[ https://issues.apache.org/jira/browse/SPARK-12922?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15236622#comment-15236622 ]
Sun Rui commented on SPARK-12922: --------------------------------- [~Narine] DataFrame and Dataset are now converged. DataFrame is a different view of Dataset, that is Dataset<Row>. So groupByKey is the same method for both Dataset and DataFrame, but the `func` is different as the data element view is different, for example: {code} val ds = Seq((1,2), (3,4)).toDS val gd = ds.groupByKey(v=>v._1) val df = ds.toDF val gd1 = df.groupByKey(r=>r.getInt(0)) {code} > 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(). -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org