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https://issues.apache.org/jira/browse/SPARK-6830?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14603619#comment-14603619
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Perinkulam I Ganesh commented on SPARK-6830:
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If we cache it locally within RDD, then it can be done as follows:
private val mycache = scala.collection.mutable.Map.empty[String, Long]
def newcount(): Long = {
mycache.getOrElseUpdate("count", sc.runJob(this, Utils.getIteratorSize
_).sum)
}
Or do we need to modify the cacheManager code to cache these results along with
others?
thanks
> Memoize frequently queried vals in RDD, such as numPartitions, count etc.
> -------------------------------------------------------------------------
>
> Key: SPARK-6830
> URL: https://issues.apache.org/jira/browse/SPARK-6830
> Project: Spark
> Issue Type: Improvement
> Components: SparkR
> Reporter: Shivaram Venkataraman
> Priority: Minor
> Labels: Starter
>
> We should memoize frequently queried vals in RDD, such as numPartitions,
> count etc.
> While using SparkR in RStudio, the `count` function seems to be called
> frequently by the IDE – I think this is to show some stats about variables in
> the workspace etc. but this is not great in SparkR as we trigger a job every
> time count is called.
> Memoization would help in this case, but we should also see if there is some
> better way to interact with RStudio.
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