We run an R shiny server (R: 3.5.1, Shiny: 1.0.3) on a virtual machine under CentOS. We are experiemcing some issues with script updates to the server.
We find that under certain conditions if we update a script to the server it may take some time (up to days occassionally) for the updated script to be actually executed.
From experience, this delay only occurs when the srcipt has multiple users. Scripts that have a sningle user (me) update instantly as soon as the updated scripts have been written to the server.
This makes sense: as long as users are running the scripts in sessions, you cannot update it. We know that resetting the Shniy server does the trick but this strikes me as a crude way of resolving this issue.
As I am not a linux expert I do not know if this is caused by Shiny or by Linux.
Does anyone know where this behaviour is documented so I can learn how to better control the process of updating?
Related
Recently I swapped my personal PC with admin rights to my employee's PC, where we use AD to log in and where I do not hold admin rights.
Since that event, I have trouble running my R code. I currently develop Shiny app. Each time I click "Run app", two things appear to behave differently versus my old setup.
First of all, I can run app only once - after that, the Rstudio is busy forever, suppressing me from running app again, accessing data frames created by my app and so on. So after each test of some changes, I literally have to reset entire RStudio.
Secondly, despite the fact that I have set my working directory to "C:/Users/mylogin/Documents", after restarting R, there are no global variables visible, event after saving workspace image. I used to use global variables to debug my app after closing it. Of course I can rewrite entire code to, for example, dump all the tables to different files.
My question is: is it possible, that this behavior is related to not having admin rights on my current PC? Or is it related to another issue and if so - may someone provide me some help in that matter? I have little to no knowledge about debugging Rstudio.
Setup: Win 10 64bit, R version 4.0.2 (2020-06-22) Rstudio desktop, 1.3.1093, Apricot Nasturtium.
For future generations: the problem perished, without any particular reason, quite frustrating that we will never know the answer.
A few days ago I noticed R was using 34% of the CPU when I have no code running. I noticed it again today and I can't figure out why. If I restart R, CPU usage returns to normal, then after 20 minutes or so it ramps up again.
I have a task scheduled that downloads a small file once a week using R, and another using wget in ubuntu (WSL). It might be the case that the constant CPU usage only happens after I download covid-related data from a github (link below). Is there a way to see if this is hijacking resources? If it is, other people should know about it.
I don't think it's a windows task reporting error since my temps are what I would expect for a constant 34% cpu usage (~56C).
Is this a security issue? Is there a way to see what R is doing? I'm sure there is a way to better inspect this but I don't know where to begin.. Glasswire hasn't reported any unusual activity.
From Win10 event viewer, I've noticed a lot of these recently but don't quite know how to read it:
The application-specific permission settings do not grant Local Activation permission for the COM Server application with CLSID {8BC3F05E-D86B-11D0-A075-00C04FB68820} and APPID {8BC3F05E-D86B-11D0-A075-00C04FB68820} to the user redacted SID (S-1-5-21-1564340199-2159526144-420669435-1001) from address LocalHost (Using LRPC) running in the application container Unavailable SID (S-1-15-2-181400768-2433568983-420332673-1010565321-2203959890-2191200666-700592917). This security permission can be modified using the Component Services administrative tool.
*edit: CPU usage seems to be positively correlated with the duration R is open.
Given the information you provided, it looks like RStudio (not R) is using a lot of resources. R and RStudio are 2 very different things. These types of issues are very difficult to investigate as one need to be able to reproduce them on another computer. One thing you can maybe do is raise the issue on github to the RStudio team.
I am used to using R in RStudio. For a new project, I have to use R on the command line, because the data storage and analysis are only allowed to be on a specific server that I connect to using ssh. This server doesn't have rstudio-server to support remote RStudio sessions.
The project involves an extremely large dataset, and some pre-written code to load/format the data that I have been told to run using "source()" before I do anything else. This takes several minutes to run and load the data each time.
What would a good workflow be for something like this? Editing my code in a .r file, saving, then running it would require taking several minutes to load the data each time. But just running R in an interactive session would make it hard to keep track of what I am doing and repeat things if necessary.
Is there some command-line equivalent to RStudio where you can have an interactive session but be editing/saving a file of your code as you go?
Sounds like JuPyteR might be your friend here.
The R kernel works great.
You can use it on a remote server either with exposing an open port (and setting up JuPyteR login credentials)
Or via port forwarding over SSH.
It is a lot like an interactive reply, except it holds state.
And you can go back and rerun cells.
(Of course state can be dangerous for reproduceability)
For RStudio you can launch console and ssh to your remote servers even if your servers don't use expensive RStudio for servers platform. You can then execute all commands from R Studio directly into the ssh with the default shortcut key. This might allow to continue using R studio, track what you're doing in the R script, execute interactively.
I am currently running R on a Microsoft Azure instance (Ubuntu virtual machine) using RStudio as my IDE, to which I connect simply through my browser. I am trying to run some commands that take quite some time to complete from within RStudio and figured that I could simply close my tab with RStudio open and the process would keep running. However, when I try to reconnect to see how the process is doing, the page keeps loading but I am unable to see RStudio.
I have a few questions regarding running RStudio on a server:
First, am I correct in thinking that I can close my tab and keep the process running?
Second, is it normal behaviour that I am unable to connect to the server while the process is running?
Third, am I going about this the correct way or are there better ways?
Yes, you can close your tab and keep it running.
RStudio Server waits on updates from the R process to update the UI. This means that if you have a long-running computation, your tab may not fully reload until it's finished. You may also have seen this in the middle of a session: when R is busy, you can have problems saving scripts that are open in the editor pane.
Logging out in the middle of a computation should be safe, but be aware that RStudio will save your workspace and shut R down after a period of inactivity. It then reloads everything when you log back in. But this only extends to objects in memory; if you have any files saved in your temp directory, they'll have disappeared when you come back. They're probably still on the disk, but since your new R session has a new temp directory, you'll have to do a manual search for them.
I have a program that is built in R and runs on the desktop version of Shiny. I am working on moving the project to a Shiny server. Would it be possible for multiple users to use the program at the same time on the Open Source version of Shiny Server or will there be an issue with concurrent users?
Thanks
According to their support page it can have an unlimited number of users:
Yes. Shiny Server is constrained only by its features.
I am not a shiny developer but yes. The limitation is that one R process will be used. So if user1 runs some process that takes some time user2's processes will wait for user1 to finish.