# Host graphql api with Flask: worker execution and job state

Deploy a graphql api 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 graphql api-specific behavior as your application code.

## 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`

## Success Checks
- Web service responds on its public URL
- Worker consumed the enqueued job

## 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 graphql api itself (your job payloads, retry policy and scheduling) is application code and was not separately tested.
- 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.

## Cost Estimate
- **Monthly Amount:** $15.00  
- Components:  
  - **Web server** (s-1vcpu-1gb): $5.00  
  - **Worker server** (s-1vcpu-1gb): $5.00  
  - **Managed PostgreSQL database** (s-1vcpu-1gb): $5.00

## Last Verified At
2026-09-20T03:56:46Z

## Evidence Summary
- Flask web service plus a separate Python worker process deployed on Ample with `ample plan --write` then `ample up`. The web service enqueued a job that the private worker service consumed from the shared managed PostgreSQL database.