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Dvir Horev 2026-02-15 18:36:24 +00:00
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coordinates_cache.json
__pycache__/
*.pyc
.env

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FROM python:3.11-slim
ENV PYTHONDONTWRITEBYTECODE=1 \
PYTHONUNBUFFERED=1
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

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# dvirlabs-project
# 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
## Getting started
- **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
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
## Prerequisites
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
- Docker
- Docker Compose
## Add your files
## Quick Start
* [Create](https://docs.gitlab.com/user/project/repository/web_editor/#create-a-file) or [upload](https://docs.gitlab.com/user/project/repository/web_editor/#upload-a-file) files
* [Add files using the command line](https://docs.gitlab.com/topics/git/add_files/#add-files-to-a-git-repository) or push an existing Git repository with the following command:
1. **Clone the repository**
```bash
cd open-meteo-service
```
```
cd existing_repo
git remote add origin https://gitlab.com/dvirlabs-group/dvirlabs-project.git
git branch -M main
git push -uf origin main
2. **Start all services**
```bash
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:**
```json
{
"source": "cache",
"data": {
"Tel Aviv": {
"name": "Tel Aviv",
"latitude": 32.08088,
"longitude": 34.78057,
"country": "Israel"
},
...
}
}
```
## Integrate with your tools
#### `GET /coordinates/{city}`
Retrieve coordinates for a specific city.
* [Set up project integrations](https://gitlab.com/dvirlabs-group/dvirlabs-project/-/settings/integrations)
**Parameters:**
- `city` (path) - City name (e.g., "Ashkelon", "London")
## Collaborate with your team
**Example:**
```bash
curl http://localhost:8000/coordinates/Paris
```
* [Invite team members and collaborators](https://docs.gitlab.com/user/project/members/)
* [Create a new merge request](https://docs.gitlab.com/user/project/merge_requests/creating_merge_requests/)
* [Automatically close issues from merge requests](https://docs.gitlab.com/user/project/issues/managing_issues/#closing-issues-automatically)
* [Enable merge request approvals](https://docs.gitlab.com/user/project/merge_requests/approvals/)
* [Set auto-merge](https://docs.gitlab.com/user/project/merge_requests/auto_merge/)
**Response:**
```json
{
"source": "open-meteo",
"data": {
"name": "Paris",
"latitude": 48.85341,
"longitude": 2.3488,
"country": "France"
}
}
```
## Test and Deploy
#### `GET /metrics`
Prometheus metrics endpoint exposing service metrics.
Use the built-in continuous integration in GitLab.
**Example:**
```bash
curl http://localhost:8000/metrics
```
* [Get started with GitLab CI/CD](https://docs.gitlab.com/ci/quick_start/)
* [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/user/application_security/sast/)
* [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/topics/autodevops/requirements/)
* [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/user/clusters/agent/)
* [Set up protected environments](https://docs.gitlab.com/ci/environments/protected_environments/)
#### `GET /healthz`
Health check endpoint.
***
**Response:**
```json
{
"status": "ok"
}
```
# Editing this README
## Metrics
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
The service exposes the following Prometheus metrics:
## Suggestions for a good README
### HTTP Metrics
- **`http_requests_total`** - Counter of total HTTP requests
- Labels: `endpoint`, `method`, `status`
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
- **`http_request_duration_seconds`** - Histogram of request durations
- Labels: `endpoint`, `method`
## Name
Choose a self-explaining name for your project.
### Cache Metrics
- **`coordinates_cache_hits_total`** - Counter of cache hits
- **`coordinates_cache_misses_total`** - Counter of cache misses
## Description
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
### External API Metrics
- **`openmeteo_api_calls_total`** - Counter of calls to Open-Meteo Geocoding API
- Labels: `city`
## Badges
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
## Grafana Dashboard
## Visuals
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
The pre-configured dashboard includes 5 panels:
## Installation
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
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
## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
Access the dashboard at http://localhost:3000 after logging in with `admin/admin`.
## Support
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
## Caching
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
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
## Contributing
State if you are open to contributions and what your requirements are for accepting them.
## Development
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
### 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
```
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
### Stop Services
```bash
docker compose down
```
## Authors and acknowledgment
Show your appreciation to those who have contributed to the project.
### View Logs
```bash
docker compose logs -f open-meteo-service
```
### Rebuild After Code Changes
```bash
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:
```bash
# 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
For open source projects, say how it is licensed.
