Monitoring
Five simulated services generate availability, HTTP status and latency observations.
Noise Reduction
An incident is created only after three consecutive failures instead of the first failed check.
Incident Lifecycle
Outages create incidents automatically and recovery closes them with recorded duration.
Operational Metrics
Uptime, average latency, outage count, failure streaks and monitoring history remain visible.
RECRUITER DEMO
Simulate an outage in under 30 seconds
Recommended flow:
Simulate Failure → Run Health Check 3× → Incident appears → Restore Service → Incident resolves
Service Health
Live browser simulation of status, latency and consecutive failure state.
Latency History
Recent simulated response times across monitored services.
Availability Snapshot
Current online/offline service distribution.
Incident History
Incidents appear only after the configured failure threshold is reached.
| Incident | Service | Status | Opened | Resolved | Duration |
|---|
Monitoring Logs
Each health check records state, HTTP result and latency.
| Time | Service | State | HTTP | Latency |
|---|
FULL ENGINEERING IMPLEMENTATION
The GitHub repository contains the real backend project
This live page intentionally runs in the browser so it opens instantly on GitHub Pages. The repository contains the actual FastAPI backend, SQLAlchemy data model, SQLite/PostgreSQL configuration, Docker Compose stack, background monitoring worker, automated tests, API documentation and optional AI Helpdesk Copilot integration.
Python
FastAPI
SQLAlchemy
PostgreSQL
Docker
pytest
Chart.js
GitHub Actions