This project showcases a fully automated log analysis and reporting system using modern DevOps practices.

• 📜 Parses structured web server logs
• 📊 Generates analytics report (CSV & Graph)
• ⚙️ Automates analysis with GitHub Actions CI/CD
• 🐳 Dockerized Python application
• 🔁 Workflow runs on every code or log update

• ✅ Real-time log parsing and summary reporting
• ✅ Error trend visualization (line chart)
• ✅ Auto CI/CD via GitHub Actions
• ✅ Docker-based reproducibility
• ✅ Structured codebase with modular design

27.0.0.1 - - [16/Jul/2025:10:00:01 +0000] "GET /index.html HTTP/1.1" 200 1024

• Extracts: IP, Time, Method, Endpoint, Status Code, Response Size
• Summarizes:
  • Total Requests
  • Error Counts (4xx & 5xx)
  • Error Rate (%)
• Visualizes: Errors over time (HH:MM)

1. 📦 Checks out repo
2. 🐍 Installs dependencies
3. 🧪 Runs log analyzer
4. 📁 Uploads summary and chart as artifacts

Trigger Paths: ``yaml on: push: branches: - main

🔧 Build the image bash Copy Edit docker build -t log-analyzer . ▶️ Run the container bash Copy Edit docker run --rm log-analyzer 📂 Folder Structure pgsql Copy Edit ├── analyzer/ │ └── analyze.py ├── logs/ │ └── sample.log ├── reports/ │ ├── summary.csv │ └── error_graph.png ├── .github/ │ └── workflows/ │ └── analyze.yml ├── Dockerfile ├── requirements.txt └── README.md

CI/CD workflow integration via GitHub Actions

Automating Python log analysis

Handling time formats and regex parsing

Dockerizing analytics tools

Working with GitHub artifact uploads

🙌 Acknowledgements Log format inspired by Apache HTTPD Logs

DevOps Automation implemented by Adharsh

📣 Contact 📧 Adharsh – LinkedIn 🌍 GitHub – adharsh277

python Copy Edit

Let me know if you'd like this as a downloadable README.md file or added to your repo automatically. You're ready to proudly showcase this, Captain! 🫡