الملفات

Gamal0909 439a31ba93 Add CI/CD pipeline, architecture documentation, and monitoring application

- Created a GitHub Actions workflow for CI/CD to deploy to Ghyamah, including testing, building, and pushing Docker images.
- Added architecture design document for handling 15,000 requests per second, detailing system components, capacity planning, and cold start strategies.
- Introduced a Python-based uptime/latency/SSL monitor with a static dashboard, utilizing standard libraries only.
- Included Dockerfile and entrypoint script for the monitoring application, ensuring it runs as a non-root user and handles process management.
- Added a .dockerignore file to exclude unnecessary files from the Docker build context.
- Created an HTML dashboard for visualizing monitoring metrics, including uptime, latency, and SSL certificate status.

2026-07-27 23:08:39 +03:00

.dockerignore

Add CI/CD pipeline, architecture documentation, and monitoring application

2026-07-27 23:08:39 +03:00

Dockerfile

Add CI/CD pipeline, architecture documentation, and monitoring application

2026-07-27 23:08:39 +03:00

Entrypoint.sh

Add CI/CD pipeline, architecture documentation, and monitoring application

2026-07-27 23:08:39 +03:00

index.html

Add CI/CD pipeline, architecture documentation, and monitoring application

2026-07-27 23:08:39 +03:00

monitor.py

Add CI/CD pipeline, architecture documentation, and monitoring application

2026-07-27 23:08:39 +03:00

README.md

Add CI/CD pipeline, architecture documentation, and monitoring application

2026-07-27 23:08:39 +03:00

README.md

mithal-space-monitor

A lightweight, stdlib-only uptime/latency/SSL monitor with a single-page Chart.js dashboard, packaged into one container.

What's inside

File	Purpose
`monitor.py`	Standard-library-only Python script. Every `CHECK_INTERVAL` seconds it checks DNS resolution time, HTTP status + latency, search-endpoint latency, and SSL cert expiry, then writes a rolling `RETENTION_HOURS` window to a JSON file.
`index.html`	Single-page dashboard (HTML/CSS/JS + Chart.js via CDN). Polls the JSON file every 30s and renders 24h uptime %, a 60-minute latency line chart, SSL expiry, and a table of the last 10 checks.
`entrypoint.sh`	Starts `monitor.py` and `python -m http.server` side by side, forwards signals, and exits the container if either process dies (so the orchestrator restarts it).
`Dockerfile`	`python:3.12-alpine` base, non-root user, healthcheck, no external Python deps.

Configuration (environment variables)

Variable	Default	Description
`TARGET_URL`	`https://mithal.space`	URL to monitor
`SEARCH_PATH`	`/search?q=test`	Path appended to the target's origin for the search-latency check
`CHECK_INTERVAL`	`60`	Seconds between checks
`RETENTION_HOURS`	`24`	Rolling window kept in the JSON log
`REQUEST_TIMEOUT`	`10`	Per-request timeout (seconds)
`PORT`	`8080`	Dashboard HTTP server port

Build & run locally

docker build -t mithal-space-monitor .

docker run -d \
  --name mithal-monitor \
  -p 8080:8080 \
  -e TARGET_URL="https://mithal.space" \
  -e SEARCH_PATH="/search?q=test" \
  -v mithal_monitor_data:/app/data \
  mithal-space-monitor

# open http://localhost:8080

The -v mithal_monitor_data:/app/data volume is optional but recommended so your 24h history survives a container restart/redeploy.

Push to a registry

docker tag mithal-space-monitor registry.example.com/yourorg/mithal-space-monitor:latest
docker push registry.example.com/yourorg/mithal-space-monitor:latest

Deploy

This image is a single process group exposing one HTTP port, so it runs as-is on most container platforms:

Cloud Run / Container Apps / Fly.io: deploy the image, set PORT to match the platform's expected port (Cloud Run injects PORT automatically - the entrypoint already respects it), mount a persistent volume if the platform supports one (otherwise history resets on redeploy, which is fine - it just rebuilds over the next RETENTION_HOURS).
Kubernetes: run as a Deployment with 1 replica, a Service of type ClusterIP/LoadBalancer, and optionally a PersistentVolumeClaim mounted at /app/data. The built-in HEALTHCHECK maps naturally to a liveness probe on GET /.
Plain VM / docker-compose: use the docker run command above behind your existing reverse proxy / TLS terminator.

Notes & extension points

Everything in monitor.py uses only the Python standard library (urllib, socket, ssl, json) - no pip install step, no dependency surface in the image.
Data is written atomically (write → temp file → os.replace) so the dashboard never reads a half-written JSON file.
To monitor multiple targets, run one container per target (each with its own TARGET_URL/port), or extend monitor.py to loop over a list of targets and extend index.html with a target selector.
Add basic auth / IP allowlisting at your reverse proxy if the dashboard shouldn't be public.