[R] Memory Experimentation: Rule of Thumb = 10-15 Times the Memory
Gabor Grothendieck
ggrothendieck at gmail.com
Thu Aug 9 20:33:58 CEST 2007
Another thing you could try would be reading it into a data base and then
from there into R.
The devel version of sqldf has this capability. That is it will use RSQLite
to read the file directly into the database without going through R at all
and then read it from there into R so its a completely different process.
The RSQLite software has no capability of dealing with quotes (they will
be regarded as ordinary characters) but a single gsub can remove them
afterwards. This won't work if there are commas within the quotes but
in that case you could read each row as a single record and then
split it yourself in R.
Try this
library(sqldf)
# next statement grabs the devel version software that does this
source("http://sqldf.googlecode.com/svn/trunk/R/sqldf.R")
gc()
f <- file("big.csv")
DF <- sqldf("select * from f", file.format = list(header = TRUE,
row.names = FALSE))
gc()
For more info see the man page from the devel version and the home page:
http://sqldf.googlecode.com/svn/trunk/man/sqldf.Rd
http://code.google.com/p/sqldf/
On 8/9/07, Michael Cassin <michael at cassin.name> wrote:
> Thanks for looking, but my file has quotes. It's also 400MB, and I don't
> mind waiting, but don't have 6x the memory to read it in.
>
>
> On 8/9/07, Gabor Grothendieck <ggrothendieck at gmail.com> wrote:
> > If we add quote = FALSE to the write.csv statement its twice as fast
> > reading it in.
> >
> > On 8/9/07, Michael Cassin <michael at cassin.name> wrote:
> > > Hi,
> > >
> > > I've been having similar experiences and haven't been able to
> > > substantially improve the efficiency using the guidance in the I/O
> > > Manual.
> > >
> > > Could anyone advise on how to improve the following scan()? It is not
> > > based on my real file, please assume that I do need to read in
> > > characters, and can't do any pre-processing of the file, etc.
> > >
> > > ## Create Sample File
> > >
> write.csv(matrix(as.character(1:1e6),ncol=10,byrow=TRUE),"big.csv",row.names=FALSE)
> > > q()
> > >
> > > **New Session**
> > > #R
> > > system("ls -l big.csv")
> > > system("free -m")
> > >
> big1<-matrix(scan("big.csv",sep=",",what=character(0),skip=1,n=1e6),ncol=10,byrow=TRUE)
> > > system("free -m")
> > >
> > > The file is approximately 9MB, but approximately 50-60MB is used to
> > > read it in.
> > >
> > > object.size(big1) is 56MB, or 56 bytes per string, which seems
> excessive.
> > >
> > > Regards, Mike
> > >
> > > Configuration info:
> > > > sessionInfo()
> > > R version 2.5.1 (2007-06-27)
> > > x86_64-redhat-linux-gnu
> > > locale:
> > > C
> > > attached base packages:
> > > [1] "stats" "graphics" "grDevices" "utils" "datasets"
> "methods"
> > > [7] "base"
> > >
> > > # uname -a
> > > Linux ***.com 2.6.9-023stab044.4-smp #1 SMP Thu May 24 17:20:37 MSD
> > > 2007 x86_64 x86_64 x86_64 GNU/Linux
> > >
> > >
> > >
> > > ====== Quoted Text ====
> > > From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>
> > > Date: Tue, 26 Jun 2007 17:53:28 +0100 (BST)
> > >
> > >
> > >
> > >
> > > The R Data Import/Export Manual points out several ways in which you
> > > can use read.csv more efficiently.
> > >
> > > On Tue, 26 Jun 2007, ivo welch wrote:
> > >
> > > > dear R experts:
> > > >
> > > > I am of course no R experts, but use it regularly. I thought I would
> > > > share some experimentation with memory use. I run a linux machine
> > > > with about 4GB of memory, and R 2.5.0.
> > > >
> > > > upon startup, gc() reports
> > > >
> > > > used (Mb) gc trigger (Mb) max used (Mb)
> > > > Ncells 268755 14.4 407500 21.8 350000 18.7
> > > > Vcells 139137 1.1 786432 6.0 444750 3.4
> > > >
> > > > This is my baseline. linux 'top' reports 48MB as baseline. This
> > > > includes some of my own routines that are always loaded. Good..
> > > >
> > > >
> > > > Next, I created a s.csv file with 22 variables and 500,000
> > > > observations, taking up an uncompressed disk space of 115MB. The
> > > > resulting object.size() after a read.csv() is 84,002,712 bytes (80MB).
> > > >
> > > >> s= read.csv("s.csv");
> > > >> object.size(s);
> > > >
> > > > [1] 84002712
> > > >
> > > >
> > > > here is where things get more interesting. after the read.csv() is
> > > > finished, gc() reports
> > > >
> > > > used (Mb) gc trigger (Mb) max used (Mb)
> > > > Ncells 270505 14.5 8349948 446.0 11268682 601.9
> > > > Vcells 10639515 81.2 34345544 262.1 42834692 326.9
> > > >
> > > > I was a big surprised by this---R had 928MB intermittent memory in
> > > > use. More interestingly, this is also similar to what linux 'top'
> > > > reports as memory use of the R process (919MB, probably 1024 vs. 1000
> > > > B/MB), even after the read.csv() is finished and gc() has been run.
> > > > Nothing seems to have been released back to the OS.
> > > >
> > > > Now,
> > > >
> > > >> rm(s)
> > > >> gc()
> > > > used (Mb) gc trigger (Mb) max used (Mb)
> > > > Ncells 270541 14.5 6679958 356.8 11268755 601.9
> > > > Vcells 139481 1.1 27476536 209.7 42807620 326.6
> > > >
> > > > linux 'top' now reports 650MB of memory use (though R itself uses only
> > > > 15.6Mb). My guess is that It leaves the trigger memory of 567MB plus
> > > > the base 48MB.
> > > >
> > > >
> > > > There are two interesting observations for me here: first, to read a
> > > > .csv file, I need to have at least 10-15 times as much memory as the
> > > > file that I want to read---a lot more than the factor of 3-4 that I
> > > > had expected. The moral is that IF R can read a .csv file, one need
> > > > not worry too much about running into memory constraints lateron. {R
> > > > Developers---reducing read.csv's memory requirement a little would be
> > > > nice. of course, you have more than enough on your plate, already.}
> > > >
> > > > Second, memory is not returned fully to the OS. This is not
> > > > necessarily a bad thing, but good to know.
> > > >
> > > > Hope this helps...
> > > >
> > > > Sincerely,
> > > >
> > > > /iaw
> > > >
> > > > ______________________________________________
> > > > R-help_at_stat.math.ethz.ch 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.
> > > >
> > > --
> > > Brian D. Ripley,
> ripley_at_stats.ox.ac.uk
> > > Professor of Applied Statistics,
> http://www.stats.ox.ac.uk/~ripley/
> > > University of Oxford, Tel: +44 1865 272861 (self)
> > > 1 South Parks Road, +44 1865 272866 (PA)
> > > Oxford OX1 3TG, UK Fax: +44 1865 272595
> > >
> > > ______________________________________________
> > > R-help at stat.math.ethz.ch 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.
> > >
> >
>
>
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