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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:

  1. R can do all stats, math and data science related stuff, but why R MapReduce?
  2. Is there any new task I can achieve by using R MapReduce instead of Hadoop MapReduce? If yes, please specify.
  3. 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?
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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.

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  • $\begingroup$ Can we do Regresssion,clustering,classifications using Rmr...... If it is possible then we can do using R directly.. only because of Parallelism we are using Rmr.. If i am correct.. Is there any main Difference between Hadoop Mapreduce and R mapreduce (apart from Parallelism)... $\endgroup$ – user3782364 Jun 28 '14 at 16:21
  • $\begingroup$ rmr is a framework for running R functions in MapReduce. That is the thing it lets you do that you could not do before. It is not a library of new statistical functions of course. $\endgroup$ – Sean Owen Jun 28 '14 at 17:32
  • $\begingroup$ Then any other specific feature where hadoop mapreduce can't handle?..... $\endgroup$ – user3782364 Jun 29 '14 at 11:32

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