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.
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.
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.
The latest source code is at https://github.com/distributed-system-analysis/pbench.
Yes, we use Google Groups
Please use GitHub's issues.
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.
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.
$ git clone https://github.com/distributed-system-analysis/pbench
$ cd pbench
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 environmenttox -e py310-- run all tests in a Python 3.10 environmenttox -e pypy3-- run all tests in a PyPy 3 environmenttox -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 teststox -- 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
- python -- runs the python tests (via
-
server
- python -- runs the python tests (via python)
For example:
tox -- agent legacy-- run agent legacy teststox -- server python-- run server python tests (viapytest)
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-CLtox -- 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-fiotox -- 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.
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.
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.
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).
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"Majorreleased image -
v<Major>.<Minor>-latest- always points to the "latest" release forMajor.Minorreleased images -
<SHA1 git hash>(9 characters) - commit hash of the checked out code
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.
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