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From Shivaram Venkataraman <>
Subject Re: Support R in Spark
Date Thu, 04 Sep 2014 17:58:43 GMT
Thanks Kui. SparkR is a pretty young project, but there are a bunch of
things we are working on. One of the main features is to expose a data
frame API ( and we will
be integrating this with Spark's MLLib.  At a high-level this will
allow R users to use a familiar API but make use of MLLib's efficient
distributed implementation. This is the same strategy used in Python
as well.

Also we do hope to merge SparkR with mainline Spark -- we have a few
features to complete before that and plan to shoot for integration by
Spark 1.3.


On Wed, Sep 3, 2014 at 9:24 PM, oppokui <> wrote:
> Thanks, Shivaram.
> No specific use case yet. We try to use R in our project as data scientest
> are all knowing R. We had a concern that how R handles the mass data. Spark
> does a better work on big data area, and Spark ML is focusing on predictive
> analysis area. Then we are thinking whether we can merge R and Spark
> together. We tried SparkR and it is pretty easy to use. But we didn’t see
> any feedback on this package in industry. It will be better if Spark team
> has R support just like scala/Java/Python.
> Another question is that MLlib will re-implement all famous data mining
> algorithms in Spark, then what is the purpose of using R?
> There is another technique for us H2O which support R natively. H2O is more
> friendly to data scientist. I saw H2O can also work on Spark (Sparkling
> Water).  It is better than using SparkR?
> Thanks and Regards.
> Kui
> On Sep 4, 2014, at 1:47 AM, Shivaram Venkataraman
> <> wrote:
> Hi
> Do you have a specific use-case where SparkR doesn't work well ? We'd love
> to hear more about use-cases and features that can be improved with SparkR.
> Thanks
> Shivaram
> On Wed, Sep 3, 2014 at 3:19 AM, oppokui <> wrote:
>> Does spark ML team have plan to support R script natively? There is a
>> SparkR project, but not from spark team. Spark ML used netlib-java to talk
>> with native fortran routines or use NumPy, why not try to use R in some
>> sense.
>> R had lot of useful packages. If spark ML team can include R support, it
>> will be a very powerful.
>> Any comment?
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