Open-Meteo Coordinates Service

A FastAPI-based microservice that queries the Open-Meteo Geocoding API to retrieve coordinates for various cities. The service includes caching, Prometheus metrics, and Grafana dashboards for monitoring.

Features

• RESTful API for retrieving city coordinates
• Intelligent caching to reduce external API calls
• Prometheus metrics for observability
• Pre-configured Grafana dashboards with 5 panels
• Docker Compose setup for easy deployment

Prerequisites

• Docker
• Docker Compose

Quick Start

1. Clone the repository

   cd open-meteo-service
   

2. Start all services

   docker compose up --build
   

3. Access the services

   • API: http://localhost:8000/docs
   • Prometheus: http://localhost:9090
   • Grafana: http://localhost:3000 (admin/admin)

API Documentation

Endpoints

GET /coordinates

Retrieve coordinates for all configured cities.

Response:

{
  "source": "cache",
  "data": {
    "Tel Aviv": {
      "name": "Tel Aviv",
      "latitude": 32.08088,
      "longitude": 34.78057,
      "country": "Israel"
    },
    ...
  }
}

GET /coordinates/{city}

Retrieve coordinates for a specific city.

Parameters:

• city (path) - City name (e.g., "Ashkelon", "London")

Example:

curl http://localhost:8000/coordinates/Paris

Response:

{
  "source": "open-meteo",
  "data": {
    "name": "Paris",
    "latitude": 48.85341,
    "longitude": 2.3488,
    "country": "France"
  }
}

GET /metrics

Prometheus metrics endpoint exposing service metrics.

Example:

curl http://localhost:8000/metrics

GET /healthz

Health check endpoint.

Response:

{
  "status": "ok"
}

Metrics

The service exposes the following Prometheus metrics:

HTTP Metrics

• http_requests_total - Counter of total HTTP requests

  • Labels: endpoint, method, status

• http_request_duration_seconds - Histogram of request durations

  • Labels: endpoint, method

Cache Metrics

• coordinates_cache_hits_total - Counter of cache hits
• coordinates_cache_misses_total - Counter of cache misses

External API Metrics

• openmeteo_api_calls_total - Counter of calls to Open-Meteo Geocoding API
  • Labels: city

Grafana Dashboard

The pre-configured dashboard includes 5 panels:

1. Request Rate - Requests per second by endpoint
2. Request Duration p95 - 95th percentile latency
3. Cache Hits vs Misses - Cache effectiveness
4. Open-Meteo Calls by City - External API usage per city
5. Requests by Status - HTTP status code distribution

Access the dashboard at http://localhost:3000 after logging in with admin/admin.

Caching

The service uses a local JSON file (coordinates_cache.json) to cache city coordinates:

• Reduces external API calls
• Shared across all API endpoints
• Persists between requests (not container restarts)
• Automatically updated when new cities are queried

Development

Project Structure

.
├── app/
│   ├── main.py          # FastAPI application
│   ├── service.py       # Business logic & caching
│   └── metrics.py       # Prometheus metrics definitions
├── grafana/
│   ├── provisioning/    # Auto-configured datasources & dashboards
│   └── dashboards/      # Dashboard JSON definitions
├── docker-compose.yml   # Service orchestration
├── Dockerfile           # Python app container
├── prometheus.yml       # Prometheus scrape configuration
└── requirements.txt     # Python dependencies

Stop Services

docker compose down

View Logs

docker compose logs -f open-meteo-service

Rebuild After Code Changes

docker compose up --build

Configuration

Environment Variables

• CACHE_FILE - Path to cache file (default: coordinates_cache.json)

Scrape Interval

Edit prometheus.yml to adjust the scrape interval (default: 15s).

Testing

Generate test traffic to populate metrics:

# Test all endpoints
curl http://localhost:8000/coordinates
curl http://localhost:8000/coordinates/Paris
curl http://localhost:8000/coordinates/London

# Generate load
for i in {1..10}; do curl -s http://localhost:8000/coordinates > /dev/null; done

License

MIT