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Pbench

A Benchmarking and Performance Analysis Framework

The code base includes three sub-systems. The first is the collection agent, Pbench Agent, responsible for collecting configuration data for test systems, managing the collection of performance tool data from those systems (sar, vmstat, perf, etc.), and executing and postprocessing standardized or arbitrary benchmarked workloads (uperf, fio, linpack, as well as real system activity).

The second sub-system is the Pbench Server, which is responsible for archiving result tar balls and providing a secure RESTful API to client applications, such as the Pbench Dashboard. The API supports curation of results data, the ability to annotate results with arbitrary metadata, and to explore the results and collected data.

The third sub-system is the Pbench Dashboard, which provides a web-based GUI for the Pbench Server allowing users to list and view public results. After logging in, users can view their own results, publish results for others to view, and delete results which are no longer of use. On the User Profile page, a logged-in user can generate API keys for use with the Pbench Server API or with the Agent pbench-results-move command. The Pbench Dashboard also serves as a platform for exploring and visualizing result data.

How is it installed?

Instructions for installing pbench-agent, can be found in the Pbench Agent Getting Started Guide.

For Fedora, CentOS, and RHEL users, we have made available COPR RPM builds for the pbench-agent and some benchmark and tool packages.

You might want to consider browsing through the rest of the documentation.

You can also use podman or docker to pull Pbench Agent containers from Quay.io.

How do I use pbench?

Refer to the Pbench Agent Getting Started Guide.

TL;DR? See "TL;DR - How to set up the pbench-agent and run a benchmark " in the main documentation for a super quick set of introductory steps.

Where is the source kept?

The latest source code is at https://github.com/distributed-system-analysis/pbench.

Is there a mailing list for discussions?

Yes, we use Google Groups

How do I report an issue?

Please use GitHub's issues.

Is there a place to track current and future work items?

Yes, we are using GitHub Projects. Please find projects covering the Agent, Server, Dashboard, and a project that is named the same as the current milestone.

How can I contribute?

Below are some simple steps for setting up a development environment for working with the Pbench code base. For more detailed instructions on the workflow and process of contributing code to Pbench, refer to the Guidelines for Contributing.

Getting the Code

$ git clone https://github.com/distributed-system-analysis/pbench
$ cd pbench

Running the Unit Tests

Install tox properly in your environment (Fedora/CentOS/RHEL):

$ sudo dnf install -y perl-JSON python3-pip python3-tox

Once tox is installed you can run the unit tests against different versions of python using the python environment short-hands:

  • tox -e py36 -- run all tests in a Python 3.6 environment (our default)
  • tox -e py39 -- run all tests in a Python 3.9 environment
  • tox -e py310 -- run all tests in a Python 3.10 environment
  • tox -e pypy3 -- run all tests in a PyPy 3 environment
  • tox -e pypy3.8 -- run all tests in a PyPy 3.8 environment

See https://tox.wiki/en/latest/example/basic.html#a-simple-tox-ini-default-environments.

You can provide arguments to the tox invocation to request sub-sets of the available tests be run.

For example, if you want to just run the agent or server tests, you'd invoke tox as follows:

  • tox -- agent -- runs only the agent tests
  • tox -- server -- runs only the server tests

Each of the "agent" and "server" tests can be further subsetted as follows:

  • agent

    • python -- runs the python tests (via pytest)
    • legacy -- runs all the legacy tests
    • datalog -- runs only the legacy tool data-log tests, agent/tool-scripts/datalog/unittests
    • postprocess -- runs only the legacy tool/bench-scripts post-processing tests, agent/tool-scripts/postprocess/unittests
    • tool-scripts -- runs only the legacy tool-scripts tests, agent/tool-scripts/unittests
    • util-scripts -- runs only the legacy util-scripts tests, agent/util-scripts/unittests
    • bench-scripts -- runs only the legacy bench-scripts tests, agent/bench-scripts/unittests

  • server

    • python -- runs the python tests (via python)

For example:

  • tox -- agent legacy -- run agent legacy tests
  • tox -- server python -- run server python tests (via pytest)

For any of the test sub-sets on either the agent or server sides of the tree, one can pass additional arguments to the specific sub-system test runner. This allows one to request a specific test, or set of tests, or command line parameters to modify the test behavior:

  • tox -- agent bench-scripts test-CL -- run bench-scripts' test-CL
  • tox -- server python -v -- run server python tests verbosely

For the agent/bench-scripts tests, one can run entire sub-sets of tests using a sub-directory name found in agent/bench-scripts/tests. For example:

  • tox -- agent bench-scripts pbench-fio
  • tox -- agent bench-scripts pbench-uperf pbench-linpack

The first runs all the pbench-fio tests, while the second runs all the pbench-uperf and pbench-linpack tests.

You can run the build.sh script to execute the linters, to run the unit tests for the Agent, Server, and Dashboard code, and to build installations for the Agent, Server, and Dashboard.

Finally, see the jenkins/Pipeline.gy file for how the unit tests are run in our CI jobs.

Python formatting

This project uses the flake8 method of code style enforcement, linting, and checking.

All python code contributed to pbench must match the style requirements. These requirements are enforced by the pre-commit hook. In addition to flake8, pbench uses the black Python code formatter and the isort Python import sorter.

Use pre-commit to set automatic commit requirements

This project makes use of pre-commit to do automatic lint and style checking on every commit containing Python files.

To install the pre-commit hook, run the executable from your Python 3 framework while in your current pbench git checkout:

$ cd ~/pbench
$ pip3 install pre-commit
$ pre-commit install --install-hooks

Once installed, all commits will run the test hooks. If your changes fail any of the tests, the commit will be rejected.

Pbench Release Tag Scheme (GitHub)

We employ a simple major, minor, release, build (optional) scheme for tagging starting with the v0.70.0 release (v<Major>.<Minor>.<Release>[-<Build>]). Prior to the v0.70.0 release, the scheme used was mostly v<Major>.<Minor>, where we only had minor releases (Major = 0).

Container Image Tags

This same GitHub "tag" scheme is used with tags applied to container images we build, with the following exceptions for tag names:

  • latest - always points to the latest released container image pushed to a repository

  • v<Major>-latest - always points to the "latest" Major released image

  • v<Major>.<Minor>-latest - always points to the "latest" release for Major.Minor released images

  • <SHA1 git hash> (9 characters) - commit hash of the checked out code

References to Container Image Repositories

The operation of our functional tests, the Pbench Server "in-a-can" used in the functional tests, and other verification and testing environments use container images from remote image registries. The CI jobs obtain references to those repositories using Jenkins credentials. When running those same jobs locally, you can provide the registry via ${HOME}/.config/pbench/ci_registry.name.

If this file is not provided, local execution will report an error.