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From "Oscar D. Lara Yejas (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-17774) Add support for head on DataFrame Column
Date Wed, 05 Oct 2016 02:49:20 GMT

    [ https://issues.apache.org/jira/browse/SPARK-17774?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15547429#comment-15547429
] 

Oscar D. Lara Yejas commented on SPARK-17774:
---------------------------------------------

[~shivaram]: I concur with Shivaram. Besides, I already implemented method head() in my PR
11336:

https://github.com/apache/spark/pull/11336

If you wanted to implement method head() alone, you'll still need to do all changes I did
for PR 11336 except for the 5 lines of code of method collect(). If that's the case, I'd rather
suggest to merge PR 11336.

[~falaki]: In the corner cases where there's no parent DataFrame, we can return an empty value
as opposed to throwing an error. This behavior is already implemented in PR 11336. Also, though
R doesn't have method collect(), I think it's still useful to turn a Column into an R vector.
Perhaps a function called as.vector()?

Thanks folks!





> Add support for head on DataFrame Column
> ----------------------------------------
>
>                 Key: SPARK-17774
>                 URL: https://issues.apache.org/jira/browse/SPARK-17774
>             Project: Spark
>          Issue Type: Sub-task
>          Components: SparkR
>    Affects Versions: 2.0.0
>            Reporter: Hossein Falaki
>
> There was a lot of discussion on SPARK-9325. To summarize the conversation on that ticket
regardign {{collect}}
> * Pro: Ease of use and maximum compatibility with existing R API
> * Con: We do not want to increase maintenance cost by opening arbitrary API. With Spark's
DataFrame API {{collect}} does not work on {{Column}} and there is no need for it to work
in R.
> This ticket is strictly about {{head}}. I propose supporting {{head}} on {{Column}} because:
> 1. R users are already used to calling {{head(iris$Sepal.Length)}}. When they do that
on SparkDataFrame they get an error. Not a good experience
> 2. Adding support for it does not require any change to the backend. It can be trivially
done in R code. 



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