Difference Between Hadoop Mapreduce(Java) and RHadoop mapreduce I understand Hadoop MapReduce and its features but I am confused about R MapReduce.

One difference I have read is that R utilizes maximum RAM. So do perform parallel processing integrated R with Hadoop.

My doubt is:


R can do all stats, math and data science related stuff, but why R MapReduce?
Is there any new task I can achieve by using R MapReduce instead of Hadoop MapReduce? If yes, please specify.
We can achieve the task by using R with Hadoop (directly) but what is the importance of MapReduce in R and how it is different from normal MapReduce?
rhadoop (the part you are interested in is now called rmr2) is simply a client API for MapReduce written in R. You invoke MapReduce using R package APIs, and send an R function to the workers, where it is executed by an R interpreter locally. But it is otherwise exactly the same MapReduce.

You can call anything you like in R this way, but no R functions are themselves parallelized to use MapReduce in this way. The point is simply that you can invoke M/R from R. I don't think it somehow lets you do anything more magical than that.