The files within this directory can be used to generate a 'metrics report' for Kubernetes.
The primary workflow consists of two stages:
- Run the provided report metrics data gathering scripts on the system(s) you wish to analyze.
- Run the provided report generation script to analyze the data and generate a report file.
Data gathering is provided by the grabdata.sh script. When run, this script
executes a set of tests from the cloud-native-setup/metrics directory. The JSON results files
will be placed into the cloud-native-setup/metrics/results directory.
Once the results are generated, create a suitably named subdirectory of
tests/metrics/results, and move the JSON files into it.
Repeat this process if you want to compare multiple sets of results. Note, the
report generation scripts process all subfolders of tests/metrics/results when
generating the report.
You can restrict the subset of tests run by grabdata.sh via its commandline parameters:
| Option | Description |
|---|---|
| -a | Run all tests (default) |
| -s | Run the scaling tests |
| -h | Print this help |
Report generation is provided by the makereport.sh script. By default this script
processes all subdirectories of the cloud-native-setup/metrics/results directory to generate the report.
To run in the default mode, execute the following:
$ ./makereport.shThe report generation tool uses Rmarkdown,
R and pandoc to produce
a PDF report. To avoid the need for all users to set up a working environment
with all the necessary tooling, the makereport.sh script utilises a Dockerfile with
the environment pre-defined in order to produce the report. Thus, you need to
have Docker installed on your system in order to run the report generation.
The resulting metrics_report.pdf is generated into the output subdir of the report
directory.
To aid in script development and debugging, the makereport.sh script offers a debug
facility via the -d command line option. Using this option will place you into a bash
shell within the running Dockerfile image used to generate the report, whilst also
mapping your host side R scripts from the report_dockerfile subdirectory into the
container, thus facilitating a 'live' edit/reload/run development cycle.
From there you can examine the Docker image environment, and execute the generation scripts.
E.g., to test the tidy_scaling.R script, you can execute:
$ ./makereport.sh -d
...
Successfully built eea7d6ac6fa7
Successfully tagged metrics-report:latest
root@<hostname>:/# R
> source('/inputdir/Env.R')
> source('/scripts/tidy_scaling.R')
## Edit script on host, and re-load/run...
> source('/scripts/tidy_scaling.R')