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
MIT

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from fastapi import FastAPI, Response
from prometheus_client import generate_latest, CONTENT_TYPE_LATEST
from .service import get_all_coordinates, get_coordinates_for_city
from .metrics import RequestTimer
app = FastAPI(title="Open-Meteo Coordinates Service")
@app.get("/coordinates")
def coordinates():
with RequestTimer(endpoint="/coordinates", method="GET") as t:
try:
return get_all_coordinates()
except Exception:
t.set_status("500")
raise
@app.get("/coordinates/{city}")
def coordinates_for_city(city: str):
with RequestTimer(endpoint="/coordinates/{city}", method="GET") as t:
try:
return get_coordinates_for_city(city)
except Exception:
t.set_status("500")
raise
@app.get("/metrics")
def metrics():
return Response(generate_latest(), media_type=CONTENT_TYPE_LATEST)
@app.get("/healthz")
def healthz():
return {"status": "ok"}

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import time
from prometheus_client import Counter, Histogram
HTTP_REQUESTS_TOTAL = Counter(
"http_requests_total",
"Total HTTP requests",
["endpoint", "method", "status"],
)
HTTP_REQUEST_DURATION_SECONDS = Histogram(
"http_request_duration_seconds",
"HTTP request duration in seconds",
["endpoint", "method"],
)
CACHE_HITS_TOTAL = Counter(
"coordinates_cache_hits_total",
"Total cache hits for coordinates",
)
CACHE_MISSES_TOTAL = Counter(
"coordinates_cache_misses_total",
"Total cache misses for coordinates",
)
OPENMETEO_CALLS_TOTAL = Counter(
"openmeteo_api_calls_total",
"Total calls made to Open-Meteo Geocoding API",
["city"],
)
class RequestTimer:
"""Small helper to measure request duration and emit metrics."""
def __init__(self, endpoint: str, method: str):
self.endpoint = endpoint
self.method = method
self.start = None
self.status = "200"
def __enter__(self):
self.start = time.time()
return self
def set_status(self, status: str):
self.status = status
def __exit__(self, exc_type, exc, tb):
HTTP_REQUESTS_TOTAL.labels(
endpoint=self.endpoint, method=self.method, status=self.status
).inc()
HTTP_REQUEST_DURATION_SECONDS.labels(
endpoint=self.endpoint, method=self.method
).observe(time.time() - self.start)

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import json
import os
from typing import Dict, Any
import requests
from .metrics import CACHE_HITS_TOTAL, CACHE_MISSES_TOTAL, OPENMETEO_CALLS_TOTAL
API_URL = "https://geocoding-api.open-meteo.com/v1/search"
CITIES = ["Tel Aviv", "Beer Sheva", "Jerusalem", "Szeged"]
CACHE_FILE = os.environ.get("CACHE_FILE", "coordinates_cache.json")
def _fetch_coordinates(city: str) -> Dict[str, Any]:
OPENMETEO_CALLS_TOTAL.labels(city=city).inc()
params = {"name": city, "count": 1}
r = requests.get(API_URL, params=params, timeout=10)
r.raise_for_status()
data = r.json()
if "results" not in data or not data["results"]:
raise ValueError(f"No results found for {city}")
result = data["results"][0]
return {
"name": result.get("name"),
"latitude": result.get("latitude"),
"longitude": result.get("longitude"),
"country": result.get("country"),
}
def _load_cache() -> Dict[str, Any] | None:
if os.path.exists(CACHE_FILE):
with open(CACHE_FILE, "r", encoding="utf-8") as f:
return json.load(f)
return None
def _save_cache(data: Dict[str, Any]) -> None:
with open(CACHE_FILE, "w", encoding="utf-8") as f:
json.dump(data, f, indent=2)
def get_all_coordinates() -> Dict[str, Any]:
cached = _load_cache()
if cached:
CACHE_HITS_TOTAL.inc()
return {"source": "cache", "data": cached}
CACHE_MISSES_TOTAL.inc()
results: Dict[str, Any] = {}
for city in CITIES:
results[city] = _fetch_coordinates(city)
_save_cache(results)
return {"source": "open-meteo", "data": results}
def get_coordinates_for_city(city: str) -> Dict[str, Any]:
cached = _load_cache()
if cached and city in cached:
CACHE_HITS_TOTAL.inc()
return {"source": "cache", "data": cached[city]}
CACHE_MISSES_TOTAL.inc()
result = _fetch_coordinates(city)
# Update cache with the new city
if cached is None:
cached = {}
cached[city] = result
_save_cache(cached)
return {"source": "open-meteo", "data": result}

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version: '3.8'
services:
open-meteo-service:
build:
context: .
