advanced-yaml 0.3.5

A suite of tools to advance your usage of yaml. YASL provides schema definition and verification. YAQL let's you query your yalm as if it were a database. YARL provides analysis and reporting. And YATL allows you to transform yaml from one structure (schema) to another.

Advanced YAML

Advanced YAML is a suite of tools that streamline data management of structured data in YAML.

These tools include:

• YASL - YAML Advanced Schema Language
• YAQL - YAML Advanced Query Language
• YARL - YAML Advanced Reporting Language

Vision & Inspiration

Teams and organizations often manage a diverse, complex set data as they collaborate to achieve their objectives. This data is commonly persisted in a database. When the data evolves rapidly (i.e. has high transaction rates), this makes sense. But often our data may be considered 'semi-static', changing infrequently over time. Visibility into and integrity of changes to this 'semi-static' data as it evolves can be valuable. What changed? Who made the change? Was the change valid? Establishing this visibility within databases can become complex and costly.

The vision of advanced-yaml is to allow for 'semi-static' data to be persisted in structured human and machine readable files that can be validated for correctness and referential integrity. We see clear examples of this concept in infrastructure-as-code tools. advanced-yaml seeks to explore that paradigm for data in a highly flexible manner, suitable for a variety of domains. This would allow teams to leverage robust version control systems to rigorously manage change. Teams can create automations to ensure data validity and integrity. And this could be done without compromising the common user experiences teams are accustomed to when working with their data.

advanced-yaml attempts to enable this by providing robust solutions for defining and validating data structure and integrity through schemas in the yasl tool. The yaql tool builds upon this by providing a query language and database fascade for your data. And the yarl tool further enhances this by enabling analysis and reporting for your data. Over time additional tools will be added to support other use cases and streamline development of products that utilize this 'semi-static' data management concept.

Developer Setup

Advanced YAML is written in python and managed with the UV tool.

• Install UV:

  curl -LsSf https://astral.sh/uv/install.sh | sh
  

• Clone the advanced-yaml repo

  git clone [e-mail]:jondavid-black/advanced-yaml.git
  cd advanced-yaml
  

• Setup the virtual environment and install dependencies

  uv venv
  source .venv/bin/activate
  uv pip install -e .[dev]
  

• Run tests

  uv run pytest
  

• Run tests w/ coverage

  uv run pytest --cov=src --cov-fail-under=75
  

• Run Formatter

  uv run ruff check src/ tests/
  

Use the standard github-flow working process to best leverage feedback from the CI process.

To Perform a release:

• Update the version in pyproject.toml
• Complete and merge the pull request to main.
• Get on the main branch:

  git checkout main
  git pull origin main
  

• Tag and push the repo:

  git tag "v$(uv run python -c "import toml; print(toml.load('pyproject.toml')['project']['version'])")"
  git push --tags