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https://issues.apache.org/jira/browse/SPARK-12922?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15351311#comment-15351311
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Timothy Hunter commented on SPARK-12922:
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[~Narine] while working on a similar function for python [1], we found it
easier to have the following changes:
- the keys are appended by default to the spark dataframe being returned
- the output schema that the users provides is the schema of the R data frame
and does not include the keys
Here were our reasons to depart from the R implementation of gapply:
- in most cases, users will want to know the key associated with a result ->
appending the key is the sensible default
- most functions in the SQL interface and in MLlib append columns, and gapply
departs from this philosophy
- for the cases when they do not need it, adding the key is a fraction of the
computation time and of the output size
- from a formal perspective, it makes calling gapply fully transparent to the
type of the key: it is easier to build a function with gapply because it does
not need to know anything about the key
I think it would make sense to make this change to the R's gapply
implementation. Let me know what you think about it.
[1]
https://github.com/databricks/spark-sklearn/blob/master/python/spark_sklearn/group_apply.py
> 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().
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