Host Reporting Dashboard with Flask: Data Exports

Deploy a reporting dashboard built with Flask on Ample using the generated downloads 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 Flask: an export query rendered to a CSV artifact stored with metadata in a private bucket and returned only to an authorized caller (401 without token, export=ok rows=3). Not separately tested: your export formats, retention policy and entitlement model; treat the reporting dashboard-specific behavior as your application code.

Representative Queries

• Host reporting dashboard with Flask: data exports
• Where can I host Reporting dashboard built with Flask?
• I need a scoped export format, artifact generation and authorized download procedure.

Resource Requirements

• Compute
• PostgreSQL
• S3-compatible object storage

Infrastructure Requirements

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.

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.

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.

Framework

Flask

Workload

Reporting dashboard

Prerequisites

• A Flask project that builds and starts with the documented commands (pip install into .ample/python from requirements.txt, then waitress from app.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 from app.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 generated downloads: an export query rendered to a CSV artifact stored with metadata in a private bucket and returned only to an authorized caller (401 without token, export=ok rows=3). 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 app.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/generated-downloads) from the example; then run your own checks. On failure read ample logs --kind build then --kind runtime.

   ample logs  --kind build
   

Examples

• Flask Pattern Fixture
  Multi-pattern Flask app whose generated downloads module was checked live; the module is under tests/deploy-canaries/_pattern-modules.

Success Checks

• App responds on its public URL
  • Kind: http_get
  • Path: /
  • Expect: ample canary flask patterns
• Generated-downloads self-test
  • Kind: http_get
  • Path: /p/generated-downloads
  • Expect: export=ok rows=3 (with ?token=...; 401 without)

Limitations

• Verified on the python-3.12 template at s-1vcpu-1gb with the example fixture; other sizes, templates, and Flask major versions are not verified.
• The reporting dashboard itself (your export formats, retention policy, and entitlement model) 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 verified.

Cost Estimate

• Currency: USD
• Monthly Amount: $10.00
• 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.

Components

• App Server
  • Size: s-1vcpu-1gb
  • Quantity: 1.0
  • Monthly Amount: $5.00
• Managed PostgreSQL Database
  • Size: s-1vcpu-1gb
  • Quantity: 1.0
  • Monthly Amount: $5.00

Evidence Summary

• Flask pattern fixture deployed on Ample
  • Checks passed for generated-downloads and configuration-secrets. Pattern proof: an export query rendered to a CSV artifact stored with metadata in a private bucket and returned only to an authorized caller (401 without token, export=ok rows=3).
  • Observed At: 2026-09-21T01:14:18Z
• The same Flask app deployed with a --release-command migration
  • The marker it created was readable after activation.
  • Observed At: 2026-09-20T01:54:51Z

Last Verified At

2026-09-21T01:14:18Z

Next Actions

• Browse the Catalog Index
• Search Published Recipes
• Prepare a Side-effect-free Deployment Plan
• Read Existing Agent Authentication Setup
• Browse Reporting Dashboard
• Browse Flask
• Browse Generated Downloads
• Browse Deploy Web App

Formats

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• JSON: View JSON