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https://issues.apache.org/jira/browse/SPARK-12922?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15244918#comment-15244918
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Narine Kokhlikyan commented on SPARK-12922:
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Hi [~sunrui],
I’ve made some progress in putting logical and physical plans together and
calling R workers, however I still have some questions.
1. I’m still not quite sure about the number of partitions. As you wrote in
https://issues.apache.org/jira/browse/SPARK-6817 we need to
tune the number of partitions based on “spark.sql.shuffle.partitions”. What
do you exactly mean by tuning? Repartitioning ?
2. I have another question about grouping by keys:
groupByKey with one key is fine, however if we have more than one key we
probably need to introduce a case class. With a case
class it looks okay too, but I’m not sure how convenient it is. Any ideas
?
case class KeyData(a: Int, b: Int)
val gd1 = df.groupByKey(r=>KeyData(r.getInt(0), r.getInt(1)))
Thanks,
Narine
> Implement gapply() on DataFrame in SparkR
> -----------------------------------------
>
> Key: SPARK-12922
> URL: https://issues.apache.org/jira/browse/SPARK-12922
> Project: Spark
> Issue Type: Sub-task
> Components: SparkR
> Affects Versions: 1.6.0
> Reporter: Sun Rui
>
> gapply() applies an R function on groups grouped by one or more columns of a
> DataFrame, and returns a DataFrame. It is like GroupedDataSet.flatMapGroups()
> in the Dataset API.
> Two API styles are supported:
> 1.
> {code}
> gd <- groupBy(df, col1, ...)
> gapply(gd, function(grouping_key, group) {}, schema)
> {code}
> 2.
> {code}
> gapply(df, grouping_columns, function(grouping_key, group) {}, schema)
> {code}
> R function input: grouping keys value, a local data.frame of this grouped
> data
> R function output: local data.frame
> Schema specifies the Row format of the output of the R function. It must
> match the R function's output.
> Note that map-side combination (partial aggregation) is not supported, user
> could do map-side combination via dapply().
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