Network Analysis / Monitoring

MomentoMonto

MomentoMonto is a realtime server monitoring system that continuously tracks HTTP response times, uptime, and availability across multiple endpoints — providing instant visibility into server health, detecting degradation patterns early, and surfacing actionable KPI insights through a live dashboard.

Python Network Analysis KPI Realtime Monitoring HTTP Uptime Tracking Response Time DevOps
⚡ RT
Response Time Tracking (ms)
99.9%
Uptime Visibility
Live
Real-time Dashboard Updates

Live Monitor Preview

api.service.io
42ms ✓
cdn.assets.io
120ms ✓
db.cluster.io
380ms ⚠
auth.service.io
68ms ✓
Project Overview

MomentoMonto is designed for developers and DevOps engineers who need instant visibility into the health of their server infrastructure without complex setup. The system pings configured endpoints at regular intervals, measures response times using Python's network stack, classifies server health into status tiers, and presents the data in a clean, real-time dashboard — all deployed on Railway for always-on monitoring.

Key Features
⏱️

Response Time Monitoring

Continuous millisecond-precision HTTP response time measurement across all configured endpoints — with rolling averages and percentile breakdowns.

🟢

Uptime Tracking

Calculates real-time uptime percentage per endpoint, detecting outages the moment they occur and logging downtime duration automatically.

📊

KPI Dashboard

Live KPI cards showing current response time, uptime %, health status, and trend direction — updated in real-time without page refresh.

🔔

Degradation Detection

Smart threshold-based alerting flags when response times exceed acceptable limits — differentiating between healthy, degraded, and down states.

📡

Multi-Endpoint Support

Monitor any number of HTTP/HTTPS endpoints simultaneously — APIs, web apps, CDNs, auth services, and database health probes.

🛤️

Railway Deployment

Always-on monitoring deployed on Railway — ensuring the monitor itself never goes down, with zero-configuration auto-restart on failure.

How It Works
1

Endpoint Configuration

Target URLs and check intervals are configured in a simple JSON/YAML config file — no code changes needed to add new endpoints.

2

Scheduled HTTP Probing

Python scheduler fires HTTP GET requests at each endpoint on the configured interval — capturing status codes, response times, and any errors.

3

Network Analysis & Classification

Response data is analyzed against threshold rules — endpoints are classified as UP (green), DEGRADED (amber), or DOWN (red) with confidence levels.

4

KPI Aggregation

Per-endpoint and aggregate KPIs are computed — including current RT, 24h average, uptime %, incident count, and MTTR (mean time to recovery).

5

Live Dashboard Rendering

The web dashboard polls the backend for fresh data and updates KPI cards, status indicators, and response time charts in real time.

Tech Stack
Python
Requests
Network Analysis
HTTP Monitoring
KPI Design
Flask / FastAPI
Scheduler
Realtime Dashboard
JSON Config
DevOps
Railway (Deployment)
Uptime Tracking