loom-kernel documentation

           

Framework-agnostic toolkit to build backend applications with typed use cases, repository contracts, and transport adapters.

Official hosted documentation: loom-py.readthedocs.io. Companion demo application: dummy-loom.

Getting Started

• Quickstart
  • Install
  • 60-second demo: full CRUD from a model
  • Add business logic
  • Scope resources under a parent
  • Structured queries
  • Background jobs
  • Dispatch jobs from use cases + callbacks
  • Chain use cases (workflow pattern)
  • Declare REST interfaces
  • Bootstrap with YAML
  • Rules + Computes (advanced)
  • Next steps
• ETL Quickstart
  • Install
  • Minimal pipeline
  • Basic write modes
  • YAML config
  • File aliases
  • Next steps

REST

• Auto-CRUD
  • Minimal example
  • Restrict which routes are generated
  • Multiple read profiles
  • Mix auto-CRUD with custom routes
  • Bootstrap
  • When to add explicit use cases
  • Full working example
• Use-case DSL
  • Anatomy of a use case
  • Commands
  • F — typed field reference
  • Input markers
  • Compute — derive and normalise fields
  • Rule — validate before execute
  • Predicate composition
  • Full use case example — update with all DSL features
  • Declaring use cases on a REST interface
  • Cross-use-case calls with ApplicationInvoker
  • DSL quick-reference
• Celery worker
  • Install
  • Core concepts
  • Define a job
  • Dispatch a job
  • Run a job inline
  • Callbacks
  • YAML configuration reference
  • Worker bootstrap
  • Callbacks and ApplicationInvoker — how it works
  • Start the worker
  • Integration tests
  • Docker-compose stack
• Examples repo
  • 1. Model layer
  • 2. Commands
  • 3. Use cases
  • 4. Background jobs
  • 5. Job dispatch from use cases
  • 6. Job callbacks
  • 7. Workflow: chain a use case + dispatch
  • 8. REST interface
  • 9. Bootstrap
  • Source files
• REST Testing
  • Unit-test a use case directly
  • HTTP-level testing
  • Golden harness
  • Integration tests
  • Snapshot testing

ETL

• ETL
  • Install
  • Minimal pipeline
  • File source with JSON payload + final CSV report
  • File aliases (FileLocator)
  • YAML config (Polars path)
  • Write modes
  • Running only selected stages
  • Spark runtime
  • Testing ETL steps
  • File aliases (FileLocator)
  • Loading config from cloud storage
  • Pluggable config resolvers
  • End-to-end example
  • API reference
• ETL Testing
  • Test a single step with Polars
  • Reusable seed datasets with ETLScenario
  • Spark integration tests
  • Compile-time stubs
• ETL Examples
  • What it covers
  • Quick start
  • Next steps

Reference

• Reference
  • Core API
  • ETL API
  • Repository API
  • REST API
  • Testing API