[R] Any chance R will ever get beyond the 2^31-1 vector size limit?

Matthew Keller mckellercran at gmail.com
Fri Apr 16 01:46:45 CEST 2010

HI Duncan and R users,

Duncan, thank you for taking the time to respond. I've had several
other comments off the list, and I'd like to summarize what these have
to say, although I won't give sources since I assume there was a
reason why people chose not to respond to the whole list. The long and
short of it is that there is hope for people who want R to get beyond
the 2^31-1 vector size limit.

First off, I received a couple of responses from people who wanted to
commiserate and me to summarize what I learned. Here you go.

Second, the package bigmemory and ff can both help with memory issues.
I've had success using bigmemory before, and found it to be quite

Third, one knowledgeable responder doubted that changing the 2^31-1
limit would 'break' old datasets. He says, "This might be true for
isolated cases of objects stored in binary formats or in workspaces,
but I don't see that as anywhere near as important as the change you
(and we) would like to see."

Fourth, another knowledgeable responder felt it was likely that, given
the demand driven by the huge increases in dataset sizes, this
limitation would likely be overcome within the next few years.



On Fri, Apr 9, 2010 at 6:36 PM, Duncan Murdoch <murdoch at stats.uwo.ca> wrote:
> On 09/04/2010 7:38 PM, Matthew Keller wrote:
>> Hi all,
>> My institute will hopefully be working on cutting-edge genetic
>> sequencing data by the Fall of 2010. The datasets will be 10's of GB
>> large and growing. I'd like to use R to do primary analyses. This is
>> OK, because we can just throw $ at the problem and get lots of RAM
>> running on 64 bit R. However, we are still running up against the fact
>> that vectors in R cannot contain more than 2^31-1. I know there are
>> "ways around" this issue, and trust me, I think I've tried them all
>> (e.g., bringing in portions of the data at a time; using large-dataset
>> packages in R; using SQL databases, etc). But all these 'solutions'
>> are, at the end of the day, much much more cumbersome,
>> programming-wise, than just doing things in native R. Maybe that's
>> just the cost of doing what I'm doing. But my questions, which  may
>> well be naive (I'm not a computer programmer), are:
>> 1) Is there an *inherent* limit to vectors being < 2^31-1 long? I.e.,
>> in an alternative history of R's development, would it have been
>> feasible for R to not have had this limitation?
> The problem is that we use "int" as a vector index.  On most platforms,
> that's a signed 32 bit integer, with max value 2^31-1.
>> 2) Is there any possibility that this limit will be overcome in future
>> revisions of R?
> Of course, R is open source.  You could rewrite all of the internal code
> tomorrow to use 64 bit indexing.
> Will someone else do it for you?  Even that is possible.  One problem are
> that this will make all of your data incompatible with older versions of R.
>  And back to the original question:  are you willing to pay for the
> development?  Then go ahead, you can have it tomorrow (or later, if your
> budget is limited).  Are you waiting for someone else to do it for free?
>  Then you need to wait for someone who knows how to do it to want to do it.
>> I'm very very grateful to the people who have spent important parts of
>> their professional lives developing R. I don't think anyone back in,
>> say, 1995, could have foreseen that datasets would be >>2^32-1 in
>> size. For better or worse, however, in many fields of science, that is
>> routinely the case today. *If* it's possible to get around this limit,
>> then I'd like to know whether the R Development Team takes seriously
>> the needs of large data users, or if they feel that (perhaps not
>> mutually exclusively) developing such capacity is best left up to ad
>> hoc R packages and alternative analysis programs.
> There are many ways around the limit today.  Put your data in a dataframe
> with many columns each of length 2^31-1 or less.  Put your data in a
> database, and process it a block at a time.  Etc.
> Duncan Murdoch
>> Best,
>> Matt

Matthew C Keller
Asst. Professor of Psychology
University of Colorado at Boulder

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