Hi,

Our company is doing processing and analysis of (mostly) quantitative
data. Some of our researchers are R users, some are Python users. Our
data is accessed via thrift-based services. Currently, we have
developed parallel R/Python clients that access the data. This has
been done using thrift generated c++ objects in R (accessed via a
dyn.loaded shared library) and thrift generated python objects in
python. We would like to merge those duplicate threads of development,
and have a single client speaking (via thrift) to the data service.
There is a fair amount of work marshaling the thrift objects into both
python and R and we would like to halve that effort by only doing it
once and (somehow) sharing the result across the two languages.

So, I am seeking advice on how to achieve this, possibly using rpy.

It is easier working with thrift from python and we want a solid
python thrift client for other purposes. So ideally, we would like
someway to get the python objects into an R process. Our objects are
relatively simple, matrices with accompanying scalar metadata. I have
not done a thorough reading of the rpy documentation, but on the face
of it the it appears the R engine is wrapped by the python one. My
naive approach to bring the data into a running R process would be to
first bring the objects into a python process, inject those into
R-space using rpy, save that, and then attach to it using R. Transfer
performance is not critical, and the data path is one way (server ->
client).

If anyone has comments or suggestions, that would be greatly appreciated.

Matt

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