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Linux /proc filesystem 기반 시스템 메트릭 수집 백엔드 프로젝트

Overview

Linux 시스템의 CPU, memory, network, disk 메트릭을 /proc 파일시스템에서 직접 파싱하여 수집하고, 이를 FastAPI 기반 REST API로 제공하는 백엔드 프로젝트입니다.

이 프로젝트는 Linux 내부 동작, /proc 기반 메트릭 수집 방식, FastAPI API 설계, 그리고 Linux 실행 환경(Ubuntu VM)에서의 개발 흐름을 학습하기 위해 시작했습니다.

Goals

• Linux 시스템이 메트릭 정보를 어떻게 제공하는지 이해
• /proc 파일을 직접 파싱하는 방식 학습
• FastAPI 기반 REST API 설계 및 구현
• Docker / systemd 같은 Linux 실행 환경 학습
• 문서화와 기록을 통한 학습 과정 정리

Tech Stack

• Python 3.x
• FastAPI
• Pydantic
• Linux /proc filesystem
• Uvicorn
• Ubuntu 24.04 VM
• VSCode Remote SSH

Current Features

• GET /health
• GET /metrics/cpu
• GET /metrics/memory
• GET /metrics/network
• GET /metrics/disk

API Endpoints

• GET /health
• GET /metrics/cpu
• GET /metrics/memory
• GET /metrics/network
• GET /metrics/disk

Architecture

The backend is organized with a layered structure:

• Parser Layer
  • Reads and parses raw data from /proc
• Service Layer
  • Calculates metrics and prepares response data
• API Layer
  • Exposes FastAPI endpoints
• Schema Layer
  • Defines response models with Pydantic

Metric Sources

• CPU: /proc/stat
• Memory: /proc/meminfo
• Network: /proc/net/dev
• Disk: /proc/diskstats

Development Environment

• Windows host
• Ubuntu 24.04 VM
• VSCode Remote SSH
• Python virtual environment created inside Ubuntu VM

Project Documents

• docs/architecture.md
• docs/api-spec.md
• docs/dev-log.md
• docs/decision-log.md
• docs/troubleshooting.md

How to Run

1. Move to backend

cd backend

2. Create virtual environment

python3 -m venv .venv
source .venv/bin/activate

3. Install dependencies

pip install fastapi "uvicorn[standard]"

4. Run server

uvicorn app.main:app --reload

5. Test endpoints

curl http://127.0.0.1:8000/health
curl http://127.0.0.1:8000/metrics/memory
curl http://127.0.0.1:8000/metrics/network
curl http://127.0.0.1:8000/metrics/cpu
curl http://127.0.0.1:8000/metrics/disk

Current Status

• Core REST API endpoints implemented
• Memory, network, CPU, and disk metric parsing completed
• Documentation for architecture and API specification completed
• Initial validation against /proc files completed

Future Work

• Dockerize the backend
• Learn and apply systemd service setup
• Add CLI dashboard with rich
• Consider simple frontend or visualization layer later
• Add optional alerting / filtering / rate-based metrics in future versions