platformics 1.0.3

Codegen Python GraphQL Entity Framework

Platformics

Platformics is a GraphQL API framework that relies on code generation to implement a full featured GraphQL API on top of a PostgreSQL database, with support for authorization policy enforcement and file persistence via S3. It's built on top of the best available Python tools and frameworks!

The libraries and tools that make Platformics work:

Links to these tools/libraries

• LinkML - Schema modeling language
• FastAPI - Async HTTP router
• Strawberry - GraphQL Framework
• Pydantic - Data validation
• Cerbos - Authorization
• SQLAlchemy - Database Access / ORM
• factory_boy - Test fixtures
• Alembic - Database migrations

Current Features

• Express your schema in a straightforward YAML format
• GraphQL Dataloader pattern (no n+1 queries!)
• Authorization policy enforcement
• Flexible Filtering
• Data aggregation
• Top-level pagination
• Relationship traversal
• DB migrations
• Generated Test fixtures
• pytest wiring
• VSCode debugger integration
• Add custom REST endpoints to generated API
• Add custom GQL queries/mutations to generated API
• Support arbitrary class inheritance hierarchies (1-level deep)
• Package and publish to PyPI

Roadmap

• Plugin hooks to add business logic to generated GQL resolvers

How to set up your own platformics API

1. Copy the test_app boilerplate code to your own repository.
2. Edit schema/schema.yml to reflect your application's data model.
3. Run make build and then make init to build and run your own GraphQL API service.
4. Browse to http://localhost:9009/graphql to interact with your api!
5. Run make token to generate an authorization token that you can use to interact with the API. The make target copies the necessary headers to the system clipboard. Paste the token into the headers section at the bottom of the GraphQL explorer API

Versioning platformics

Platformics can be used in downstream applications by either

• using the platformics image as the base Docker image. To select a version of platformics, add the appropriate version tags to the docker image
• installing as a python dependency, pip install platformics.

The version in pyproject.toml is managed using poetry-dynamic-versioning which determines version based on git tags.

Iterating on your schema

1. Make changes to schema/schema.yml
2. Run make codegen to re-run code gen and restart the API service
3. If your changes require DB schema changes, run make alembic-autogenerate and make alembic-upgrade-head to generate DB migrations and run them.

HOWTO

• Work with platformics
• Extend the generated API
• Customize Codegen templates
• Override Default Authorization Behaviors

Contributing

This project adheres to the Contributor Covenant code of conduct. By participating, you are expected to uphold this code. Please report unacceptable behavior to [e-mail].

Reporting Security Issues

Please disclose security issues responsibly by contacting [e-mail].