This project should help with executing the same tasks on pre-defined scenarios/profiles. In case the tasks provide pbench-like json results it also allows tools to analyze and compare the results with main focus on monitoring performance in time.

The main usecase of this tool is a performance regression CI.


Run-perf is available from pip so one can install it by executing:

python3 -m pip install runperf

or to install directly the latest version from git:

python3 -m pip install

For development purposes please check-out the Clone and deploy section.


  • run-perf => run perf test(s) and report results
  • compare-perf => compare 2 or more runperf results together reporting human as well as machine readable output optionally supporting model to smooth the comparisons
  • analyze-perf => calculate a model based on one or multiple results

Basic usage

Execute uperf and fio (with custom params) on machine that will be provisioned via beaker to Fedora-32. Execute the tests under Localhost (directly on the machine) and TunedLibvirt (configures host, fetches guest image, configures it and spawns guest VM) profiles and report results in ./result_$date directory:

run-perf -vvv --hosts --provisioner beaker --distro Fedora-32 --default-password password --profiles Localhost TunedLibvirt -- uperf fio:'{"type":"read", "ramptime":"1", "runtime":"10", "samples":"1", "file-size": "100", "targets": "/fio"}'

Process result* directories, compare the ranges and create a linear model that normalizes the ranges to <-3, +3> range:

analyze-perf -vvv -l model1.json -t 3 -- result*

Compare src and dst results using model1.json linear model and report the comparison in human readable form to the console, in XUNIT format in result.xml file and as a standalone html page in result.html. For some tasks the result* results are also added as reference for better visualization of the changes:

compare-perf -vvv --tolerance 5 --stddev-tolerance 10 -l model1.json --xunit result.xml --html result.html --references result* -- src dst

Travis CI Status Documentation Status LGTM alerts LGTM Python code quality Maintainability Test Coverage

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