A CLI tool for extracting patterns from streaming unstructured log messages using the Drain parser algorithm.
logu is a command-line tool that extracts patterns from unstructured, streaming log messages using the Drain parsing algorithm. It tokenizes log data, builds a tree structure, and clusters similar logs to transform chaotic log streams into organized, analyzable formats. This helps developers and operators identify recurring issues and monitor system behavior in real-time.
DevOps engineers, SREs, and developers who need to analyze log streams from applications, containers, or infrastructure in real-time, especially those using tools like stern for Kubernetes log tailing.
logu offers a lightweight, efficient alternative to heavy log management systems by providing real-time pattern extraction directly in the terminal. It leverages the proven Drain algorithm for accurate clustering and is easy to integrate into existing logging pipelines without complex setup.
Extract patterns from unstructured log messages
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Processes log messages as they arrive, supporting continuous monitoring pipelines, which is ideal for live log analysis in tools like Kubernetes with stern.
Leverages the established Drain parser for tokenization and clustering, ensuring efficient and reliable pattern extraction from unstructured logs.
Renders extracted patterns in real-time with configurable refresh intervals (e.g., --render-interval), making it easy to monitor logs directly in the CLI.
Designed as a command-line utility without heavy infrastructure, allowing quick integration into existing workflows via simple installation methods like Homebrew or Cargo.
As noted in the README, features for identifying attributes such as IP and port are marked as not yet implemented, limiting detailed log analysis.
Requires configuring multiple thresholds (e.g., --sim-th, --max-clusters) which can be challenging for users unfamiliar with the Drain algorithm's nuances.
Focused solely on real-time streaming, so it doesn't store logs for historical querying or offline analysis, reducing its utility for long-term debugging.