[Rd] Rapid Random Access
tplate at acm.org
Fri Dec 14 20:31:01 CET 2007
Barry Rowlingson wrote:
> I have some code that can potentially produce a huge number of
> large-ish R data frames, each of a different number of rows. All the
> data frames together will be way too big to keep in R's memory, but
> we'll assume a single one is manageable. It's just when there's a
> million of them that the machine might start to burn up.
This is exactly the type of situation that the trackObjs package is
designed for. It will automatically (and invisibly) store each object
in its own .RData file so that objects can be accessed as ordinary R
objects, but are not kept in memory (actually, there are options to
control whether or not objects are cached in memory). It also caches
some characteristics of objects so that a brief summary of objects can
be provided without having to read each object. The g.data package and
the filehash package also do similar things wrt to providing automatic
access to objects in .RData files (and were part of the inspiration for
the trackObjs package.)
-- Tony Plate
> However I might, for example, want to compute some averages over the
> elements in the data frames. Or I might want to sample ten of them at
> random and do some plots. What I need is rapid random access to data
> stored in external files.
> Here's some ideas I've had:
> * Store all the data in an HDF-5 file - problem here is that the
> current HDF package for R reads the whole file in at once.
> * Store the data in some other custom binary format with an index for
> rapid access to the N-th elements. Problems: feels like reinventing HDF,
> cross-platform issues, etc.
> * Store the data in a number of .RData files in a directory. Hence to
> get the N-th element just attach(paste("foo/A-",n,'.RData')) give or
> take a parameter or two.
> * Use a database. Seems a bit heavyweight, but maybe using RSQLite
> could work in order to keep it local.
> What I'm currently doing is keeping it OO enough that I can in theory
> implement all of the above. At the moment I have an implementation that
> does keep them all in R's memory as a list of data frames, which is fine
> for small test cases but things are going to get big shortly. Any other
> ideas or hints are welcome.
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