[R] R tools for large files
andy_liaw at merck.com
Tue Aug 26 04:15:46 CEST 2003
> From: Richard A. O'Keefe [mailto:ok at cs.otago.ac.nz]
> Murray Jorgensen <maj at stats.waikato.ac.nz> wrote:
> "Large" for my purposes means "more than I really want to read
> into memory" which in turn means "takes more than 30s". I'm at
> home now and the file isn't so I'm not sure if the file is large
> or not.
> I repeat my earlier observation. The AMOUNT OF DATA is
> easily handled a typical desktop machine these days. The
> problem is not the amount of data. The problem is HOW LONG
> IT TAKES TO READ. I made several attempts to read the test
> file I created yesterday, and each time gave up impatiently
> after 5+ minutes elapsed time. I tried again today (see
> below) and went away to have a cop of tea &c; took nearly 10
> minute that time and still hadn't finished. 'mawk' read _and
> processed_ the same file happily in under 30 seconds.
> One quite serious alternative would be to write a little C
> function to read the file into an array, and call that from R.
> > system.time(m <- matrix(1:(41*250000), nrow=250000, ncol=41))
>  3.28 0.79 4.28 0.00 0.00
> > system.time(save(m, file="m.bin"))
>  8.44 0.54 9.08 0.00 0.00
> > m <- NULL
> > system.time(load("m.bin"))
>  11.25 0.19 11.51 0.00 0.00
> > length(m)
>  10250000
I tried the following on my IBM T22 Thinkpad (P3-933 w/ 512MB):
> system.time(x <- matrix(runif(41*250000), 250000, 41))
 6.02 0.40 6.52 NA NA
> system.time(write(t(x), file="try.dat", ncol=41))
 192.12 81.60 279.64 NA NA
> system.time(xx <- matrix(scan("try.dat"), byrow=TRUE, ncol=41))
Read 10250000 items
 110.90 1.09 126.89 NA NA
> system.time(xx <- read.table("try.dat", header=FALSE,
+ colClasses=rep("numeric", 41)))
 106.61 0.48 110.66 NA NA
> system.time(save(x, file="try.rda"))
 9.15 1.05 19.12 NA NA
 10.22 0.33 10.69 NA NA
The last few lines show that the timing I get is approximately the
same as yours, so the other timings shouldn't be too different.
I don't think I can make coffee that fast. (No, I don't drink it black!)
> The binary file m.bin is 41 million bytes.
> This little transcript shows that a data set of this size can
> be comfortably read from disc in under 12 seconds, on the
> same machine where scan() took about 50 times as long before
> I killed it.
> So yet another alternative is to write a little program that
> converts the data file to R binary format, and then just read
> the whole thing in. I think readers will agree that 12
> seconds on a 500MHz machine counts as "takes less than 30s".
> It's just that R is so good in reading in initial
> segments of a file that I
> can't believe that it can't be effective in reading more general
> (pre-specified) subsets.
> R is *good* at it, it's just not *quick*. Trying to select a
> subset in scan() or read.table() wouldn't help all that much,
> because it would still have to *scan* the data to determine
> what to skip.
> Two more times:
> An unoptimised C program writing 0:(41*250000-1) as a file of
> 41-number lines: f% time a.out >m.txt 13.0u 1.0s 0:14 94%
> 0+0k 0+0io 0pf+0w
> > system.time(m <- read.table("m.txt", header=FALSE))
> Timing stopped at: 552.01 15.48 584.51 0 0
> To my eyes, src/main/scan.c shows no signs of having been
> tuned for speed. The goals appear to have been power (the R
> scan() function has LOTS of
> options) and correctness, which are perfectly good goals, and
> the speed of scan() and read.table() with modest data sizes
> is quite good enough.
> The huge ratio (>552)/(<30) for R/mawk does suggest that
> there may be room for some serious improvement in scan(),
> possibly by means of some extra hints about total size,
> possibly by creating a fast path through the code.
> Of course the big point is that however long scan() takes to
> read the data set, it only has to be done once. Leave R
> running overnight and in the morning save the dataset out as
> an R binary file using save(). Then you'll be able to load it
> again quickly.
> R-help at stat.math.ethz.ch mailing list
> https://www.stat.math.ethz.ch/mailman/listinfo> /r-help
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