In andrie/rrd: Import Data from a RRD (Round Robin Database) File

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rrd

The rrd package allows you to read data from an RRD Round Robin Database.

Installation

System requirements

In order to build the package from source you need librrd. Installing RRDtool from your package manager will usually also install the library.

| Platform | Installation | |-----------------|------------------------------| | Debian / Ubuntu | apt-get install librrd-dev | | RHEL / CentOS | yum install rrdtool-devel | | Fedora | yum install rrdtool-devel | | Solaris / CSW | Install rrdtool | | OSX | brew install rrdtool | | Windows | Not available |

Note: on OSX you may have to update xcode, using xcode-select --install.

Package installation

You can install the stable version of the package from CRAN:

install.packages("rrd")

And the development version from GitHub:

# install.packages("remotes")
remotes::install_github("andrie/rrd")

About RRD and RRDtool

The rrd package is a wrapper around RRDtool. Internally it uses librrd to import the binary data directly into R without exporting it to an intermediate format first.

For an introduction to RRD database, see https://oss.oetiker.ch/rrdtool/tut/rrd-beginners.en.html

Example

The package contains some example RRD files that originated in an instance of RStudio Connect. In this example, you analyze CPU data in the file cpu-0.rrd.

Load the package and assign the location of the cpu-0.rrd file to a variable:

library(rrd)
rrd_cpu_0 <- system.file("extdata/cpu-0.rrd", package = "rrd")

To describe the contents of an RRD file, use describe_rrd():

describe_rrd(rrd_cpu_0)

To read an entire RRD file, i.e. all of the RRA archives, use read_rrd(). This returns a list of tibble objects:

cpu <- read_rrd(rrd_cpu_0)

str(cpu, max.level = 1)

Since the resulting object is a list of tibbles, you can easily work with individual data frames:

names(cpu)

cpu[[1]]

tail(cpu$AVERAGE60$sys)

To read a single RRA archive from an RRD file, use read_rra(). To use this function, you must specify several arguments that define the specific data to retrieve. This includes the consolidation function (e.g. "AVERAGE") and time step (e.g. 60), the end time. You must also specifiy either the start time, or the number of steps, n_steps.

In this example, you extract the average for 1 minute periods (step = 60), for one entire day (n_steps = 24 * 60):

end_time <- as.POSIXct("2018-05-02") # timestamp with data in example
avg_60 <- read_rra(rrd_cpu_0, cf = "AVERAGE", step = 60, n_steps = 24 * 60,
                     end = end_time)

avg_60

And you can easily plot using your favourite packages:

library(ggplot2)
ggplot(avg_60, aes(x = timestamp, y = user)) + 
  geom_line() +
  stat_smooth(method = "loess", span = 0.125, se = FALSE) +
  ggtitle("CPU0 usage, data read from RRD file")