di-logging

di-logging

Product logging library to provide consistent logging across components.

Loggers can be obtained thusly:

from di_logging import get_logger

logger = get_logger(__name__)

This code should be placed at the top level within a module and this logger used throughout the component.

If an application or system is being written the logging should be configured within the main entry point;

from di_logging import configure_logging

if __name__ == "__main__":
    configure_logging()
    do_the_stuff()

This will setup the logging for the system.

Release Process

☛ Includes a semantic release system, which generates a new release on GitHub, including a changelog.

      The version is automatically changed accordingly to the commit message that results from merging the PR.

       ☛ breaking-change/CAP-123 Something runs the pipeline and bumps major version: 1.0.0 → 2.0.0

       ☛ feature/CAP-123 Something runs the pipeline and bumps minor version: 1.0.0 → 1.1.0

       ☛ fix/CAP-123 Something runs the pipeline and bumps patch version: 1.0.0 → 1.0.1

       ☛ CAP-123 Something just runs the pipeline

                         ⚠ If this flow is unclear to you, don't hesitate in contacting DevOps team ⚠

How to run locally

Create a .env file from the existent .env.example file. Make sure to update it accordingly with your environment variables.

Using poetry

poetry config virtualenvs.create true poetry config virtualenvs.in-project true poetry install

Using docker container

Execute make start-dependencies in order to start whichever dependencies your project might have. You should always have the base Python image.

Execute make run and your application should be running.

Execute make stop-dependencies when you no longer need to execute your project.

Execute make sync-requirements to rebuild the docker image.

How to know which make recipes are available

Run make

How to run tests

Run make test

How to run tests with coverage report

Run make coverage