- 1 Basic
- 2 Packages
- 3 Properties
- 3.1 Running Rserve under Windows is very limited
- 3.2 Rserve needs to take double space compared to running under R gui
- 3.3 Rserve has a limit of maximum size of a single REXP
- 3.4 Difference in Windows and Linux
- 3.5 Some advice when running Rserve under Windows
- 3.6 Do you need to have Rserve subfolder created under library folder in order to use Rserve?
- 3.7 Can we use parallel/snow package with Rserve
- 4 Applications
- 5 Quirks
Rserve uses socket connections between R and other applications.
Running Rserve under Windows is very limited
Windows lacks important features that make the separation of namespaces possible, therefore Rserve for Windows works in cooperative mode only, that is only one connection at a time is allowed and all subsequent connections share the same namespace.
This has an immediate implication that we cannot make use of Rserve.cluster package for high performance computing.
Rserve needs to take double space compared to running under R gui
For example, x <- rnorm(450000*200) The R gui takes 0.7GB but Rserve uses 1.4GB. See a comprehensive comparison of running Rserve on Windows and Linux.
Rserve has a limit of maximum size of a single REXP
Maximum size of a single REXP: 2GB (on 32-bit platforms), theoretical limit is 2^55 on 64-bit platforms. Packet size is auto-adjusted, configured by maxinbuf and maxsendbuf config entries. (maximum 2GB) The maxinbuf (max. packet from client to Rserve) and maxsendbuf (max. packet from Rserve to client) options in the configuration file allow you to set limits in order to prevent memory overflow on machines that act as servers for multiple connections. The defaults are 16MB and unlimited respectively.
If we run a statement like
RSeval(cc, "x <- rnorm(450000*400)")
we will see an error "Error in RSeval(cc, "x <- rnorm(450000*400)") : remote evaluation failed" in the R console. The Rserve window shows the problem
1: In rnorm(450000 * 400) : Reached total allocation of 2047Mb: see help(memory.size)
But if we try to use memory.limit(4000) to allocate 4GB space on 64-bit Rserve, we still get the same error on R console, but no error message on Rserve.
Test the statement RSeval(cc, 'x <- rnorm(450000*narray)'). That creating a matrix of 450k genes and various number of arrays.
|narray||space in R||Windows||Linux|
The Windows box has 12GB physical installed ram and Linux has 16GB.
Some advice when running Rserve under Windows
Do not use memory hungry script through Rserve. Use R in batch mode to run it.
Do you need to have Rserve subfolder created under library folder in order to use Rserve?
No. As long as Rserve.exe has been copied to i386\bin or x64\bin folder, Rserve will work. So don't be surprised if you run library(Rserve) and get an error saying there is no package called ‘Rserve’ .
Can we use parallel/snow package with Rserve
Yes and no. We cannot directly use parallel package with Rserve. But with Rserve.cluster package, we can run parallel computing with Rserve. See the the package official website .
Rserve will be opened in a new command prompt window. So a new application appears on the task bar.
Bio7 - An IDE for Ecological Modeling
Rserve is started within Bio7 Console so no new application is shown on the task bar.
Programming languages are Java, Groovy and R. User interface is Eclipse, Java SWT and Java Swing.
Given an R function and its manual page, make the documented function available in Galaxy.
Reval error in unserialize unknown input format
In one instance, we got the above error on R 3.5.0 + Windows 7.
- Start up RServe. Copy RServe.exe to bin\x64 directory. Open a command prompt
cd C:\Program Files\R\r-2.14.1\bin\x64 R CMD Rserve
- Connect through an R client
library(RSclient) cc <- RSconnect() RSeval(cc, quote(x2 <- date())) RSeval(cc, quote(cat(ls(), file = "C:/Users/brb/Documents/out.txt"))) RSclose(cc)
- Shutdown Rserve
library(Rserve) cc <- RSconnect() RSshutdown(cc)