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Sun Rui commented on SPARK-12922: --------------------------------- [~Narine], 1. Typically users don't care number of partitions in SparkSQL. If they care, they can tune it by setting “spark.sql.shuffle.partitions”. It seems not related to implementation of gapply? 2. I think we need support groupBy instead of groupByKey for DataFrame. for groupBy, users can specify multiple key columns at once. So a list should be used to hold the key columns. FYI, I have basically implemented dapply(), and is debugging it > 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