Host mobile backend with Flask: worker execution and job state

Deploy a mobile backend built with Flask as a public web service plus a dedicated worker process on Ample. ample plan --write discovers both services and their shared database and writes ample.toml; ample up reconciles the topology: a managed PostgreSQL 16 database, a public web microVM and a private worker microVM (kind worker, no public URL), both reading DATABASE_URL. Verified on Flask: a separately deployed worker service (kind worker, no public URL) sharing a declared managed PostgreSQL database with the web service, consuming a jobs table with row locks and marking jobs done once (worker=done attempts=1). Not separately tested: your job payloads, retry policy and scheduling; treat the mobile backend-specific behavior as your application code.

Representative Queries

• Host mobile backend with Flask: worker execution and job state
• Where can I host Mobile backend built with Flask?
• I need a separately runnable worker, durable job records and idempotent retries; not a managed queue.

Resource Requirements

• Compute
• Postgres

Infrastructure Requirements

• Compute: Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.
• 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.

Framework

Flask

Workload

Mobile backend

Release Status

Published

Support Status

Verified

Execution Status

Ready

Prerequisites

• A repository with the web app and the worker as separate service directories (the fixture uses apps/web and apps/worker), each building and starting 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; the worker starts with python3 worker.py and binds no port)
• Both services read DATABASE_URL at runtime and share one jobs table; the worker claims rows with SELECT ... FOR UPDATE SKIP LOCKED and marks them done once
• An Ample account token with servers:write and databases:write (ample up creates the database)

Workflow Steps

1. Plan the topology
   Run the planner once. It discovers the web and worker services, infers kind = "worker" for the process without a port, declares the database each service needs, and writes ample.toml. Exit 2 means it left questions in the manifest; answer them with --answer or by editing the file.
   Command: ample plan --write .

2. Share one database
   Keep a single [databases.main] with engine = "postgres", drop any per-service database the planner added, and set DATABASE_URL = { database = "main" } under both [services.web.env] and [services.worker.env]. Python services also need an explicit start command in the manifest (the fixture sets start for both services).
   Command: ample plan --offline .

3. Apply
   Reconcile the whole manifest in dependency order: the database first, then both services. It is idempotent and never destructive; exit 0 means everything applied, 2 needs input, 1 a failed resource (independent siblings still proceed and re-running resumes).
   Command: ample up .

4. Verify
   Fetch the web service URL and its worker status route (/p/worker-queue/status in the example) and confirm the worker processed the enqueued job; on failure read the worker service's runtime logs.
   Command: ample logs --kind runtime

Examples

• Flask web + worker fixture: A Flask web app (apps/web) and a worker process (apps/worker) sharing one managed PostgreSQL database; the queue module is under tests/deploy-canaries/_pattern-modules.

Success Checks

• Web service responds on its public URL:
  Path: /
  Expect: ample canary flask patterns
• Worker consumed the enqueued job:
  Path: /p/worker-queue/status
  Expect: worker=done attempts=1 result=processed-by-worker

Limitations

• Verified on the python-3.12 template at s-1vcpu-1gb for both services with the example fixture; other sizes, templates and Flask major versions are not verified.
• The mobile backend itself (your job payloads, retry policy and scheduling) is application code and was not separately tested; the worker-queue check is what was verified.
• The worker is a single long-running process supervised in its own microVM; there is no scheduler, cron or horizontal worker scaling in this recipe, and the check ran immediately after deploy so idle behavior of the worker VM is not verified.
• The worker service is private: it has no public URL and is reached only through the shared database; both services are placed with their database.
• Managed PostgreSQL 16 only; extensions, connection limits and backup or restore procedures are not verified; apps and their databases are placed together.
• Region, compliance attestations and request-duration limits are unknown and not claimed.

Cost Estimate

• Currency: USD
• Monthly Amount: 15.0
• Note: Always-on monthly price of the tested sizes for the web service, the worker service and the database; apps auto-pause when idle.

Components

1. Web server: s-1vcpu-1gb, Quantity: 1.0, Monthly Amount: 5.0
2. Worker server: s-1vcpu-1gb, Quantity: 1.0, Monthly Amount: 5.0
3. Managed PostgreSQL database: s-1vcpu-1gb, Quantity: 1.0, Monthly Amount: 5.0

Evidence Summary

• Canary run: Flask web service plus a separate Python worker process deployed on Ample with ample plan --write then ample up (pip install into .ample/python from requirements.txt, then waitress from app.py reading PORT on the python-3.12 template); the web service enqueued a job that the private worker service consumed from the shared managed PostgreSQL database. Pattern proof: a separately deployed worker service (kind worker, no public URL) sharing a declared managed PostgreSQL database with the web service, consuming a jobs table with row locks and marking jobs done once (worker=done attempts=1).

Last Verified At

• 2026-09-20T03:56:46Z

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