fn_registry/dev/flows/0002-aemet-madrid.md

name, id, status, created, updated, priority, risk, related_issues, apps, trigger, schedule, expected_runtime_s, tags

name	id	status	created	updated	priority	risk	related_issues	apps	trigger	schedule	expected_runtime_s	tags
aemet-madrid	0002	pending	2026-05-16	2026-05-16	medium	low	0097	dag_engine data_factory footprint_geo_stack	cron	0 * * * *	10	api weather geo http-only

Goal

Probar path HTTP-only (sin Chrome/CDP). Extractor REST -> data_factory -> sink geo (PostGIS via footprint_geo_stack). Demuestra que el stack tambien sirve para APIs publicas + datos georeferenciados.

Pre-requisitos

• API key AEMET (gratis, signup). Guardar en pass insert aemet/api-key.
• footprint_geo_stack corriendo (PostGIS :5432 + Martin tiles :3000).
• dag_engine activo.

Flow

1. Crear funcion del registry aemet_get_weather_py_infra (o usar http_get_json_py_infra directamente si la API responde JSON simple).
2. Endpoint AEMET observacion convencional: GET https://opendata.aemet.es/opendata/api/observacion/convencional/datos/estacion/<id> con header api_key.
3. Schema esperado: [{ts, temp, humidity, pressure, wind_speed, lat, lon}, ...].
4. Sink: INSERT en PostGIS tabla weather_madrid (ts, temp, humidity, pressure, geom geometry(Point, 4326)).
5. Crear node en data_factory: {kind: 'database', label: 'postgis_weather'} (sink declarado).
6. DAG aemet_madrid_hourly.yaml:

   name: aemet-madrid
   schedule: "0 * * * *"
   steps:
     - name: extract
       function: aemet_get_weather_py_infra
       args: ["--station", "3195", "--out", "/tmp/aemet.json"]
     - name: load_postgis
       function: db_insert_row_go_infra
       args: ["--db", "postgres://...", "--table", "weather_madrid", "--from-json", "/tmp/aemet.json"]
       depends: [extract]
   

7. Verificar Martin tiles renderiza overlay (opcional).

Acceptance

• Funcion AEMET extractor existe (creada o reusada http_get_json_*).
• DAG corre 2x consecutivas via scheduler.
• PostGIS tabla weather_madrid tiene >=2 filas.
• data_factory muestra node aemet_madrid kind=extractor + node postgis_weather kind=database con last_seen_at reciente.
• Martin tile server sirve overlay weather (opcional).

Telemetria esperada

• function_stats.http_get_json_py_infra o aemet_get_weather_py_infra: calls_24h += 24 (1/hora).
• data_factory.runs: 24 entries/dia.
• data_factory.databases.last_seen_at actualizado por sink.

Notas

• Sin LLM/CDP. Mas barato que flow 0001.
• Caso minimal para servicios geo. Si funciona, sirve de plantilla para mas extractores API.