Visualize sar CPU data with R

From braindump
Jump to: navigation, search

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. The examples outlined below were done in CentOS 6.5. For other distros or Unix flavors your milage may vary.


Create CPU graphs in R from the sar utility without massaging the output data too much.


  • The Linux sysstat package installed and configured to report performance data.
  • R
  • ggplot2 R library


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' \

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 ) <- read.csv( file="<SADF-OUTPUT>", sep=";" )$timestamp <- as.POSIXct($timestamp )$CPU[$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 should look like the below data.

str( ) 
'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(, 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.


Combining it with more data

To really make this graph useful a few more data points need to be added in this case iowait and system. To make sure they are not overlapping the data needs to be stacked. Firstly a new graph variable needs to be defined. And then for each y axis a new geom_line is defined. There is probably better ways but the of each is calculated and then plotted. The facet_grid got a bit of an upgrade and has better labeling. The last three lines are sugar coating to make the graph fit for management :).

cpu.stacked <- ggplot(, aes( x=timestamp, group=CPU ) ) 
cpu.stacked + 
  geom_line( aes( y=iowait+user+system, colour="system" ) ) + 
  geom_line( aes( y=iowait+user, colour="user" ) ) + 
  geom_line( aes( y=iowait, colour="iowait" ) ) + 
  facet_grid( CPU~., labeller=label_both ) + 
  xlab( "Datetime" ) +
  ylab( "CPU usage" ) + 
  theme( legend.title=element_blank() ) 

The result is:


Showing more detail by reducing the window

When using SAR data with a high frequency probe over a long time span it is good to show an overall trend. When locating a hotspot it makes sense to zoom in by reducing the window. This is done by subsetting the data. <- subset(, timestamp %in% as.POSIXct( "2014-01-29 02:00:00" ):as.POSIXct( "2014-01-29 04:00:00" ) )

Then run the graph code above again with the newly created data frame.

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~. )