page:blueprints:restaurants:inventory manager:django

Restaurants: Inventory Manager Using Django

Summary

A inventory manager blueprint for restaurants built with Django on Ample. Domain schema:

• menu_items (sku, name, section, price, allergens, active): the menu as a SKU catalog;
• stock_movements (sku, delta, reason, moved_at): ingredient and prepared-item stock;
• orders (order_no, service_type, status, placed_at): dine-in, pickup, and delivery orders;
• order_lines (order_no, sku, qty, modifiers): order contents;
• events (seq, record_type, record_id, kind, at): application-owned history written in the same transaction.

Technical Basis

• An optimistic version check and an event row written in the same transaction.
• A stale update is rejected, and a history query over the application-owned events (history=2 conflict=rejected).

Public Information

• Menu with allergens and prices, locations, hours, reservations contact, and catering information.
• Customer contact and payment details, staff schedules are kept out of scope until handling is reviewed.
• Allergen information must be kept accurate by the operator; nothing here validates food-safety claims.

Prerequisites

• A Django project (pip install into .ample/python from requirements.txt, then waitress serving project.wsgi from run.py reading PORT on the python-3.12 template).
• A PostgreSQL driver reading DATABASE_URL (auto-provisioned when omitted).
• Bucket credentials from ample bucket create passed as encrypted S3_* environment variables.
• A review of which restaurants data classes may be handled at all; this blueprint models public information only.

Workflow Steps

1. Model the restaurants domain: Create the tables menu_items, stock_movements, orders, order_lines, events. The menu as a sku catalog lives in menu_items; keep the sensitive classes (customer contact and payment details, staff schedules) out of this schema.
2. Workflow step 1: Model SKUs and stock movements; derive stock from movements.
3. Workflow step 2: Apply movements transactionally with an optimistic version check on the SKU.
4. Workflow step 3: Record an event per movement for history.
5. Workflow step 4: Deploy and verify history and rejected stale updates.
6. Deploy: Run the synchronous deploy once and read the result. Re-running with no change is a no-op.
7. Verify: Run the pattern self-test(s) from the example (/p/transactional-workflows) and your own acceptance checks for the restaurants workflow.

Cost Estimate

• Currency: USD
• Monthly Amount: 10.0
• Components:
  • App server: s-1vcpu-1gb, quantity: 1.0, monthlyAmount: 5.0
  • Managed PostgreSQL database: s-1vcpu-1gb, quantity: 1.0, monthlyAmount: 5.0
• Note: Always-on monthly price of the tested sizes; apps and databases auto-pause when idle. Buckets are allocation-priced per quota and not included.

Limitations

• The technical basis was verified with the pattern fixture; the restaurants schema and workflow are an original design for this blueprint and were not executed as a separate application.
• Allergen information must be kept accurate by the operator; nothing here validates food-safety claims.

Evidence Summary

• Kind: canary_run
• Summary: Django pattern fixture deployed on Ample; the transactional workflows checks passed.
• Observed At: 2026-09-20T03:31:44Z

Examples

• Django pattern fixture: Verified transactional workflows basis for this blueprint.