author: felixcheung):
since we are undecided on what to export for RDD, should we just go ahead and
rename in these internal methods so we won't match the generics we are
exporting? That should eliminate the warnings.
If that makes sense, I could start this shortly.
> Handle SparkR RDD generics that create warnings in R CMD check
> --------------------------------------------------------------
>
> Key: SPARK-16519
> URL: https://issues.apache.org/jira/browse/SPARK-16519
> Project: Spark
> Issue Type: Sub-task
> Components: SparkR
> Reporter: Shivaram Venkataraman
>
> One of the warnings we get from R CMD check is that RDD implementations of
> some of the generics are not documented. These generics are shared between
> RDD, DataFrames in SparkR. The list includes
> {quote}
> WARNING
> Undocumented S4 methods:
> generic 'cache' and siglist 'RDD'
> generic 'collect' and siglist 'RDD'
> generic 'count' and siglist 'RDD'
> generic 'distinct' and siglist 'RDD'
> generic 'first' and siglist 'RDD'
> generic 'join' and siglist 'RDD,RDD'
> generic 'length' and siglist 'RDD'
> generic 'partitionBy' and siglist 'RDD'
> generic 'persist' and siglist 'RDD,character'
> generic 'repartition' and siglist 'RDD'
> generic 'show' and siglist 'RDD'
> generic 'take' and siglist 'RDD,numeric'
> generic 'unpersist' and siglist 'RDD'
> {quote}
> As described in
> https://stat.ethz.ch/pipermail/r-devel/2003-September/027490.html this looks
> like a limitation of R where exporting a generic from a package also exports
> all the implementations of that generic.
> One way to get around this is to remove the RDD API or rename the methods in
> Spark 2.1
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