Read logs, count error types, flag anomalies, output a summary report.
Linux systems generate large volumes of logs that can be difficult to scan manually.
This tool provides a quick way to:
- Identify errors and warnings
- Summarize log severity levels
- Review recent log activity
- Support troubleshooting workflows
It’s intended as a learning and demonstration tool rather than a production log pipeline.
Sample log file:
Jan 10 10:17:45 system ERROR Failed to connect to database
Jan 10 10:18:22 system INFO Retrying connection
Jan 10 10:19:01 system ERROR Connection timeout
=== Log Summary ===
ERROR: 2
INFO: 1
=== Recent Log Entries ===
Jan 10 10:17:45 system ERROR Failed to connect to database
Jan 10 10:18:22 system INFO Retrying connection
Jan 10 10:19:01 system ERROR Connection timeout
My professional work has involved Linux infrastructure, automation, and debugging workflows.
I wanted a small project to:
- Reinforce log analysis skills
- Practice building Linux-friendly CLI tools
- Explore observability concepts
- Demonstrate systems-oriented thinking publicly
- Structured log parsing techniques
- Practical Linux troubleshooting workflows
- Importance of logging consistency
- CLI usability considerations
- Writing clear technical documentation
Clone the repo:
git clone <your-repo-url>
cd linux-log-analyzerRun the analyzer:
python log_analyzer.py sample_logs/system.log- Regex-based parsing
- Timestamp filtering
- Service-specific analysis
- Integration with journalctl
- Output formatting improvements
This project is intended for learning, demonstration, and portfolio purposes. It does not process sensitive production logs.