Full-Stack Monitoring & Operations Platform (FSMOP)

A comprehensive full-stack platform for system monitoring, metrics aggregation, operational visibility, and scalable observability workflows.

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Abstract

This repository presents a full-stack operational monitoring and analytics platform designed to collect, process, and visualize system and application metrics. FSMOP integrates backend services with frontend dashboards to enable real-time operational insights and metrics-driven decision making.

The platform emphasizes:

• Modularity
• Reproducibility
• Scalable monitoring pipelines

This project bridges the gap between system engineering and applied software development in production-grade environments.

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Problem Motivation

Modern distributed systems generate large volumes of metrics, logs, and operational signals that must be ingested, processed, and visualized with minimal overhead. Traditional monitoring scripts and ad-hoc dashboards are insufficient for scalable environments. FSMOP addresses this need by providing a structured platform for:

• Centralized metric collection
• Interactive operational dashboards
• Background processing and task orchestration

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System Architecture

FSMOP is divided into modular layers:

1. Data Ingestion & Backend

   • Flask API for metric submission
   • REST endpoints for operational queries
   • PostgreSQL for structured storage

2. Frontend Dashboard

   • Real-time visualization of key performance metrics
   • Built with React for responsive, modular UI components

3. Processing & Workers

   • Background workers process incoming metric streams
   • Docker-based deployment for scalability

4. Containerization & Deployment

   • Docker and Docker Compose configurations for reproducible deployments

Figure 1: High-level system architecture illustrating backend, frontend, workers, and storage layers.

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Methodology

Data Model

Metrics are collected as time-series records with defined schema:

• timestamp
• source
• metric type
• value
• labels

Real-Time Processing

Incoming metrics are queued and processed in background workers to:

• normalize metric values
• filter noise
• aggregate over preconfigured intervals

Evaluation Metrics

To validate monitoring effectiveness:

• Throughput (metrics per second)
• Dashboard latency (ms)
• Storage consistency and indexing performance

These performance measurements are captured and analyzed as part of FSMOP’s evaluation.

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Reproducibility

FSMOP is engineered with reproducible deployment as a first-class constraint:

• All dependencies are defined in requirements.txt
• Dockerfile and docker-compose.yml enable environment consistency
• Modular code facilitates extension and experimentation

Quickstart

1. Clone the repository

git clone https://github.com/talhayilmazc/Full-Stack-Monitoring-Operations-Platform-FSMOP-
cd Full-Stack-Monitoring-Operations-Platform-FSMOP-
2. Build and run with Docker Compose

bash
Kodu kopyala
docker compose up --build
3. Access the Dashboard

Open your browser and navigate to:

dts
Kodu kopyala
http://localhost:3000
Results and Visualization
The platform produces operational charts and dashboards illustrating:

System health and resource usage

Event arrival rates

Performance over time

<p align="center"> <img src="docs/sample_dashboard.png" width="70%"> </p>
Figure 2: Example operational dashboard snapshot (placeholder).

Note: Actual dashboard visuals should be included under docs/ and referenced here.

Impact and Use Cases
FSMOP is suitable for:

DevOps observability pipelines

Production system monitoring and alerting

Performance analytics and bottleneck exploration

This project validates how full-stack systems integrate backend, frontend, and processing logic into a coherent platform.

Future Work
Integrate with Prometheus and Grafana for enriched alerting

Add plugin support for custom metric exporters

Distributed worker clusters with performance autoscaling

Time-series database support (InfluxDB, TimescaleDB)