python-devops-logging-decorators 0.1.4

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

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

• PowerShell:

.\deploy.ps1 -Profile dev

• Bash:

./deploy.sh dev

Valid profiles are runtime, dev, and docs.

Build artifacts are written to dist/.

PyPI deployment config (Bitbucket)

A top-level TOML config now exists at pyproject.toml with deployment metadata for publishing the root distribution to PyPI from Bitbucket pipelines.

Published distribution name:

• 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

Install command:

pip install python-devops-logging-decorators

After install, you can import both packages:

import logging_decorators
import batch_etl

It includes:

• project/dist paths
• requirements profiles
• PyPI and TestPyPI upload URLs
• expected Bitbucket env var names for secrets
• canonical install/build/check/upload command strings

Set these repository variables in Bitbucket before publishing:

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

Recommended pipeline flow:

1. install dependencies
2. build package
3. run twine check
4. upload to TestPyPI
5. upload to PyPI (on release/tag)

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.