spark-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Shivaram Venkataraman (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-12922) Implement gapply() on DataFrame in SparkR
Date Thu, 16 Jun 2016 05:01:05 GMT

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

Shivaram Venkataraman resolved SPARK-12922.
-------------------------------------------
       Resolution: Fixed
         Assignee: Narine Kokhlikyan
    Fix Version/s: 2.0.0

Resolved by https://github.com/apache/spark/pull/12836

> 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
>            Assignee: Narine Kokhlikyan
>             Fix For: 2.0.0
>
>
> 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().



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org


Mime
View raw message