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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-12922) Implement gapply() on DataFrame in SparkR
Date Mon, 02 May 2016 06:39:13 GMT

     [ https://issues.apache.org/jira/browse/SPARK-12922?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-12922:
------------------------------------

    Assignee:     (was: Apache Spark)

> 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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