python-devops-logging-decorators 0.1.5

Factory-based logging decorators and batch ETL utilities

python_devops

This repository is intended to be a DevOps toolkit workspace.

It contains practical, reusable projects and utilities that support common engineering and operations workflows, such as automation, observability, reliability, and deployment-related tasks.

Root-level deployment workflow

You can now deploy/build python-devops-logging-decorators directly from the python_devops root.

1) Create virtual environment (once)

py -m venv .venv

To activate

Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

.venv/Scripts.Activate.ps1

2) Install dependencies from root requirements

• Runtime only:

.\.venv\Scripts\python.exe -m pip install -r requirements.txt

• Development profile:

.\.venv\Scripts\python.exe -m pip install -r requirements-dev.txt

• Docs profile:

.\.venv\Scripts\python.exe -m pip install -r requirements-docs.txt

3) Run deployment/build from root

Before running deployment, do this preflight checklist:

1. Run tests:

.\.venv\Scripts\python.exe -m pytest batch_etl/tests logging_decorators/tests -q -vv

2. Confirm package metadata/version in pyproject.toml (name/version should match intended release).

3. Ensure PyPI credentials are available as environment variables:

$env:PYPI_TOKEN = "<pypi-token>"
$env:TEST_PYPI_TOKEN = "<testpypi-token>"  # optional

4. (Optional) clear old build artifacts:

Remove-Item -Recurse -Force .\dist -ErrorAction SilentlyContinue

• PowerShell:

.\deploy.ps1 -Profile dev

• Bash:

./deploy.sh dev

Valid profiles are runtime, dev, and docs.

Build artifacts are written to dist/.

After build, validate distributions:

.\.venv\Scripts\python.exe -m twine check .\dist\*

Optional manual uploads:

• TestPyPI

.\.venv\Scripts\python.exe -m twine upload --repository-url https://test.pypi.org/legacy/ -u __token__ -p $env:TEST_PYPI_TOKEN .\dist\*

• PyPI

.\.venv\Scripts\python.exe -m twine upload --repository-url https://upload.pypi.org/legacy/ -u __token__ -p $env:PYPI_TOKEN .\dist\*

Tag note: tag releases as v<version> (example: v0.1.4) so the tag-based pipeline path can publish to PyPI.

PyPI deployment config (Bitbucket)

Use this release checklist for Bitbucket-driven publishing.

1. Confirm release metadata in pyproject.toml:

   • distribution name: python-devops-logging-decorators
   • version: project.version
   • deploy commands: [tool.python_devops.pypi.commands]

2. Ensure repository variables are set:

   • PYPI_TOKEN
   • TEST_PYPI_TOKEN (optional; falls back to PYPI_TOKEN for TestPyPI)

3. Validate locally before pushing:

   • run tests
   • build package from root
   • run twine check dist/*

4. Push to main to trigger TestPyPI publishing.

5. Create/push a version tag (v<version>, for example v0.1.4) to trigger PyPI publishing.

git tag -a v<version> -m "<some message>"

git push origin v<version>

6. Verify the release from PyPI install:

pip install python-devops-logging-decorators

Import instructions

Install from PyPI:

pip install python-devops-logging-decorators

Or install locally from this repository root (editable):

pip install -e .

Then import both subprojects as:

import logging_decorators
import batch_etl

Bitbucket pipeline file

Top-level pipeline config: bitbucket-pipelines.yml

• pushes to main run publish to TestPyPI
• tags matching v* run publish to PyPI

The pipeline reads [tool.python_devops.pypi.commands] from the root pyproject.toml, so deployment commands stay centralized in one file.

Component projects

logging_decorators

logging_decorators is a component library that provides Python decorators for standardized function logging.

It uses a factory pattern to create decorators by:

• log level (debug, info, warning, error, critical)
• handler type (stream, file, rotating_file, timed_rotating_file)
• level + handler combinations (for example error_file, info_rotating_file)

It also supports optional formatter overrides and includes examples/tests so it can be adopted quickly in application or automation code.

batch_etl

batch_etl is a DAG-oriented ETL component library included in the same published distribution.

It provides:

• abstract ETL primitives (BaseExtractor, BaseTransformer, BaseLoader)
• a pipeline orchestrator (Pipeline) with dependency traversal and cycle detection
• concrete CSV implementations (CsvFileExtractor, PassthroughRowsTransformer, CsvFileLoader)
• demo utilities for OHLC CSV workflows (batch_etl.demo)

For detailed usage examples, see batch_etl/README.md.