On Fri, Apr 1, 2011 at 1:46 PM, Henri Mone <henrim...@gmail.com> wrote: > Dear R Users, > > I use for my data crunching a combination of MySQL and GNU R. I have > to handle huge/ middle seized data which is stored in a MySql > database, R executes a SQL command to fetch the data and does the > plotting with the build in R plotting functions. > > The (low level) calculations like summing, dividing, grouping, sorting > etc. can be done either with the sql command on the MySQL side or on > the R side. > My question is what is faster for this low level calculations / data > rearrangement MySQL or R? Is there a general rule of thumb what to > shift to the MySql side and what to the R side? > > Thanks > Henri > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >
I would assume RDBMS have advanced memory management capabilities and are designed for the manipulation and handling of large amounts of data. These are primary features for most database management server software. This way the database management server software should (in most cases) be used to store, manipulate then return only the processed and qualifying records to the client or other application for further specialized processing and/or data visualization. Allan. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
