Plot of metric aggregates in R
You probably have seen that with RHQ 4.2 you can export the aggregate metrics for the last n (default 8) hours via the REST api by calling:
where <scheduleId> must be a valid numerical schedule.
The statistical tool R allows via "RCurl" to load data from remote URLs e.g. like this:
json_file <- getURL("http://localhost:7080/rest/1/metric/data/10013", httpheader=c(Accept = "application/json"),userpwd="rhqadmin:rhqadmin")
RCurl allows you to specify username and password unlike the simple
json_file <- "http://..."calls. The next step is then to transform the received data with the help of the "rjson" library into R data structures:
## convert json to list of vectors
json_data <- fromJSON(paste(json_file, collapse=""))
## convert the embedded data points into a data frame
df <- data.frame(do.call(rbind,json_data$dataPoints))
which you can then access and e.g. plot:
## plot the data
plot(df$timeStamp,df$value,xlab="time",ylab="Free memory (bytes)",xaxt='n',type='l')
You can find the full example as plot_metrics.r in the RHQ samples project on GitHub.
Some like it raw...
RHQ master (this will make it into RHQ 4.3) is now also able to export raw metrics via REST api
Like above you provide the schedule id and optionally a startTime and endTime or an endTime and a duration (in seconds). If nothing is provided, the data for the last 8h is exported. The following screenshot shows an example plot:
The metrics are plotted in black, the average in blue, the 5% and 95% quantils in orange and green and with the help of the TTR library, the 50 samples moving average is plotted in red. The sample code (with slightly different parameters) is also available online as plot_raw.r.
If you find other cool usages like e.g. Holt-Winters prediction on the data, please consider submitting them as example to the samples project.