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?

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.

  • $\begingroup$ 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)... $\endgroup$ 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?..... $\endgroup$ Jun 29 '14 at 11:32

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.