Next Steps

Next Steps #

Upcoming topics and expansions for Karma

Graph Schema #

Document Karma’s graph model in Neptune.

  • Node Types: Component (with type, environment, config_path, runtime_path)
  • Edge Types: depends_on, emits_runtime_to, consumes_runtime_from
  • Note: Components do not store their own nickname — it’s tracked externally.
  • TODO: Add edge metadata and example diagrams.

Querying the Graph #

Gremlin or Karma CLI queries against Neptune.

karma graph query --gremlin 'g.V().count()'
  • Common queries:
    • Downstream components
    • Orphans
    • Drift detection
    • Runtime consumers
  • TODO: SPARQL support notes

Change Coordination #

How Karma safely applies config updates:

  1. Accept proposed change
  2. Validate input config
  3. Analyze graph impact
  4. Plan or apply
  5. Update Parameter Store
  6. Write graph delta to Neptune
  7. Log result

Planned features:

  • Queued change sets
  • Dry-run mode
  • Approval hooks

Developer Onboarding #

Fast path to contributing:

git clone https://github.com/usekarma/karma.git
cd karma
poetry install
poetry run pytest
  • Requires Python 3.10+ and AWS credentials
  • TODO: Add directory structure overview and contribution guide

Future Pages #

Design Principles #

  • Capture architectural beliefs (e.g. config/runtime separation, graph-first modeling)

Why Neptune? #

  • Justify the use of a graph DB vs. SQL or NoSQL alternatives

Karma API #

  • Document CLI and REST endpoints (e.g., /graph, /request-change, /log)

Example Graph Visualization #

  • Small visual system (3–5 components) with labeled nodes and runtime refs

UI / Viewer Preview #

  • Placeholder or screenshot of future graph navigation interface

Gremlin Cheatsheet #

  • Quick reference of common queries for graph analysis and validation

This page will evolve as implementation progresses. If you’re contributing or extending Karma, these are the building blocks ahead.