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https://issues.apache.org/jira/browse/SPARK-12922?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15233886#comment-15233886
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Narine Kokhlikyan commented on SPARK-12922:
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Hi [~sunrui],
I have a question regarding your suggestion about adding a new
"GroupedData.flatMapRGroups" function according to the following document:
https://docs.google.com/presentation/d/1oj17N5JaE8JDjT2as_DUI6LKutLcEHNZB29HsRGL_dM/edit#slide=id.p9
It seems that some changes has happened in SparkSQL. According to 1.6.1 there
was a scala class called:
https://github.com/apache/spark/blob/v1.6.1/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
This doesn't seem to exist in 2.0.0
I was thinking to add the flatMapRGroups helper function to
org.apache.spark.sql.KeyValueGroupedDataset or
org.apache.spark.sql.RelationalGroupedDataset. What do you think ?
Thank you,
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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