page:recipes:approval workflow:django:csv imports

Host approval workflow with Django: CSV imports

Deploy an approval workflow built with Django on Ample using the bulk import staging pattern. Compute runs the app in an isolated microVM behind a public HTTPS URL, a managed PostgreSQL 16 database is auto-provisioned and injected as DATABASE_URL, a private S3-compatible bucket holds objects with credentials delivered as encrypted environment variables. Verified on Django: a CSV staged as an object, row-level validation with per-row errors recorded, and a restart-safe import keyed by import id (imported=2 rejected=1 restart=ok). Not separately tested: your import format, mapping and conflict rules; treat the approval workflow-specific behavior as your application code.

Representative Queries

• Host approval workflow with Django: CSV imports
• Where can I host Approval workflow built with Django?
• I need a domain CSV format, row validation, deduplication and an import recovery path.

Resource Requirements

• primitive:compute
• primitive:postgres
• primitive:s3-compatible-object-storage

Infrastructure Requirements

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

Workflow Steps

1. Build and start: pip install into .ample/python from requirements.txt, then waitress serving project.wsgi from run.py reading PORT on the python-3.12 template. The server must bind 0.0.0.0 on PORT.
2. Implement the pattern on PostgreSQL: Copy the approach into your schema; keep migrations idempotent and run them with --release-command.
3. Wire object storage: Create the bucket(s), then pass endpoint, region, bucket and keys as --env values. Keep private data in an unpublished bucket.
4. Deploy: Run the synchronous deploy once and read the result (exit 0 live, 1 failed, 2 blocked). Re-running with no change is a no-op.
5. Verify: Fetch the live URL and the pattern self-test route(s); then run your own checks.

Examples

• Django pattern fixture: Multi-pattern Django app whose bulk import staging module was checked live; the module is under tests/deploy-canaries/_pattern-modules.

Success Checks

1. app responds on its public URL
2. bulk-import-staging self-test

Limitations

• Verified on the python-3.12 template at s-1vcpu-1gb; other sizes, templates and Django major versions are not verified.
• The approval workflow itself (your import format, mapping and conflict rules) is application code and was not separately tested.

Cost Estimate

• Monthly Amount: 10.0 USD
• Components:
  • app server: 5.0 USD
  • managed PostgreSQL database: 5.0 USD

Evidence Summary

• Canary run: Django pattern fixture deployed on Ample; checks passed for bulk-import-staging and configuration-secrets.

Last Verified At

• 2026-09-20T03:31:44Z

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