New to R, sorry if one or either of these is an inappropriate list for a
question like this below; please let me know if this is a general help
question.
Jill Willie
Open Seas
Safeco Insurance
jilwil at safeco.com
-----Original Message-----
From: WILLIE, JILL
Sent: Thursday, January 25, 2007 2:27 PM
To: r-help at stat.math.ethz.ch
Subject: reducing RODBC odbcQuery memory use?
Basic Questions:
1. Can I avoid having RODBC use so much memory (35 times the data size
or more) making a data.frame & then .rda file via. sqlQuery/save?
2. If not, is there some more appropriate way from w/in R to pull large
data sets (2-5GB) into .rda files from sql?
Testing details (R transcript below): 1GB CPU, 1GB RAM windows machine.
1. testing bigger input table (AUTCombinedWA_BILossCost_1per), size is
20MB in sql, 100000 rows, 2 numeric columns, 55 integer columns;
consumes 350000kb of memory & 800000kb of virtual memory to execute the
sqlQuery command. Memory not released after the step finishes or upon
execution of odbcCloseAll(), or gc().
2. tested small input table, size is 2MB in sql, 10000 rows, 2 numeric
columns, 55 integer columns; consumes 55000kb of memory & 515000kb (vm
seems oddly high to me) of virtual memory to execute the sqlQuery
command.
3. concluded the high memory use is isolated to the odbcQuery step w/in
the sqlQuery function as opposed to sqlGetResults or ODBC itself.
Relevant R session transcript:
>library(RODBC)
>channel<-odbcConnect("psmrd")
>df_OnePer <-data.frame(sqlQuery(channel, "select * from
AUTCombinedWA_BILossCost_1per"))>save(df_OnePer, file = "df_OnePer.rda")
Additional testing details:
I exited R which released all memory cleanly, then started R again,
loaded the .rda saved in prior step as below. This confirmed relatively
little of the memory is consumed going from .rda to data frame;
isolating to the RODBC step:
> load("df_OnePer.rda")
> df <- data.frame(df_OnePer)
I closed R & opened MS Access & used the same DSN "psmrd",
& to import
the AUTCombinedWA_BILossCost_1per into MS Access which required about
30000kb of memory & 20000kb of virtual.
And finally, I have this excerpt from Prof Brian Ripley that seems
potentially relevant (if it's not just confusion because I called them
'byte-size' when really I should have said they're integers just
having
values limited to 1-255). In any case I'm unable to see from the RODBC
help how to specify this: "...sqlQuery returns a data frame directly.
I think you need to tell RODBC to translate your 'byte-sized factors' to
numeric, as it will be going through character if these are a type it
does not know about."
Read all the RODBC help, read all the data import guide & searched help
archives...can't find an answer. Would appreciate advice, experience,
or direction.
Jill Willie
Open Seas
Safeco Insurance
jilwil at safeco.com
-----Original Message-----
From: Prof Brian Ripley [mailto:ripley at stats.ox.ac.uk]
Sent: Thursday, January 25, 2007 12:05 AM
To: WILLIE, JILL
Cc: r-help at stat.math.ethz.ch
Subject: Re: [R] Size of data vs. needed memory...rule of thumb?
On Wed, 24 Jan 2007, WILLIE, JILL wrote:
> I have been searching all day & most of last night, but can't find
any
> benchmarking or recommendations regarding R system requirements for
very> large (2-5GB) data sets to help guide our hardware configuration. If
> anybody has experience with this they're willing to share or could
> anybody point me in a direction that might be productive to research,
it> would be much appreciated. Specifically: will R simply use as much
> memory as the OS makes available to it, unlimited?
Under most OSes. Because Windows has no means to limit the amount made
available, R under Windows does have it own limiting mechanism (which
you
hit in the examples below). R under Linux will allow you to run a 4GB
process on a machine with 2GB RAM, but you are likely to get around 0.7%
usage. (One of my colleagues did that on a server earlier this week,
hence the very specific answer.)
> Is there a multi-threading version R, packages?
Not to run computations in R. Some parts of R (e.g. GUIs) and some
libraries (e.g. some BLAS) are multithreaded. There are multiprocess
packages, e.g. Rmpi, rpvm, snow.
> Does the core R package support 64-bit
Yes, and has for many years.
> & should I expect to see any difference in how memory's handled
under
> that version?
yes, because the address space will not get seriously fragmented. See
the
appropriate section in R-admin.html (referenced from INSTALL).
> Is 3 GB of memory to 1GB of data a reasonable ballpark?
I'd say it was a bit low, but it really depends on the analysis you are
doing, how 1GB of data is made up (many rows?, many cols?, etc) and so
on.
Had you asked me to suggest a ratio I would have said 5.
> Our testing thus far has been on a windows 32-bit box w/1GB of RAM & 1
> CPU; it appears to indicate something like 3GB of RAM for every 1GB of
> sql table (ex-indexes, byte-sized factors). At this point, we're
> planning on setting up a dual core 64-bit Linux box w/16GB of RAM for
> starters, since we have summed-down sql tables of approx 2-5GB
> generally.
>
> Here's details, just for context, or in case I'm misinterpreting
the
> results, or in case there's some more memory-efficient way to get data
> in R's binary format than going w/the data.frame.
Well, sqlQuery returns a data frame directly. I think you need to tell
RODBC to translate your 'byte-sized factors' to numeric, as it will be
going through character if these are a type it does not know about.
> R session:
> > library(RODBC)
> > channel<-odbcConnect("psmrd")
> > FivePer <-data.frame(sqlQuery(channel, "select * from
> AUTCombinedWA_BILossCost_5per"))
>
> Error: cannot allocate vector of size 2000 Kb
> In addition: Warning messages:
> 1: Reached total allocation of 1023Mb: see
> help(memory.size)
> 2: Reached total allocation of 1023Mb: see
> help(memory.size)
>
>
> ODBC connection:
> Microsoft SQL Server ODBC Driver Version 03.86.1830
>
> Data Source Name: psmrd
> Data Source Description:
> Server: psmrdcdw01\modeling
> Database: OpenSeas_Work1
> Language: (Default)
> Translate Character Data: Yes
> Log Long Running Queries: No
> Log Driver Statistics: No
> Use Integrated Security: Yes
> Use Regional Settings: No
> Prepared Statements Option: Drop temporary procedures on
> disconnect
> Use Failover Server: No
> Use ANSI Quoted Identifiers: Yes
> Use ANSI Null, Paddings and Warnings: Yes
> Data Encryption: No
>
> Please be patient, I'm a new R user (or at least I'm trying to
be...at
> this point I'm mostly a new R-help-reader); I'd appreciated being
> pointed in the right direction if this isn't the right help list to
send> this question to...or if this question is poorly worded (I did read
the> posting guide).
>
> Jill Willie
> Open Seas
> Safeco Insurance
> jilwil at safeco.com
>
>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> 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
