Hello Splunkers!
I'm trying to get our admin to install the R Project app. Native R uses memory to perform statistical computations on the data source(s). He is trying to get information on potential impact (CPU, memory, jobs, etc.) on the shared Splunk environment. Any information (even anecdote) is welcome. Thank you.
Rufus
At the moment, the R Project app (more specifically the "r" search command) doesn't make use of the map/reduce functionality.
But you can use Splunk search commands like search, stats, tstats, etc that does the map/reduct tricks to collect data and then send the data to your R script.
So the script is only executed on the search head in the context of a R sub-process which is spawned every time the search is executed.
That means it's still native R environment which uses memory to perform computations. The app uses temporary files to send the input data from Splunk to R and the results back from R to Splunk. I'm thinking about changing that so that the data is streamed instead of copied in a whole.
At the moment, the R Project app (more specifically the "r" search command) doesn't make use of the map/reduce functionality.
But you can use Splunk search commands like search, stats, tstats, etc that does the map/reduct tricks to collect data and then send the data to your R script.
So the script is only executed on the search head in the context of a R sub-process which is spawned every time the search is executed.
That means it's still native R environment which uses memory to perform computations. The app uses temporary files to send the input data from Splunk to R and the results back from R to Splunk. I'm thinking about changing that so that the data is streamed instead of copied in a whole.
Thank you for the rapid response. I can't wait to use the app that you created @rfujara_splunk!