Skip to content
Lightweight Performance Control System
Python C++ HTML C Perl Makefile
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
docs
examples
figs
perun
tests
.gitignore
.travis.yml
AUTHORS.rst
CHANGELOG.rst
CONTRIBUTING
LICENSE
Makefile
README.md
_config.yml
requirements.txt
setup.py

README.md

Perun: Lightweight Performance Version System

image codecov Codacy Badge Maintainability GitHub tag

Perun is an open source light-weight Performance Version System, which works as a wrapper over existing Version Control Systems and in parallel manages performance profiles corresponding to different versions of projects. Moreover, it offers a tool suite suitable for automation of the performance regression test runs, postprocessing of existing profiles or effective interpretation of the results.

In particular, Perun has the following advantages over using databases or sole Version Control Systems for the same purpose:

  1. Preserves Context---each performance profile is assigned to a concrete minor version adding the functional context (i.e. code changes) of profiles.

  2. Provides Automation---Perun allows one to easily automate the process of profile collection, eventually reducing the whole process to a single command. The specification of jobs is read from YAML files.

  3. Is Highly Generic---supported format of the performance profiles is based on JSON. Perun tool suite contains a basic set of visualizations, postprocessing and collection modules, but it is easily extensible.

  4. Is Easy to use---the workflow, interface and storage of Perun is heavily inspired by the git systems aiming at natural use.

Perun is intented to be used in two ways: (1) for a single developer (or a small team) as a complete solution for automating, storing and interpreting performance of project or (2) as a dedicated store for a bigger projects and teams. Its git-like design aims at easy distribution and simple interface makes it a good store of profiles along with the context.

Installation

You can install Perun as follows:

git clone https://github.com/tfiedor/perun.git
cd perun
make init
make install

These commands installs Perun to your system as a runnable python package. You can then run Perun safely from the command line using the perun command. Run either perun --help or see the cli documentation for more information about running Perun commands from command line. Note that depending on your OS and the location of Python libraries, you might require root permissions to install Perun.

It is advised to verify that Perun is running correctly in your environment as follows:

make test

Developing

Alternatively you can install Perun in development mode:

git clone https://github.com/tfiedor/perun.git
cd perun
make init
make dev

This method of installation allows you to make a changes to the code, which will be then reflected by the installation.

Again, it is advised to verify that Perun is running correctly in your environment as follows:

make test

If you are interested in contributing to Perun project, please refer to contributing section. If you think your results could help others, please send us PR, we will review the code and in case it is suitable for wider audience, we will include it in our upstream.

But, please be understanding, we cannot fix and merge everything.

Getting Started

In order to start managing performance of your project tracked by git, go to its directory and run the following:

perun init --vcs-type=git --configure

This creates a parallel directory structure for Perun storage (stored in .perun), and runs the initial configuration of the local project settings in text editor (by default vim). There you can chose the set of collectors, postprocessors and specify which commands (and with which configurations) should be profiled. See configuration for more details about perun's configuration.

Now start collecting the profiles for current version of your project:

perun run matrix

This command collects set of profiles, according to the previously set configuration (see specification of job matrix for more details). You can then view the list of collected and registered profiles, and visualize the profiles (see visualization overview), or check for possible performance changes (see degradation documentation):

# Show list of profiles
perun status

# Show the first generated profile using scatter plot
perun show 0@p scatter -v

# Register the first generated profile to current minor version
perun add 0@p

Now anytime one can do code changes, commit them, rerun the collection phase, register new profiles and check whether any change in performance can be detected:

# Rerun the collection
perun run matrix

# Register the profiles for current minor version
perun add 0@p

# Run the degradation check
perun check head

Features

In the following, we list the foremost features and advantages of Perun:

  • Unified format---we base our format of performance profiles on JSON.

  • Natural specification of Profiling Runs---we base the specification of profiling jobs in Yaml format.

  • Git-inspired Interface---the cli is inspired by git version control systems and specifies commands like e.g. add, remove, status, or log.

  • Efficient storage---performance profiles are stored compressed in the storage in parallel to versions of the profiled project inspired by git.

  • Multiplatform-support---Perun is implemented in Python 3 and its implementation is supported both by Windows and Unix-like platforms.

  • Regression Analysis---Perun's suite contains a postprocessing module for regression analysis of profiles (see regression analysis documentation), which supports several different strategies for finding the best predicting model for given data (such as linear, quadratic, or constant model).

  • Interactive Visualizations---Perun's tool suite includes several visualization modules, some of them based on Bokeh visualization library, which provides nice and interactive plots, in exchange of scalability.

  • Useful API for profile manipulation---helper modules are provided for working with our profiles in external applications ---we have API for executing simple queries over the resources or other parts of the profiles, or convert and transform the profiles to different representations (e.g. pandas data frame). See conversion api and query api for overview.

  • Automatic Detection of Performance Degradation---we are currently exploring effective heuristics for automatic detection of performance degradation between two project versions (e.g. between two commits).

As a sneak peek, we are currently working and exploring the following featurues in near future of the project:

  • Regular Expression Driven Collector---collector based on parsing the standard text output for a custom specified metrics, specified by regular expressions.

  • Fuzzing Collector---collector based on method of fuzz testing ---i.e. modifying inputs in order to force error or, in our case, a performance change.

  • Clustering Postprocessor---we are exploring now how to make any profile usable for regression analysis.

  • Automatic Hooks---the automatic hooks, that will allow to automate the runs of job matrix, automatic detection of degradation and efficient storage.

For more information about Perun's feature, please refer to our extensive list of features!

Contributing

If you'd like to contribute, please fork the repository and use a feature branch. Pull requests are warmly welcome.

In case you run in some unexpected behaviour, error or anything suspicious, either contact us directly through mail or create a new Issue.

The architecture of Perun allows easy extension. In case you are interested in extending our tool suite with new kinds of collectors, postprocessors or visualization methods, please refer to appropriate sections in Perun's documentation (i.e. Create your own collector, postprocessor or visualization).

If you are interested in contributing to Perun project, please first refer to contributing section. If you think your custom module could help others, please send us PR, we will review the code and in case it is suitable for wider audience, we will include it in our upstream.

But, please be understanding, we cannot fix and merge everything.

Links

Unrelated links:

Licensing

The code in this project is licensed under GNU GPLv3 license.

Acknowledgements

We thank for the support received from Red Hat (especially branch of Brno), Brno University of Technology (BUT FIT) and H2020 ECSEL project Aquas.

Further we would like to thank the following individuals (in the alphabetic order) for their (sometimes even just a little) contributions:

  • Jan Fiedor (Honeywell)---for feedback, and technical discussions;
  • Jirka Hladky and his team (RedHat)---for technical discussions;
  • Martin Hruska (BUT FIT)---for feedback, and technical discussions;
  • Petr Müller (SAP)---for nice discussion about the project;
  • Michal Kotoun (BUT FIT)---for feedback, and having faith in this repo;
  • Hanka Pluhackova (BUT FIT)---for awesome logo, theoretical discussions about statistics, feedback, and lots of ideas;
  • Adam Rogalewicz (BUT FIT)---for support, theoretical discussions, feedback;
  • Tomas Vojnar (BUT FIT)---for support, theoretical discussions, feedback;
  • Jan Zeleny (Red Hat)---for awesome support, and feedback.
You can’t perform that action at this time.