[R-SIG-Finance] xts timeseries as shared-memory objects with bigmemory package ?

[me at censix.com]

Jeff, that's amazing. Thanks for sharing!!


> Hi Soren,
>
> There are a couple alternatives that are closer to the 'metal' if you
> will.  The first, and (maybe) recommended way would be to use the mmap
> package and a struct.  mmap doesn't support dimensioned data (yet),
> but the struct is quite useful in that you can have different types
> embedded in something of a list format.
>
> I'll be giving a short lightning talk on this at R/Finance 2011 that
> we are hosting in Chicago next month - which I hope to see everyone at
> ;-)  In case anyone missed the emails
> http://www.RinFinance.com/register
>
> For now, the free, quick, incomplete and ugly version:
>
>> library(mmap)
>> library(xts)
> Loading required package: zoo
>> x <- xts(1:10,Sys.time()+1:10)
>> x
>                     [,1]
> 2011-03-17 12:48:16    1
> 2011-03-17 12:48:17    2
> 2011-03-17 12:48:18    3
> 2011-03-17 12:48:19    4
> 2011-03-17 12:48:20    5
> 2011-03-17 12:48:21    6
> 2011-03-17 12:48:22    7
> 2011-03-17 12:48:23    8
> 2011-03-17 12:48:24    9
> 2011-03-17 12:48:25   10
>
> # create an on disk object of the correct size
>> tmp <- tempfile()
>> length(coredata(x)) * sizeof(double) + length(.index(x)) *
>> sizeof(double)
> [1] 160
>> writeBin(raw(160),tmp)
>
> # create a mapping to it using the struct() construct in mmap
>
>> m <- mmap(tmp, struct(double(),double()))
>
> # assign the components, keeping in mind that you'd need a multiple
> columns to handle multiple xts columns
>> m[,1] <- .index(x)
>> m[,2] <- coredata(x)[,1]
>
> # the raw on disk data
>> m[]
> [[1]]
>  [1] 1300384097 1300384098 1300384099 1300384100 1300384101 1300384102
>  [7] 1300384103 1300384104 1300384105 1300384106
>
> [[2]]
>  [1]  1  2  3  4  5  6  7  8  9 10
>
> # the 'magic'
>> extractFUN(m) <- function(x) .xts(x[[2]],x[[1]])
>> m[]
>                     [,1]
> 2011-03-17 12:48:16    1
> 2011-03-17 12:48:17    2
> 2011-03-17 12:48:18    3
> 2011-03-17 12:48:19    4
> 2011-03-17 12:48:20    5
> 2011-03-17 12:48:21    6
> 2011-03-17 12:48:22    7
> 2011-03-17 12:48:23    8
> 2011-03-17 12:48:24    9
> 2011-03-17 12:48:25   10
>
> Some things lost will be tz, time class, colnames, and misc
> attributes. (same as bigmemory, ff of course)
>
> All of the above is admittedly very, very ugly .. but will work across
> processes.  You just need to know the name of the file that is being
> mmapped/shared.  (i.e. don't use tempfile() ... make your own name).
> The advantage of a struct is that we can find rows/columns without
> loading the whole into memory. The disadvantage in some cases will be
> that a struct in mmap is like a row-major db, whereas xts is like a
> col-major one.  If that doesn't make sense to you, pretend I didn't
> say it. ;-)
>
> By the conference I'll have some simple wrappers to make this as easy
> as the rest of xts - e.g. as.mmap will likely get an xts method.  I'll
> also talk more about sharing objects across sessions in my Friday
> morning workshop at the conference.
>
> Another alternate approach that works well is using a key-value db -
> something like RBerkeley from R.  This is incredibly fast and very
> simple (see the vignette in the package) - with the only caveat being
> that I can't get Oracle's BDB to compile using the R windows
> toolchain, so RBerkeley gets nothing to link against if you are one of
> the luckless souls bound to the Redmond OS.  If someone solves that
> problem for the community (read: won't likely be me), it too will be a
> solid cross-platform solution.
>
> Jeff
>
> On Thu, Mar 17, 2011 at 12:40 PM,  <me at censix.com> wrote:
>> Hi to the list
>>
>> I am looking into inter-process communication between one or more
>> instances of R (i.e. one process to receive marketdata, the other one to
>> execute a strategy and place orders)
>>
>> I have seen that, with the 'bigmemory' package, a matrix defined as
>>
>> shared <- big.matrix(....)
>>
>> can be accessed by several different R processes, quite easily. Is there
>> an easy way to used this mechanism for sharing an xts timeseries ? I
>> havent seen how to do that.
>>
>> My best guess would be to define the shared matrix as type='double' and
>> every time I need to read from or write to it, make the corresponding
>> matrix<==>xts conversion.  That would probably work since the matrix
>> itself is not huge, but it would be a bit more elegant if I could share
>> the actual xts object.
>>
>> Thankful for any ideas
>>
>> regards
>>
>> Soren
>>
>> http://censix.com
>>
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>
>
>
> --
> Jeffrey Ryan
> jeffrey.ryan at lemnica.com
>
> www.lemnica.com
>
> R/Finance 2011 April 29th and 30th in Chicago
> www.RinFinance.com
>