bedatadriven: R native code is usually slow and always memory hungry. Nonetheless, running R on Google AppEngine is very tempting. Could you give us some idea how the memory usage looks like using Renjin when compared to any other R distribution? Here is example how to measure memory:
Re: performance of R language code, Renjin is a bit faster there than R2.X, and should get faster the more we get into byte code. (Though renjin is currently slower in other areas like giant matrix manipulation)
As for memory usage, I believe object.size() will double-count your input data when it is referenced by the resulting model objects. Better to check memory.profile()
At present, Renjin benefits from the JVM's state-of-the art garbage collection, so you may see some improvements even at present, but I expect the big difference will be once we roll out non-memory-backed stores for R Vectors. Then your input data could be stored in a database and only partially loaded into memory as needed.
http://heuristically.wordpress.com/2010/01/04/r-memory-usage...