Visualize sar CPU data with R
This is a five minute guide how to visualize Linux's sar data provided by the sysstat utility without a lot of mangeling the data.
Goal
Create CPU graphs in R from the sar utility without massaging the output data too much.
Prerequisites
- The Linux sysstat package installed and configured to report performance data.
- R
- ggplot2 R library
Howto
Dumping the sar data with sadf
The data sar collects is in binary format and needs to be converted first to a format that can be imported into R. This is done with the sadf command which converts the collected data into tabular data delimited by semicolon.
Note: On CentOS 6 and higher the sadf command also prints a header file to make most use of it we need to slightly changes it like remove the leading #, plus remove the % from the cpu data but only in the first. Other lines starting with # or containing a LINUX-RESTART should also be removed. Your milage may vary!
sadf -t -d -P ALL <SAR-FILE> | \ sed -e '1,1s/\(^#\|%\)//g' \ -e '/\(^#\|LINUX-RESTART\)/d' \ > <SADF-OUTPUT>
Importing the data into R
The next step is to read the tabular data into R and print the graphs there are just a handful of commands to do this. In R type the following commands.
library( ggplot2 )
cpu.data <- read.csv( file="<SADF-OUTPUT>", sep=";" )
cpu.data$timestamp <- as.POSIXct( cpu.data$timestamp )
cpu.data$CPU[ cpu.data$CPU == "-1" ] <- "all"
With very little effort data is read into data.table format. Then we have to change the format of two fields. Namely timestamp needs conversion from a string to a proper time format like POSIXct. Plus the value for all CPUs is "-1" and to make it clear to the viewer we want it to be "all".
After the changes the structure of the cpu.data should look like the below data.
str( cpu.data ) 'data.frame': 429 obs. of 10 variables: $ hostname : Factor w/ 1 level "hostname.local": 1 1 1 1 1 1 1 1 1 1 ... $ interval : int 588 588 588 590 590 590 589 589 589 588 ... $ timestamp: POSIXct, format: "2014-05-04 00:10:01" "2014-05-04 00:10:01" ... $ CPU : chr "all" "0" "1" "all" ... $ user : num 8.05 9.39 6.73 5.28 5.02 ... $ nice : num 0 0 0 0 0 0 0 0 0 0 ... $ system : num 3.83 4.32 3.34 2.75 3.21 2.3 4.59 4.99 4.19 5.1 ... $ iowait : num 0.71 1.1 0.33 0.7 1.1 0.3 0.7 1.12 0.29 0.81 ... $ steal : num 0 0 0 0 0 0 0 0 0 0 ... $ idle : num 87.4 85.2 89.6 91.3 90.7 ...
Setting up the graph
The next step is to store the ggplot data into a variable for further processing. It's important to group the data by the CPU field and assign a different color to each CPU with the colour= assignment.
cpu.graph <- ggplot( data=cpu.data, aes( x=timestamp, y=user, group=CPU, colour=CPU ) )
Display the result as a line graph
To display the graph a simply calling the stored graph data and assigning a layer with geom_line().
cpu.graph + geom_line()
Will result in a graph like this:
Plotting each CPU separately
To show which CPU or core is used most it is probably better to separately print the CPUs. The ggplot2 library comes with a nifty command called facet_grid(). To print each separately simply add it to the end of the previous command.
cpu.graph + geom_line() + facet_grid( CPU~. )
Which will result in a graph like this.
Saving the graph to a file
Especially when automation comes into play saving to a file is a must this example shows how to save to PNG.
setwd( "/home/r-user/cpu-data" ) png( "cpu-graph-grid.png", width=600, height=360, res=72 ) cpu.graph + geom_line() + facet_grid( CPU~. ) dev.off()