Host reporting dashboard with Django: structured data imports

Deploy a reporting dashboard 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 reporting dashboard-specific behavior as your application code.

Representative Queries

• Host reporting dashboard with Django: structured data imports
• Where can I host Reporting dashboard built with Django?
• I need a workload-specific import mapping, conflict rules and restart behavior.

Resource Requirements

• Compute
• Postgres
• S3-compatible object storage

Infrastructure Requirements

1. Compute
   Status: verified
   Summary: 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
   Status: verified
   Summary: 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
   Status: verified
   Summary: Buckets are S3-compatible with issued credentials; PutObject and GetObject are verified by canary. Other S3 operations are not verified.

Prerequisites

• A Django project that builds and starts with the documented commands (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 at runtime (auto-provisioned when omitted, or supplied with --env)
• A bucket from ample bucket create with its credentials passed as encrypted S3_* environment variables
• An Ample account token with servers:write, databases:read, buckets:read

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: The fixture's module implements bulk import staging: 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). 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. Use path-style addressing. 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.

   ample deploy . --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=...
   

5. Verify: Fetch the live URL and the pattern self-test route(s) (/p/bulk-import-staging) from the example; then run your own checks. On failure read ample logs --kind build then --kind runtime.

   ample logs --kind build
   

Limitations

• Verified on the python-3.12 template at s-1vcpu-1gb with the example fixture; other sizes, templates and Django major versions are not verified.
• The reporting dashboard itself (your import format, mapping and conflict rules) is application code and was not separately tested; the pattern checks are what was verified.
• Region, compliance attestations and request-duration limits are unknown and not claimed.
• Apps auto-pause when idle and wake on the next request; always-on is an operator setting, not a plan feature.
• Managed PostgreSQL 16 only; extensions, connection limits and backup or restore procedures are not verified; apps and their databases are placed together.
• PutObject and GetObject with path-style addressing are verified; multipart upload, listing, presigned URLs and lifecycle rules are not.

Cost Estimate

• Currency: USD
• Monthly Amount: 10.0
• Components:
  • App server: monthlyAmount 5.0
  • Managed PostgreSQL database: 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.

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. Source: tests/deploy-canaries/django-patterns.

Success Checks

• App responds on its public URL: expected "ample canary django patterns".
• Bulk-import-staging self-test at path /p/bulk-import-staging expected "imported=2 rejected=1 restart=ok".

Evidence Summary

• Canary Run: Django pattern fixture deployed on Ample; checks passed for bulk-import-staging and configuration-secrets.
• Observed At: 2026-09-20T03:31:44Z
  • Implementation Revision: 03b6402b1b3e19178985f08ea4033804521d85d4-dirty (CLI b75e104)
  • Expires At: 2027-03-19T03:31:44Z

Formats

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• JSON View