Making sense of MapReduce

September 24, 2010

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

From guest blogger Joseph Rickert.

Last night I went to hear Ken Krugler of Bixolabs talk about Hadoop at the monthly meeting of the Software Developers Forum. Maybe because Ken is an unusually lucid speaker, or maybe because I just reached some sort of cumulative tipping point through the prep work of all those patient people who have tried to help me in the past; but finally, I think I get Hadoop and MapReduce.

First of all, it’s MapReduce and then Hadoop: not Hadoop with MapReduce. The way to think about Hadoop is that it is the open source choice for implementing MapReduce like algorithms. Hadoop may provide technical advantages over competitors (Twister, Greenplum and others) about which I am in no position to comment, but it is nevertheless only one of several MapReduce frameworks.

The second big “revelation” was the nature of MapReduce. When dealing with massive data, it is very likely that the size of the data to be processed is many orders of magnitude larger than size of the software required to process it. The revelation is: not only does it make sense to move the code to the data, but one ought also to do as much parallel processing as possible. Looked at this way, MapReduce is a variation of the flow based programming techniques developed by mainframe programmers in the 60’s and 70’s which were part of a tremendous effort to understanding parallel programming during that time period. In fact, the tuples of Linda spaces, the precursor of Network Spaces which forms the conceptual foundation Revolution’s parallel R programming package, ParallelR, is probably a direct ancestor of MapReduce’s key – value pairs. Furthermore, the buzz in the R community about Hadoop makes sense if one thinks about Hadoop as the open source way to implement sophisticated R based MapReduce algorithms. R should be an ideal choice for implementing parallel algorithms that work on independent chunks of data.

To leave a comment for the author, please follow the link and comment on their blog: Revolutions. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Tags: ,

Comments are closed.


Mango solutions

RStudio homepage

Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training


CRC R books series

Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)