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From "Narine Kokhlikyan (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-12922) Implement gapply() on DataFrame in SparkR
Date Sun, 17 Apr 2016 21:23:25 GMT

    [ https://issues.apache.org/jira/browse/SPARK-12922?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15244918#comment-15244918
] 

Narine Kokhlikyan commented on SPARK-12922:
-------------------------------------------

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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