Rsyslog is a high-performance, modular system for log processing, designed for ingesting, transforming, and outputting log data from diverse sources to various destinations. It targets system administrators and developers needing robust and scalable logging solutions, offering over a million messages per second throughput.

How It Works

Rsyslog employs a modular architecture with a core engine and pluggable modules for input, output, and processing. This design allows for extreme flexibility and extensibility, enabling it to handle complex routing, filtering, and transformation rules efficiently. Its performance is attributed to an event-driven, multi-threaded architecture optimized for high-volume log streams.

Quick Start & Requirements

• Installation: Typically installed via distribution package managers (e.g., apt, yum). Latest versions are available via official builds for RPM, Debian, and Ubuntu.
• Prerequisites: Building from source requires pkg-config, libestr, and liblogging (stdlog component). Development containers are available in packaging/docker/dev_env.
• Resources: Minimal for basic operation; high-volume processing may require significant CPU and I/O.
• Links: Official Builds, Build Instructions, rsyslog Assistant

Highlighted Details

• "Rocket-fast" performance claims over 1 million messages/sec.
• Experimental AI Assistant for configuration and troubleshooting.
• AI-based code review for pull requests using Google Gemini.
• Supports external plugin development in Python and Perl.

Maintenance & Community

Primarily sponsored by Adiscon GmbH, with technical decisions made via mailing list consensus. Community contributions are encouraged. Resources include GitHub Discussions and a Mailing List.

Licensing & Compatibility

Licensed under the GPLv3. Compatibility for commercial use or closed-source linking depends on adherence to GPLv3 terms, which may require making derivative works available under the same license.

Limitations & Caveats

The AI Assistant is experimental and may generate incorrect configurations. AI code reviews are informational only and do not replace human expert review. GDPR compliance requires careful consideration of data stored in git history.