Sports clubs: event registration using Django

Summary

A event registration blueprint for sports clubs built with Django on Ample. Domain schema: course_groups (team_or_class, season, age_group): teams and classes; memberships (course_group_id, member_reference, guardian_reference, role): players, guardians and coaches; sessions (course_group_id, starts_at, venue, type): training and fixtures; materials (course_group_id, title, object_key, visibility): schedules and coaching resources; events (seq, record_type, record_id, kind, at): application-owned history written in the same transaction. Public information: teams, seasons and fixtures, venues, membership fees. Kept out of scope until handling is reviewed: minor members and medical notes, safeguarding records. Medical and safeguarding records are sensitive; only schedules and coaching materials are modeled. Technical basis verified on Django: an optimistic version check and an event row written in the same transaction, a stale update rejected, and a history query over the application-owned events (history=2 conflict=rejected).

Infrastructure requirements

• Compute: verified (Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.)
• Postgres: verified (Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database and no DATABASE_URL is supplied.)
• S3-compatible object storage: verified (Buckets are S3-compatible with issued credentials; PutObject and GetObject are verified by canary. Other S3 operations are not verified.)

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 sports clubs data classes may be handled at all; this blueprint models public information only

Exact tested configuration

• template: python-3.12
• runtime: python
• size: s-1vcpu-1gb
• install: python3 -m pip install --target .ample/python -r requirements.txt
• start: PYTHONPATH=.ample/python:${PYTHONPATH:-} python3 run.py

Steps

1. Model the sports clubs domain. Create the tables course_groups, memberships, sessions, materials, events. Teams and classes lives in course_groups; keep the sensitive classes (minor members and medical notes, safeguarding records) out of this schema.

2. Workflow step 1. Model sessions with capacity and registrations with status

3. Workflow step 2. Register transactionally with a capacity check and an event per registration

4. Workflow step 3. Reject stale concurrent updates

5. Workflow step 4. Deploy and verify history and conflict handling

6. Deploy. Run the synchronous deploy once and read the result. Re-running with no change is a no-op.

   ample deploy . --name <app-name> --public --start "python3 run.py" --env S3_ENDPOINT=... --env S3_REGION=... --env S3_BUCKET=... --env S3_ACCESS_KEY_ID=... --env S3_SECRET_ACCESS_KEY=...
   

7. Verify. Run the pattern self-test(s) from the example (/p/transactional-workflows) and your own acceptance checks for the sports clubs workflow.

   ample logs <deployment_id> --kind build
   

Tested examples

• Django pattern fixture (tests/deploy-canaries/django-patterns): Verified transactional workflows basis for this blueprint.

Success checks

• app responds on its public URL (/ on the live URL, expect ample canary django patterns)
• transactional-workflows self-test from the example (/p/transactional-workflows on the live URL, expect see the pattern fixture checks)

Limitations

• The technical basis (transactional workflows on Django) was verified with the pattern fixture; the sports clubs schema and workflow are an original design for this blueprint and were not executed as a separate application.
• No health, financial, privacy or other compliance claim is made. Medical and safeguarding records are sensitive; only schedules and coaching materials are modeled.
• Public content and synthetic examples only until actual data-handling requirements have been reviewed.
• Verified on the python-3.12 template at s-1vcpu-1gb; region, request-duration limits and other sizes are unknown or unverified.
• Managed PostgreSQL 16 only; extensions, connection limits and backup or restore procedures are not verified.
• PutObject and GetObject with path-style addressing are verified; other S3 operations and CDN cache rules are not.