[
https://issues.apache.org/jira/browse/SPARK-12922?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15247047#comment-15247047
]
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: [email protected]
For additional commands, e-mail: [email protected]