dockerfile: Dockerfile
image: open-meteo-service:local
container_name: open-meteo-service
ports:
- "8000:8000"
networks:
- monitoring
prometheus:
image: prom/prometheus:latest
container_name: prometheus
ports:
- "9090:9090"
volumes:
- ./prometheus.yml:/etc/prometheus/prometheus.yml
- prometheus_data:/prometheus
command:
- "--config.file=/etc/prometheus/prometheus.yml"
- "--storage.tsdb.path=/prometheus"
networks:
- monitoring
depends_on:
- open-meteo-service
grafana:
image: grafana/grafana:latest
container_name: grafana
ports:
- "3000:3000"
environment:
- GF_SECURITY_ADMIN_PASSWORD=admin
- GF_SECURITY_ADMIN_USER=admin
volumes:
- grafana_data:/var/lib/grafana
- ./grafana/provisioning:/etc/grafana/provisioning
- ./grafana/dashboards:/var/lib/grafana/dashboards
networks:
- monitoring
depends_on:
- prometheus
networks:
monitoring:
driver: bridge
volumes:
prometheus_data:
grafana_data:

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{
"uid": "open-meteo-service",
"title": "Open-Meteo Service",
"timezone": "browser",
"schemaVersion": 38,
"version": 1,
"refresh": "10s",
"time": {
"from": "now-15m",
"to": "now"
},
"panels": [
{
"id": 1,
"type": "timeseries",
"title": "Request Rate",
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"gridPos": { "x": 0, "y": 0, "w": 12, "h": 8 },
"targets": [
{
"expr": "sum(rate(http_requests_total[5m])) by (endpoint, method)",
"legendFormat": "{{endpoint}} {{method}}",
"refId": "A"
}
]
},
{
"id": 2,
"type": "timeseries",
"title": "Request Duration p95",
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"gridPos": { "x": 12, "y": 0, "w": 12, "h": 8 },
"targets": [
{
"expr": "histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, endpoint, method))",
"legendFormat": "{{endpoint}} {{method}}",
"refId": "A"
}
]
},
{
"id": 3,
"type": "timeseries",
"title": "Cache Hits vs Misses",
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"gridPos": { "x": 0, "y": 8, "w": 12, "h": 8 },
"targets": [
{
"expr": "rate(coordinates_cache_hits_total[5m])",
"legendFormat": "hits",
"refId": "A"
},
{
"expr": "rate(coordinates_cache_misses_total[5m])",
"legendFormat": "misses",
"refId": "B"
}
]
},
{
"id": 4,
"type": "timeseries",
"title": "Open-Meteo Calls by City",
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"gridPos": { "x": 12, "y": 8, "w": 12, "h": 8 },
"targets": [
{
"expr": "sum(rate(openmeteo_api_calls_total[5m])) by (city)",
"legendFormat": "{{city}}",
"refId": "A"
}
]
},
{
"id": 5,
"type": "timeseries",
"title": "Requests by Status",
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"gridPos": { "x": 0, "y": 16, "w": 24, "h": 8 },
"targets": [
{
"expr": "sum(rate(http_requests_total[5m])) by (status)",
"legendFormat": "{{status}}",
"refId": "A"
}
]
}
],
"templating": {
"list": []
}
}

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apiVersion: 1
providers:
- name: default
type: file
disableDeletion: false
editable: true
updateIntervalSeconds: 10
options:
path: /var/lib/grafana/dashboards

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apiVersion: 1
datasources:
- name: Prometheus
type: prometheus
access: proxy
url: http://prometheus:9090
isDefault: true
uid: prometheus

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global:
scrape_interval: 15s
scrape_configs:
- job_name: "prometheus"
static_configs:
- targets: ["prometheus:9090"]
- job_name: "open-meteo-service"
metrics_path: "/metrics"
static_configs:
- targets: ["open-meteo-service:8000"]

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fastapi==0.115.8
uvicorn[standard]==0.30.6
requests==2.32.3
prometheus-client==0.21.1