This python module includes data model implementation of DRB (Data Request Broker). Implementations are provided outside this project and shall be included into the application according to the needs. See documentation for details.
Installing drb with execute the following in a terminal:
pip install drb
The development environment includes all the features required to execute this module unitary test and to deploy it. When loading the project with pycharm, the tool automatically reads requirements file and sets up the virtual environment.
Installation of a virtual environment is not mandatory but greatly recommended in order to avoid any confusion between system or other projects libraries. Linux package python3-venv shall be installed (sudo apt install python3-venv on ubuntu)
The command line to prepare the virtual environment:
python3 -m venv venv
source venv/bin/activate
Once successfully run, the command line prompt is updated with the virtual environment name:
(venv) $~> _
Once the virtual environment installed, the dependencies shall be downloaded:
(venv) $~> pip install -r requirements.txt --no-cache-dir
Collecting pip==21.1.2
Downloading pip-21.1.2-py3-none-any.whl (1.5 MB)
|████████████████████████████████| 1.5 MB 54 kB/s
Collecting setuptools==57.0.0
Downloading setuptools-57.0.0-py3-none-any.whl (821 kB)
|████████████████████████████████| 821 kB 73 kB/s
Installing collected packages: pip, setuptools
Attempting uninstall: pip
Found existing installation: pip 20.0.2
Uninstalling pip-20.0.2:
Successfully uninstalled pip-20.0.2
Attempting uninstall: setuptools
Found existing installation: setuptools 44.0.0
Uninstalling setuptools-44.0.0:
Successfully uninstalled setuptools-44.0.0
Successfully installed pip-21.1.2 setuptools-57.0.0
At this stage the environment is installed.
Other environment virtualization solution such as Vagrant are also stable solutions for production.
We are pleased to receive all users contributions. Each contribution shall be documented in english, and code styling shall follow pep8 recommendations. You can also join the moderators team. Please contact me.
Contribution process is based on gitlab best practice and processes the following schema:
Contributor shall work in its own fork of the project and provides its contributions via the merge request process. The merge request shall be approved and merged by a maintainers. So the step-by-step procedure to contribute is:
Draft proposal state.Draft flag shall be removed and the assignees/maintainers notified for merge.Before submission, contributor shall clean up, squash and rebase its commits to be easily inserted as fast-forward into the main branch. Please read https://chris.beams.io/posts/git-commit as commit writing best practices for this project. The related issue/bug identifier shall be reported into the commit message, otherwise, the issue shall be commented/closed accordingly.
The project uses the versioneer tool to manage releases deployement.
Then, the version management and deployment are coupled and can be performed in the same process.
This process secures the deployment process preventing a developer from accidentally deploying and erasing a release version. Versioneer tool checks the local repository then generates a dirty release when repository is not clean. This process can also be useful when developer needs to deploy snapshots.
Multiple and dirty deployement of versions are forbidden in pypi.org repository, this behavior also secures the version erasing risks.
The environment shall be configured to deploy the python library onto Pypi public repository.
The application used to manage module deployment is twine. This application shall be configured via ${HOME}/.pypirc file as followed:
Alternatively, the private gael's repository can be set (See [gael] entry.
[distutils]
index-servers =
pypi
drb
[pypi]
username: __token__
password: pypi-XXX
[drb]
repository = https://upload.pypi.org/legacy/
username: __token__
password: pypi-YYY
[gael]
repository: https://repository.gael-systems.com/repository/python-gael/
username: username
password: password
The important part is the drb section which defines the remote repository and credentials or token for the deployment (See https://pypi.org/help/#apitoken for details).
The version management is performed automatically with git tags. Setting the version is coupled with the deployment process within the CI/CD process.
To generate a new version, tag the master branch with the expected version and push the new tag version into git:
git tag 1.0-rc1
git push origin 1.0-rc1
On pushing new tag, a pipeline is automatically executed to control
NB: if the runner is not properly configured to upload distribution to the remote repository, the pipeline executed at tag push time might fail. To perform the distribution manually it shall be possible to run the following command lines:
make dist-clean
make dist-deploy
See the makefile details in command line section here after.
In git, thanks to the tag oriented release, the branching model fixes the following rules:
main branch.release-xx branch can be created for post-release fixes(hotfixes).The project release version is based on 3 digits "M.m.f" :
Drb project follows the pep440 recommendations for the tag representation. Versions are represented with the couple of (Major, Minor) version numbers for all pre-releases. Modifiers such as alpha (aN), beta(bN), release candidate(rcN), or post release (.postN) are possible as followed:
1.0a11.0b11.0rc11.0.0Note that (a,b,rc) are based on 2 digits (Major.Minor) whereas Final release are identified on 3 digits as desibed in the previous chapter: The third digit is used to manage fixes, waiting minor releases.
The environment comes with a preconfigured Makefile able to set up and prepare python environment to run tests and coverage. It also provides target to deploy new release manually.
make clean
Clean-up the environment from cache and lightweight components. It does not removed downloaded dependencies (from venv directory), nor distributions.
make dist-clean
The dist-clean command full cleans the repository as it has been cloned first.
Following the call of dist-clean the virtual environment and all the caches will be removed.
make test
Run the unitary tests.
make lint
Check if the source code properly follows the pep8 directives. This test is also used in gitlab piplines to accept pushed sources.
make coverage
Run the test coverage and generates a html report into htmlcov directory.
make dist
Prepare a distribution locally into dist directory. When no tag is present on the current commit, or modified files are present into the local directory, the distribution process creates a dirty version to be safety deploy to the repository.
make dist-deploy
Prepare and deploy a distribution into the gael's remote Pipy repository. This command is run automatically when pushing a new tag.
Currently, DRB project is not tested on Windows environment and some issues may appear on Windows systems.
If any troubleshoot occurred with the DRB library, please report us, opening a new issue or accident on the project GitLab page.
FAQs
Data Request Broker
We found that drb demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 4 open source maintainers collaborating on the project.
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